wanyu/IteraTeR_v2 huggingface dataset:
Original:  The derived solution is applicable to the most general form of linear advection-diffusion-reaction equation, a finite medium comprising an arbitrary number of layers, continuity of concentration and diffusive flux at the interfaces between adjacent layers and transient boundary conditions of arbitrary type at the two boundaries.
Revised:  The derived solution is applicable to the most general form of linear advection-dispersion-reaction equation, a finite medium comprising an arbitrary number of layers, continuity of concentration and diffusive flux at the interfaces between adjacent layers and transient boundary conditions of arbitrary type at the two boundaries.
Edit intent: clarity
Original:  The derived solution is applicable to the most general form of linear advection-diffusion-reaction equation, a finite medium comprising an arbitrary number of layers, continuity of concentration and diffusive flux at the interfaces between adjacent layers and transient boundary conditions of arbitrary type at the two boundaries.
Revised:  The derived solution is applicable to the most general form of linear advection-diffusion-reaction equation, a finite medium comprising an arbitrary number of layers, continuity of concentration and dispersive flux at the interfaces between adjacent layers and transient boundary conditions of arbitrary type at the two boundaries.
Edit intent: clarity
Original:  Our derived semi-analytical solution extends and addresses deficiencies of existing Laplace-transform based solutions in a layered medium, which consider  diffusion or reaction-diffusion only  . Code implementing our semi-analytical solution is provided and applied to a selection of test cases, with the reported results in excellent agreement with a standard numerical solution and other analytical results available in the literature.
Revised:  Our derived semi-analytical solution extends and addresses deficiencies of existing analytical solutions in a layered medium, which consider diffusion or reaction-diffusion only . Code implementing our semi-analytical solution is provided and applied to a selection of test cases, with the reported results in excellent agreement with a standard numerical solution and other analytical results available in the literature.
Edit intent: coherence
Original:  Our derived semi-analytical solution extends and addresses deficiencies of existing Laplace-transform based solutions in a layered medium, which consider  diffusion or reaction-diffusion only  . Code implementing our semi-analytical solution is provided and applied to a selection of test cases, with the reported results in excellent agreement with a standard numerical solution and other analytical results available in the literature.
Revised:  Our derived semi-analytical solution extends and addresses deficiencies of existing Laplace-transform based solutions in a layered medium, which consider analogous processes such as diffusion or reaction-diffusion only . Code implementing our semi-analytical solution is provided and applied to a selection of test cases, with the reported results in excellent agreement with a standard numerical solution and other analytical results available in the literature.
Edit intent: clarity
Original:  Our derived semi-analytical solution extends and addresses deficiencies of existing Laplace-transform based solutions in a layered medium, which consider  diffusion or reaction-diffusion only  . Code implementing our semi-analytical solution is provided and applied to a selection of test cases, with the reported results in excellent agreement with a standard numerical solution and other analytical results available in the literature.
Revised:  Our derived semi-analytical solution extends and addresses deficiencies of existing Laplace-transform based solutions in a layered medium, which consider diffusion or reaction-diffusion only . Code implementing our semi-analytical solution is supplied and applied to a selection of test cases, with the reported results in excellent agreement with a standard numerical solution and other analytical results available in the literature.
Edit intent: clarity
Original:  Analyzing the graph of this network yields meaningful information about the expertise of these experts and their subject areas.
Revised:  Analyzing this network yields meaningful information about the expertise of these experts and their subject areas.
Edit intent: clarity
Original:  To perform the analysis, graph embedding techniques have emerged as a promising tool.
Revised:  To perform the analysis, graph embedding techniques have emerged as an effective and promising tool.
Edit intent: clarity
Original:  In this paper, we propose a graph embedding method, called ExEm, which using dominating-set theory and deep learning approaches .
Revised:  In this paper, we propose a graph embedding method, called ExEm, that uses dominating-set theory and deep learning approaches .
