wanyu/IteraTeR_v2 huggingface dataset:
Original:  Although other Boolean counter-examples have been found recently, this result illustrates the general relevance of our method for the study of multilevel logical models.
Revised:  This result illustrates the general relevance of our method for the study of multilevel logical models.
Edit intent: coherence
Original: In this work we study the limit distribution of an appropriately normalized cophenetic index of the pure birth tree on n contemporary tips.
Revised: In this work we study the limit distribution of an appropriately normalized cophenetic index of the pure-birth tree conditioned on n contemporary tips.
Edit intent: clarity
Original:  We link our work with studies on trees without branch lengths and show that in this case the limit distribution is a contraction type distribution, similar to the Quicksort limit distribution.
Revised:  We link our work with studies on trees without branch lengths and show that in this case the limit distribution is a contraction-type distribution, similar to the Quicksort limit distribution.
Edit intent: fluency
Original:  Therefore, we postulate using quantiles of the derived distributions for hypothesis testing, whether an observed phylogenetic tree is consistent with the pure birth process.
Revised:  Therefore, we postulate using quantiles of the derived distributions for hypothesis testing, whether an observed phylogenetic tree is consistent with the pure-birth process.
Edit intent: fluency
Original:  Therefore, we postulate using quantiles of the derived distributions for hypothesis testing, whether an observed phylogenetic tree is consistent with the pure-birth process.
Revised:  Therefore, we propose a way based on the quantiles of the derived distributions for hypothesis testing, whether an observed phylogenetic tree is consistent with the pure-birth process.
Edit intent: clarity
Original:  Simulating a sample by the proposed heuristics is rapid  while exact simulation (simulating the tree and then calculating the index) is a time-consuming procedure.
Revised:  Simulating a sample by the proposed heuristics is rapid , while exact simulation (simulating the tree and then calculating the index) is a time-consuming procedure.
Edit intent: fluency
Original:  We conduct a power study that indicates that the cophenetic index (with branch lengths) is much better at detecting deviations from the Yule tree and apply the indices to empirical phylogenies.
Revised:  We conduct a power study to investigate how well the cophenetic indices detect deviations from the Yule tree and apply the indices to empirical phylogenies.
Edit intent: clarity
Original:  We conduct a power study that indicates that the cophenetic index (with branch lengths) is much better at detecting deviations from the Yule tree and apply the indices to empirical phylogenies.
Revised:  We conduct a power study that indicates that the cophenetic index (with branch lengths) is much better at detecting deviations from the Yule tree and apply the methodology to empirical phylogenies.
Edit intent: clarity
Original:  Dimensionality reduction is often sought  since many high-dimensional tensors have low intrinsic rank relative to the dimension of the ambient measurement space.
Revised:  Dimensionality reduction is often sought after since many high-dimensional tensors have low intrinsic rank relative to the dimension of the ambient measurement space.
Edit intent: clarity
Original:  Leveraging modern randomized algorithms, we demonstrate that the coherent structure can be learned from a smaller representation of the tensor in a fraction of the time .
Revised:  By leveraging modern randomized algorithms, we demonstrate that the coherent structure can be learned from a smaller representation of the tensor in a fraction of the time .
Edit intent: fluency
Original:  Leveraging modern randomized algorithms, we demonstrate that the coherent structure can be learned from a smaller representation of the tensor in a fraction of the time .
Revised:  Leveraging modern randomized algorithms, we demonstrate that coherent structures can be learned from a smaller representation of the tensor in a fraction of the time .
Edit intent: fluency
Original: Recommender systems for research papers are offered only by few digital libraries and reference managers , although they could help users of digital libraries etc. to better deal with information overload.
Revised: Only few digital libraries and reference managers , although they could help users of digital libraries etc. to better deal with information overload.
Edit intent: clarity
Original: Recommender systems for research papers are offered only by few digital libraries and reference managers , although they could help users of digital libraries etc. to better deal with information overload.
Revised: Recommender systems for research papers are offered only by few digital libraries and reference managers offer recommender systems, although such systems could assist users facing information overload.
Edit intent: clarity
Original:  One reason might be that operators of digital libraries do not have the resources to develop and maintain a recommender system.
Edit intent: coherence
Original: When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network -- conditioning the language model by injecting image features -- or in a layer following the recurrent neural network -- conditioning the language model by merging the image features.
Revised: When a recurrent neural network language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network -- conditioning the language model by injecting image features -- or in a layer following the recurrent neural network -- conditioning the language model by merging the image features.
