wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our MATLAB code is available at URL .
Revised:  Our MATLAB code is available at URL 
Edit intent: fluency
Original:  Therefore, we propose a simulation-based optimization approach for improving crop yield and reducing the greenhouse gas emissions (GHG) of the agriculture system.
Revised:  Therefore, we propose a simulation-based optimization approach for improving crop yield and reducing the greenhouse gas emissions (GHG) of agriculture system.
Edit intent: fluency
Original:  Using the experimental data provided by Syngenta France, we simulated and verified the crop yield and carbon/nitrogen cycle with Denitrification-Decomposition (DNDC) model.
Revised:  We simulated and verified the crop yield and carbon/nitrogen cycle with Denitrification-Decomposition (DNDC) model.
Edit intent: coherence
Original:  The results show the necessity of optimal planning and usage of fertilizer in the agriculture system.
Revised:  The results show the necessity of optimal planning and usage of fertilizer in agriculture system.
Edit intent: fluency
Original: This paper presents a comprehensive literature review on  applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks.
Revised: This paper presents a literature review on applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks.
Edit intent: clarity
Original:  As such, the IRS's phase control  combined with the conventional transmission control  can potentially bring performance gain compared to the conventional wireless networks without using the IRS.
Revised:  As such, the IRS's phase control , combined with the conventional transmission control can potentially bring performance gain compared to the conventional wireless networks without using the IRS.
Edit intent: fluency
Original:  As such, the IRS's phase control  combined with the conventional transmission control  can potentially bring performance gain compared to the conventional wireless networks without using the IRS.
Revised:  As such, the IRS's phase control combined with the conventional transmission control , can potentially bring performance gain compared to the conventional wireless networks without using the IRS.
Edit intent: fluency
Original:  As such, the IRS's phase control  combined with the conventional transmission control  can potentially bring performance gain compared to the conventional wireless networks without using the IRS.
Revised:  As such, the IRS's phase control combined with the conventional transmission control can potentially bring performance gain compared to wireless networks without using the IRS.
Edit intent: fluency
Original:  As such, the IRS's phase control  combined with the conventional transmission control  can potentially bring performance gain compared to the conventional wireless networks without using the IRS.
Revised:  As such, the IRS's phase control combined with the conventional transmission control can potentially bring performance gain compared to the conventional wireless networks without IRS.
Edit intent: clarity
Original:  To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g.,  rate maximization and power minimization problems.
Revised:  To exploit the performance gain, we discuss the joint optimization of the IRS's phase control and the transceivers' transmission control in different network design problems, e.g., ~ rate maximization and power minimization problems.
Edit intent: fluency
Original:  Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging wireless applications .
Revised:  Furthermore, we extend the discussion of IRS-assisted wireless networks to some emerging use cases .
Edit intent: clarity
Original:  Finally, we highlight important practical challenges and future research directions of realizing IRS-assisted wireless communications in beyond 5G networks .
Revised:  Finally, we highlight important practical challenges and future research directions for realizing IRS-assisted wireless communications in beyond 5G networks .
Edit intent: fluency
Original:  Finally, we highlight important practical challenges and future research directions of realizing IRS-assisted wireless communications in beyond 5G networks .
Revised:  Finally, we highlight important practical challenges and future research directions of realizing IRS-assisted wireless networks in beyond 5G networks .
Edit intent: clarity
Original:  Finally, we highlight important practical challenges and future research directions of realizing IRS-assisted wireless communications in beyond 5G networks .
Revised:  Finally, we highlight important practical challenges and future research directions of realizing IRS-assisted wireless communications in beyond 5G communications .
Edit intent: clarity
Original:  Indeed,  optimal control problems require a huge computational effort in order to be solved, most of all in a physical and/or geometrical parametrized setting . Reduced order methods are a reliably suitable approach, increasingly gaining popularity, to achieve rapid and accurate optimal solutions in several fields, such as in biomedical and environmental sciences.
Revised:  Indeed, the optimal control problems require a huge computational effort in order to be solved, most of all in a physical and/or geometrical parametrized setting . Reduced order methods are a reliably suitable approach, increasingly gaining popularity, to achieve rapid and accurate optimal solutions in several fields, such as in biomedical and environmental sciences.
