wanyu/IteraTeR_v2 huggingface dataset:
Original:  Traditional \mbox{GMC methods typically adopt graph regularization to impose smoothness priors for \mbox{MC . Recently, geometric deep learning on graphs ~(\mbox{GDLG ) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods.
Revised:  Traditional \mbox{GMC methods typically adopt graph regularization to impose smoothness priors for MC . Recently, geometric deep learning on graphs ~(\mbox{GDLG ) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods.
Edit intent: fluency
Original:  Traditional \mbox{GMC methods typically adopt graph regularization to impose smoothness priors for \mbox{MC . Recently, geometric deep learning on graphs ~(\mbox{GDLG ) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods.
Revised:  Traditional \mbox{GMC methods typically adopt graph regularization to impose smoothness priors for \mbox{MC . Recently, geometric deep learning on graphs (GDLG ) is proposed to solve the GMC problem, showing better performance than existing GMC methods including traditional graph regularization based methods.
Edit intent: coherence
Original:  To the best of our knowledge, there exists only one GDLG method for GMC, which is called \mbox{RMGCNN . RMGCNN combines graph convolutional network ~ (GCN) and recurrent neural network ~ (RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method.
Revised:  To the best of our knowledge, there exists only one GDLG method for GMC, which is called RMGCNN . RMGCNN combines graph convolutional network ~ (GCN) and recurrent neural network ~ (RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method.
Edit intent: fluency
Original:  To the best of our knowledge, there exists only one GDLG method for GMC, which is called \mbox{RMGCNN . RMGCNN combines graph convolutional network ~ (GCN) and recurrent neural network ~ (RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method.
Revised:  To the best of our knowledge, there exists only one GDLG method for GMC, which is called \mbox{RMGCNN . RMGCNN combines graph convolutional network (GCN) and recurrent neural network ~ (RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method.
Edit intent: fluency
Original:  To the best of our knowledge, there exists only one GDLG method for GMC, which is called \mbox{RMGCNN . RMGCNN combines graph convolutional network ~ (GCN) and recurrent neural network ~ (RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method.
Revised:  To the best of our knowledge, there exists only one GDLG method for GMC, which is called \mbox{RMGCNN . RMGCNN combines graph convolutional network ~ (GCN) and recurrent neural network (RNN) together for GMC. In the original work of RMGCNN, RMGCNN demonstrates better performance than pure GCN-based method.
Edit intent: fluency
Original:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Revised:  In this paper, we propose a new GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Edit intent: fluency
Original:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Revised:  In this paper, we propose a new \mbox{GMC method, called onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Edit intent: fluency
Original:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Revised:  In this paper, we propose a new \mbox{GMC method, called c \underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Edit intent: fluency
Original:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Revised:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{ eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Edit intent: fluency
Original:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Revised:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g \underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Edit intent: fluency
Original:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Revised:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{ atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Edit intent: fluency
Original:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Revised:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m \underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Edit intent: fluency
Original:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{c ompletion~ (CGMC), for recommendation with graphs among users/items.
Revised:  In this paper, we propose a new \mbox{GMC method, called c onvolutional\underline{g eometric\underline{m atrix\underline{ ompletion~ (CGMC), for recommendation with graphs among users/items.
Edit intent: fluency
Original:  NetGAN is able to produce graphs that exhibit the well-known network patterns without explicitly specifying them in the model definition.
Revised:  NetGAN is able to produce graphs that exhibit well-known network patterns without explicitly specifying them in the model definition.
Edit intent: clarity
Original:  Being the first approach to combine both of these desirable properties, NetGAN opens exciting further avenues for research.
Revised:  Being the first approach to combine both of these desirable properties, NetGAN opens exciting avenues for further research.
Edit intent: fluency
Original:  In the semidefinite formulation there are O(N^2) dual variables and a standard primal-dual interior point method requires at least O(N^6) flops per iteration .
Revised:  In the semidefinite formulation there are O(N^2) dual variables which complicates the implementation of a standard primal-dual interior point method requires at least O(N^6) flops per iteration .
