wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this regard, we propose a novel approach of multimodal relational graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information through interactive message propagation in the latent feature space.
Revised:  In this regard, we propose a novel online approach of multi-modal relational graph network (i.e., MRG-Net) to dynamically integrate visual and kinematics information through interactive message propagation in the latent feature space.
Edit intent: meaning-changed
Original: {\bf Purpose}: Earlier work showed that IVIM-NET_{orig}, an unsupervised physics-informed deep neural network, was faster and more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to DWI.
Revised: {\bf Purpose}: Earlier work showed that IVIM-NET_{orig}, an unsupervised physics-informed deep neural network, was more accurate than other state-of-the-art intravoxel-incoherent motion (IVIM) fitting approaches to DWI.
Edit intent: clarity
Original:  This study presents  : IVIM-NET_{optim}, overcoming IVIM-NET_{orig . {\bf Method}: In simulations (SNR=20), the accuracy, independence and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, # hidden layers, dropout, batch normalization, learning rate), by calculating the NRMSE, Spearman's \rho, and the coefficient of variation (CV_{NET}), respectively.
Revised:  This study presents an improved version : IVIM-NET_{optim}, overcoming IVIM-NET_{orig . {\bf Method}: In simulations (SNR=20), the accuracy, independence and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, # hidden layers, dropout, batch normalization, learning rate), by calculating the NRMSE, Spearman's \rho, and the coefficient of variation (CV_{NET}), respectively.
Edit intent: meaning-changed
Original:  This study presents  : IVIM-NET_{optim}, overcoming IVIM-NET_{orig . {\bf Method}: In simulations (SNR=20), the accuracy, independence and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, # hidden layers, dropout, batch normalization, learning rate), by calculating the NRMSE, Spearman's \rho, and the coefficient of variation (CV_{NET}), respectively.
Revised:  This study presents : IVIM-NET_{optim}, and characterizes its superior performance in pancreatic ductal adenocarcinoma (PDAC) patients . {\bf Method}: In simulations (SNR=20), the accuracy, independence and consistency of IVIM-NET were evaluated for combinations of hyperparameters (fit S0, constraints, network architecture, # hidden layers, dropout, batch normalization, learning rate), by calculating the NRMSE, Spearman's \rho, and the coefficient of variation (CV_{NET}), respectively.
Edit intent: meaning-changed
Original:  The best performing network, IVIM-NET_{optim} was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET_{optim}'s performance was evaluated in 23 pancreatic ductal adenocarcinoma (PDAC ) patients.
Revised:  The best performing network, IVIM-NET_{optim} was compared to least squares (LS) and a Bayesian approach at different SNRs. IVIM-NET_{optim}'s performance was evaluated in 23 PDAC patients.
Edit intent: clarity
Original:  {\bf Results}: In simulations, IVIM-NET_{optim} outperformed IVIM-NET_{orig} in accuracy (NRMSE(D)= 0.14 vs 0.17 ;
Revised:  {\bf Results}: In simulations, IVIM-NET_{optim} outperformed IVIM-NET_{orig} in accuracy (NRMSE(D)= 0.18 vs 0.20 ;
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Original:  NMRSE(D*)= 0.46 vs 0.49 ), independence (\rho(D*,f)= 0.32 vs 0.95 ) and consistency (CV_{NET} (D)= 0.028 vs 0.185 ;
Revised:  NMRSE(D*)= 0.39 vs 0.39 ), independence (\rho(D*,f)= 0.32 vs 0.95 ) and consistency (CV_{NET} (D)= 0.028 vs 0.185 ;
Edit intent: meaning-changed
Original:  NMRSE(D*)= 0.46 vs 0.49 ), independence (\rho(D*,f)= 0.32 vs 0.95 ) and consistency (CV_{NET} (D)= 0.028 vs 0.185 ;
Revised:  NMRSE(D*)= 0.46 vs 0.49 ), independence (\rho(D*,f)= 0.22 vs 0.74 ) and consistency (CV_{NET} (D)= 0.028 vs 0.185 ;
Edit intent: meaning-changed
Original:  NMRSE(D*)= 0.46 vs 0.49 ), independence (\rho(D*,f)= 0.32 vs 0.95 ) and consistency (CV_{NET} (D)= 0.028 vs 0.185 ;
Revised:  NMRSE(D*)= 0.46 vs 0.49 ), independence (\rho(D*,f)= 0.32 vs 0.95 ) and consistency (CV_{NET} (D)= 0.01 vs 0.10 ;
Edit intent: meaning-changed
Original:  Due to the lack of methods to aware the environment, although many promising methods were proposed to improve the mobile machines' efficiency, most construction machinery engineers must focus on the most hazardous situation and have to give the highest priority to safety when designing. Consequently, the industry does not fully benefited from the existing research. Also, due to the dynamically changing of the construction site and the consequent absence of a high definition map, the localization of mobile machines is still a challengingtask.