Edit intent: clarity
Original: When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network -- conditioning the language model by injecting image features -- or in a layer following the recurrent neural network -- conditioning the language model by merging the image features.
Revised: When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network -- conditioning the language model by `injecting' image features -- or in a layer following the recurrent neural network -- conditioning the language model by merging the image features.
Edit intent: fluency
Original: When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network -- conditioning the language model by injecting image features -- or in a layer following the recurrent neural network -- conditioning the language model by merging the image features.
Revised: When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network -- conditioning the language model by injecting image features -- or in a layer following the RNN -- conditioning the language model by merging the image features.
Edit intent: clarity
Original: When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network -- conditioning the language model by injecting image features -- or in a layer following the recurrent neural network -- conditioning the language model by merging the image features.
Revised: When a neural language model is used for caption generation, the image information can be fed to the neural network either by directly incorporating it in a recurrent neural network -- conditioning the language model by injecting image features -- or in a layer following the recurrent neural network -- conditioning the language model by `merging' image features.
Edit intent: fluency
Original:  While merging implies that visual features are bound at the end of the caption generation process, injecting can bind the visual features at a variety stages .
Revised:  While both options are attested in the literature, there is as yet no systematic comparison between the two .
Edit intent: coherence
Original:  This suggests that the different modalities ( visual and linguistic ) for caption generation should not be jointly encoded by the RNN  ;
Revised:  This suggests that the different modalities ( visual and linguistic modalities for caption generation should not be jointly encoded by the RNN ;
Edit intent: clarity
Original:  This suggests that the different modalities ( visual and linguistic ) for caption generation should not be jointly encoded by the RNN  ;
Revised:  This suggests that the different modalities ( visual and linguistic ) for caption generation need not be jointly encoded by the RNN ;
Edit intent: clarity
Original:  We propose to use a pretrained convnet feature, a concatenated feature vector using  activations of feature maps of multiple layers in a trained convolutional network.
Revised:  We propose to use a pre-trained convnet feature, a concatenated feature vector using activations of feature maps of multiple layers in a trained convolutional network.
Edit intent: fluency
Original:  We propose to use a pretrained convnet feature, a concatenated feature vector using  activations of feature maps of multiple layers in a trained convolutional network.
Revised:  We propose to use a pretrained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network.
Edit intent: fluency
Original:  We show that how this convnet feature can serve as a general-purpose music representation.
Revised:  We show how this convnet feature can serve as a general-purpose music representation.
Edit intent: fluency
Original:  In the experiment , a convnet is trained for music tagging and then transferred for many music-related classification and regression tasks as well as an audio-related classification task.
Revised:  In the experiments , a convnet is trained for music tagging and then transferred for many music-related classification and regression tasks as well as an audio-related classification task.
Edit intent: clarity
Original:  In the experiment , a convnet is trained for music tagging and then transferred for many music-related classification and regression tasks as well as an audio-related classification task.
Revised:  In the experiment , a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks as well as an audio-related classification task.
Edit intent: clarity
Original:  In experiments, the convnet feature outperforms the baseline MFCC feature in all tasks and many reported approaches of aggregating MFCCs and low- and high-level music features.
Revised:  In experiments, the convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches aggregating MFCCs as well as low- and high-level music features.
Edit intent: clarity
Original:  We show how this convnet feature can serve as a general-purpose music representation.
Revised:  We show how this convnet feature can serve as general-purpose music representation.
Edit intent: fluency
Original:  The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches  aggregating MFCCs as well as low- and high-level music features.
Revised:  The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as low- and high-level music features.
Edit intent: clarity
Original: Community detection algorithms are a family of unsupervised graph mining algorithms which group vertices into clusters ( i.e., \ie communities).
Revised: Community detection algorithms are a family of unsupervised graph mining algorithms which group vertices into clusters ( \ie communities).
Edit intent: coherence
Original:  The fluid communities algorithm is based on the efficient propagation method, which makes it very competitive in computational cost and scalability.
Revised:  The fluid communities algorithm is based on the propagation methodology, the most efficient approach to community detection in terms of computational cost and scalability.
Edit intent: clarity
Original:  At the same time, the quality of its results is close to that of  current state-of-the-art community detection algorithms .
Revised:  At the same time, the quality of the communities it finds is close to that of current state-of-the-art community detection algorithms .
Edit intent: clarity
Original:  At the same time, the quality of its results is close to that of  current state-of-the-art community detection algorithms .
Revised:  At the same time, the quality of its results is close to that of the current state-of-the-art community detection algorithms .