Edit intent: coherence
Original:  Indeed,  optimal control problems require a huge computational effort in order to be solved, most of all in a physical and/or geometrical parametrized setting . Reduced order methods are a reliably suitable approach, increasingly gaining popularity, to achieve rapid and accurate optimal solutions in several fields, such as in biomedical and environmental sciences.
Revised:  Indeed, optimal control problems require a huge computational effort in order to be solved, most of all in physical and/or geometrical parametrized setting . Reduced order methods are a reliably suitable approach, increasingly gaining popularity, to achieve rapid and accurate optimal solutions in several fields, such as in biomedical and environmental sciences.
Edit intent: fluency
Original:  Indeed,  optimal control problems require a huge computational effort in order to be solved, most of all in a physical and/or geometrical parametrized setting . Reduced order methods are a reliably suitable approach, increasingly gaining popularity, to achieve rapid and accurate optimal solutions in several fields, such as in biomedical and environmental sciences.
Revised:  Indeed, optimal control problems require a huge computational effort in order to be solved, most of all in a physical and/or geometrical parametrized settings . Reduced order methods are a reliably suitable approach, increasingly gaining popularity, to achieve rapid and accurate optimal solutions in several fields, such as in biomedical and environmental sciences.
Edit intent: fluency
Original:  Indeed,  optimal control problems require a huge computational effort in order to be solved, most of all in a physical and/or geometrical parametrized setting . Reduced order methods are a reliably suitable approach, increasingly gaining popularity, to achieve rapid and accurate optimal solutions in several fields, such as in biomedical and environmental sciences.
Revised:  Indeed, optimal control problems require a huge computational effort in order to be solved, most of all in a physical and/or geometrical parametrized setting . Reduced order methods are a reliable and suitable approach, increasingly gaining popularity, to achieve rapid and accurate optimal solutions in several fields, such as in biomedical and environmental sciences.
Edit intent: fluency
Original:  In this work, we exploit POD-Galerkin reduction  over a parametrized optimality system, derived from  Karush-Kuhn-Tucker conditions.
Revised:  In this work, we employ a POD-Galerkin reduction over a parametrized optimality system, derived from Karush-Kuhn-Tucker conditions.
Edit intent: fluency
Original:  In this work, we exploit POD-Galerkin reduction  over a parametrized optimality system, derived from  Karush-Kuhn-Tucker conditions.
Revised:  In this work, we exploit POD-Galerkin reduction approach over a parametrized optimality system, derived from Karush-Kuhn-Tucker conditions.
Edit intent: clarity
Original:  In this work, we exploit POD-Galerkin reduction  over a parametrized optimality system, derived from  Karush-Kuhn-Tucker conditions.
Revised:  In this work, we exploit POD-Galerkin reduction over a parametrized optimality system, derived from the Karush-Kuhn-Tucker conditions.
Edit intent: fluency
Original:  We study accessible paths on random fitness landscapes of the House-of-Cards type, which implies that the fitness values are independent, identically and continuously distributed random variables.
Revised:  We study accessible paths on random fitness landscapes of the House-of-Cards type, on which fitness values are independent, identically and continuously distributed random variables.
Edit intent: clarity
Original: Video Quality Assessment (VQA) methods have been designed with a focus on particular degradation types, usually artificially induced on a small set of reference videos.
Revised: Video quality assessment (VQA) methods have been designed with a focus on particular degradation types, usually artificially induced on a small set of reference videos.
Edit intent: fluency
Original: Video Quality Assessment (VQA) methods have been designed with a focus on particular degradation types, usually artificially induced on a small set of reference videos.
Revised: Video Quality Assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos.
Edit intent: coherence
Original:  It consists of a coarsely annotated set of 153,841 videos having 5 quality ratings each, and 1600 videos with a minimum of 89 ratings each.
Revised:  It consists of a coarsely annotated set of 153,841 videos having five quality ratings each, and 1600 videos with a minimum of 89 ratings each.