Revised:  Due to the dynamically changing of the construction site and the consequent absence of a high definition map, the localization of mobile machines is still a challengingtask.
Edit intent: coherence
Original:  Also, due to the dynamically changing of the construction site and the consequent absence of a high definition map, the localization of mobile machines is still a challengingtask.
Revised:  Also, due to the dynamically changing of the construction site and the consequent absence of a high definition map, the Simultaneous Localization and Mapping (SLAM) offering the terrain information for construction machines is still a challengingtask.
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Original:  Also, due to the dynamically changing of the construction site and the consequent absence of a high definition map, the localization of mobile machines is still a challengingtask.
Revised:  Also, due to the dynamically changing of the construction site and the consequent absence of a high definition map, the localization of mobile machines is still challenging. Current SLAM technologies proposed for mobile machines are strongly dependent on costly or computationally expensive sensors, such as RTK GPS and cameras, so that commercial use is rare.
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Original:  As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of 1 IMU and 2 differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 2 \% in the harsh environment.
Revised:  As a result of our research gathered from Gazebo, we showed that a suitable layout is the combination of 1 IMU and 2 differential GPS antennas using the unscented Kalman filter, which keeps the average distance error lower than 2m and the mapping error lower than 1.3 \% in the harsh environment.
Edit intent: meaning-changed
Original:  Our DL models predict excellently the chronic stress exposure  , the individual psychological score , and FSI values at 34 weeks of gestation and maternal cortisol at birth  .
Revised:  Our DL models predict excellently the chronic stress exposure group (AUROC 0.982+/-0.002) , the individual psychological score , and FSI values at 34 weeks of gestation and maternal cortisol at birth .
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Original:  Our DL models predict excellently the chronic stress exposure  , the individual psychological score , and FSI values at 34 weeks of gestation and maternal cortisol at birth  .
Revised:  Our DL models predict excellently the chronic stress exposure , the individual psychological stress score (R2 0.943+/-0.009) , and FSI values at 34 weeks of gestation and maternal cortisol at birth .
Edit intent: meaning-changed
Original:  Our DL models predict excellently the chronic stress exposure  , the individual psychological score , and FSI values at 34 weeks of gestation and maternal cortisol at birth  .
Revised:  Our DL models predict excellently the chronic stress exposure , the individual psychological score , and FSI values at 34 weeks of gestation (R2 0.946+/-0.013) as well as the maternal hair cortisol at birth .
Edit intent: meaning-changed
Original:  Our DL models predict excellently the chronic stress exposure  , the individual psychological score , and FSI values at 34 weeks of gestation and maternal cortisol at birth  .
Revised:  Our DL models predict excellently the chronic stress exposure , the individual psychological score , and FSI values at 34 weeks of gestation and maternal cortisol at birth reflecting chronic stress exposure (R2 0.931+/-0.006) .
Edit intent: meaning-changed
Original:  Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about the utility of DL in non-invasive biometrics during pregnancy.
