wanyu/IteraTeR_v2 huggingface dataset:
Original:  (ii) that the distance correlation of the power-law curves between countries are statistically highly correlated, showing the universality of such curves around the World;
Revised:  (ii) that the distance correlation of the power-law curves between countries are statistically highly correlated, suggesting the universality of such curves around the World;
Edit intent: coherence
Original:  Furthermore, we present a model and strategies which allow the government to reach the flattening in the power-law curves.
Revised:  Furthermore, we present a model and strategies which allow the government to reach the flattening of the power-law curves.
Edit intent: clarity
Original:  We found that, besides the social isolation of individuals, of well known relevance, the strategy of identifying and isolating infected individuals could be of more relevance to flatten the power-laws.
Revised:  We found that, besides the social distance of individuals, of well known relevance, the strategy of identifying and isolating infected individuals could be of more relevance to flatten the power-laws.
Edit intent: clarity
Original:  In fact, our results suggest that the allowed balance between social isolation and degrees of isolation of infected individuals can be used to prevent economic catastrophes.
Edit intent: coherence
Original:  The high correlation between the power-law curves of different countries strongly suggest that the government containment measures can be applied with success around the whole World.
Revised:  The high correlation between the power-law curves of different countries strongly indicate that the government containment measures can be applied with success around the whole World.
Edit intent: clarity
Original: The paper presents a framework to reconstruct explanations for multiple choices science questions through explanation-centred corpora.
Revised: This paper presents a framework to reconstruct explanations for multiple choices science questions through explanation-centred corpora.
Edit intent: fluency
Original:  (b) A Unification Score (US) that takes into account the explanatory power of a fact, determined according to its frequency in explanations for similar questions  .
Revised:  (b) A Unification Score (US) that takes into account the explanatory power of a fact, determined according to its frequency in explanations for similar questions in the corpus .
Edit intent: clarity
Original:  An extensive evaluation of the framework is performed on the Worldtree corpus, adopting IRweighting schemes for its implementation.
Revised:  An extensive evaluation is performed on the Worldtree corpus, adopting IRweighting schemes for its implementation.
Edit intent: clarity
Original:  The following findings are presented : (1) The proposed approach achieves competitive results when compared to state-of-the-art Transformers, yet possessing the property of being scalable to large explanatory knowledge bases;
Revised:  The following findings are presented : (1) The proposed method achieves results competitive with Transformers, yet possessing the property of being scalable to large explanatory knowledge bases;
Edit intent: clarity
Original:  The following findings are presented : (1) The proposed approach achieves competitive results when compared to state-of-the-art Transformers, yet possessing the property of being scalable to large explanatory knowledge bases;
Revised:  The following findings are presented : (1) The proposed approach achieves competitive results when compared to state-of-the-art Transformers, yet possessing the property of being scalable to large explanatory corpora
Edit intent: clarity
Original:  ( 3) The constructed explanations can support downstream models for answer prediction , improving the accuracy of BERT for multiple choices QA on both ARC easy (+6.92\% ) and challenge (+15.69\%) questions .
Revised:  ( 3) Crucially, the constructed explanations can support downstream models for answer prediction , improving the accuracy of BERT for multiple choices QA on both ARC easy (+6.92\% ) and challenge (+15.69\%) questions .
Edit intent: coherence
Original:  ( 3) The constructed explanations can support downstream models for answer prediction , improving the accuracy of BERT for multiple choices QA on both ARC easy (+6.92\% ) and challenge (+15.69\%) questions .
Revised:  ( 3) The constructed explanations can support downstream QA models , improving the accuracy of BERT for multiple choices QA on both ARC easy (+6.92\% ) and challenge (+15.69\%) questions .
Edit intent: clarity
Original:  First, we present HXPath=, a multi-modal version of XPath with data  extended with nominals and the hybrid operator @.
Revised:  First, we present HXPath=, a multi-modal version of XPath with data , extended with nominals and the hybrid operator @.
Edit intent: fluency
Original:  We prove a general completeness result similar to the one presented in, e.g., Blackburn and ten Cate 2006, that ensures that certain extensions of the axiomatic system we introduce are also complete.
Revised:  We prove a general completeness result similar to the one presented in, e.g., BtC06 that ensures that certain extensions of the axiomatic system we introduce are also complete.
Edit intent: clarity
Original:  We show that a strongly complete, finite , first-order axiomatization of hybrid XPath over trees does not exist, and we propose two alternatives to deal with this issue.
Revised:  We show that a strongly complete, finitary , first-order axiomatization of hybrid XPath over trees does not exist, and we propose two alternatives to deal with this issue.
Edit intent: clarity
Original:  We prove a general completeness result similar to the one presented in, e.g., [BtC06]  that ensures that certain extensions of the axiomatic system we introduce are also complete.
