wanyu/IteraTeR_v2 huggingface dataset:
Original:  Convergence analysis of exact two-grid methods (i.e., the Galerkin coarse-grid system is solved exactly) has been well developed: the convergence factor of exact two-grid methods can be characterized by an identity.
Revised:  Convergence analysis of exact two-grid methods (i.e., the Galerkin coarse-grid system is mature, and the convergence factor of exact two-grid methods can be characterized by an identity.
Edit intent: coherence
Original:  However, convergence theory of inexact ones (i.e., the coarse-grid problem is solved approximately) is still less mature .
Revised:  However, convergence theory of inexact ones (i.e., the coarse-grid system is solved approximately) is still less mature .
Edit intent: clarity
Original:  In this paper,  a theoretical framework for the convergence analysis of inexact two-grid methods is developed .
Revised:  In this paper, we develop a theoretical framework for the convergence analysis of inexact two-grid methods is developed .
Edit intent: coherence
Original:  In this paper,  a theoretical framework for the convergence analysis of inexact two-grid methods is developed .
Revised:  In this paper, a theoretical framework for the convergence analysis of inexact two-grid methods .
Edit intent: clarity
Original:  More specifically,  two-sided bounds for the energy norm of the error propagation matrix are established under different approximation conditions , from which one can readily get the identity for the convergence factor of exact two-grid methods .
Revised:  More specifically, we present two-sided bounds for the energy norm of the error propagation matrix are established under different approximation conditions , from which one can readily get the identity for the convergence factor of exact two-grid methods .
Edit intent: coherence
Original:  More specifically,  two-sided bounds for the energy norm of the error propagation matrix are established under different approximation conditions , from which one can readily get the identity for the convergence factor of exact two-grid methods .
Revised:  More specifically, two-sided bounds for the energy norm of the error propagation matrix of inexact two-grid methods , from which one can readily get the identity for the convergence factor of exact two-grid methods .
Edit intent: clarity
Original:  More specifically,  two-sided bounds for the energy norm of the error propagation matrix are established under different approximation conditions , from which one can readily get the identity for the convergence factor of exact two-grid methods .
Revised:  More specifically, two-sided bounds for the energy norm of the error propagation matrix are established under different approximation conditions , from which one can readily obtain the identity for the convergence factor of exact two-grid methods .
Edit intent: fluency
Original:  More specifically,  two-sided bounds for the energy norm of the error propagation matrix are established under different approximation conditions , from which one can readily get the identity for the convergence factor of exact two-grid methods .
Revised:  More specifically, two-sided bounds for the energy norm of the error propagation matrix are established under different approximation conditions , from which one can readily get the identity for exact two-grid methods .
Edit intent: clarity
Original:  } We further give the first sublinear-time algorithm for the probabilistic embedding of edit distance to Hamming distance.
Revised:  } We further give the first sublinear-time probabilistic embedding of edit distance to Hamming distance.
Edit intent: clarity
Original:  We prove that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network, up to a limit that is dictated by the extent of the input and output fields-of-view.
Revised:  We show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network, up to a limit that is dictated by the extent of the input and output fields-of-view.
Edit intent: coherence
Original:  The main ingredient in the proof is a polynomial (in k) lower bound on the typical support of a random closed walk of length 2k in any connected regular graph, which further relies on new lower bounds for the entries of the Perron eigenvector of a connected irregular graph .
Revised:  The main ingredient in the proof is a polynomial (in k) lower bound on the typical support of a closed random walk of length 2k in any connected regular graph, which further relies on new lower bounds for the entries of the Perron eigenvector of a connected irregular graph .
Edit intent: fluency
Original:  The main ingredient in the proof is a polynomial (in k) lower bound on the typical support of a random closed walk of length 2k in any connected regular graph, which further relies on new lower bounds for the entries of the Perron eigenvector of a connected irregular graph .
Revised:  The main ingredient in the proof is a polynomial (in k) lower bound on the typical support of a random closed walk of length 2k in any connected graph, which further relies on new lower bounds for the entries of the Perron eigenvector of a connected irregular graph .