Revised:  Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about DL's utility in non-invasive biometrics during pregnancy.
Edit intent: clarity
Original:  Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about the utility of DL in non-invasive biometrics during pregnancy.
Revised:  Deep learning (DL) is capable of pattern detection in complex medical data with high accuracy in noisy real-life environments, but little is known about the utility of DL in non-invasive biometric monitoring during pregnancy.
Edit intent: clarity
Original:  We hypothesized that a recently established self-supervised learning (SSL) approach that provides emotional recognition from ECG will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG.
Revised:  A recently established self-supervised learning (SSL) approach that provides emotional recognition from ECG will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG.
Edit intent: coherence
Original:  We hypothesized that a recently established self-supervised learning (SSL) approach that provides emotional recognition from ECG will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG.
Revised:  We hypothesized that a recently established self-supervised learning (SSL) approach to DL provides emotional recognition from ECG will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG.
Edit intent: clarity
Original:  We hypothesized that a recently established self-supervised learning (SSL) approach that provides emotional recognition from ECG will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG.
Revised:  We hypothesized that a recently established self-supervised learning (SSL) approach that provides emotional recognition from electrocardiogram (ECG). We hypothesized that SSL will identify chronically stressed mother-fetus dyads from the raw maternal abdominal electrocardiograms (aECG), containing fetal and maternal ECG.
Edit intent: coherence
Original:  Our DL models predict excellently the chronic stress exposure group (AUROC  0.982+/-0.002), the individual psychological stress score (R2  0.943+/-0.009) , and FSI at 34 weeks of gestation (R2  0.946+/-0.013)  as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Revised:  Our DL models accurately detect the chronic stress exposure group (AUROC 0.982+/-0.002), the individual psychological stress score (R2 0.943+/-0.009) , and FSI at 34 weeks of gestation (R2 0.946+/-0.013) as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Edit intent: clarity
Original:  Our DL models predict excellently the chronic stress exposure group (AUROC  0.982+/-0.002), the individual psychological stress score (R2  0.943+/-0.009) , and FSI at 34 weeks of gestation (R2  0.946+/-0.013)  as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Revised:  Our DL models predict excellently the chronic stress exposure group (AUROC = 0.982+/-0.002), the individual psychological stress score (R2 0.943+/-0.009) , and FSI at 34 weeks of gestation (R2 0.946+/-0.013) as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Edit intent: clarity
Original:  Our DL models predict excellently the chronic stress exposure group (AUROC  0.982+/-0.002), the individual psychological stress score (R2  0.943+/-0.009) , and FSI at 34 weeks of gestation (R2  0.946+/-0.013)  as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Revised:  Our DL models predict excellently the chronic stress exposure group (AUROC 0.982+/-0.002), the individual psychological stress score (R2 = 0.943+/-0.009) , and FSI at 34 weeks of gestation (R2 0.946+/-0.013) as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Edit intent: clarity
Original:  Our DL models predict excellently the chronic stress exposure group (AUROC  0.982+/-0.002), the individual psychological stress score (R2  0.943+/-0.009) , and FSI at 34 weeks of gestation (R2  0.946+/-0.013)  as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Revised:  Our DL models predict excellently the chronic stress exposure group (AUROC 0.982+/-0.002), the individual psychological stress score (R2 0.943+/-0.009) and FSI at 34 weeks of gestation (R2 0.946+/-0.013) as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Edit intent: fluency
Original:  Our DL models predict excellently the chronic stress exposure group (AUROC  0.982+/-0.002), the individual psychological stress score (R2  0.943+/-0.009) , and FSI at 34 weeks of gestation (R2  0.946+/-0.013)  as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Revised:  Our DL models predict excellently the chronic stress exposure group (AUROC 0.982+/-0.002), the individual psychological stress score (R2 0.943+/-0.009) , and FSI at 34 weeks of gestation (R2 = 0.946+/-0.013) as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Edit intent: clarity
Original:  Our DL models predict excellently the chronic stress exposure group (AUROC  0.982+/-0.002), the individual psychological stress score (R2  0.943+/-0.009) , and FSI at 34 weeks of gestation (R2  0.946+/-0.013)  as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Revised:  Our DL models predict excellently the chronic stress exposure group (AUROC 0.982+/-0.002), the individual psychological stress score (R2 0.943+/-0.009) , and FSI at 34 weeks of gestation (R2 0.946+/-0.013) , as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Edit intent: fluency
Original:  Our DL models predict excellently the chronic stress exposure group (AUROC  0.982+/-0.002), the individual psychological stress score (R2  0.943+/-0.009) , and FSI at 34 weeks of gestation (R2  0.946+/-0.013)  as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( R2 0.931+/-0.006).
