wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To quantify the impact of government actions and support existing theories as well as policy makers, a data-driven model is developed . Data for two nations, Italy and Taiwan, is gathered and used to train several neural networks.
Revised:  To quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by means of training neural network models to predict the daily slope increase of cumulative infected and by using such models in an optimisation study aimed at understanding the best set of mitigating actions . Data for two nations, Italy and Taiwan, is gathered and used to train several neural networks.
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Original:  To quantify the impact of government actions and support existing theories as well as policy makers, a data-driven model is developed . Data for two nations, Italy and Taiwan, is gathered and used to train several neural networks. The results of the best-performing model , the Long Short Term Neural Network, are optimized to investigate a different scenario of mitigation measures for both nations .
Revised:  To quantify the impact of government actions and support existing theories as well as policy makers, a data-driven model is developed . Data for two nations, Italy and Taiwan, have been gathered to model government restrictions in traveling, testing and enforcement of social distance measures as well as people connectivity and adherence to government actions .
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Original:  Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of a nationwide lockdown
Revised:  Interestingly, this scenario couples with an earlier but milder implementation of nationwide restrictions for Italy, thus supporting Taiwan's lack of nationwide lockdown. The results, found with a purely data-driven approach, are in line with the main findings of mathematical epidemiological models, proving that the proposed approach has value and that the data alone contains valuable knowledge to inform decision makers.
Edit intent: meaning-changed
Original:  This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by means of training neural network models to predict the daily slope increase of cumulative infected and by using such models in an optimisation study aimed at understanding the best set of mitigating actions.
Revised:  This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by a novel soft computing approach that makes concurrent use of a neural network model, to predict the daily slope increase of cumulative infected and by using such models in an optimisation study aimed at understanding the best set of mitigating actions.
Edit intent: meaning-changed
Original:  This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by means of training neural network models to predict the daily slope increase of cumulative infected and by using such models in an optimisation study aimed at understanding the best set of mitigating actions.
Revised:  This study seeks to quantify the impact of government actions in mitigating viral transmission of SARS-CoV-2 by means of training neural network models to predict the daily slope increase of cumulative infected , and an optimiser, with a parametrisation of the government restriction time series, to understanding the best set of mitigating actions.
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Original:  For that, we pick a  penalty function F: {\Bbb R} \longrightarrow [0, 1] with F(0)=1 and F(y) < 1 for y \ne 0 and compute the expectation of F(q_1(x)) \cdots F(q_m(x)) for a random x sampled from the standard Gaussian measure in {\Bbb R}^n.
Revised:  For that, we pick a delta-shaped penalty function F: {\Bbb R} \longrightarrow [0, 1] with F(0)=1 and F(y) < 1 for y \ne 0 and compute the expectation of F(q_1(x)) \cdots F(q_m(x)) for a random x sampled from the standard Gaussian measure in {\Bbb R}^n.
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Original:  We choose F(y)=y^{-2}\sin^2 y and show that the expectation can be approximated within relative error 0< \epsilon < 1 in quasi-polynomial time (m+n)^{O(\ln (m+n)-\ln \epsilon)}, provided each form q_i depends on not more than r real variables, has common variables with at most r-1 other forms and satisfies |q_i(x)| \leq \gamma \|x\|^2/r, where \gamma >0 is an absolute constant  .
Revised:  We choose F(y)=y^{-2}\sin^2 y and show that the expectation can be approximated within relative error 0< \epsilon < 1 in quasi-polynomial time (m+n)^{O(\ln (m+n)-\ln \epsilon)}, provided each form q_i depends on not more than r real variables, has common variables with at most r-1 other forms and satisfies |q_i(x)| \leq \gamma \|x\|^2/r, where \gamma >0 is an absolute constant . This allows us to distinguish between "easily solvable" and "badly unsolvable" systems in some non-trivial situations .
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Original:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient as a free hyperparameter, and finally inject appropriate noise and discretize the  diffusion process.
Revised:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient as a free hyperparameter, and finally inject appropriate noise and discretize the diffusion process.
Edit intent: coherence
Original:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient as a free hyperparameter, and finally inject appropriate noise and discretize the  diffusion process.
Revised:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, lift its high-resolution coefficient as a free hyperparameter, and finally inject appropriate noise and discretize the diffusion process.
