wanyu/IteraTeR_v2 huggingface dataset:
Original:  We propose a canonical evaluation ordering that both reduces FP error and the size of the search space based on Huffman coding  .
Revised:  We propose a canonical evaluation ordering based on Huffman coding .
Edit intent: clarity
Original:  We study point selection experimentally, and find empirically good points .
Revised:  We study the selection of sampling "points" experimentally and find empirically good points .
Edit intent: clarity
Original:  We also identify the main factors that associated with sets of pointsthat result in a low error .
Revised:  We also identify the main factors associated with good points .
Edit intent: clarity
Original:  In addition, we explore other methods to reduce FP error, including mixed-precision convolution, and pairwise addition across DNN channels.
Revised:  In addition, we explore other methods to reduce FP error, including mixed-precision convolution, and pairwise summation across DNN channels.
Edit intent: clarity
Original: In mathematical models with uncertainties and noise , the calculation of a deterministic " quantity of interest " (model output ) is often replaced by the calculation of its moments (mean, standard deviation, etc.) and probability density function.
Revised: The effect of model uncertainties and noise , the calculation of a deterministic " quantity of interest " (model output ) is often replaced by the calculation of its moments (mean, standard deviation, etc.) and probability density function.
Edit intent: clarity
Original: In mathematical models with uncertainties and noise , the calculation of a deterministic " quantity of interest " (model output ) is often replaced by the calculation of its moments (mean, standard deviation, etc.) and probability density function.
Revised: In mathematical models with uncertainties and noise on a quantity of interest " (model output ) is often replaced by the calculation of its moments (mean, standard deviation, etc.) and probability density function.
Edit intent: clarity
Original: In mathematical models with uncertainties and noise , the calculation of a deterministic " quantity of interest " (model output ) is often replaced by the calculation of its moments (mean, standard deviation, etc.) and probability density function.
Revised: In mathematical models with uncertainties and noise , the calculation of a deterministic " quantity of interest ("model output" ) is often replaced by the calculation of its moments (mean, standard deviation, etc.) and probability density function.
Edit intent: fluency
Original:  In this paper we present a novel spline-based algorithm for these tasks .
Revised:  In this paper we present a novel spline-based algorithm for this task .
Edit intent: clarity
Original:  Our method offers significant advantages over the existing methods for density estimation, including a guaranteed convergence rate which is at least cubic in the number of samples .
Revised:  Our method offers significant advantages over existing methods for density estimation, including a guaranteed convergence rate which is at least cubic in the number of samples .
Edit intent: fluency
Original:  Furthermore, although spectral methods can approximate moments with exponential accuracy  , the spline-based approximation is often more accurate when the sample size is small  .
Revised:  Furthermore, although spectral methods approximate moments with exponential accuracy , the spline-based approximation is often more accurate when the sample size is small .
Edit intent: clarity
Original:  Furthermore, although spectral methods can approximate moments with exponential accuracy  , the spline-based approximation is often more accurate when the sample size is small  .
Revised:  Furthermore, although spectral methods can approximate moments with exponential accuracy , the spline-based approximation can be more accurate when the sample size is small .
Edit intent: clarity
Original: The effect of model uncertainties and noise on a quantity of interest ( "model output" ) is often better described by its probability density function (PDF) than by its moments (mean, standard deviation, etc. ).
Revised: The effect of uncertainties and noise on a quantity of interest ( "model output" ) is often better described by its probability density function (PDF) than by its moments (mean, standard deviation, etc. ).
Edit intent: fluency
Original: The effect of model uncertainties and noise on a quantity of interest ( "model output" ) is often better described by its probability density function (PDF) than by its moments (mean, standard deviation, etc. ).
Revised: The effect of model uncertainties and noise on a quantity of interest ( model output ) is often better described by its probability density function (PDF) than by its moments (mean, standard deviation, etc. ).
Edit intent: clarity
Original: The effect of model uncertainties and noise on a quantity of interest ( "model output" ) is often better described by its probability density function (PDF) than by its moments (mean, standard deviation, etc. ).
Revised: The effect of model uncertainties and noise on a quantity of interest ( "model output" ) is often better described by its probability density function (PDF) than by its moments .
Edit intent: clarity
Original:  Although density estimation is a common uncertainty-quantification (UQ) task, the adequacy of approximation methods ( surrogates ) for density estimation has scarcely been analyzed before  .
Revised:  Although density estimation is a common task, the adequacy of approximation methods ( surrogates ) for density estimation has scarcely been analyzed before .
Edit intent: clarity
Original:  Although density estimation is a common uncertainty-quantification (UQ) task, the adequacy of approximation methods ( surrogates ) for density estimation has scarcely been analyzed before  .