Edit intent: fluency
Original:  Word2vec, fastText and their combination are employed to train the neural network of the SKIP-GRAM model.
Revised:  Word2vec, fastText and the SKIP-GRAM model.
Edit intent: clarity
Original:  Finally, the result is the low dimensional vectors of experts, called expert embeddings.
Revised:  Finally, the result is the low-dimensional vectors of experts, called expert embeddings.
Edit intent: fluency
Original:  Expert embeddings can be used for various purposes including accurately modeling experts' expertise or computing experts' scores in expert recommendation systems.
Revised:  The extracted expert embeddings can be used for various purposes including accurately modeling experts' expertise or computing experts' scores in expert recommendation systems.
Edit intent: clarity
Original:  The effectiveness of ExEm is validated through assessing its performance on multi-label classification, link prediction, and recommendation tasks .
Revised:  The effectiveness of ExEm through assessing its performance on multi-label classification, link prediction, and recommendation tasks .
Edit intent: clarity
Original:  The effectiveness of ExEm is validated through assessing its performance on multi-label classification, link prediction, and recommendation tasks .
Revised:  The effectiveness of ExEm is validated through assessing its performance over the multi-label classification, link prediction, and recommendation tasks .
Edit intent: clarity
Original:  We call these classesstructurally sparse which means that they are sparse because of their geometric nature (planar, bounded genus and unit-disk graphs) and/or because they have bounded parameters (arboricity, expansion, growth, independence) or forbidden structures (forbidden minors).
Revised:  We call these classes which means that they are sparse because of their geometric nature (planar, bounded genus and unit-disk graphs) and/or because they have bounded parameters (arboricity, expansion, growth, independence) or forbidden structures (forbidden minors).
Edit intent: clarity
Original:  We call these classesstructurally sparse which means that they are sparse because of their geometric nature (planar, bounded genus and unit-disk graphs) and/or because they have bounded parameters (arboricity, expansion, growth, independence) or forbidden structures (forbidden minors).
Revised:  We call these classesstructurally sparse which means that they are sparse because of their geometric nature (planar, bounded genus and unit-disk graphs) and/or have bounded parameters (arboricity, expansion, growth, independence) or forbidden structures (forbidden minors).
Edit intent: fluency
Original: The ubiquitous existence of cycles is one of important originations of network complexity, as cycle is the simplest structure that brings redundant paths in  connectivity and feedback effects in dynamics.
Revised: Cycle is the simplest structure that brings redundant paths in connectivity and feedback effects in dynamics.
Edit intent: coherence
Original: The ubiquitous existence of cycles is one of important originations of network complexity, as cycle is the simplest structure that brings redundant paths in  connectivity and feedback effects in dynamics.
Revised: The ubiquitous existence of cycles is one of important originations of network complexity, as cycle is the simplest structure that brings redundant paths in network connectivity and feedback effects in dynamics.
Edit intent: fluency
Original:  Experimental tests on real networks suggest that cycle ratio contains rich information in addition to well-known benchmark indices, that is , the node rankings by cycle ratio are largely different from rankings by degree, H-index and coreness, while the rankings by the three benchmarks are very similar to each other.
Revised:  Experimental tests on real networks suggest that cycle ratio contains rich information in addition to well-known benchmark indices, for example , the node rankings by cycle ratio are largely different from rankings by degree, H-index and coreness, while the rankings by the three benchmarks are very similar to each other.
Edit intent: coherence
Original:  Experimental tests on real networks suggest that cycle ratio contains rich information in addition to well-known benchmark indices, that is , the node rankings by cycle ratio are largely different from rankings by degree, H-index and coreness, while the rankings by the three benchmarks are very similar to each other.
Revised:  Experimental tests on real networks suggest that cycle ratio contains rich information in addition to well-known benchmark indices, that is , the node rankings by cycle ratio are largely different from rankings by degree, H-index and coreness, while the rankings by degree, H-index, coreness are very similar to each other.
