wanyu/IteraTeR_v2 huggingface dataset:
Original:  We, thus, make a distinction between items that are good for everyone (i.e. goods), bad for everyone (i.e. bads) , and good for some agents and bad for other agents (i.e. mixed ).
Revised:  We, thus, make a distinction between items that are good for everyone (i.e. mixed), good for everyone (i.e. bads) , and good for some agents and bad for other agents (i.e. mixed ).
Edit intent: clarity
Original:  We, thus, make a distinction between items that are good for everyone (i.e. goods), bad for everyone (i.e. bads) , and good for some agents and bad for other agents (i.e. mixed ).
Revised:  We, thus, make a distinction between items that are good for everyone (i.e. goods), bad for everyone (i.e. goods) or bad for everyone (i.e. mixed ).
Edit intent: clarity
Original:  We, thus, make a distinction between items that are good for everyone (i.e. goods), bad for everyone (i.e. bads) , and good for some agents and bad for other agents (i.e. mixed ).
Revised:  We, thus, make a distinction between items that are good for everyone (i.e. goods), bad for everyone (i.e. bads) , and good for some agents and bad for other agents (i.e. bads ).
Edit intent: clarity
Original:  Strain is estimated from the recorded neutron transmission data by analysing features known as Bragg-edges for which several methods exist.
Revised:  Strain is estimated from such data by analysing features known as Bragg-edges for which several methods exist.
Edit intent: clarity
Original:  It is important for these methods to provide both accurate estimates of strain and an accurate quantification of certainty for these estimates .
Revised:  It is important for these methods to provide both accurate estimates of strain and an accurate quantification the associated uncertainty .
Edit intent: clarity
Original:  Our algorithm is based on the quantum cluster expansion of Netoc n\`y and Redig and the cluster expansion approach to designing algorithms due to Helmuth, Perkins, and Regts.
Revised:  Our algorithm is based on the quantum cluster expansion of Netoc n\'y and Redig and the cluster expansion approach to designing algorithms due to Helmuth, Perkins, and Regts.
Edit intent: fluency
Original: We prove, under a computational complexity hypothesis, that it is consistent with the true universal theory of p-time algorithms that a specific p-time function extending n bits to m \geq n^2 bits violates the dual weak pigeonhole principle: every string y of length m equals to the value of the function for some x of length n.
Revised: We prove, under a computational complexity hypothesis, that it is consistent with the true universal theory of p-time algorithms that a specific p-time function extending n bits to m \geq n^2 bits violates the dual weak pigeonhole principle: every string y of length m equals the value of the function for some x of length n.
Edit intent: fluency
Original: Users' reviews have been demonstrated to be effective in solving different recommendation problems.
Revised: The user review data have been demonstrated to be effective in solving different recommendation problems.
Edit intent: clarity
Original:  Previous review-based recommendation methods usually employ sophisticated compositional models , e.g., Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) , to learn semantic representations from the review data for recommendation.
Revised:  Previous review-based recommendation methods usually employ sophisticated compositional models such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) , to learn semantic representations from the review data for recommendation.
Edit intent: clarity
Original:  Previous review-based recommendation methods usually employ sophisticated compositional models , e.g., Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) , to learn semantic representations from the review data for recommendation.
Revised:  Previous review-based recommendation methods usually employ sophisticated compositional models , e.g., Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) to learn semantic representations from the review data for recommendation.
Edit intent: fluency
Original:  Extensive experiments on real datasets have been performed to demonstrate the effectiveness of RGNN, compared with state-of-the-art review-based recommendation approaches  .
Revised:  We compared RGNN with state-of-the-art review-based recommendation approaches .
Edit intent: clarity
Original:  Previous review-based recommendation methods usually employ sophisticated compositional models  such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN)  to learn semantic representations from the review data for recommendation.
Revised:  Previous review-based recommendation methods usually employ sophisticated compositional models , such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) to learn semantic representations from the review data for recommendation.
Edit intent: fluency
Original:  Previous review-based recommendation methods usually employ sophisticated compositional models  such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN)  to learn semantic representations from the review data for recommendation.
Revised:  Previous review-based recommendation methods usually employ sophisticated compositional models such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) , to learn semantic representations from the review data for recommendation.
Edit intent: fluency
Original:  However, these methods mainly capture the local and consecutive dependency between neighbouring words in a word window  .
Revised:  However, these methods mainly capture the local dependency between neighbouring words in a word window .
Edit intent: clarity
Original:  Therefore, they may not be effective in capturing the long-term, non-consecutive, and global dependency between words coherently .
Revised:  Therefore, they may not be effective in capturing the global dependency between words coherently .