Edit intent: clarity
Original:  Aiming at beautifying the  appearance of QR codes, existing works have developed a series of techniques to make the QR code more visual-pleasant .
Revised:  Aiming at beautifying the appearance of QR codes, existing works have developed a series of techniques .
Edit intent: coherence
Original:  However, these works still leave much to be desired, such as visual diversity, aesthetic quality, flexibility, universal property, and robustness.
Revised:  However, these works still leave much to be desired, such as personalization, artistry, and robustness.
Edit intent: clarity
Original:  To address these issues, in this paper, we propose a novel type of aesthetic QR codes, SEE (Stylize aEsthEtic) QR Code , and a three-stage automatic approach to produce such robust art-style-oriented code .
Revised:  To address these issues, in this paper, we propose a novel type of aesthetic QR codes, SEE (Stylize aEsthEtic) QR code , and a three-stage automatic approach to produce such robust art-style-oriented code .
Edit intent: fluency
Original:  To address these issues, in this paper, we propose a novel type of aesthetic QR codes, SEE (Stylize aEsthEtic) QR Code , and a three-stage automatic approach to produce such robust art-style-oriented code .
Revised:  To address these issues, in this paper, we propose a novel type of aesthetic QR codes, SEE (Stylize aEsthEtic) QR Code , and a three-stage approach to automatically produce such robust art-style-oriented code .
Edit intent: fluency
Original:  To address these issues, in this paper, we propose a novel type of aesthetic QR codes, SEE (Stylize aEsthEtic) QR Code , and a three-stage automatic approach to produce such robust art-style-oriented code .
Revised:  To address these issues, in this paper, we propose a novel type of aesthetic QR codes, SEE (Stylize aEsthEtic) QR Code , and a three-stage automatic approach to produce such robust style-oriented codes .
Edit intent: fluency
Original:  Specifically, in the first stage, we propose a method to generate the optimized baseline aesthetic QR code, which reduces the visual contrast between the noise-like black/white modules and the blended image.
Revised:  Specifically, in the first stage, we propose a method to generate an optimized baseline aesthetic QR code, which reduces the visual contrast between the noise-like black/white modules and the blended image.
Edit intent: fluency
Original:  In the second stage, to obtain style-orient QR code, we adjust an appropriate neural style transformation network to endowing the baseline aesthetic QR code with abstract art elements.
Revised:  In the second stage, to obtain art style QR code, we adjust an appropriate neural style transformation network to endowing the baseline aesthetic QR code with abstract art elements.
Edit intent: fluency
Original:  In the second stage, to obtain style-orient QR code, we adjust an appropriate neural style transformation network to endowing the baseline aesthetic QR code with abstract art elements.
Revised:  In the second stage, to obtain style-orient QR code, we tailor an appropriate neural style transformation network to endowing the baseline aesthetic QR code with abstract art elements.
Edit intent: clarity
Original:  In the second stage, to obtain style-orient QR code, we adjust an appropriate neural style transformation network to endowing the baseline aesthetic QR code with abstract art elements.
Revised:  In the second stage, to obtain style-orient QR code, we adjust an appropriate neural style transformation network to endow the baseline aesthetic QR code with abstract art elements.
Edit intent: fluency
Original:  In the second stage, to obtain style-orient QR code, we adjust an appropriate neural style transformation network to endowing the baseline aesthetic QR code with abstract art elements.
Revised:  In the second stage, to obtain style-orient QR code, we adjust an appropriate neural style transformation network to endowing the baseline aesthetic QR code with artistic elements.
Edit intent: clarity
Original:  In the third stage, we design an error-correction mechanism by balancing two competing terms, visual quality and readability, to ensure performance robustly . Extensive experiments demonstrate that SEE QR Code has a high quality in terms of both visual appearance and robustness, and also enables users to have more personalized choices .
Revised:  In the third stage, we design an error-correction mechanism by balancing two competing terms, visual quality and readability, to ensure the performance robust . Extensive experiments demonstrate that SEE QR Code has a high quality in terms of both visual appearance and robustness, and also enables users to have more personalized choices .