Revised:  Our DL models predict excellently the chronic stress exposure group (AUROC 0.982+/-0.002), the individual psychological stress score (R2 0.943+/-0.009) , and FSI at 34 weeks of gestation (R2 0.946+/-0.013) as well as the maternal hair cortisol at birth reflecting chronic stress exposure ( 0.931+/-0.006).
Edit intent: fluency
Original:  We do so by initializing a high-dimensional value function via supervision from a low-dimensional value function  obtained by applying model-based techniques on a low-dimensional problem featuring an approximate system model.
Revised:  We do so by initializing a high-dimensional value function via supervision from a low-dimensional value function . Such a lower-dimensional value function can be obtained by applying model-based techniques on a low-dimensional problem featuring an approximate system model.
Edit intent: meaning-changed
Original:  As an application of our results, for the hard-core model on independent sets weighted by a fugacity \lambda, we establish O(nn) mixing time for the Glauber dynamics on any n-vertex graph of constant maximum degree \Delta when \lambda<\lambda_c(\Delta) where \lambda_c(\Delta) is the critical point for the uniqueness/non-uniqueness phase transition on the \Delta-regular tree. More generally, for any antiferromagnetic 2-spin system we prove O(n\log{n) mixing time of the Glauber dynamics on any bounded degree graph in the corresponding tree uniqueness region.
Revised:  As an application of our results, for the hard-core model on independent sets weighted by a fugacity \lambda, we establish O(nn) mixing time for the Glauber dynamics on any n-vertex graph of constant maximum degree \Delta when ) mixing time of the Glauber dynamics on any bounded degree graph in the corresponding tree uniqueness region.
Edit intent: coherence
Original:  for example, we also obtain O(nn) mixing for q-colorings of triangle-free graphs of maximum degree \Delta when the number of colors satisfies q \geq \alpha \Delta + 1 } ) mixing time of the Glauber dynamics on any bounded degree graph in the corresponding tree uniqueness region.
Revised:  for example, we also obtain O(nn) mixing for q-colorings of triangle-free graphs of maximum degree \Delta when the number of colors satisfies q \geq \alpha \Delta + 1 } \lambda<\lambda_c(\Delta) where \lambda_c(\Delta) is the critical point for the uniqueness/non-uniqueness phase transition on the \Delta-regular tree. More generally, for any antiferromagnetic 2-spin system we prove O(n\log{n) mixing time of the Glauber dynamics on any bounded degree graph in the corresponding tree uniqueness region.
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Original:  This premise is evidenced in many empirical results. For example, the European Central Bank's Survey of Professional Forecasters on Euro-area real GDP growth demonstrates that the professional forecasters tend to jointly understate or overstate GDP growth. Motivated by this stylized fact, we develop a novel framework which exploits the factor structure of forecast errors and the sparsity in the precision matrix of the idiosyncratic components of the forecast errors.
Revised:  We propose a new approach, Factor Graphical Model (FGM), to forecast combinations that separates idiosyncratic forecast errors from the common errors. FGM exploits the factor structure of forecast errors and the sparsity in the precision matrix of the idiosyncratic components of the forecast errors.