Edit intent: fluency
Original:  Additionally, we propose new efficient VQA approaches (MLSP-VQA) relying on multi-level spatially pooled deep features (MLSP).
Revised:  Additionally, we propose new efficient VQA approaches (MLSP-VQA) relying on multi-level spatially pooled deep-features (MLSP).
Edit intent: fluency
Original:  They are extremely well suited for training at scale, compared to deep transfer learning approaches.
Revised:  They are exceptionally well suited for training at scale, compared to deep transfer learning approaches.
Edit intent: clarity
Original:  Our best method  MLSP-VQA-FF  improves the Spearman Rank-order Correlation Coefficient (SRCC) performance metric on the standard KonVid-1k in-the-wild benchmark dataset to 0.83 surpassing the best existing deep-learning model ( 0.8 SRCC) and hand-crafted feature-based method (0.78 SRCC).
Revised:  Our best method , MLSP-VQA-FF improves the Spearman Rank-order Correlation Coefficient (SRCC) performance metric on the standard KonVid-1k in-the-wild benchmark dataset to 0.83 surpassing the best existing deep-learning model ( 0.8 SRCC) and hand-crafted feature-based method (0.78 SRCC).
Edit intent: fluency
Original:  Our best method  MLSP-VQA-FF  improves the Spearman Rank-order Correlation Coefficient (SRCC) performance metric on the standard KonVid-1k in-the-wild benchmark dataset to 0.83 surpassing the best existing deep-learning model ( 0.8 SRCC) and hand-crafted feature-based method (0.78 SRCC).
Revised:  Our best method MLSP-VQA-FF , improves the Spearman Rank-order Correlation Coefficient (SRCC) performance metric on the standard KonVid-1k in-the-wild benchmark dataset to 0.83 surpassing the best existing deep-learning model ( 0.8 SRCC) and hand-crafted feature-based method (0.78 SRCC).
Edit intent: fluency
Original:  Our best method  MLSP-VQA-FF  improves the Spearman Rank-order Correlation Coefficient (SRCC) performance metric on the standard KonVid-1k in-the-wild benchmark dataset to 0.83 surpassing the best existing deep-learning model ( 0.8 SRCC) and hand-crafted feature-based method (0.78 SRCC).
Revised:  Our best method MLSP-VQA-FF improves the Spearman rank-order correlation coefficient (SRCC) performance metric on the standard KonVid-1k in-the-wild benchmark dataset to 0.83 surpassing the best existing deep-learning model ( 0.8 SRCC) and hand-crafted feature-based method (0.78 SRCC).
Edit intent: fluency
Original:  Finally, we show that MLSP-VQA-FF trained on FlickrVid-150k sets the new state-of-the-art for cross-test performance on KonVid-1k and LIVE-Qualcomm with a 0.79 and 0.58 SRCC, respectively  , showing excellent generalization.
Revised:  Finally, we show that the MLSP-VQA models trained on KonVid-150k sets the new state-of-the-art for cross-test performance on KonVid-1k and LIVE-Qualcomm with a 0.79 and 0.58 SRCC, respectively , showing excellent generalization.
Edit intent: clarity
Original: The model of programs over (finite) monoids, introduced by Barrington and Th \'e%DIFDELCMD < }%%% rien , gives an interesting way to characterise the circuit complexity class NC^1 and its subclasses and showcases deep connections with algebraic automata theory.
Revised: The model of programs over (finite) monoids, introduced by Barrington and \'e%DIFDELCMD < }%%% rien , gives an interesting way to characterise the circuit complexity class NC^1 and its subclasses and showcases deep connections with algebraic automata theory.
Edit intent: fluency
Original: The model of programs over (finite) monoids, introduced by Barrington and Th \'e%DIFDELCMD < }%%% rien , gives an interesting way to characterise the circuit complexity class NC^1 and its subclasses and showcases deep connections with algebraic automata theory.
Revised: The model of programs over (finite) monoids, introduced by Barrington and Th %DIFDELCMD < }%%% rien , gives an interesting way to characterise the circuit complexity class NC^1 and its subclasses and showcases deep connections with algebraic automata theory.