Edit intent: clarity
Original:  In the third stage, we design an error-correction mechanism by balancing two competing terms, visual quality and readability, to ensure performance robustly . Extensive experiments demonstrate that SEE QR Code has a high quality in terms of both visual appearance and robustness, and also enables users to have more personalized choices .
Revised:  In the third stage, we design an error-correction mechanism by balancing two competing terms, visual quality and readability, to ensure performance robustly . Extensive experiments demonstrate that SEE QR code has high quality in terms of both visual appearance and robustness, and also enables users to have more personalized choices .
Edit intent: fluency
Original:  In the third stage, we design an error-correction mechanism by balancing two competing terms, visual quality and readability, to ensure performance robustly . Extensive experiments demonstrate that SEE QR Code has a high quality in terms of both visual appearance and robustness, and also enables users to have more personalized choices .
Revised:  In the third stage, we design an error-correction mechanism by balancing two competing terms, visual quality and readability, to ensure performance robustly . Extensive experiments demonstrate that SEE QR Code has a high quality in terms of both visual appearance and robustness, and also offers a greater variety of personalized choices to users .
Edit intent: clarity
Original: We prove that two polygons A and B have a reversible hinged dissection (a chain hinged dissection that reverses inside and outside boundaries when folding between A and B) if and only if A and B are two non-crossing nets of a common polyhedron.
Revised: We prove that two polygons A and B have a reversible hinged dissection (a chain hinged dissection that reverses inside and outside boundaries when folding between A and B) if and only if A and B are two noncrossing nets of a common polyhedron.
Edit intent: fluency
Original:  Furthermore, monotone  hinged dissections (where all hinges rotate in the same direction when changing from A to B  correspond exactly to non-crossing nets of a common convex polyhedron.
Revised:  Furthermore, monotone hinged dissections (where all hinges rotate in the same direction when changing from A to B ) correspond exactly to non-crossing nets of a common convex polyhedron.
Edit intent: fluency
Original:  Furthermore, monotone  hinged dissections (where all hinges rotate in the same direction when changing from A to B  correspond exactly to non-crossing nets of a common convex polyhedron.
Revised:  Furthermore, monotone hinged dissections (where all hinges rotate in the same direction when changing from A to B correspond exactly to noncrossing nets of a common convex polyhedron.
Edit intent: fluency
Original: A robust methodology for obtaining the steady-state solution of the power grid is essential for reliable operation as well as planning of the future transmission and distribution grid .
Revised: Robust simulation is essential for reliable operation as well as planning of the future transmission and distribution grid .
Edit intent: clarity
Original: A robust methodology for obtaining the steady-state solution of the power grid is essential for reliable operation as well as planning of the future transmission and distribution grid .
Revised: A robust methodology for obtaining the steady-state solution of the power grid is essential for reliable operation and planning of transmission and distribution grid .
Edit intent: clarity
Original: A robust methodology for obtaining the steady-state solution of the power grid is essential for reliable operation as well as planning of the future transmission and distribution grid .
Revised: A robust methodology for obtaining the steady-state solution of the power grid is essential for reliable operation as well as planning of the future transmission and distribution power grids .
Edit intent: clarity
Original:  All existing alternating current (AC) power flow and three-phase power flow analyses formulate a non-linear problem that generally lacks the ability to ensure convergence to the correct physical solution from an arbitrary set of initial guesses.
Revised:  Due to the non-linear nature of the problem, it is difficult for alternating current (AC) power flow and three-phase power flow analyses formulate a non-linear problem that generally lacks the ability to ensure convergence to the correct physical solution from an arbitrary set of initial guesses.
Edit intent: clarity
Original:  All existing alternating current (AC) power flow and three-phase power flow analyses formulate a non-linear problem that generally lacks the ability to ensure convergence to the correct physical solution from an arbitrary set of initial guesses.
Revised:  All existing alternating current (AC) power flow and three-phase power flow analyses to ensure convergence to the correct physical solution from an arbitrary set of initial guesses.