Edit intent: clarity
Original:  We compared RGNN with state-of-the-art review-based recommendation approaches on real datasets.
Revised:  We compared RGNN with state-of-the-art review-based recommendation approaches on two real-world datasets.
Edit intent: clarity
Original:  The experimental results indicate that RGNN usually outperforms baseline methods  in terms of mean square error .
Revised:  The experimental results indicate that RGNN consistently outperforms baseline methods in terms of mean square error .
Edit intent: clarity
Original:  The experimental results indicate that RGNN usually outperforms baseline methods  in terms of mean square error .
Revised:  The experimental results indicate that RGNN usually outperforms baseline methods , in terms of mean square error .
Edit intent: fluency
Original:  Although this field is rather new, over the last years, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research.
Revised:  Although this field is rather new, a broad range of techniques have been developed for various data sources and problems, resulting in a large body of research.
Edit intent: clarity
Original:  This paper reviews pertinent publications and tries to present an exhaustive e view of the field.
Revised:  This paper reviews pertinent publications and tries to present an exhaustive overview of the field.
Edit intent: fluency
Original:  This is the task of finding a set that contains the orbit of the system, is itself invariant under the dynamics , and is disjoint from the a given set of error configurations.
Revised:  This is the task of finding a set of states that is closed under the dynamics , and is disjoint from the a given set of error configurations.
Edit intent: clarity
Original:  This is the task of finding a set that contains the orbit of the system, is itself invariant under the dynamics , and is disjoint from the a given set of error configurations.
Revised:  This is the task of finding a set that contains the orbit of the system, is itself invariant under the dynamics of the system and is disjoint from the a given set of error configurations.
Edit intent: fluency
Original:  This is the task of finding a set that contains the orbit of the system, is itself invariant under the dynamics , and is disjoint from the a given set of error configurations.
Revised:  This is the task of finding a set that contains the orbit of the system, is itself invariant under the dynamics , and is disjoint from a given set of error configurations.
Edit intent: fluency
Original:  Assuming Schanuel's conjecture in transcendental number theory, we establish effective synthesis for o-minimal invariants and semi-algebraic error sets.
Revised:  Assuming Schanuel's conjecture in transcendental number theory, we establish effective synthesis of o-minimal invariants and semi-algebraic error sets.
Edit intent: fluency
Original:  Assuming Schanuel's conjecture in transcendental number theory, we establish effective synthesis for o-minimal invariants and semi-algebraic error sets.
Revised:  Assuming Schanuel's conjecture in transcendental number theory, we establish effective synthesis for o-minimal invariants in the case of semi-algebraic error sets.
Edit intent: clarity
Original:  Without  Schanuel's conjecture, we give a procedure for synthesizing semi-algebraic invariants that contain all but a bounded initial segment of the orbit and are disjoint from the error set.
Revised:  Without using Schanuel's conjecture, we give a procedure for synthesizing semi-algebraic invariants that contain all but a bounded initial segment of the orbit and are disjoint from the error set.
Edit intent: fluency
Original:  Without  Schanuel's conjecture, we give a procedure for synthesizing semi-algebraic invariants that contain all but a bounded initial segment of the orbit and are disjoint from the error set.
Revised:  Without Schanuel's conjecture, we give a procedure for synthesizing semi-algebraic invariants that contain all but a bounded initial segment of the orbit and are disjoint from a given semi-algebraic error set.
Edit intent: clarity
Original:  We further prove that unconditional effective synthesis of semi-algebraic invariants that contain the whole orbit, is at least as hard as a certain open problem in transcendental number theory.
Revised:  We further prove that effective synthesis of semi-algebraic invariants that contain the whole orbit, is at least as hard as a certain open problem in transcendental number theory.
Edit intent: clarity
Original:  The first approach provides a normative rule for action selection in the face of model uncertainty or when information-processing capabilities are limited.
Revised:  The first approach provides a normative rule for action selection in the face of model uncertainty or when information processing capabilities are limited.
Edit intent: clarity
Original:  We elucidate the main ideas and discuss critical technical and conceptual issues revolving around these two notions of free energy that both claim to apply at all levels of decision-making, from the high-level deliberation of reasoning down to the low-level information-processing of perception.
Revised:  We elucidate the main ideas and discuss critical technical and conceptual issues revolving around these two notions of free energy that both claim to apply at all levels of decision-making, from the high-level deliberation of reasoning down to the low-level information processing of perception.
Edit intent: fluency
Original:  The high-dimensionality of the data space implies extremely high computational cost already for the evaluation of the forward operator , which makes a numerical solution of the inverse problem, e.g., by iterative regularization methods, practically infeasible.