Edit intent: fluency
Original: Community detection algorithms are a family of unsupervised graph mining algorithms which group vertices into clusters (%DIFDELCMD < \ie %%% communities).
Revised: %DIFDELCMD < \ie %%% communities).
Edit intent: coherence
Original:  In this paper we propose a novel community detection algorithm  based on the simple idea of fluids interacting in an environment, expanding and contracting in contact with one another.
Revised:  In this paper we propose a novel community detection algorithm based on the idea of fluids interacting in an environment, expanding and contracting in contact with one another.
Edit intent: clarity
Original:  Undecided voters typify the uncertainty of swing voters in polls but are often ignored or allocated to each candidate in a simplistic manner.
Revised:  Undecided voters typify the uncertainty of swing voters in polls but are often ignored or allocated to each candidate in a simple, deterministic manner.
Edit intent: clarity
Original:  Historically this has been adequate because first, the undecided tend to settle on a candidate as the election day draws closer, and second, they are comparatively small enough to assume that the undecided voters do not affect the relative proportions of the decided voters.
Revised:  Historically this may have been adequate because first, the undecided tend to settle on a candidate as the election day draws closer, and second, they are comparatively small enough to assume that the undecided voters do not affect the relative proportions of the decided voters.
Edit intent: clarity
Original:  Historically this has been adequate because first, the undecided tend to settle on a candidate as the election day draws closer, and second, they are comparatively small enough to assume that the undecided voters do not affect the relative proportions of the decided voters.
Revised:  Historically this has been adequate because the undecided were comparatively small enough to assume that the undecided voters do not affect the relative proportions of the decided voters.
Edit intent: coherence
Original:  Historically this has been adequate because first, the undecided tend to settle on a candidate as the election day draws closer, and second, they are comparatively small enough to assume that the undecided voters do not affect the relative proportions of the decided voters.
Revised:  Historically this has been adequate because first, the undecided tend to settle on a candidate as the election day draws closer, and second, they are comparatively small enough to assume that they do not affect the relative proportions of the decided voters.
Edit intent: fluency
Original:  These assumptions are used by poll authors and meta-poll analysts, but in the presence of high numbers of undecided voters  these static rules may  bias election predictions  .
Revised:  However, in the presence of high numbers of undecided voters these static rules may bias election predictions .
Edit intent: coherence
Original:  These assumptions are used by poll authors and meta-poll analysts, but in the presence of high numbers of undecided voters  these static rules may  bias election predictions  .
Revised:  These assumptions are used by poll authors and meta-poll analysts, but in the presence of high numbers of undecided voters , these static rules may bias election predictions .
Edit intent: fluency
Original:  These assumptions are used by poll authors and meta-poll analysts, but in the presence of high numbers of undecided voters  these static rules may  bias election predictions  .
Revised:  These assumptions are used by poll authors and meta-poll analysts, but in the presence of high numbers of undecided voters these static rules may in fact bias election predictions .
Edit intent: clarity
Original:  These assumptions are used by poll authors and meta-poll analysts, but in the presence of high numbers of undecided voters  these static rules may  bias election predictions  .
Revised:  These assumptions are used by poll authors and meta-poll analysts, but in the presence of high numbers of undecided voters these static rules may bias election predictions for election poll authors and meta-poll analysts .
Edit intent: clarity
Original:  However, in the presence of high numbers of undecided voters, these static rules may in fact bias election predictions for election poll authors and meta-poll analysts.
Revised:  However, in the presence of high numbers of undecided voters, these static rules may in fact bias election predictions from election poll authors and meta-poll analysts.
Edit intent: clarity
Original:  In this paper, we examine the effect of undecided voters in the 2016 US presidential election , with respect to the previous three presidential elections.
Revised:  In this paper, we examine the effect of undecided voters in the 2016 US presidential election to the previous three presidential elections.
Edit intent: clarity
Original:  We find evidence that increasing numbers of undecided voters combined with static allocation regimes are inadequate for election prediction models and that probabilistic allocations may be superior.
Revised:  We find evidence that static allocation regimes are inadequate for election prediction models and that probabilistic allocations may be superior.
Edit intent: clarity
Original:  We also estimate the average bias attributable to certain polling agencies, also referred to as as "house effects".
Revised:  We also estimate the bias attributable to certain polling agencies, also referred to as as "house effects".
Edit intent: clarity
Original:  We also estimate the average bias attributable to certain polling agencies, also referred to as as "house effects".