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Original:  Motivated by this stylized fact, we develop a novel framework which exploits the factor structure of forecast errors and the sparsity in the precision matrix of the idiosyncratic components of the forecast errors. The proposed algorithm is called Factor Graphical Model (FGM). Our approach overcomes the challenge of obtaining the forecasts that contain unique information, which was shown to be necessary to achieve a "winning" forecast combination . In simulation, we demonstrate the merits of the FGM in comparison with the equal-weighted forecasts and the standard graphical methods in the literature. An empirical application to forecasting macroeconomic time series in big data environment highlights the advantage of the FGM approach in comparison with the existing methods of forecast combination .
Revised:  Motivated by this stylized fact, we develop a novel framework which exploits the factor structure of forecast errors and the sparsity in the precision matrix of the idiosyncratic errors. We prove the consistency of forecast combination weights and mean squared forecast error estimated using FGM, supporting the results with extensive simulations. Empirical applications to forecasting macroeconomic time series in big data environment highlights the advantage of the FGM approach in comparison with the existing methods of forecast combination .
Edit intent: meaning-changed
Original:  Specifically, we have analyzed traffic accidents associated with various demographic groups, how traffic accidents are distributed in time and space, and the severity level of traffic accidents that both involve and  not involve other transportation modes (e.g., pedestrians and motorists).
Revised:  Specifically, we have analyzed traffic accidents associated with various demographic groups, how traffic accidents are distributed in time and space, and the severity level of traffic accidents that both involve and do not involve other transportation modes (e.g., pedestrians and motorists).
Edit intent: clarity
Original:  We borrow similar proof techniques from \mbox{%DIFAUXCMD Abboud2017 the sub-linear time PCP , and show how to use it to prove both existing and new inapproximability results .
Revised:  We borrow similar proof techniques from \mbox{%DIFAUXCMD Abboud2017 the sub-linear PCP theorem by adapting from an MA-protocol for the Set Containment problem , and show how to use it to prove both existing and new inapproximability results .
Edit intent: meaning-changed
Original:  We borrow similar proof techniques from \mbox{%DIFAUXCMD Abboud2017 the sub-linear time PCP , and show how to use it to prove both existing and new inapproximability results .
Revised:  We borrow similar proof techniques from \mbox{%DIFAUXCMD Abboud2017 the sub-linear time PCP , and show how to use the theorem to prove both existing and new inapproximability results .
Edit intent: clarity
Original:  We borrow similar proof techniques from \mbox{%DIFAUXCMD Abboud2017 the sub-linear time PCP , and show how to use it to prove both existing and new inapproximability results . These results exhibits the power of the sub-linear PCP theorem proposed in this paper . Considering the emerging research works on sub-linear time algorithms, the sub-linear PCP theorem is important in guiding the research in sub-linear time approximation algorithms.
Revised:  We borrow similar proof techniques from \mbox{%DIFAUXCMD Abboud2017 the sub-linear time PCP , and show how to use it to prove both existing and new inapproximability results , exhibiting the power of the sub-linear PCP theorem proposed in this paper . Considering the emerging research works on sub-linear time algorithms, the sub-linear PCP theorem is important in guiding the research in sub-linear time approximation algorithms.
Edit intent: coherence
Original:  These results exhibits the power of the sub-linear PCP theorem proposed in this paper . Considering the emerging research works on sub-linear time algorithms, the sub-linear PCP theorem is important in guiding the research in sub-linear time approximation algorithms.
Revised:  These results exhibits the power of the sub-linear PCP theorem . Considering the emerging research works on sub-linear time algorithms, the sub-linear PCP theorem is important in guiding the research in sub-linear time approximation algorithms.
Edit intent: coherence
Original: Many methods for debiasing classifiers have been proposed, but their effectiveness in practice remains unclear. We evaluate the performance of pre-processing and post-processing debiasers for improving fairness in random forest classifierstrained on a suite of data sets.
Revised: We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and post-processing debiasers for improving fairness in random forest classifierstrained on a suite of data sets.
Edit intent: meaning-changed