Edit intent: fluency
Original:  The main ingredient in the proof is a polynomial (in k) lower bound on the typical support of a random closed walk of length 2k in any connected regular graph, which further relies on new lower bounds for the entries of the Perron eigenvector of a connected irregular graph .
Revised:  The main ingredient in the proof is a polynomial (in k) lower bound on the typical support of a random closed walk of length 2k in any connected regular graph, which in turn relies on new lower bounds for the entries of the Perron eigenvector of a connected irregular graph .
Edit intent: clarity
Original:  To model large-vocab sparse categorical features, typical recommender models learn a joint embedding space for both queries and items.
Revised:  To model large-vocab categorical features, typical recommender models learn a joint embedding space for both queries and items.
Edit intent: clarity
Original:  Inspired by the recent success in self-supervised representation learning  research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning (SSL) framework for sparse neural models in recommendations.
Revised:  Inspired by the recent success in self-supervised representation learning (SSL) research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning (SSL) framework for sparse neural models in recommendations.
Edit intent: fluency
Original:  Inspired by the recent success in self-supervised representation learning  research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning (SSL) framework for sparse neural models in recommendations.
Revised:  Inspired by the recent success in self-supervised representation learning research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning framework for neural models in recommendations.
Edit intent: clarity
Original:  Furthermore, we propose two highly generalizable self-supervised learning tasks : (i) Feature Masking (FM) and (ii) Feature Dropout (FD) within the proposed SSL framework .
Revised:  Furthermore, we propose two self-supervised learning tasks : (i) Feature Masking (FM) and (ii) Feature Dropout (FD) within the proposed SSL framework .
Edit intent: clarity
Original:  Furthermore, we propose two highly generalizable self-supervised learning tasks : (i) Feature Masking (FM) and (ii) Feature Dropout (FD) within the proposed SSL framework .
Revised:  Furthermore, we propose two highly generalizable self-supervised learning tasks : (i) Feature Masking (FM) and (ii) Feature Dropout (FD) .
Edit intent: clarity
Original:  Our results demonstrate that the proposed framework outperforms baseline models and state-of-the-art spread-out regularization techniques in the context of retrieval.
Revised:  Our results demonstrate that the proposed framework outperforms baseline models and state-of-the-art regularization techniques in the context of retrieval.
Edit intent: clarity
Original: Large scale neural recommender models play a critical role in modern search and recommendation systems.
Revised: Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems.
Edit intent: clarity
Original:  To model  large-vocab categorical features, typical recommender models learn a joint embedding space  for both queries and items .
Revised:  To model the input space with large-vocab categorical features, typical recommender models learn a joint embedding space for both queries and items .
Edit intent: clarity
Original:  To model  large-vocab categorical features, typical recommender models learn a joint embedding space  for both queries and items .
Revised:  To model large-vocab categorical features, a typical recommender model learns a joint embedding space for both queries and items .
Edit intent: fluency
Original:  Inspired by the recent success in self-supervised representation learning (SSL) research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning framework for neural models in recommendations.
Revised:  Inspired by the recent success in self-supervised representation learning research in both computer vision and natural language understanding, we propose a multi-task self-supervised learning framework for neural models in recommendations.
Edit intent: clarity
Original:  We evaluate our framework using two large-scale datasets with ~ 500M and 1B training examples respectively.
Revised:  We evaluate our framework using two large-scale datasets with 500M and 1B training examples respectively.
Edit intent: fluency
Original:  Our results demonstrate that the proposed framework outperforms learning with the supervision task only and other state-of-the-art regularization techniques in the context of retrieval .
Revised:  Our results demonstrate that the proposed framework outperforms traditional supervised learning only models and state-of-the-art regularization techniques in the context of retrieval .
Edit intent: clarity
Original:  Our results demonstrate that the proposed framework outperforms learning with the supervision task only and other state-of-the-art regularization techniques in the context of retrieval .
Revised:  Our results demonstrate that the proposed framework outperforms learning with the supervision task only and other state-of-the-art regularization techniques in the context of item recommendations .
Edit intent: clarity
Original:  However, with millions to billions of items , the power-law user feedback makes labels very sparse for a large amount of long-tail items  .
