wanyu/IteraTeR_full_sent huggingface dataset:
Original:  From FOOwe develop fast, practical methods to compute nearly tight bounds for the optimal hit ratio, which we call practical flow-based offline optimal (P-FOO). P-FOO enables the first analysis of optimal caching on realistic traces with hundreds of millions of requests.
Revised:  Indeed, FOO's error over 10M requests of production CDN and storage traces is negligible: at most 0.3\%. FOO thus reveals, for the first time, the limits of caching with variable object sizes. While FOO is very accurate, it is computationally impractical on traces with hundreds of millions of requests.
Edit intent: meaning-changed
Original:  We evaluate P-FOO on several production traces, where results show that recent caching systems are  still far from optimal  .
Revised:  We therefore extend FOO to obtain more efficient bounds on OPT, which we call practical flow-based offline optimal (PFOO). We evaluate PFOO on several full production traces and use it to compare OPT to prior online policies. This analysis shows that current caching systems are still far from optimal .
Edit intent: meaning-changed
Original: It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas.
Revised: We consider continuous-time models with a large panel of moment conditions, where the structural parameter depends on a set of characteristics, whose effects are of interest. The leading example is the linear factor model in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas.
Edit intent: meaning-changed
Original: It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas.
Revised: It has been well known in financial economics where factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas.
Edit intent: clarity
Original: It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas.
Revised: It has been well known in financial economics that factor betas depend on observed characteristics such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas.
Edit intent: clarity
Original: It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas.
Revised: It has been well known in financial economics that factor betas depend on observed instruments such as firm specific instruments and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas.
Edit intent: clarity
Original: It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and a key object of interest is the effect of instruments on the factor betas. One of the key features of our model is that we specify the factor betas as functions of time-varying observed instruments that pick up long-run beta fluctuations, plus an orthogonal idiosyncratic component that captures high-frequency movements in beta .
Revised: It has been well known in financial economics that factor betas depend on observed instruments such as firm specific characteristics and macroeconomic variables, and their effects pick up long-run time-varying beta fluctuations. We specify the factor betas as functions of time-varying observed instruments that pick up long-run beta fluctuations, plus an orthogonal idiosyncratic component that captures high-frequency movements in beta .
Edit intent: clarity
Original:  One of the key features of our model is that we specify the factor betas as functions of time-varying observed instruments that pick up long-run beta fluctuations, plus an orthogonal idiosyncratic component that captures high-frequency movements in beta .
Revised:  One of the key features of our model is that we specify the factor betas as the sum of characteristic effects and an orthogonal idiosyncratic component that captures high-frequency movements in beta .
Edit intent: clarity
Original:  One of the key features of our model is that we specify the factor betas as functions of time-varying observed instruments that pick up long-run beta fluctuations, plus an orthogonal idiosyncratic component that captures high-frequency movements in beta .
Revised:  One of the key features of our model is that we specify the factor betas as functions of time-varying observed instruments that pick up long-run beta fluctuations, plus an orthogonal idiosyncratic parameter that captures high-frequency movements in beta .
Edit intent: clarity
Original:  One of the key features of our model is that we specify the factor betas as functions of time-varying observed instruments that pick up long-run beta fluctuations, plus an orthogonal idiosyncratic component that captures high-frequency movements in beta .
Revised:  One of the key features of our model is that we specify the factor betas as functions of time-varying observed instruments that pick up long-run beta fluctuations, plus an orthogonal idiosyncratic component that captures high-frequency movements .
Edit intent: clarity
Original:  It is often the case that researchers do not know whether or not the idiosyncratic beta exists, or its strengths, and thus uniformity is essential for inferences.
Revised:  It is often the case that researchers do not know whether or not the idiosyncratic beta exists, or its strengths, and thus the inference about the characteristic effects should be valid uniformly over a broad class of data generating processes for idiosyncratic parameters. We construct our estimation and inference in a two-step continuous-time GMM framework.
Edit intent: meaning-changed
Original:  It is found that the limiting distribution of the estimated instrument effect has a discontinuity when the strength of the idiosyncratic beta is near zero , which makes usual inferences fail to be valid and produce misleading results.
Revised:  It is found that the limiting distribution of the estimated characteristic effects has a discontinuity when the strength of the idiosyncratic beta is near zero , which makes usual inferences fail to be valid and produce misleading results.