Revised:  Although density estimation is a common uncertainty-quantification (UQ) task, the adequacy of approximation methods ( surrogate models ) for density estimation has scarcely been analyzed before .
Edit intent: clarity
Original:  Although density estimation is a common uncertainty-quantification (UQ) task, the adequacy of approximation methods ( surrogates ) for density estimation has scarcely been analyzed before  .
Revised:  Although density estimation is a common uncertainty-quantification (UQ) task, the adequacy of approximation methods ( surrogates ) for density estimation has not been analyzed before .
Edit intent: clarity
Original:  In this paper, we first show that standard methods in uncertainty propagation (such as generalized polynomial chaos), which are highly accurate for moment estimation, sometimes fail to approximate the PDF even in the case of one-dimensional noise.
Revised:  In this paper, we first show that standard surrogate models (such as generalized polynomial chaos), which are highly accurate for moment estimation, sometimes fail to approximate the PDF even in the case of one-dimensional noise.
Edit intent: clarity
Original:  In this paper, we first show that standard methods in uncertainty propagation (such as generalized polynomial chaos), which are highly accurate for moment estimation, sometimes fail to approximate the PDF even in the case of one-dimensional noise.
Revised:  In this paper, we first show that standard methods in uncertainty propagation (such as generalized polynomial chaos), which are highly accurate for moment estimation, sometimes fail to approximate the PDF , even for one-dimensional noise.
Edit intent: coherence
Original:  Finally, we demonstrate our algorithm for problems in nonlinear optics and computational fluid dynamics.
Revised:  Finally, we demonstrate our algorithm for problems in nonlinear optics and fluid dynamics.
Edit intent: clarity
Original: Power dissipation is the main limitation of all the nano electronics design techniques including Quantum-dot Cellular Automata (QCA).
Revised: Power dissipation is known as the most notable limiting factor in all nano-electronic design techniques including Quantum-dot Cellular Automata (QCA).
Edit intent: clarity
Original:  The reversible computing is considered as the reliable solutionto lower the power dissipation.
Revised:  The familiar reversible computing approach is used as a reasonably reliable solution, mitigating power dissipation.
Edit intent: clarity
Original:  Results show that our proposed method outperforms the best available methods in terms of area, complexity (cell amount), delay (clocking zones), and also in logic level in terms of levels , Control inputs,  number of majority and NOT gates.
Revised:  Results show that our proposed method outperforms the best available methods in terms of area, complexity (cell amount), delay (clocking zones), and to even logical levels including levels , Control inputs, number of majority and NOT gates.
Edit intent: clarity
Original:  Results show that our proposed method outperforms the best available methods in terms of area, complexity (cell amount), delay (clocking zones), and also in logic level in terms of levels , Control inputs,  number of majority and NOT gates.
Revised:  Results show that our proposed method outperforms the best available methods in terms of area, complexity (cell amount), delay (clocking zones), and also in logic level in terms of levels , Control inputs, the number of majority and NOT gates.
Edit intent: fluency
Original: Based on the Hermite expansion of the distribution function, we introduce a Galerkin spectral method for the spatially homogeneous Boltzmann equation with the realistic inverse- power-law models.
Revised: Based on the Hermite expansion of the distribution function, we introduce a Galerkin spectral method for the spatially homogeneous Boltzmann equation with the realistic inverse-power-law models.
Edit intent: fluency
Original:  Furthermore, many methods behave as black boxes, therefore we have very little understanding about the mechanisms that lead to the prediction provided.
Revised:  Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction provided.
Edit intent: coherence
Original:  While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power  limiting their application in a clinical setting.
Revised:  While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization power , limiting their application in a clinical setting.
Edit intent: fluency
Original:  Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction provided .
Revised:  Furthermore, many methods behave as black boxes, and we have very little understanding about the mechanisms that lead to the prediction .
Edit intent: clarity
Original:  We  show empirically that CNN prediction for a given test sample depends on the labels of the training samples in its local neighborhood.
Revised:  We first show empirically that CNN prediction for a given test sample depends on the labels of the training samples in its local neighborhood.
Edit intent: coherence
Original:  We  show empirically that CNN prediction for a given test sample depends on the labels of the training samples in its local neighborhood.
Revised:  We show empirically that a CNN's prediction for a given test sample depends on the labels of the training samples in its local neighborhood.
Edit intent: clarity
Original:  Experiments on real datasets validate our analytical expression by showing that they match the empirical results for varying degrees of label noise .
Revised:  Experiments on real datasets validate our analytical expression by showing that the more concentrated the noise is (in the data), the greater the degration in performance .