Edit intent: clarity
Original:  In this paper, we demonstrate that our equivalent circuit formulation approach can model both the positive sequence model of the transmission grid and three-phase model of the distribution grid without loss of generality.
Revised:  In this paper, we describe our equivalent circuit formulation approach can model both the positive sequence model of the transmission grid and three-phase model of the distribution grid without loss of generality.
Edit intent: clarity
Original:  In this paper, we demonstrate that our equivalent circuit formulation approach can model both the positive sequence model of the transmission grid and three-phase model of the distribution grid without loss of generality.
Revised:  In this paper, we demonstrate that our equivalent circuit formulation approach can model both the positive sequence network of the transmission grid and three-phase model of the distribution grid without loss of generality.
Edit intent: clarity
Original:  In this paper, we demonstrate that our equivalent circuit formulation approach can model both the positive sequence model of the transmission grid and three-phase model of the distribution grid without loss of generality.
Revised:  In this paper, we demonstrate that our equivalent circuit formulation approach can model both the positive sequence model of the transmission grid and three-phase network of the distribution grid without loss of generality.
Edit intent: clarity
Original:  Moreover, when combined with novel circuit simulation techniques, it can robustly solve for the steady-state solution for both these network models independent of the choice of initial conditions . Examples for positive sequence transmission and three-phase distribution systems, including  75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the robustness of our approach.
Revised:  The proposed circuit models and formalism enable the extension and application of circuit simulation techniques to solve for the steady-state solution for both these network models independent of the choice of initial conditions . Examples for positive sequence transmission and three-phase distribution systems, including 75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the robustness of our approach.
Edit intent: coherence
Original:  Moreover, when combined with novel circuit simulation techniques, it can robustly solve for the steady-state solution for both these network models independent of the choice of initial conditions . Examples for positive sequence transmission and three-phase distribution systems, including  75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the robustness of our approach.
Revised:  Moreover, when combined with novel circuit simulation techniques, it can robustly solve for the steady-state solution with excellent robustness of convergence . Examples for positive sequence transmission and three-phase distribution systems, including 75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the robustness of our approach.
Edit intent: clarity
Original:  Moreover, when combined with novel circuit simulation techniques, it can robustly solve for the steady-state solution for both these network models independent of the choice of initial conditions . Examples for positive sequence transmission and three-phase distribution systems, including  75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the robustness of our approach.
Revised:  Moreover, when combined with novel circuit simulation techniques, it can robustly solve for the steady-state solution for both these network models independent of the choice of initial conditions . Examples for positive sequence transmission and three-phase distribution systems, including actual 75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the robustness of our approach.
Edit intent: clarity
Original:  Moreover, when combined with novel circuit simulation techniques, it can robustly solve for the steady-state solution for both these network models independent of the choice of initial conditions . Examples for positive sequence transmission and three-phase distribution systems, including  75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the robustness of our approach.
Revised:  Moreover, when combined with novel circuit simulation techniques, it can robustly solve for the steady-state solution for both these network models independent of the choice of initial conditions . Examples for positive sequence transmission and three-phase distribution systems, including 75k+ nodes Eastern Interconnection transmission test cases and 8k+ nodes taxonomy distribution test cases, are solved from arbitrary initial guesses to demonstrate the efficacy of our approach.
Edit intent: clarity
Original: One of the most studied problems in network science is the identification of those nodes that, once activated, maximize the fraction of nodes that are reached by a spreading process of interest.
Revised: A pivotal idea in network science is the identification of those nodes that, once activated, maximize the fraction of nodes that are reached by a spreading process of interest.
Edit intent: clarity
Original:  Here, we connect the two problems - the influential spreaders identification and spreading processes' hitting time estimation - by introducing a centrality metric, called ViralRank, which quantifies how close a node is , on average, to the other nodes in terms of the random-walk effective distance .
Revised:  Here, we connect the two problems - the influential spreaders identification and spreading processes' hitting time estimation - by introducing a centrality metric, called ViralRank, which quantifies how close a node is to the other nodes in terms of the random-walk effective distance .
Edit intent: clarity