wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To remedy this ,  more complex notions of value-sharing from economics and game-theory, such as the Shapley value need to be used to capture the effect of mixing datasets on the accuracy of forecasting algorithms driven by them .
Revised:  To remedy this , more complex notions of value-sharing from economics and game-theory, such as the Shapley value need to be used to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms driven by them .
Edit intent: clarity
Original:  To remedy this ,  more complex notions of value-sharing from economics and game-theory, such as the Shapley value need to be used to capture the effect of mixing datasets on the accuracy of forecasting algorithms driven by them .
Revised:  To remedy this , more complex notions of value-sharing from economics and game-theory, such as the Shapley value need to be used to capture the effect of mixing datasets on the accuracy of forecasting algorithms .
Edit intent: clarity
Original:  We use structured sampling to overcome such scalability challenges and manage to compute accurately the importance of different data sources, even when their number ranges in the thousands, as in the case of all the taxi drivers in a large metropolis .
Revised:  We use structured sampling to overcome such scalability challenges and manage to compute accurately the importance of thousands of data sources. We show that the relative value of the data held by different taxi companies and drivers may differ substantially, and that its relative ranking may change from district to district within a metropolitan area .
Edit intent: meaning-changed
Original:  Using a simulation experiment, I show that their MUE procedure generates excessively large estimates of \lambda _{z} when applied to data simulated from a model where the true \lambda _{z} is equal to zero.
Revised:  Using a simulation experiment, I show that their procedure generates excessively large estimates of \lambda _{z} when applied to data simulated from a model where the true \lambda _{z} is equal to zero.
Edit intent: clarity
Original:  Using a simulation experiment, I show that their MUE procedure generates excessively large estimates of \lambda _{z} when applied to data simulated from a model where the true \lambda _{z} is equal to zero.
Revised:  Using a simulation experiment, I show that their MUE procedure generates excessively large estimates of \lambda _{z} when applied to data generated from a model where the true \lambda _{z} is equal to zero.
Edit intent: clarity
Original:  Correcting  their Stage 2 MUE procedure leads to a substantially smaller estimate of \lambda _{z a more subdued downward trending influence of ` other factor' z_{t} on the natural rate.
Revised:  Correcting the misspecification in their Stage 2 MUE procedure leads to a substantially smaller estimate of \lambda _{z a more subdued downward trending influence of ` other factor' z_{t} on the natural rate.
Edit intent: meaning-changed
Original:  Correcting  their Stage 2 MUE procedure leads to a substantially smaller estimate of \lambda _{z a more subdued downward trending influence of ` other factor' z_{t} on the natural rate.
Revised:  Correcting their Stage 2 model and the implementation of MUE leads to a substantially smaller estimate of \lambda _{z a more subdued downward trending influence of ` other factor' z_{t} on the natural rate.
Edit intent: clarity
Original:  Correcting  their Stage 2 MUE procedure leads to a substantially smaller estimate of \lambda _{z a more subdued downward trending influence of ` other factor' z_{t} on the natural rate.
Revised:  Correcting their Stage 2 MUE procedure leads to a substantially smaller \lambda _{z a more subdued downward trending influence of ` other factor' z_{t} on the natural rate.
Edit intent: clarity
Original:  Correcting  their Stage 2 MUE procedure leads to a substantially smaller estimate of \lambda _{z a more subdued downward trending influence of ` other factor' z_{t} on the natural rate.
Revised:  Correcting their Stage 2 MUE procedure leads to a substantially smaller estimate of \lambda _{z a more subdued downward trending influence of ' other factor' z_{t} on the natural rate.
Edit intent: fluency
Original:  This correction is quantitatively important. With everything else remaining the same in the model, the natural rate of interest is estimated to be 1.5\% at the end of 2019:Q2; that is, three times the 0.5\% estimate obtained from Holston et al.'s (2017) original Stage 2 MUE implementation .
Revised:  Moreover, the \lambda _{z .
Edit intent: meaning-changed
Original:  In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and }]  the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Revised:  In this paper, we provide a new feature selection method which uses singular value decomposition of a matrix and }] the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Edit intent: clarity
Original:  In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and }]  the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Revised:  In this paper, we propose a new feature selection method (DRPT) that consists of first removing the irrelevant features and then detecting correlations between the remaining features. Let D= }] the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Edit intent: meaning-changed
Original:  In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and }]  the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Revised:  In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and A\mid \mathbf{b}] the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Edit intent: meaning-changed
Original:  In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and }]  the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Revised:  In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and }] be a dataset, where \mathbf{b the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Edit intent: meaning-changed
Original:  In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and }]  the method of least squares to remove the irrelevant features and detect correlations between the remaining features . and remove those features whose weights are smaller than the threshold.
Revised:  In this paper, we propose a new feature selection method which uses singular value decomposition of a matrix and }] the corresponding column (feature). We define a threshold based on the local maxima of \mathbf{x and remove those features whose weights are smaller than the threshold.