Edit intent: fluency
Original: The model of programs over (finite) monoids, introduced by Barrington and Th \'e%DIFDELCMD < }%%% rien , gives an interesting way to characterise the circuit complexity class NC^1 and its subclasses and showcases deep connections with algebraic automata theory.
Revised: The model of programs over (finite) monoids, introduced by Barrington and Th \'e%DIFDELCMD < }%%% Th\'erien , gives an interesting way to characterise the circuit complexity class NC^1 and its subclasses and showcases deep connections with algebraic automata theory.
Edit intent: fluency
Original:  Combining quantum linear solver , quantum singular value estimation and quantum phase estimation, we propose quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: normal matrices and matrices whose eigenvalues have non-positive imaginary parts.
Revised:  Combining quantum algorithms to solve linear differential equations , quantum singular value estimation and quantum phase estimation, we propose quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: normal matrices and matrices whose eigenvalues have non-positive imaginary parts.
Edit intent: clarity
Original:  Combining quantum linear solver , quantum singular value estimation and quantum phase estimation, we propose quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: normal matrices and matrices whose eigenvalues have non-positive imaginary parts.
Revised:  Combining quantum linear solver , quantum singular value estimation and quantum phase estimation, we propose quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: matrices whose eigenvalues have non-positive imaginary parts.
Edit intent: coherence
Original:  The quantum algorithm returns a quantum state such that the first register stores eigenvalues and the second register stores  eigenvectors.
Revised:  The quantum algorithm returns a quantum state such that the first register stores eigenvalues and the second register stores the corresponding eigenvectors.
Edit intent: clarity
Original:  For normal matrices, the complexity to obtain this quantum state is dominated by the complexity to perform quantum phase estimation and quantum singular value estimation.
Revised:  For normal matrices, the complexity to obtain this quantum state is dominated by the complexity to perform quantum singular value estimation.
Edit intent: clarity
Original:  Under certain conditions (e.g. sparse or block-encoding ), the complexity is polylog on the size n of the given matrix.
Revised:  Under certain conditions (e.g. sparse ), the complexity is polylog on the size n of the given matrix.
Edit intent: clarity
Original:  Under certain conditions (e.g. sparse or block-encoding ), the complexity is polylog on the size n of the given matrix.
Revised:  Under certain conditions (e.g. sparse or block-encoding ), the complexities are polylog on the size n of the given matrix.
Edit intent: fluency
Original:  For s sparsematrix M, the complexity can be O(sn\|M\|_{\max}  /\epsilon ).
Revised:  If M is s sparse, then the complexity is O(sn\|M\|_{\max} /\epsilon ).
Edit intent: clarity
Original:  Combining quantum algorithms to solve linear differential equations , quantum singular value estimation and quantum phase estimation , we propose  quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: matrices that only have real eigenvalues and normal matrices.
Revised:  Combining quantum algorithms to solve linear differential equations and quantum singular value estimation and quantum phase estimation , we propose quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: matrices that only have real eigenvalues and normal matrices.
Edit intent: fluency
Original:  Combining quantum algorithms to solve linear differential equations , quantum singular value estimation and quantum phase estimation , we propose  quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: matrices that only have real eigenvalues and normal matrices.
Revised:  Combining quantum algorithms to solve linear differential equations , quantum singular value estimation , we propose quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: matrices that only have real eigenvalues and normal matrices.
Edit intent: coherence
Original:  Combining quantum algorithms to solve linear differential equations , quantum singular value estimation and quantum phase estimation , we propose  quantum algorithms to compute the eigenvalues of diagonalizable matrices of two types: matrices that only have real eigenvalues and normal matrices.
Revised:  Combining quantum algorithms to solve linear differential equations , quantum singular value estimation and quantum phase estimation , we propose quantum algorithms to compute the eigenvalues of diagonalizable matrices that only have real eigenvalues and normal matrices.
Edit intent: clarity
Original:  The quantum algorithms return a quantum state such that the first register stores eigenvalues and the second register stores the corresponding eigenvectors.
Revised:  The output of the quantum algorithms is a superposition of the eigenvalues and the second register stores the corresponding eigenvectors.