Revised:  The high-dimensionality of the data space implies extremely high computational cost already for the evaluation of the forward operator which makes a numerical solution of the inverse problem, e.g., by iterative regularization methods, practically infeasible.
Edit intent: fluency
Original:  To overcome this obstacle, we develop a novel model reduction approach that takes advantage of the underlying tensor product structure of the problem and which allows to obtain low-dimensional certified reduced order models of quasi-optimal rank  .
Revised:  To overcome this obstacle, we take advantage of the underlying tensor product structure of the problem and which allows to obtain low-dimensional certified reduced order models of quasi-optimal rank .
Edit intent: clarity
Original:  To overcome this obstacle, we develop a novel model reduction approach that takes advantage of the underlying tensor product structure of the problem and which allows to obtain low-dimensional certified reduced order models of quasi-optimal rank  .
Revised:  To overcome this obstacle, we develop a novel model reduction approach that takes advantage of the underlying tensor product structure of the problem and propose a strategy for constructing low-dimensional certified reduced order models of quasi-optimal rank .
Edit intent: clarity
Original:  We show that from the distribution of signals on a 2D-pC-MS map it is possible to extract the charge state of both parent and fragment ions without resolving the isotopic envelope.
Revised:  We show that from the distribution of signals on a 2D-PC-MS map it is possible to extract the charge state of both parent and fragment ions without resolving the isotopic envelope.
Edit intent: clarity
Original: We model the limit order book (LOB) as a continuous Markov process and develop an algebra to describe its dynamics based on the fundamental events of the book : order arrivals and cancellations.
Revised: We propose a microscopic model to describe the dynamics of the fundamental events of the book : order arrivals and cancellations.
Edit intent: clarity
Original:  We estimate several linearized model specifications based on the theoretical description of the LOB and conduct in- and out-of-sample forecasts on several frequencies .
Revised:  We estimate several linearized model specifications , and conduct in- and out-of-sample forecasts on several frequencies .
Edit intent: clarity
Original:  We estimate several linearized model specifications based on the theoretical description of the LOB and conduct in- and out-of-sample forecasts on several frequencies .
Revised:  We estimate several linearized model specifications based on the theoretical description of the LOB and conduct in- and out-of-sample forecasts .
Edit intent: clarity
Original: The in-sample results based on contemporaneous information suggest that our model describes up to 90\% of the variation of close-to-close returns , the adjusted R^2 still ranges at around 80\%.
Revised: The in-sample results based on contemporaneous information suggest that our model describes returns very well, resulting in an adjusted R^2 still ranges at around 80\%.
Edit intent: clarity
Original:  In the more realistic setting where only past information enters the model, we still observe an adjusted R^2 in the range of 15\%.
Revised:  In the more realistic setting where only past information enters the model, we observe an adjusted R^2 in the range of 15\%.
Edit intent: clarity
Original:  In the more realistic setting where only past information enters the model, we still observe an adjusted R^2 in the range of 15\%.
Revised:  In the more realistic setting where only past information enters the model, we still observe an adjusted R^2 around 15\%.
Edit intent: clarity
Original:  The direction of the next return can be predicted , out-of-sample , with an accuracy of over 75\% for short time horizons below 10 minutes.
Revised:  The direction of the next return can be predicted ( out-of-sample , with an accuracy of over 75\% for short time horizons below 10 minutes.
Edit intent: fluency
Original:  The direction of the next return can be predicted , out-of-sample , with an accuracy of over 75\% for short time horizons below 10 minutes.
Revised:  The direction of the next return can be predicted , out-of-sample ) with an accuracy of over 75\% for short time horizons below 10 minutes.
Edit intent: fluency
Original:  The direction of the next return can be predicted , out-of-sample , with an accuracy of over 75\% for short time horizons below 10 minutes.
Revised:  The direction of the next return can be predicted , out-of-sample , with an accuracy above 75\% for short time horizons below 10 minutes.
Edit intent: clarity
Original:  The direction of the next return can be predicted , out-of-sample , with an accuracy of over 75\% for short time horizons below 10 minutes.
Revised:  The direction of the next return can be predicted , out-of-sample , with an accuracy of over 75\% for time horizons below 10 minutes.
Edit intent: clarity
Original:  Out-of-sample, on average, we obtain R^2 values for the Mincer-Zarnowitz regression of around 2-3\% and an RMSPE that is 10 times lower than values documented in the literature.
Revised:  Out-of-sample, on average, we obtain an RMSPE that is 10 times lower than values documented in the literature.
Edit intent: coherence
Original:   We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping.
Revised:  Results: We present CLUE, an algorithm for exact model reduction of systems of polynomial differential equations by constrained linear lumping.