Edit intent: meaning-changed
Original:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}.
Revised:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde A of A. We prove that correlations are encoded in \Delta\mathbf{x=\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}.
Edit intent: meaning-changed
Original:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}.
Revised:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\mid , where\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}.
Edit intent: meaning-changed
Original:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}.
Revised:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} is the least quares solution of\tilde \tilde{\mathbf{x}}.
Edit intent: meaning-changed
Original:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}.
Revised:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde A\tilde{\mathbf{x}}.
Edit intent: meaning-changed
Original:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}.
Revised:  To detect the correlations in the reduced matrix, which we still call A, we consider a perturbation }\tilde =\mid x -}\mathbf{x}\tilde{\mathbf{x}} \tilde \tilde{\mathbf{x}}=\mathbf{b.
Edit intent: meaning-changed
Original:  } The effectiveness of our method has been verified by performing a series of comparisons with  state-of-the-art feature selection methods over ten genetic datasets ranging up from 9,117 to 267,604 features.
Revised:  } The effectiveness of DRPT has been verified by performing a series of comparisons with state-of-the-art feature selection methods over ten genetic datasets ranging up from 9,117 to 267,604 features.
Edit intent: meaning-changed
Original:  } The effectiveness of our method has been verified by performing a series of comparisons with  state-of-the-art feature selection methods over ten genetic datasets ranging up from 9,117 to 267,604 features.
Revised:  } The effectiveness of our method has been verified by performing a series of comparisons with seven state-of-the-art feature selection methods over ten genetic datasets ranging up from 9,117 to 267,604 features.
Edit intent: meaning-changed
Original:  Here, we take a simulation approach to estimate power and classification accuracy for popular analysis pipelines . We systematically varied cluster size, number of clusters, number of different features between clusters, effect sizewithin each different feature, and cluster covariance structurein generated datasets .
Revised:  Here, we estimated power and accuracy for common analysis pipelines through simulation. We varied subgroup size, number of clusters, number of different features between clusters, effect sizewithin each different feature, and cluster covariance structurein generated datasets .
Edit intent: clarity
Original:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and  cosine distance, HDBSCAN).
Revised:  We then subjected generated datasets to dimensionality reduction (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and cosine distance, HDBSCAN).
Edit intent: clarity
Original:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and  cosine distance, HDBSCAN).
Revised:  We then subjected these datasets to common dimensionality reduction approaches (none, multidimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and cosine distance, HDBSCAN).
Edit intent: fluency
Original:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and  cosine distance, HDBSCAN).
Revised:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or UMAP ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and cosine distance, HDBSCAN).
Edit intent: meaning-changed
Original:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and  cosine distance, HDBSCAN).
Revised:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, agglomerative hierarchical clustering with Ward linkage and Euclidean distance, or average linkage and cosine distance, HDBSCAN).
Edit intent: fluency
Original:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and  cosine distance, HDBSCAN).
Revised:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward or average linkage and cosine distance, HDBSCAN).
Edit intent: clarity
Original:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and  cosine distance, HDBSCAN).
Revised:  We then subjected these datasets to common dimensionality reduction approaches (none, multi-dimensional scaling, or uniform manifold approximation and projection ) and cluster algorithms (k-means, hierarchical agglomerative clustering with Ward linkage and Euclidean distance, or average linkage and Euclidean or cosine distance, HDBSCAN).
Edit intent: meaning-changed
Original:  Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious  alternative for identifying separable multivariate normal distributions, particularly those with  lower centroid separation  } .
Revised:  Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious alternative for identifying separable multivariate normal distributions, particularly those with slightly lower centroid separation } .
Edit intent: meaning-changed
Original:  Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious  alternative for identifying separable multivariate normal distributions, particularly those with  lower centroid separation  } .
Revised:  Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious alternative for identifying separable multivariate normal distributions, particularly those with lower centroid separation \Delta} .
Edit intent: meaning-changed
Original:  Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious  alternative for identifying separable multivariate normal distributions, particularly those with  lower centroid separation  } .
Revised:  Finally, we discuss whether fuzzy clustering (c-means) could provide a more parsimonious alternative for identifying separable multivariate normal distributions, particularly those with lower centroid separation }=3). Overall, we recommend that researchers 1) only apply cluster analysis when large subgroup separation is expected, 2) aim for sample sizes of N=20 to N=30 per expected subgroup, 3) use multidimensional scaling to improve cluster separation, and 4) use fuzzy clustering or finite mixture modelling approaches that are more powerful and more parsimonious with partially overlapping multivariate normal distributions .
Edit intent: meaning-changed
Original: In geographic information systems, Digital Elevation Models (DEMs) are commonly processed using radial scanning based algorithms. These algorithms are particularly popular when calculating parameters whose magnitudes decrease with the distance squared such as those related to radio signals, sound waves , and human eyesight.