Edit intent: clarity
Original: We investigate the multi-class classification performance of K-Nearest Neighbors (K-NN) and Convolutional Neural Networks (CNNs ) in the presence of label noise.
Revised: We investigate the classification performance of K-Nearest Neighbors (K-NN) and Convolutional Neural Networks (CNNs ) in the presence of label noise.
Edit intent: clarity
Original: We investigate the multi-class classification performance of K-Nearest Neighbors (K-NN) and Convolutional Neural Networks (CNNs ) in the presence of label noise.
Revised: We investigate the multi-class classification performance of K-nearest neighbors (K-NN) and Convolutional Neural Networks (CNNs ) in the presence of label noise.
Edit intent: fluency
Original:  We first show empirically that a CNN 's prediction for a given test sample depends on the labels of the training samples in its local neighborhood.
Revised:  We first show empirically that a CNN 's prediction for a given test example depends on the labels of the training samples in its local neighborhood.
Edit intent: clarity
Original:  We first show empirically that a CNN 's prediction for a given test sample depends on the labels of the training samples in its local neighborhood.
Revised:  We first show empirically that a CNN 's prediction for a given test sample depends on the labels of the training examples in its local neighborhood.
Edit intent: clarity
Original:  Our results may explain the already observed surprising resistance of CNNs to some types of label noise.
Revised:  Our result may explain the already observed surprising resistance of CNNs to some types of label noise.
Edit intent: fluency
Original:  In particular, it charcterizes an important factor in this resistance, by showing that the more concentrated the noise is (in the data), the greater the degration in performance.
Revised:  It also characterizes an important factor in this resistance, by showing that the more concentrated the noise is (in the data), the greater the degration in performance.
Edit intent: coherence
Original:  In particular, it charcterizes an important factor in this resistance, by showing that the more concentrated the noise is (in the data), the greater the degration in performance.
Revised:  In particular, it charcterizes an important factor of it showing that the more concentrated the noise is (in the data), the greater the degration in performance.
Edit intent: clarity
Original:  In particular, it charcterizes an important factor in this resistance, by showing that the more concentrated the noise is (in the data), the greater the degration in performance.
Revised:  In particular, it charcterizes an important factor in this resistance, by showing that the more concentrated the noise the degration in performance.
Edit intent: clarity
Original:  In particular, it charcterizes an important factor in this resistance, by showing that the more concentrated the noise is (in the data), the greater the degration in performance.
Revised:  In particular, it charcterizes an important factor in this resistance, by showing that the more concentrated the noise is (in the data), the greater the greater is the degradation in performance.
Edit intent: clarity
Original:  State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks which require huge amount of labeled data for training and prove impracticable in robotics.
Revised:  State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks . However, such networks require huge amount of labeled data for training and prove impracticable in robotics.
Edit intent: clarity
Original:  State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks which require huge amount of labeled data for training and prove impracticable in robotics.
Revised:  State-of-the-art robotic grasping methods perform prediction of object grasp locations based on deep neural networks which require huge amount of labeled data for training making this approach often impracticable in robotics.
Edit intent: clarity
Original:  In this paper, we propose  to generate a large scale synthetic dataset with ground truth, which we refer to as the Jacquard grasping dataset.
Revised:  In this paper, we propose a method to generate a large scale synthetic dataset with ground truth, which we refer to as the Jacquard grasping dataset.
Edit intent: clarity
Original:  Specifically, the proposed Jacquard dataset builds on a subset of ShapeNet with its numerous shape objects and features at a scale of millions varied object grasping positions for a large diversity of more than 11k objects.
Revised:  Jacquard is built on a subset of ShapeNet with its numerous shape objects and features at a scale of millions varied object grasping positions for a large diversity of more than 11k objects.
Edit intent: clarity
Original: We study how individual use of V\'elo' v - a bike sharing system located in Lyon, France - evolves over time, using a 5-year dataset covering 121,000 long-term distinct users  . Users follow two main trajectories . About 60\% stay in the system  at most one year, showing a low median activity (47 trips) .
Revised: We study how individual use of V\'elo' v - a BSS located in Lyon, France - evolves over time, using a 5-year dataset covering 121,000 long-term distinct users . Users follow two main trajectories . About 60\% stay in the system at most one year, showing a low median activity (47 trips) .
Edit intent: clarity
Original: We study how individual use of V\'elo' v - a bike sharing system located in Lyon, France - evolves over time, using a 5-year dataset covering 121,000 long-term distinct users  . Users follow two main trajectories . About 60\% stay in the system  at most one year, showing a low median activity (47 trips) .