Edit intent: clarity
Original:  It is found that the limiting distribution of the estimated instrument effect has a discontinuity when the strength of the idiosyncratic beta is near zero , which makes usual inferences fail to be valid and produce misleading results.
Revised:  It is found that the limiting distribution of the estimated instrument effect has a discontinuity when the variance of the idiosyncratic beta is near zero , which makes usual inferences fail to be valid and produce misleading results.
Edit intent: clarity
Original:  It is found that the limiting distribution of the estimated instrument effect has a discontinuity when the strength of the idiosyncratic beta is near zero , which makes usual inferences fail to be valid and produce misleading results.
Revised:  It is found that the limiting distribution of the estimated instrument effect has a discontinuity when the strength of the idiosyncratic parameter is near the boundary (zero) , which makes usual inferences fail to be valid and produce misleading results.
Edit intent: meaning-changed
Original:  It is found that the limiting distribution of the estimated instrument effect has a discontinuity when the strength of the idiosyncratic beta is near zero , which makes usual inferences fail to be valid and produce misleading results. In addition, the usual "plug-in" method using the estimated asymptotic variance is only valid pointwise .
Revised:  It is found that the limiting distribution of the estimated instrument effect has a discontinuity when the strength of the idiosyncratic beta is near zero , which makes the usual "plug-in" method using the estimated asymptotic variance is only valid pointwise .
Edit intent: coherence
Original:  In addition, the usual "plug-in" method using the estimated asymptotic variance is only valid pointwise . The central goal is to make inference about the effect on the betas of firms' instruments, and to conduct out-of-sample forecast of integrated volatilities using estimated factors. Both procedures should be valid uniformly over a broad class of data generating processes for idiosyncratic betas with various signal strengths and degrees of time-variant.
Revised:  In addition, the usual "plug-in" method using the estimated asymptotic variance is only valid pointwise and may produce either over- or under- coveraging probabilities.
Edit intent: clarity
Original: Given the recent trend towards validating the neuroimaging statistical methods, we compared the most popular functional magnetic resonance imaging (fMRI) analysis softwares : AFNI, FSL and SPM, with regard to temporal autocorrelation modelling .
Revised: Given the recent controversies in some neuroimaging statistical methods, we compared the most popular functional magnetic resonance imaging (fMRI) analysis softwares : AFNI, FSL and SPM, with regard to temporal autocorrelation modelling .
Edit intent: meaning-changed
Original: Given the recent trend towards validating the neuroimaging statistical methods, we compared the most popular functional magnetic resonance imaging (fMRI) analysis softwares : AFNI, FSL and SPM, with regard to temporal autocorrelation modelling .
Revised: Given the recent trend towards validating the neuroimaging statistical methods, we compared the most frequently used functional Magnetic Resonance Imaging (fMRI) analysis softwares : AFNI, FSL and SPM, with regard to temporal autocorrelation modelling .
Edit intent: clarity
Original: Given the recent trend towards validating the neuroimaging statistical methods, we compared the most popular functional magnetic resonance imaging (fMRI) analysis softwares : AFNI, FSL and SPM, with regard to temporal autocorrelation modelling .
Revised: Given the recent trend towards validating the neuroimaging statistical methods, we compared the most popular functional magnetic resonance imaging (fMRI) analysis packages : AFNI, FSL and SPM, with regard to temporal autocorrelation modelling .
Edit intent: clarity
Original: Given the recent trend towards validating the neuroimaging statistical methods, we compared the most popular functional magnetic resonance imaging (fMRI) analysis softwares : AFNI, FSL and SPM, with regard to temporal autocorrelation modelling .
Revised: Given the recent trend towards validating the neuroimaging statistical methods, we compared the most popular functional magnetic resonance imaging (fMRI) analysis softwares : AFNI, FSL and SPM, with regard to temporal autocorrelation modeling .
Edit intent: fluency
Original:  On the other hand, due to SPM modelling temporal autocorrelation in the least flexible way, it can introduce negative autocorrelations during pre-whitening for scans with long repetition times.
Revised:  On the other hand, due to SPM modeling temporal autocorrelation in the least flexible way, it can introduce negative autocorrelations during pre-whitening for scans with long repetition times.