Revised:  We prove a general completeness result similar to the one presented in, e.g., [BtC06] , that ensures that certain extensions of the axiomatic system we introduce are also complete.
Edit intent: fluency
Original:  The axiomatic systems that can be obtained in this way cover a large family of hybrid XPath languages over different classes of frames, and we present concrete examples.
Revised:  The axiomatic systems that can be obtained in this way cover a large family of hybrid XPath languages over different classes of frames, for which we present concrete examples.
Edit intent: coherence
Original: In this paper, we consider the problem to automatically reconstruct both garment and body shapes from a single near front view RGB image.
Revised: In this paper, we consider the problem to automatically reconstruct garment and body shapes from a single near front view RGB image.
Edit intent: clarity
Original: In this paper, we consider the problem to automatically reconstruct both garment and body shapes from a single near front view RGB image.
Revised: In this paper, we consider the problem to automatically reconstruct both garment and body shapes from a single near-front view RGB image.
Edit intent: fluency
Original:  To this end, we propose a layered garment representation on top of SMPL and novelly make the skinning weight of garment to be independent with the body mesh, which significantly improves the expression ability of our garment model.
Revised:  To this end, we propose a layered garment representation on top of SMPL and novelly make the skinning weight of garment independent of the body mesh, which significantly improves the expression ability of our garment model.
Edit intent: clarity
Original:  Compared with existing methods, our method can support more garment categories like skirts and recover more accurate garment geometry.
Revised:  Compared with existing methods, our method can support more garment categories and recover more accurate garment geometry.
Edit intent: clarity
Original:  Compared with existing methods, our method can support more garment categories like skirts and recover more accurate garment geometry.
Revised:  Compared with existing methods, our method can support more garment categories like skirts and recover more accurate geometry.
Edit intent: fluency
Original:  Meanwhile, representation learning ( %DIFDELCMD < \aka%%% ~ embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks.
Revised:  Meanwhile, representation learning ( %DIFDELCMD < \aka%%% a.k.a. embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks.
Edit intent: clarity
Original:  As the third contribution, we carefully refactor and amend the implementations of and create friendly interfaces for ten popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.
Revised:  As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for ten popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.
Edit intent: clarity
Original:  Meanwhile, representation learning ( a.k.a. \aka embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks.
Revised:  Meanwhile, representation learning ( \aka embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks.
Edit intent: coherence
Original:  Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and etc.
Revised:  Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and 
Edit intent: coherence
Original:  \etc from different sources, towards handy and fair evaluations of HNE algorithms.
Revised:  \etc.~ from different sources, towards handy and fair evaluations of HNE algorithms.
Edit intent: coherence
Original:  However, recent attempts to combine decision trees with deep learning have resulted in models that (1) achieve accuracies far lower than that of modern neural networks (e.g. ResNet) even on small datasets (e.g. MNIST), and ( 2) require significantly different architectures, forcing practitioners pick between accuracy and interpretability.
Revised:  However, recent attempts to combine decision trees with deep learning , yielding models that (1) achieve accuracies far lower than that of modern neural networks (e.g. ResNet) even on small datasets (e.g. MNIST), and ( 2) require significantly different architectures, forcing practitioners pick between accuracy and interpretability.
Edit intent: clarity
Original:  However, recent attempts to combine decision trees with deep learning have resulted in models that (1) achieve accuracies far lower than that of modern neural networks (e.g. ResNet) even on small datasets (e.g. MNIST), and ( 2) require significantly different architectures, forcing practitioners pick between accuracy and interpretability.
Revised:  However, recent attempts to combine decision trees with deep learning have resulted in models that (1) sacrifice interpretability to maintain accuracy or ( 2) require significantly different architectures, forcing practitioners pick between accuracy and interpretability.
Edit intent: clarity
Original:  However, recent attempts to combine decision trees with deep learning have resulted in models that (1) achieve accuracies far lower than that of modern neural networks (e.g. ResNet) even on small datasets (e.g. MNIST), and ( 2) require significantly different architectures, forcing practitioners pick between accuracy and interpretability.
Revised:  However, recent attempts to combine decision trees with deep learning have resulted in models that (1) achieve accuracies far lower than that of modern neural networks (e.g. ResNet) even on small datasets (e.g. MNIST), and ( 2) underperform modern neural networks to maintain interpretability.