Edit intent: clarity
Original:  Further experiments show that cycle ratio performs overall better than the three benchmarksin identifying critical nodes that maintain network connectivity and facilitate network synchronization .
Revised:  Further experiments show that cycle ratio performs overall better than other benchmarks. We believe the in-depth analyses on cycle structure may yield novel insights, metrics, models and algorithms for network science .
Edit intent: clarity
Original:  We first introduce a suitable geometric description of the set of fixed rank matrices which relies on a natural parametrization of matrices.
Revised:  We first introduce a geometric description of the set of fixed rank matrices which relies on a natural parametrization of matrices.
Edit intent: clarity
Original:  The resulting chart based algorithm can be interpreted as a particular splitting of the projection operator onto the tangent space of the low-rank matrix manifold.
Revised:  The resulting algorithm can be interpreted as a particular splitting of the projection operator onto the tangent space of the low-rank matrix manifold.
Edit intent: coherence
Original:  It is proven to be robust and exact in some particular case.
Revised:  It is proven to be exact in some particular case.
Edit intent: clarity
Original:  Numerical experiments confirm these results and illustrate the behavior of the proposed algorithm.
Revised:  Numerical experiments confirm this result and illustrate the behavior of the proposed algorithm.
Edit intent: fluency
Original:  Numerical experiments confirm these results and illustrate the behavior of the proposed algorithm.
Revised:  Numerical experiments confirm these results and illustrate the robustness of the proposed algorithm.
Edit intent: clarity
Original: Relational autocompletion is the problem of automatically filling out some missing fields in a relational database .
Revised: Relational autocompletion is the problem of automatically filling out some missing values in multi-relational data .
Edit intent: clarity
Original:  Within this framework, we introduce Dreaml -- an approach to learn both the structure and the parameters of DC programs from databases that may contain missing information .
Revised:  Within this framework, we introduce Dreaml -- an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data) .
Edit intent: clarity
Original:  To realize this, Dreaml integrates statistical modeling , distributional clauses with rule learning.
Revised:  To realize this, Dreaml integrates statistical modeling and distributional clauses with rule learning.
Edit intent: fluency
Original:  The distinguishing features of Dreaml are that it 1) tackles relational autocompletion , 2) learns distributional clauses extended with statistical models, 3) deals with both discrete and continuous distributions, 4) can exploit background knowledge, and 5) uses an expectation-maximization based algorithm to cope with  missing data.
Revised:  The distinguishing features of Dreaml are that it 1) tackles autocompletion in relational data , 2) learns distributional clauses extended with statistical models, 3) deals with both discrete and continuous distributions, 4) can exploit background knowledge, and 5) uses an expectation-maximization based algorithm to cope with missing data.
Edit intent: clarity
Original:  Within this framework, we introduce DiceML -- an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data).
Revised:  Within this framework, we introduce DiceML an approach to learn both the structure and the parameters of DC programs from relational data (with possibly missing data).
Edit intent: fluency
Original: Cloud Segmentation is one of the fundamental steps in optical remote sensing image analysis.
Revised: Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis.
Edit intent: clarity
Original:  Current methods for identification of cloudregions in aerial or satellite images are not accurate enough especially in the presence of snow and haze.
Revised:  Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze.
Edit intent: clarity
Original:  This paper presents a deep learning-based framework to address the problem of clouddetection in Landsat 8 imagery.
Revised:  This paper presents a deep learning-based framework for the detection of cloud/shadow in Landsat 8 imagery.
Edit intent: clarity
Original:  The proposed method benefits from a convolutional neural network ( Cloud-Net+ ) with multiple blocks, which is trained with a novel loss function (Filtered Jaccard loss ).
Revised:  The proposed method benefits from a convolutional neural network , Cloud-Net+ ) with multiple blocks, which is trained with a novel loss function (Filtered Jaccard loss ).