Edit intent: clarity
Original:  The quantum algorithms return a quantum state such that the first register stores eigenvalues and the second register stores the corresponding eigenvectors.
Revised:  The quantum algorithms return a quantum state such that the first register stores eigenvalues and the corresponding eigenvectors.
Edit intent: clarity
Original:  For normal matrices, the complexity to obtain this quantum state is dominated by the complexity to perform quantum singular value estimation .
Revised:  The complexities are dominated by solving a linear system of ODEs and performing quantum singular value estimation .
Edit intent: clarity
Original:  If M is s sparse, then the complexity is O( sn  \|M\|_{\max} \kappa^2 /\epsilon^2) , where \kappa is the condition number of the matrix of eigenvectors .
Revised:  If M is s sparse, then the complexity is O( sn \|M\|_{\max} /\epsilon^2) , where \kappa is the condition number of the matrix of eigenvectors .
Edit intent: coherence
Original:  The considered hybrid algorithm combine the Toom-Cook-k (or Toom-k) fast integer multiplication approach with computational complexity \Theta\left(c_kn^{\log_k \left(2k-1\right)}\right), and "standard" integer multiplication algorithms which compute \Omega\left(n^2\right) digit multiplications.
Revised:  The considered hybrid algorithms combine the Toom-Cook-k (or Toom-k) fast integer multiplication approach with computational complexity \Theta\left(c_kn^{\log_k \left(2k-1\right)}\right), and "standard" integer multiplication algorithms which compute \Omega\left(n^2\right) digit multiplications.
Edit intent: fluency
Original:  We present an \Omega\left(\left(n/\max\{M,n_0\}\right)^{\log_k \left(2k-1\right)}\left(\max\{1,n_0/M\}\right)^2M\right) lower bound for the I/O complexity  a class of "uniform, non-stationary" hybrid algorithms when executed in a two-level storage hierarchy with M words of fast memory , where n_0 denotes the threshold size of sub-problems which are computed using standard algorithms with algebraic complexity \Omega\left(n^2\right).
Revised:  We present an \Omega\left(\left(n/\max\{M,n_0\}\right)^{\log_k \left(2k-1\right)}\left(\max\{1,n_0/M\}\right)^2M\right) lower bound for the I/O complexity of a class of "uniform, non-stationary" hybrid algorithms when executed in a two-level storage hierarchy with M words of fast memory , where n_0 denotes the threshold size of sub-problems which are computed using standard algorithms with algebraic complexity \Omega\left(n^2\right).
Edit intent: fluency
Original:  As some  hybrid algorithms from this class execute a number of I/O operations that is within a O \left(k^2\right) multiplicative term of the corresponding lower bounds, the proposed lower bounds are almost asymptotically tight and indeed tight for constant values of k. Extensions of the lower bounds for a parallel model with P processors are also discussed .
Revised:  As some hybrid algorithms from this class exhibit I/O operations that is within a O \left(k^2\right) multiplicative term of the corresponding lower bounds, the proposed lower bounds are almost asymptotically tight and indeed tight for constant values of k. Extensions of the lower bounds for a parallel model with P processors are also discussed .
Edit intent: clarity
Original:  To demonstrate the latter, we perform a systematic evaluation of a diverse set of algorithms for the ridesharing problem , which is, to the best of our knowledge, one of the largest and most comprehensive to date.
Revised:  To demonstrate the latter, we perform a systematic evaluation of a diverse set of algorithms for the ridesharing problem which is, to the best of our knowledge, one of the largest and most comprehensive to date.
Edit intent: fluency
Original:  Our evaluation setting is specifically designed to resemble reality as closely as possible.
Edit intent: coherence
Original: A pure Galerkin scheme is notoriously unstable.
Revised: In the hyperbolic community, discontinuous Galerkin (DG) approaches are mainly applied when finite element methods are considered. As the name suggested, the DG framework allows a discontinuity at the element interfaces, which seems for many researchers a favorable property in case of hyperbolic balance laws. On the contrary, continuous Galerkin (CG) methods appear to be unsuitable for hyperbolic problems and there exists still the perception that continuous Galerkin methods are notoriously unstable.
Edit intent: coherence