Revised:  However, with millions to billions of items , the power-law distribution. This makes the feedback data for long-tail items .
Edit intent: clarity
Original:  Furthermore, we propose two self-supervised tasks applicable to models with categorical features within the proposed framework : (i) Feature Masking (FM) and (ii) Feature Dropout (FD) . We evaluate our framework using two large-scale datasets with 500M and 1B training examples respectively.
Revised:  Furthermore, we propose two self-supervised tasks applicable to models with categorical features within the proposed framework . We evaluate our framework using two large-scale datasets with 500M and 1B training examples respectively.
Edit intent: coherence
Original:  Furthermore, we propose two self-supervised tasks applicable to models with categorical features within the proposed framework : (i) Feature Masking (FM) and (ii) Feature Dropout (FD) . We evaluate our framework using two large-scale datasets with 500M and 1B training examples respectively.
Revised:  Furthermore, we propose two self-supervised tasks applicable to models with categorical features within the proposed framework : (i) Feature Masking (FM) and (ii) Feature Dropout (FD) . We evaluate our framework using two real-world datasets with 500M and 1B training examples respectively.
Edit intent: clarity
Original:  Our results demonstrate that the proposed framework outperforms traditional supervised learning only models and state-of-the-art regularization techniques in the context of item recommendations.
Revised:  Our results demonstrate the effectiveness of SSL regularization and show its superior performance over the state-of-the-art regularization techniques in the context of item recommendations.
Edit intent: coherence
Original:  We also apply the proposed techniques to a web-scale commercial app-to-app recommendation system, and significantly improve top-tier business metrics via A/B experiments on live traffic.
Revised:  We also apply the proposed techniques to a web-scale commercial app-to-app recommendation system, with significant improvements top-tier business metrics via A/B experiments on live traffic.
Edit intent: fluency
Original:  We also apply the proposed techniques to a web-scale commercial app-to-app recommendation system, and significantly improve top-tier business metrics via A/B experiments on live traffic.
Revised:  We also apply the proposed techniques to a web-scale commercial app-to-app recommendation system, and significantly improve top-tier business metrics demonstrated in A/B experiments on live traffic.
Edit intent: clarity
Original: The paper presents the approach for the building of consistent and applicable clinical decision support systems ( CDSS ) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications.
Revised: The paper presents an approach for building consistent and applicable clinical decision support systems ( CDSS ) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications.
Edit intent: fluency
Original: The paper presents the approach for the building of consistent and applicable clinical decision support systems ( CDSS ) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications.
Revised: The paper presents the approach for the building of consistent and applicable clinical decision support systems ( CDSSs ) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications.
Edit intent: fluency
Original: The paper presents the approach for the building of consistent and applicable clinical decision support systems ( CDSS ) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications.
Revised: The paper presents the approach for the building of consistent and applicable clinical decision support systems ( CDSS ) using a data-driven predictive model aimed at resolving the problem of low applicability and scalability of CDSS in real-world applications.
Edit intent: fluency
Original: The paper presents the approach for the building of consistent and applicable clinical decision support systems ( CDSS ) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSS in real-world applications.
Revised: The paper presents the approach for the building of consistent and applicable clinical decision support systems ( CDSS ) using a data-driven predictive model aimed to resolve a problem of low applicability and scalability of CDSSs in real-world applications.
Edit intent: fluency
Original:  The approach is based on the three-stage application of domain-specific and data-driven supportive procedures to integrate into clinical business-processes with higher trust and explainability of the prediction results and recommendations.
Revised:  The approach is based on a threestage application of domain-specific and data-driven supportive procedures to integrate into clinical business-processes with higher trust and explainability of the prediction results and recommendations.
Edit intent: fluency
Original:  The approach is based on the three-stage application of domain-specific and data-driven supportive procedures to integrate into clinical business-processes with higher trust and explainability of the prediction results and recommendations.
Revised:  The approach is based on the three-stage application of domain-specific and data-driven supportive procedures that are to be integrated into clinical business processes with higher trust and explainability of the prediction results and recommendations.
Edit intent: clarity
Original:  Within the considered three stages, the regulatory policy, data-driven modes, and interpretation procedures are integrated to enable natural domain-specific interaction with decision-makers with sequential narrowing of the intelligent decision support focus.