Edit intent: clarity
Original:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient as a free hyperparameter, and finally inject appropriate noise and discretize the  diffusion process.
Revised:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient to be a hyperparameter, and finally inject appropriate noise and discretize the diffusion process.
Edit intent: clarity
Original:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient as a free hyperparameter, and finally inject appropriate noise and discretize the  diffusion process.
Revised:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient as a free hyperparameter, and then inject appropriate noise and discretize the diffusion process.
Edit intent: fluency
Original:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient as a free hyperparameter, and finally inject appropriate noise and discretize the  diffusion process.
Revised:  It relies on a modification of the Nesterov's accelerated gradient method for strongly convex functions (NAG-SC): We first reformulate NAG-SC as a Hessian-Free High-Resolution ODE, then release the high-resolution coefficient as a free hyperparameter, and finally inject appropriate noise and discretize the resulting diffusion process.
Edit intent: clarity
Original:  Accelerated sampling enabled by this new hyperparameter is not only experimentally demonstrated on several learning tasks, but also theoretically quantified, both at the continuous level and after discretization. For (not-necessarily-strongly-) convex and L-smooth potentials, exponential convergence in \chi^2 divergence  is proved, with a rate analogous to  state-of-the-art results of underdamped Langevin dynamics, plus an additional acceleration.
Revised:  Accelerated sampling enabled by this new hyperparameter is theoretically quantified. At continuous-time level, for log-concave/log-strongly-concave target measures, exponential convergence in \chi^2 divergence is proved, with a rate analogous to state-of-the-art results of underdamped Langevin dynamics, plus an additional acceleration.
Edit intent: clarity
Original:  For (not-necessarily-strongly-) convex and L-smooth potentials, exponential convergence in \chi^2 divergence  is proved, with a rate analogous to  state-of-the-art results of underdamped Langevin dynamics, plus an additional acceleration.
Revised:  For (not-necessarily-strongly-) convex and L-smooth potentials, exponential convergence in \chi^2 divergence /2-Wasserstein distance is proved, with a rate analogous to state-of-the-art results of underdamped Langevin dynamics, plus an additional acceleration.
Edit intent: meaning-changed
Original:  For (not-necessarily-strongly-) convex and L-smooth potentials, exponential convergence in \chi^2 divergence  is proved, with a rate analogous to  state-of-the-art results of underdamped Langevin dynamics, plus an additional acceleration.
Revised:  For (not-necessarily-strongly-) convex and L-smooth potentials, exponential convergence in \chi^2 divergence is proved, with a rate analogous to the state-of-the-art results of underdamped Langevin dynamics, plus an additional acceleration.
Edit intent: fluency
Original:  For (not-necessarily-strongly-) convex and L-smooth potentials, exponential convergence in \chi^2 divergence  is proved, with a rate analogous to  state-of-the-art results of underdamped Langevin dynamics, plus an additional acceleration. At the same time, the method also works for nonconvex potentials, for which we also establish exponential convergence as long as the potential satisfies a Poincar\'e inequality\tilde{\mathcal{O}}}{\epsilon}) iteration complexity in 2-Wasserstein distance, same as underdamped Langevin dynamics, but with a reduced constant.
Revised:  For (not-necessarily-strongly-) convex and L-smooth potentials, exponential convergence in \chi^2 divergence is proved, with a rate analogous to state-of-the-art results of underdamped Langevin dynamics, plus an additional acceleration\tilde{\mathcal{O}}}{\epsilon}) iteration complexity in 2-Wasserstein distance, same as underdamped Langevin dynamics, but with a reduced constant.
Edit intent: coherence
Original:  At the same time, the method also works for nonconvex potentials, for which we also establish exponential convergence as long as the potential satisfies a Poincar\'e inequality\tilde{\mathcal{O}}}{\epsilon}) iteration complexity in 2-Wasserstein distance, same as underdamped Langevin dynamics, but with a reduced constant.
Revised:  At the same time, the method also works for nonconvex potentials, for which we also establish exponential convergence as long as the potential satisfies a Poincar\'e inequality. At discrete algorithm level, a dedicated discretization algorithm is proposed to simulate the Hessian-Free High-Resolution SDE in a cost-efficient manner. For log-strong-concave target measures, the proposed algorithm achieves\tilde{\mathcal{O}}}{\epsilon}) iteration complexity in 2-Wasserstein distance, same as underdamped Langevin dynamics, but with a reduced constant.