Revised: In geographic information systems, Digital Elevation Models (DEMs) are important datasets for modelling the line of sight, such as radio signals, sound waves , and human eyesight.
Edit intent: clarity
Original:  These algorithms are particularly popular when calculating parameters whose magnitudes decrease with the distance squared such as those related to radio signals, sound waves , and human eyesight. However, radial scanning algorithms imply a large number of accesses to 2D arrays , which despite being regular, results in poor data locality .
Revised:  These algorithms are particularly popular when calculating parameters whose magnitudes decrease with the distance squared such as those related to radio signals, sound waves and human vision. These are commonly analyzed using rotational sweep algorithms. However, such algorithms require large numbers of memory accesses to 2D arrays , which despite being regular, results in poor data locality .
Edit intent: meaning-changed
Original:  However, radial scanning algorithms imply a large number of accesses to 2D arrays , which despite being regular, results in poor data locality . This paper proposes a new methodology , termed sDEM , which substantially improves the locality of memory accesses and largely increases the inherent parallelism involved in the computation of radial scanning algorithms.
Revised:  However, radial scanning algorithms imply a large number of accesses to 2D arrays , which despite being regular, results in poor data locality in memory. Here, we propose a new methodology , termed sDEM , which substantially improves the locality of memory accesses and largely increases the inherent parallelism involved in the computation of radial scanning algorithms.
Edit intent: meaning-changed
Original:  This paper proposes a new methodology , termed sDEM , which substantially improves the locality of memory accesses and largely increases the inherent parallelism involved in the computation of radial scanning algorithms.
Revised:  This paper proposes a new methodology called skewed Digital Elevation Model (sDEM) , which substantially improves the locality of memory accesses and largely increases the inherent parallelism involved in the computation of radial scanning algorithms.
Edit intent: meaning-changed
Original:  This paper proposes a new methodology , termed sDEM , which substantially improves the locality of memory accesses and largely increases the inherent parallelism involved in the computation of radial scanning algorithms.
Revised:  This paper proposes a new methodology , termed sDEM , which substantially improves the locality of memory accesses and largely increases the inherent parallelism involved in the computation of rotational sweep-based algorithms.
Edit intent: meaning-changed
Original:  In order to demonstrate the high efficiency of sDEM, we use the problem of total viewshed computation as a case study . Sequential, parallel , single-GPU and multi-GPU implementations are analyzed and compared with the state-of-the-art total viewshed computation algorithm.
Revised:  In order to demonstrate the high efficiency of sDEM, we use the problem of total viewshed computation as a case study considering different implementations for single-core, multi-core , single-GPU and multi-GPU implementations are analyzed and compared with the state-of-the-art total viewshed computation algorithm.
Edit intent: meaning-changed
Original:  Sequential, parallel , single-GPU and multi-GPU implementations are analyzed and compared with the state-of-the-art total viewshed computation algorithm.
Revised:  Sequential, parallel , single-GPU and multi-GPU platforms. We conducted two experiments to compare sDEM with (i) the most commonly used geographic information systems (GIS) software and (ii) the state-of-the-art total viewshed computation algorithm.
Edit intent: meaning-changed
Original:  Sequential, parallel , single-GPU and multi-GPU implementations are analyzed and compared with the state-of-the-art total viewshed computation algorithm. Experiments show that sDEM achieves an acceleration rate of up to 827.3 times for the best multi-GPU execution approach with respect to the best multi-core implementation .
Revised:  Sequential, parallel , single-GPU and multi-GPU implementations are analyzed and compared with the state-of-the-art algorithm. In the first experiment, sDEM is on average 8.8x faster than current GIS software despite being able to consider only few points because of their limitations. In the second experiment, sDEM is 827.3 times for the best multi-GPU execution approach with respect to the best multi-core implementation .
Edit intent: meaning-changed
Original: We show that computing the Tutte polynomial of a linear matroid of dimension k on k^{O(1)} points over a field of k^{O(1)} elements requires k^{\Omega(k)} time unless the  ETH---a counting extension of the Exponential Time Hypothesis of Impagliazzo and Paturi [CCC 1999] due to Dell {\em et al.
Revised: We show that computing the Tutte polynomial of a linear matroid of dimension k on k^{O(1)} points over a field of k^{O(1)} elements requires k^{\Omega(k)} time unless the \# ETH---a counting extension of the Exponential Time Hypothesis of Impagliazzo and Paturi [CCC 1999] due to Dell {\em et al.
Edit intent: meaning-changed
Original:  Moreover, we also show that the same is true for computing the Tutte polynomial of a binary matroid of dimension k on k^{O(1)} points with at most three nonzero coordinates in each point's vector.
Revised:  We also show that the same is true for computing the Tutte polynomial of a binary matroid of dimension k on k^{O(1)} points with at most three nonzero coordinates in each point's vector.
Edit intent: coherence