Edit intent: fluency
Original:   An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at URL 
Revised:  Availability: An implementation of the algorithm and relevant resources to replicate the experiments herein reported are freely available for download at URL 
Edit intent: coherence
Original: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS) that can be used to construct optimistic parameter estimates using significantly fewer samples from the posterior distributions compared to existing oversampling frameworks.
Revised: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS) to construct optimistic parameter estimates using significantly fewer samples from the posterior distributions compared to existing oversampling frameworks.
Edit intent: coherence
Original: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS) that can be used to construct optimistic parameter estimates using significantly fewer samples from the posterior distributions compared to existing oversampling frameworks.
Revised: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS) that can be used to construct optimistic parameter estimates using significantly fewer samples than existing oversampling frameworks.
Edit intent: clarity
Original:  We apply CO to a new budgeted variant of the Influence Maximization (IM) semi-bandits with linear generalization of edge weights.
Revised:  We apply CO to a novel budgeted variant of the Influence Maximization (IM) semi-bandits with linear generalization of edge weights.
Edit intent: clarity
Original:  Combining CO with the oracle we designed for the offline problem, our online learning algorithm tackles the budget allocation, parameter learning, and reward maximization challenges simultaneously.
Revised:  Combining CO with the oracle we design for the offline problem, our online learning algorithm tackles the budget allocation, parameter learning, and reward maximization challenges simultaneously.
Edit intent: fluency
Original:  Combining CO with the oracle we designed for the offline problem, our online learning algorithm tackles the budget allocation, parameter learning, and reward maximization challenges simultaneously.
Revised:  Combining CO with the oracle we designed for the offline problem, our online learning algorithm simultaneously tackles budget allocation, parameter learning, and reward maximization challenges simultaneously.
Edit intent: fluency
Original:  We prove that our online learning algorithm achieves a scaled regret comparable to that of the  UCB-based algorithms for IM semi-bandits.
Revised:  We prove that our online learning algorithm achieves a scaled regret comparable to that of the best UCB-based algorithms for IM semi-bandits.
Edit intent: clarity
Original: In software-defined networks (SDN)  a controller program is in charge of deploying diverse network functionality across a large number of switches, but this comes at a great risk: deploying buggy controller code could result in network and service disruption and security loopholes.
Revised: In software-defined networks (SDN) , a controller program is in charge of deploying diverse network functionality across a large number of switches, but this comes at a great risk: deploying buggy controller code could result in network and service disruption and security loopholes.
Edit intent: fluency
Original:  In this work, we develop sublinear time algorithms that find the planted clique when k=\omega(n \log \log n).
Revised:  In this work, we initiate the development of sublinear time algorithms that find the planted clique when k=\omega(n \log \log n).
Edit intent: clarity
Original:  For this multivariate screening, we prove the asymptotic between-stage independence, required for the family-wise error rate control, under the biomarker-treatment independence.
Revised:  For this multivariate screening, we prove the asymptotic between-stage independence, required for family-wise error rate control, under the biomarker-treatment independence.
Edit intent: fluency
Original:  For this multivariate screening, we prove the asymptotic between-stage independence, required for the family-wise error rate control, under the biomarker-treatment independence.
Revised:  For this multivariate screening, we prove the asymptotic between-stage independence, required for the family-wise error rate control, under biomarker-treatment independence.
Edit intent: fluency
Original: The gradient discretisation method (GDM) is a generic framework, covering many classical methods (Finite Elements, Finite Volumes, Discontinuous Galerkin, etc.), for designing and analysing numerical schemes for diffusion models.
Revised: *The gradient discretisation method (GDM) is a generic framework, covering many classical methods (Finite Elements, Finite Volumes, Discontinuous Galerkin, etc.), for designing and analysing numerical schemes for diffusion models.
Edit intent: fluency
Original: As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for the analysis and diagnosis of biomedical and health care problemsin the last decade , mobile healthcare applications have received unprecedented attention.
Revised: As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for biomedical and healthcare problems , mobile healthcare applications have received unprecedented attention.
Edit intent: clarity
Original: As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for the analysis and diagnosis of biomedical and health care problemsin the last decade , mobile healthcare applications have received unprecedented attention.
Revised: As the advancement of deep learning (DL), the Internet of Things and cloud computing techniques for the analysis and diagnosis of biomedical and health care problemsin the last decade , mobile healthcare systems have received unprecedented attention.
Edit intent: clarity
Original:  Since DL techniques usually require enormous amount of computation, most of them cannot be directly deployed on the computation-constrained and energy limited mobile and IoT devices.
Revised:  Since DL techniques usually require enormous amount of computation, most of them cannot be directly deployed on the resource-constrained mobile and IoT devices.
Edit intent: clarity