Revised:  We also estimate the average bias attributable to polling agencies, also referred to as as "house effects".
Edit intent: clarity
Original:  We also estimate the average bias attributable to certain polling agencies, also referred to as as "house effects".
Revised:  We also estimate the average bias attributable to certain polling agencies, often referred to as "house effects".
Edit intent: fluency
Original: Symmetric Positive Definite (SPD) matrices have been widely used as feature descriptors in image recognition .
Revised: Symmetric positive definite (SPD) matrices have been widely used as feature descriptors in image recognition .
Edit intent: fluency
Original: Symmetric Positive Definite (SPD) matrices have been widely used as feature descriptors in image recognition .
Revised: Symmetric Positive Definite (SPD) matrices used as feature descriptors in image recognition .
Edit intent: clarity
Original:  The existing manifold learning algorithms only apply to reduce the dimension of high dimensional vector-form data.
Revised:  The existing manifold learning algorithms only apply to reduce the dimension of high-dimensional vector-form data.
Edit intent: fluency
Original:  For high dimensional SPD matrices, it is impossible to directly use manifold learning algorithms to reduce the dimension of matrix-form data , but we need first transform the matrix into a long vector and then reduce the dimension of this vector .
Revised:  For high-dimensional SPD matrices, it is impossible to directly use manifold learning algorithms to reduce the dimension of matrix-form data , but we need first transform the matrix into a long vector and then reduce the dimension of this vector .
Edit intent: fluency
Original:  For high dimensional SPD matrices, it is impossible to directly use manifold learning algorithms to reduce the dimension of matrix-form data , but we need first transform the matrix into a long vector and then reduce the dimension of this vector .
Revised:  For high dimensional SPD matrices, directly using manifold learning algorithms to reduce the dimension of matrix-form data , but we need first transform the matrix into a long vector and then reduce the dimension of this vector .
Edit intent: clarity
Original:  For high dimensional SPD matrices, it is impossible to directly use manifold learning algorithms to reduce the dimension of matrix-form data , but we need first transform the matrix into a long vector and then reduce the dimension of this vector .
Revised:  For high dimensional SPD matrices, it is impossible to directly use manifold learning algorithms to reduce the dimension of matrix-form data , but we need first transform the matrix into a long vector , and then the dimension of this vector .
Edit intent: fluency
Original:  To overcome this limitation, we propose a new dimension reduction algorithm on SPD matrix space to transform the high dimensional SPD matrices to lower dimensional SPD matrices.
Revised:  To overcome this limitation, we propose a new dimension reduction algorithm on SPD matrix space to transform high-dimensional SPD matrices into low-dimensional SPD matrices.
Edit intent: coherence
Original:  Our work is based on the fact that the set of all SPD matrices with the same size is known to have a Lie group structure and we aims to transform the manifold learning algorithm to SPD matrix Lie group.
Revised:  Our work is based on the fact that the set of all SPD matrices with the same size has a Lie group structure and we aims to transform the manifold learning algorithm to SPD matrix Lie group.
Edit intent: clarity
Original:  Our work is based on the fact that the set of all SPD matrices with the same size is known to have a Lie group structure and we aims to transform the manifold learning algorithm to SPD matrix Lie group.
Revised:  Our work is based on the fact that the set of all SPD matrices with the same size is known to have a Lie group structure , and we aim to transform the manifold learning algorithm to SPD matrix Lie group.
Edit intent: fluency
Original:  Our work is based on the fact that the set of all SPD matrices with the same size is known to have a Lie group structure and we aims to transform the manifold learning algorithm to SPD matrix Lie group.
Revised:  Our work is based on the fact that the set of all SPD matrices with the same size is known to have a Lie group structure and we aims to transform the manifold learning to the SPD matrix Lie group.
Edit intent: clarity
Original:  We make use of the basic idea of  manifold learning algorithm LPP ( locality preserving projection  ) to construct the corresponding Laplacian matrix on  SPD matrix Lie group.
Revised:  We use the basic idea of manifold learning algorithm LPP ( locality preserving projection ) to construct the corresponding Laplacian matrix on SPD matrix Lie group.
Edit intent: coherence
Original:  We make use of the basic idea of  manifold learning algorithm LPP ( locality preserving projection  ) to construct the corresponding Laplacian matrix on  SPD matrix Lie group.
Revised:  We make use of the basic idea of the manifold learning algorithm LPP ( locality preserving projection ) to construct the corresponding Laplacian matrix on SPD matrix Lie group.
Edit intent: fluency