Edit intent: fluency
Original:  The proposed method benefits from a convolutional neural network ( Cloud-Net+ ) with multiple blocks, which is trained with a novel loss function (Filtered Jaccard loss ).
Revised:  The proposed method benefits from a convolutional neural network ( Cloud-Net+ ) with multiple blocks, which is trained with a novel loss function (Filtered Jaccard Loss ).
Edit intent: fluency
Original:  The combination of Cloud-Net+ and Filtered Jaccard loss function delivers superior results over four public cloud  detection datasets.
Revised:  The combination of Cloud-Net+ and Filtered Jaccard loss function delivers superior results on four public cloud detection datasets.
Edit intent: clarity
Original:  Our experiments on one of the most common public datasets in computer vision ( Pascal VOC dataset ) show that the proposed network/loss function could be used in other segmentation tasks for more accurate performance/evaluation .
Revised:  Our experiments on Pascal VOC dataset ) show that the proposed network/loss function could be used in other segmentation tasks for more accurate performance/evaluation .
Edit intent: clarity
Original:  Our experiments on one of the most common public datasets in computer vision ( Pascal VOC dataset ) show that the proposed network/loss function could be used in other segmentation tasks for more accurate performance/evaluation .
Revised:  Our experiments on one of the most common public datasets in computer vision ( Pascal VOC dataset exemplifies the applicability and quality of our proposed network and loss function in other computer vision applications .
Edit intent: clarity
Original:  A major challenge in the learning is the incentive issue.
Revised:  A major challenge in learning is the incentive issue.
Edit intent: fluency
Original:  We call this setting\emph{mechanism design with type-imposed externalities .
Revised:  We call this setting\emph{ .
Edit intent: coherence
Original:  Second, each agent can only misreport a lower type, but not the other way round.
Revised:  Second, each agent can only misreport a type lower than his true type, but not the other way round.
Edit intent: clarity
Original:  Finally, we show the existence of such mechanisms are highly affected by the market growth rate and give empirical analysis.
Revised:  Finally, we show the existence of such mechanisms is highly affected by the market growth rate and give empirical analysis.
Edit intent: fluency
Original:  Finally, we show the existence of such mechanisms are highly affected by the market growth rate and give empirical analysis.
Revised:  Finally, we show the existence of such mechanisms are highly affected by the market growth rate and provide empirical analysis.
Edit intent: clarity
Original:  dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components, 2) global context aware inter-intra cross-parallel BiLSTM, 3) local context-aware network with attention BiLSTM, 4) multiple spiral iterative refinement dual-path BiLSTM (this method is also called PitchFork) , that all these networks take the mixed utterance of two speakers and map it to two separated utterances, where each utterance contains only one speaker's voice.
Revised:  dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components, 2) global context aware inter-intra cross-parallel BiLSTM, 3) multiple spiral iterative refinement dual-path BiLSTM (this method is also called PitchFork) , that all these networks take the mixed utterance of two speakers and map it to two separated utterances, where each utterance contains only one speaker's voice.
Edit intent: coherence
Original:  dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components, 2) global context aware inter-intra cross-parallel BiLSTM, 3) local context-aware network with attention BiLSTM, 4) multiple spiral iterative refinement dual-path BiLSTM (this method is also called PitchFork) , that all these networks take the mixed utterance of two speakers and map it to two separated utterances, where each utterance contains only one speaker's voice.
Revised:  dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components, 2) global context aware inter-intra cross-parallel BiLSTM, 3) local context-aware network with attention BiLSTM, 4) multiple spiral iterative refinement dual-path BiLSTM , that all these networks take the mixed utterance of two speakers and map it to two separated utterances, where each utterance contains only one speaker's voice.
Edit intent: coherence
Original:  Our experiments on the public WSJ0-2mix data corpus results in 19.86 dB SDR improvement, 3.63 of PESQ, and 94.2 \\%DIF < of ESTOI, which shows our proposed networks can lead to %DIF > of ESTOI, which shows our proposed networks can lead to performance improvement on the speaker separation task.