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Original:  At the same time, the method also works for nonconvex potentials, for which we also establish exponential convergence as long as the potential satisfies a Poincar\'e inequality\tilde{\mathcal{O}}}{\epsilon}) iteration complexity in 2-Wasserstein distance, same as underdamped Langevin dynamics, but with a reduced constant.
Revised:  At the same time, the method also works for nonconvex potentials, for which we also establish exponential convergence as long as the potential satisfies a Poincar\'e inequality\tilde{\mathcal{O}}(\frac{\sqrt{d}{\epsilon}) iteration complexity in 2-Wasserstein distance, same as underdamped Langevin dynamics, but with a reduced constant.
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Original:  For this, the relation between any two nodes is characterized by the length of the shortest path between them , including the special case when there is no such path. The Graformer learns to weigh these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph.
Revised:  For this, the relation between any two nodes is characterized by the length of the shortest path between them . Graformer learns to weigh these node-node relations differently for different attention heads, thus virtually learning differently connected views of the input graph.
Edit intent: clarity
Original:  We evaluate the Graformer on two  graph-to-text generation benchmarks, the AGENDA dataset and the WebNLGchallenge dataset , where it achieves strong performance while using significantly less parameters than other approaches.
Revised:  We evaluate Graformer on two graph-to-text generation benchmarks, the AGENDA dataset and the WebNLGchallenge dataset , where it achieves strong performance while using significantly less parameters than other approaches.
Edit intent: clarity
Original:  We evaluate the Graformer on two  graph-to-text generation benchmarks, the AGENDA dataset and the WebNLGchallenge dataset , where it achieves strong performance while using significantly less parameters than other approaches.
Revised:  We evaluate the Graformer on two popular graph-to-text generation benchmarks, the AGENDA dataset and the WebNLGchallenge dataset , where it achieves strong performance while using significantly less parameters than other approaches.
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Original:  We evaluate the Graformer on two  graph-to-text generation benchmarks, the AGENDA dataset and the WebNLGchallenge dataset , where it achieves strong performance while using significantly less parameters than other approaches.
Revised:  We evaluate the Graformer on two graph-to-text generation benchmarks, AGENDA and WebNLG , where it achieves strong performance while using significantly less parameters than other approaches.
Edit intent: clarity
Original:  We evaluate the Graformer on two  graph-to-text generation benchmarks, the AGENDA dataset and the WebNLGchallenge dataset , where it achieves strong performance while using significantly less parameters than other approaches.
Revised:  We evaluate the Graformer on two graph-to-text generation benchmarks, the AGENDA dataset and the WebNLGchallenge dataset , where it achieves strong performance while using many fewer parameters than other approaches.
Edit intent: clarity
Original:  It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Lehman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures.
Revised:  It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, from which they inherit proven limitations such as the inability to detect and count graph substructures.
Edit intent: clarity
Original:  On the other hand, there is significant empirical evidence, e.g. in network science and bioinformatics, that substructures are often informative for downstream tasks, suggesting that it is desirable to design GNNs capable of leveraging this important source of information .
Revised:  On the other hand, there is significant empirical evidence, e.g. in network science and bioinformatics, that substructures are often intimately related to downstream tasks .
Edit intent: clarity
Original: Mathematical proofs can be mechanised using proof assistants to eliminate gaps and errors. However, mechanisation still requires intensive labour. To promote automation, it is essential to capture high-level human mathematical reasoning , which we address as the problem of generating suitable propositions .
Revised: A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models. In this paper, we present a benchmark for high-level human mathematical reasoning , which we address as the problem of generating suitable propositions .
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Original:  We build a non-synthetic dataset from the largest repository of mechanised proofs and propose a task on causal reasoning, where a model is required to fill in a missing intermediate proposition given a causal context .
Revised:  We build a non-synthetic dataset from the largest repository of proofs written by human experts in a theorem prover. The dataset has a broad coverage of undergraduate and research-level mathematical and computer science theorems. In our defined task, a model is required to fill in a missing intermediate proposition given a causal context .
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Original:  We build a non-synthetic dataset from the largest repository of mechanised proofs and propose a task on causal reasoning, where a model is required to fill in a missing intermediate proposition given a causal context .