Edit intent: fluency
Original:  Interestingly, because pre-whitening in FSL and SPM does not remove a substantial part of the temporal autocorrelation in the noise, we observed a relationship that the lower the assumed experimental design frequency, the more likely it was to observe significant activation.
Revised:  Interestingly, because pre-whitening in FSL and SPM does not remove a substantial part of the temporal autocorrelation in the noise, we found a strong relationship in which the lower the assumed experimental design frequency, the more likely it was to observe significant activation.
Edit intent: clarity
Original:  Though temporal autocorrelation modelling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modelling in FSL and SPM. FSL and SPM could improve their autocorrelation modelling approaches for example  adopting a noise model similar to the one used by AFNI.
Revised:  Though temporal autocorrelation modeling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modelling in FSL and SPM. FSL and SPM could improve their autocorrelation modelling approaches for example adopting a noise model similar to the one used by AFNI.
Edit intent: fluency
Original:  Though temporal autocorrelation modelling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modelling in FSL and SPM. FSL and SPM could improve their autocorrelation modelling approaches for example  adopting a noise model similar to the one used by AFNI.
Revised:  Though temporal autocorrelation modelling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modeling in FSL and SPM. FSL and SPM could improve their autocorrelation modelling approaches for example adopting a noise model similar to the one used by AFNI.
Edit intent: fluency
Original:  Though temporal autocorrelation modelling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modelling in FSL and SPM. FSL and SPM could improve their autocorrelation modelling approaches for example  adopting a noise model similar to the one used by AFNI.
Revised:  Though temporal autocorrelation modelling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modelling in FSL and SPM. FSL and SPM could improve their autocorrelation modeling approaches for example adopting a noise model similar to the one used by AFNI.
Edit intent: fluency
Original:  Though temporal autocorrelation modelling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modelling in FSL and SPM. FSL and SPM could improve their autocorrelation modelling approaches for example  adopting a noise model similar to the one used by AFNI.
Revised:  Though temporal autocorrelation modelling in AFNI was not perfect, its performance was much higher than the performance of temporal autocorrelation modelling in FSL and SPM. FSL and SPM could improve their autocorrelation modelling approaches for example by adopting a noise model similar to the one used by AFNI.
Edit intent: fluency
Original:  Here , we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise  on the states and parameters, as given in the Langevin framework.
Revised:  In this chapter , we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise on the states and parameters, as given in the Langevin framework.
Edit intent: coherence
Original:  Here , we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise  on the states and parameters, as given in the Langevin framework.
Revised:  Here , we address this issue for biomolecular systems using a combination of Monte Carlo simulations and experimental data, exploiting the dependence of the process noise covariance on the states and parameters, as given in the Langevin framework.
Edit intent: meaning-changed
Original:  We find that the extended Kalman filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving  balance between the mean square estimation error and parameter convergence time.
Revised:  We find that the Extended Kalman Filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving balance between the mean square estimation error and parameter convergence time.
Edit intent: fluency
Original:  We find that the extended Kalman filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving  balance between the mean square estimation error and parameter convergence time.
Revised:  We find that the extended Kalman filter with such process noise covariance update is closer to the optimality condition in the sense that the innovation sequence becomes white and in achieving balance between the mean square estimation error and parameter convergence time.
Edit intent: meaning-changed
Original:  We find that the extended Kalman filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving  balance between the mean square estimation error and parameter convergence time.
Revised:  We find that the extended Kalman filter with such process noise covariance update can be optimal in the sense that the innovation sequence becomes white and in achieving a balance between the mean square estimation error and parameter convergence time.
Edit intent: fluency
Original:  Given a finite dynamic metric space (DMS ), we construct the zigzag simplicial filtration arising from applying the Rips simplicial complex construction (with a fixed scale parameter) to this finite DMS. Upon passing to 0-th homology with field coefficients, we obtain a zigzag persistence module and, based on standard results, we in turn obtain a persistence diagram or barcode from this zigzag persistence module.
Revised:  Given a finite dynamic graph (DG ), we construct the zigzag simplicial filtration arising from applying the Rips simplicial complex construction (with a fixed scale parameter) to this finite DMS. Upon passing to 0-th homology with field coefficients, we obtain a zigzag persistence module and, based on standard results, we in turn obtain a persistence diagram or barcode from this zigzag persistence module.