Revised: We study how individual use of V\'elo' v - a bike sharing system located in Lyon, France - evolves over time, using a 5-year dataset covering 121,000 long-term distinct users . Users follow two main trajectories : about 60\% stay in the system at most one year, showing a low median activity (47 trips) .
Edit intent: fluency
Original: We study how individual use of V\'elo' v - a bike sharing system located in Lyon, France - evolves over time, using a 5-year dataset covering 121,000 long-term distinct users  . Users follow two main trajectories . About 60\% stay in the system  at most one year, showing a low median activity (47 trips) .
Revised: We study how individual use of V\'elo' v - a bike sharing system located in Lyon, France - evolves over time, using a 5-year dataset covering 121,000 long-term distinct users . Users follow two main trajectories . About 60\% remain in the system at most one year, showing a low median activity (47 trips) .
Edit intent: fluency
Original: We study how individual use of V\'elo' v - a bike sharing system located in Lyon, France - evolves over time, using a 5-year dataset covering 121,000 long-term distinct users  . Users follow two main trajectories . About 60\% stay in the system  at most one year, showing a low median activity (47 trips) .
Revised: We study how individual use of V\'elo' v - a bike sharing system located in Lyon, France - evolves over time, using a 5-year dataset covering 121,000 long-term distinct users . Users follow two main trajectories . About 60\% stay in the system for at most one year, showing a low median activity (47 trips) .
Edit intent: fluency
Original:  The remaining 40\% correspond to more active users (median activity of 96 trips on their first year) that remain continuously active for several years (mean time = 2.9 years) and show a relatively stable activity, slightly decreasing over the years.
Revised:  The remaining 40\% correspond to more active users (median activity of 96 trips in their first year) that remain continuously active for several years (mean time = 2.9 years) and show a relatively stable activity, slightly decreasing over the years.
Edit intent: clarity
Original:  The remaining 40\% correspond to more active users (median activity of 96 trips on their first year) that remain continuously active for several years (mean time = 2.9 years) and show a relatively stable activity, slightly decreasing over the years.
Revised:  The remaining 40\% correspond to more active users (median activity of 96 trips on their first year) that remain continuously active for several years (mean time = 2.9 years) . This latter class exhibits a relatively stable activity, slightly decreasing over the years.
Edit intent: clarity
Original:  The remaining 40\% correspond to more active users (median activity of 96 trips on their first year) that remain continuously active for several years (mean time = 2.9 years) and show a relatively stable activity, slightly decreasing over the years.
Revised:  The remaining 40\% correspond to more active users (median activity of 96 trips on their first year) that remain continuously active for several years (mean time = 2.9 years) and show a relatively stable activity, decreasing slightly over the years.
Edit intent: fluency
Original:  Old , male and urban users are far more likely to represent "stable" users.
Revised:  Old , male and urban users are over represented among the 'stable' users.
Edit intent: clarity
Original:  Codes without local obstructions are called locally good, because the obstruction precludes the code from arising from open sets that form a good cover.
Revised:  Codes without local obstructions are called locally good, because the obstruction precludes the code from encoding the intersections of open sets that form a good cover.
Edit intent: clarity
Original:  First we take an audio stream recorded within the aviary near the time of death to unfold the local embedding dimension, thereby enumerating the suspects.
Revised:  First we take an audio stream recorded within the aviary near the time of death to identify the local embedding dimension, thereby enumerating the suspects.
Edit intent: clarity
Original:  Then we uniquely identify the murderer.
Revised:  Third, we uniquely identify the murderer.
Edit intent: fluency
Original:  Third, we uniquely identify the murderer.
Revised:  Then we uniquely identify the murderer.
Edit intent: fluency
Original:  thus the expert' s perspectives have influenced the current systems of the scientific classification.
Revised:  thus , the experts' perspectives have influenced the current systems of the scientific classification.
Edit intent: fluency
Original:  thus the expert' s perspectives have influenced the current systems of the scientific classification.
Revised:  thus the expert' s perspectives have influenced the current systems of scientific classification.
Edit intent: fluency
Original:  Those systems archived  current state-of-art in practice, yet acceleration of scientific turnover over time reported so that the global effect of accelerating make it impossible to respond to update of the classifications system by experts .
Revised:  Those systems archived the current state-of-art in practice, yet acceleration of scientific turnover over time reported so that the global effect of accelerating make it impossible to respond to update of the classifications system by experts .
Edit intent: fluency
Original:  Those systems archived  current state-of-art in practice, yet acceleration of scientific turnover over time reported so that the global effect of accelerating make it impossible to respond to update of the classifications system by experts .
Revised:  Those systems archived current state-of-art in practice, yet the global effect of the accelerating scientific change over time has made the updating of the classifications system by experts .
Edit intent: clarity