Revised:  We build a non-synthetic dataset from the largest repository of mechanised proofs and propose a task on causal reasoning, where a model is required to fill in a missing intermediate proposition given surrounding proofs. This task provides a starting point for the long-term goal of having machines generate human-readable proofs automatically .
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Original:  We first provide an alternate  proof to show that vanilla Transformers are Turing-complete and then we prove that Transformers with  positional masking and without any positional encoding are also Turing-complete.
Revised:  We first provide an alternate and simpler proof to show that vanilla Transformers are Turing-complete and then we prove that Transformers with positional masking and without any positional encoding are also Turing-complete.
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Original:  We first provide an alternate  proof to show that vanilla Transformers are Turing-complete and then we prove that Transformers with  positional masking and without any positional encoding are also Turing-complete.
Revised:  We first provide an alternate proof to show that vanilla Transformers are Turing-complete and then we prove that Transformers with only positional masking and without any positional encoding are also Turing-complete.
Edit intent: clarity
Original: We show that for the cardinality constrained monotone submodular maximization problem, there exists a (1-1/e-\varepsilon) -approximate deterministic algorithm with linear query complexity , which performs O(n / \varepsilon) queries in total .
Revised: In this work, we present the first linear query complexity algorithm for maximizing a monotone submodular function subject to a cardinality constraint, which achieves the approximation ratio of (1-1/e-\varepsilon) -approximate deterministic algorithm with linear query complexity , which performs O(n / \varepsilon) queries in total .
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Original: We show that for the cardinality constrained monotone submodular maximization problem, there exists a (1-1/e-\varepsilon) -approximate deterministic algorithm with linear query complexity , which performs O(n / \varepsilon) queries in total .
Revised: We show that for the cardinality constrained monotone submodular maximization problem, there exists a (1-1/e-\varepsilon) using O(n / \varepsilon) queries in total .
Edit intent: clarity
Original: We show that for the cardinality constrained monotone submodular maximization problem, there exists a (1-1/e-\varepsilon) -approximate deterministic algorithm with linear query complexity , which performs O(n / \varepsilon) queries in total .
Revised: We show that for the cardinality constrained monotone submodular maximization problem, there exists a (1-1/e-\varepsilon) -approximate deterministic algorithm with linear query complexity , which performs O(n / \varepsilon) queries. To the best of our knowledge, this is the first deterministic algorithm that achieves the almost optimal approximation and optimal query complexity simultaneously. Query complexity lower bound of submodular maximization problems is also studied in this paper. We show that there exists no (randomized) (1/4+\varepsilon)-approximate algorithm using o(n/\log n) queries for unconstrained submodular maximization. Combining with existing results, we present a complete characterization of the query complexity of unconstrained submodular maximization .
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Original: Cross-lingual transfer learning studies how datasets, annotations, and models can be transferred from resource-rich languagesto improve language technologies in resource-poor settings. Recent works have shown that we can further benefit from the selection of the best transfer language. In this paper, we propose three pragmatically-motivated features that can help guide the optimal transfer language selection problem for cross-lingual transfer. Specifically, the proposed features operationalize cross-cultural similarities that manifest in various linguistic patterns : language context-level, sharing multi-word expressions , and the use of emotion concepts.
Revised: Much work in cross-lingual transfer learning explored how to select better transfer languages for multilingual tasks, primarily focusing on typological and genealogical similarities between languages. We hypothesize that these measures of linguistic proximity are not enough when working with pragmatically-motivated tasks, such as sentiment analysis. As an alternative, we introduce three linguistic features that capture cross-cultural similarities that manifest in various linguistic patterns : language context-level, sharing multi-word expressions , and the use of emotion concepts.
Edit intent: coherence
Original:  Our experimental results show that these features significantly improve the prediction of optimal transfer languages over baselines in sentiment analysis, but are less useful for dependency parsing. Further analyses show that the proposed features indeed capture the intended cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology  .
Revised:  Our analyses show that the proposed features indeed capture the intended cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology .
Edit intent: coherence
Original:  Further analyses show that the proposed features indeed capture the intended cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology  .
Revised:  Further analyses show that the proposed features indeed capture the intended cross-cultural similarities and align well with existing work in sociolinguistics and linguistic anthropology . We further corroborate the effectiveness of pragmatically-driven transfer in the downstream task of choosing transfer languages for cross-lingual sentiment analysis .
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