Revised:  Our experiments on the public WSJ0-2mix data corpus results in 19.86 dB SDR improvement, 3.63 of PESQ, and 94.2 \\%DIF < of ESTOI, which %DIF > of ESTOI, which shows our proposed networks can lead to performance improvement on the speaker separation task.
Edit intent: coherence
Original:  We have open-sourced our re-implementation of the DPRNN-TasNet in URL and our `La Furca' is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be smoothly reproduced.
Revised:  We have open-sourced our re-implementation of the DPRNN-TasNet in URL and our LaFurca is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be smoothly reproduced.
Edit intent: fluency
Original:  In this paper, we propose several improvements of dual-path BiLSTM based network for end-to-end approach to monaural speech separation , which consists of 1)
Revised:  In this paper, we propose several improvements of dual-path BiLSTM based network for end-to-end approach to monaural speech separation . Firstly a
Edit intent: coherence
Original:  dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components , 2) global context aware inter-intra cross-parallel BiLSTM , 3) multiple spiral iterative refinement dual-path BiLSTM , that all these networks take the mixed utterance of two speakers and map it to two separated utterances, where each utterance contains only one speaker's voice.
Revised:  dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components , 2) global context aware inter-intra cross-parallel BiLSTM to further perceive the global contextual information. Finally, a spiral multi-stage dual-path BiLSTM , that all these networks take the mixed utterance of two speakers and map it to two separated utterances, where each utterance contains only one speaker's voice.
Edit intent: clarity
Original:  dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components , 2) global context aware inter-intra cross-parallel BiLSTM , 3) multiple spiral iterative refinement dual-path BiLSTM , that all these networks take the mixed utterance of two speakers and map it to two separated utterances, where each utterance contains only one speaker's voice.
Revised:  dual-path network with intra-parallel BiLSTM and inter-parallel BiLSTM components , 2) global context aware inter-intra cross-parallel BiLSTM , 3) multiple spiral iterative refinement dual-path BiLSTM , that all these networks take the mixed utterance of two speakers and map it to two separate utterances, where each utterance contains only one speaker's voice.
Edit intent: fluency
Original:  For the objective, we propose to train the network by directly optimizing  utterance level scale-invariant signal-to-distortion ratio (SI-SDR) in a permutation invariant training (PIT) style.
Revised:  For the objective, we propose to train the network by directly optimizing the utterance level scale-invariant signal-to-distortion ratio (SI-SDR) in a permutation invariant training (PIT) style.
Edit intent: fluency
Original:  Our experiments on the public WSJ0-2mix data corpus results in 20.55dB SDR improvement, 20.35dB SI-SDR improvement, 3.69 of PESQ, and 94.86\\%DIF < of ESTOI, which shows our proposed networks can lead to %DIF > of  performance improvement on the speaker separation task.
Revised:  Our experiments on the public WSJ0-2mix data corpus results in 20.55dB SDR improvement, 20.35dB SI-SDR improvement, 3.69 of PESQ, and 94.86\\%DIF < of ESTOI, which shows our proposed networks can lead to %DIF > of ESTOI, which shows our proposed networks can lead to performance improvement on the speaker separation task.
Edit intent: fluency
Original:  We have open-sourced our re-implementation of the DPRNN-TasNet in URL and our LaFurca is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be smoothly reproduced .
Revised:  We have open-sourced our re-implementation of the DPRNN-TasNet in URL and our LaFurca is realized based on this implementation of DPRNN-TasNet, it is believed that the results in this paper can be reproduced with ease .
Edit intent: clarity
Original: We present the MagNet, a  multi-agent interaction network to discover governing dynamics and predict evolution of a complex  system from observations.
Revised: We present the MagNet, a multi-agent interaction model to discover the governing dynamics and predict evolution of a complex system from observations.
Edit intent: clarity