Edit intent: meaning-changed
Original:  Given a finite dynamic metric space (DMS ), we construct the zigzag simplicial filtration arising from applying the Rips simplicial complex construction (with a fixed scale parameter) to this finite DMS. Upon passing to 0-th homology with field coefficients, we obtain a zigzag persistence module and, based on standard results, we in turn obtain a persistence diagram or barcode from this zigzag persistence module.
Revised:  Given a finite dynamic metric space (DMS ), we first construct a zigzag persistence module arising from linearizing the dynamic transitive graph naturally induced from the input DG. Based on standard results, we in turn obtain a persistence diagram or barcode from this zigzag persistence module.
Edit intent: meaning-changed
Original:  Given a finite dynamic metric space (DMS ), we construct the zigzag simplicial filtration arising from applying the Rips simplicial complex construction (with a fixed scale parameter) to this finite DMS. Upon passing to 0-th homology with field coefficients, we obtain a zigzag persistence module and, based on standard results, we in turn obtain a persistence diagram or barcode from this zigzag persistence module.
Revised:  Given a finite dynamic metric space (DMS ), we construct the zigzag simplicial filtration arising from applying the Rips simplicial complex construction (with a fixed scale parameter) to this finite DMS. Upon passing to 0-th homology with field coefficients, we obtain a zigzag persistence module and, based on standard results, we then obtain a persistence diagram or barcode from this zigzag persistence module.
Edit intent: clarity
Original:  We prove that these barcodes are stable under perturbations in the input DMS. In order to formalize the notion of perturbation we introduce a suitable distance between DMSs and we then prove that the value of this distance between any two DMSs admits as a lower bound the bottleneck distance between the Rips barcodes associated to each of two input DMSs.
Revised:  We prove that these barcodes are stable under perturbations in the input DG under a suitable distance between DMSs and we then prove that the value of this distance between any two DMSs admits as a lower bound the bottleneck distance between the Rips barcodes associated to each of two input DMSs.
Edit intent: meaning-changed
Original:  We prove that these barcodes are stable under perturbations in the input DMS. In order to formalize the notion of perturbation we introduce a suitable distance between DMSs and we then prove that the value of this distance between any two DMSs admits as a lower bound the bottleneck distance between the Rips barcodes associated to each of two input DMSs.
Revised:  We prove that these barcodes are stable under perturbations in the input DMS. In order to formalize the notion of perturbation we introduce a suitable distance between DGs that we identify. More precisely, our stability theorem can be interpreted as providing a lower bound the bottleneck distance between the Rips barcodes associated to each of two input DMSs.
Edit intent: meaning-changed
Original:  We prove that these barcodes are stable under perturbations in the input DMS. In order to formalize the notion of perturbation we introduce a suitable distance between DMSs and we then prove that the value of this distance between any two DMSs admits as a lower bound the bottleneck distance between the Rips barcodes associated to each of two input DMSs. This lower bound can be computed in polynomial time from the DMS inputs.
Revised:  We prove that these barcodes are stable under perturbations in the input DMS. In order to formalize the notion of perturbation we introduce a suitable distance between DMSs and we then prove that the value of this distance between any two DMSs admits as a lower bound for the distance between DGs. Since it relies on barcodes, and their bottleneck distance, this lower bound can be computed in polynomial time from the DMS inputs.
Edit intent: clarity
Original:  In order to elucidate the relationship between our distance between two dynamic metric spaces and the bottleneck distance between their Rips zigzag barcodes, we exploit recent advances in the stability of zigzag persistence ( due to Botnan and Lesnick ).
Revised:  In order to elucidate the relationship between our distance between two DGs and the bottleneck distance between their Rips zigzag barcodes, we exploit recent advances in the stability of zigzag persistence ( due to Botnan and Lesnick ).
Edit intent: clarity
Original:  In order to elucidate the relationship between our distance between two dynamic metric spaces and the bottleneck distance between their Rips zigzag barcodes, we exploit recent advances in the stability of zigzag persistence ( due to Botnan and Lesnick ).
Revised:  In order to elucidate the relationship between our distance between two dynamic metric spaces and the bottleneck distance between their associated barcodes, we exploit recent advances in the stability of zigzag persistence ( due to Botnan and Lesnick ).
Edit intent: clarity