Revised:  Within the considered three stages, the regulatory policy, data-driven modes, and interpretation procedures are integrated to enable natural domain-specific interaction with decisionmakers with sequential narrowing of the intelligent decision support focus.
Edit intent: fluency
Original:  The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSS . The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling  to improve known clinical scales (such as FINDRISK) , keeping the problem-specific reasoning interface similar to existing applications.
Revised:  The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSSs . The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling to improve known clinical scales (such as FINDRISK) , keeping the problem-specific reasoning interface similar to existing applications.
Edit intent: fluency
Original:  The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSS . The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling  to improve known clinical scales (such as FINDRISK) , keeping the problem-specific reasoning interface similar to existing applications.
Revised:  The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSS . The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling us to improve known clinical scales (such as FINDRISK) , keeping the problem-specific reasoning interface similar to existing applications.
Edit intent: fluency
Original:  The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSS . The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling  to improve known clinical scales (such as FINDRISK) , keeping the problem-specific reasoning interface similar to existing applications.
Revised:  The proposed methodology enables a higher level of automation, scalability, and semantic interpretability of CDSS . The approach was implemented in software solutions and tested within a case study in T2DM prediction, enabling to improve known clinical scales (such as FINDRISK) while keeping the problem-specific reasoning interface similar to existing applications.
Edit intent: coherence
Original:  Such inheritance, together with the three-stages approach, provide higher compatibility of the solution and leads to trust, valid, and explainable application of data-driven solution in real-world cases.
Revised:  Such inheritance, together with the three-staged approach, provide higher compatibility of the solution and leads to trust, valid, and explainable application of data-driven solution in real-world cases.
Edit intent: fluency
Original:  Such inheritance, together with the three-stages approach, provide higher compatibility of the solution and leads to trust, valid, and explainable application of data-driven solution in real-world cases.
Revised:  Such inheritance, together with the three-stages approach, provide higher compatibility of the solution and leads to trust, valid, and explainable application of data-driven solutions in real-world cases.
Edit intent: fluency
Original: Finite state transducers, multitape automata and weighted probabilistic automata have a lot in common.
Revised: Finite state transducers, multitape automata and weighted automata have a lot in common.
Edit intent: clarity
Original:  We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a `` best-effort '' at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient.
Revised:  We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a " best-effort '' at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient.
Edit intent: fluency
Original:  We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a `` best-effort '' at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient.
Revised:  We propose two methods to solve the resulting non-convex mixed integer program: semi-exhaustive search optimization, which represents a `` best-effort " at obtaining the optimal solution, and efficient alternate optimization, which is more computationally efficient.
Edit intent: fluency
Original:  In this paper, we develop an optimization methodology that considers topology configuration jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing.
Revised:  In this paper, we develop an optimization methodology that considers the network topology jointly with device and network resource allocation to minimize total D2D overhead, which we quantify in terms of time and energy required for task processing.
Edit intent: clarity
Original:  Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple input multiple output (MIMO) wireless devices.
Revised:  Variables in our model include task assignment, CPU allocation, subchannel selection, and beamforming design for multiple-input multiple-output (MIMO) wireless devices.
Edit intent: fluency
Original: Complex reasoning problems are most clearly and easily specified using logical rules, especially recursive rules with aggregation such as counts and sums for practical applications.
Revised: Complex reasoning problems are most clearly and easily specified using logical rules, but require recursive rules with aggregation such as counts and sums for practical applications.
Edit intent: coherence
Original:  Unfortunately, the meaning of such rules has been a significant challenge, leading to many different conflicting semantics.
Revised:  Unfortunately, the meaning of such rules has been a significant challenge, leading to many disagreeing semantics.
Edit intent: clarity
Original:  The key idea is to support simple expression of the different assumptions underlying different semantics, and orthogonally interpret aggregation operations straightforwardly using their simple usual meaning  .
Revised:  The key idea is to support simple expression of the different assumptions underlying different semantics, and orthogonally interpret aggregation operations using their simple usual meaning .
Edit intent: clarity