Edit intent: clarity
Original:  We forgo this dilemma by creating Neural-Backed Decision Trees (NBDTs) that (1) achieve neural network accuracy and (2) require no architectural changes to a neural network . NBDTs achieve accuracywithin 1\% of the base neural network  on CIFAR10, CIFAR100, TinyImageNet, using recently state-of-the-art WideResNet;
Revised:  We forgo this dilemma by proposing Neural-Backed Decision Trees (NBDTs) that (1) achieve neural network accuracy and (2) require no architectural changes to a neural network . NBDTs achieve accuracywithin 1\% of the base neural network on CIFAR10, CIFAR100, TinyImageNet, using recently state-of-the-art WideResNet;
Edit intent: clarity
Original:  We forgo this dilemma by creating Neural-Backed Decision Trees (NBDTs) that (1) achieve neural network accuracy and (2) require no architectural changes to a neural network . NBDTs achieve accuracywithin 1\% of the base neural network  on CIFAR10, CIFAR100, TinyImageNet, using recently state-of-the-art WideResNet;
Revised:  We forgo this dilemma by creating Neural-Backed Decision Trees (NBDTs) that (1) achieve neural network accuracy and (2) neural network on CIFAR10, CIFAR100, TinyImageNet, using recently state-of-the-art WideResNet;
Edit intent: clarity
Original:  This yields state-of-the-art explainable models on ImageNet, with NBDTs improving the baseline by ~14\% to  75.30\% top-1 accuracy.
Revised:  This yields state-of-the-art interpretable models on ImageNet, with NBDTs improving the baseline by ~14\% to 75.30\% top-1 accuracy.
Edit intent: clarity
Original:  This yields state-of-the-art explainable models on ImageNet, with NBDTs improving the baseline by ~14\% to  75.30\% top-1 accuracy.
Revised:  This yields state-of-the-art explainable models on ImageNet, with NBDTs improving the baseline by ~14\% to attain 75.30\% top-1 accuracy.
Edit intent: clarity
Original:  Furthermore, we show interpretability of our model's decisions both qualitatively and quantitatively via a semi-automatic process.
Edit intent: coherence
Original:  Code and pretrained NBDTs can be found at URL
Revised:  Code and pretrained NBDTs are at URL
Edit intent: clarity
Original:  In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability to maintain accuracy or (2) underperform modern neural networks to maintain interpretability.
Revised:  In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) underperform modern neural networks to maintain interpretability.
Edit intent: fluency
Original:  In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability to maintain accuracy or (2) underperform modern neural networks to maintain interpretability.
Revised:  In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability to maintain accuracy or (2) sacrifice accuracy for interpretability.
Edit intent: clarity
Original: We prove lower bounds for the worst case error of quadrature formulas that use given sample points \mathcal X \X _n = \{ x_1, \dots , x_n \}.
Revised: We prove lower bounds for the worst case error of quadrature formulas that use given sample points \X _n = \{ x_1, \dots , x_n \}.
Edit intent: coherence
Original:  Our goal is to combine logical constraints, that is, deterministic information from a type system, with natural constraints, uncertain information about types  from sources like identifier names.
Revised:  Our goal is to combine logical constraints, that is, deterministic information from a type system, with natural constraints, uncertain information about types learnt from sources like identifier names.
Edit intent: clarity
Original:  The main insight of our method is to constrain the predictions from the learning procedure to respect the logical constraints, which we achieve by relaxing the logical inference problem of type prediction into a continuous optimisation problem.
Revised:  The foremost insight of our method is to constrain the predictions from the learning procedure to respect the logical constraints, which we achieve by relaxing the logical inference problem of type prediction into a continuous optimisation problem.
Edit intent: fluency
Original:  To evaluate the idea, we built a tool called OptTyper to predict a TypeScript declaration file for a JavaScript library .
Revised:  As proof of concept, we build a tool called OptTyper to predict a TypeScript declaration file for a JavaScript library .
Edit intent: clarity
Original:  To evaluate the idea, we built a tool called OptTyper to predict a TypeScript declaration file for a JavaScript library .
Revised:  To evaluate the idea, we built a tool called OptTyper to predict missing types for TypeScript files .
Edit intent: clarity
Original:  Our goal is to combine logical constraints, that is, deterministic information from a type system, with natural constraints, that is, uncertain statistical information about types learnt from sources like identifier names.
Revised:  Our goal is to combine logical constraints, that is, deterministic information from a type system, with natural constraints, that is, uncertain statistical information about types learnt from sources like identifier names.
Edit intent: fluency
Original:  To this end, we introduce a framework for probabilistic type inference that combines logic and learning: logical constraints on the types are extracted from the program, and deep learning is applied to predict types from surface-level code properties that are statistically associated , such as variable names .
Revised:  To this end, we introduce a framework for probabilistic type inference that combines logic and learning: logical constraints on the types are extracted from the program, and deep learning is applied to predict types from surface-level code properties that are statistically associated .
Edit intent: clarity
Original:  As proof of concept, we build a tool called OptTyper to predict missing types for TypeScript files.
Revised:  We build a tool called OptTyper to predict missing types for TypeScript files.
Edit intent: coherence
Original:  OptTyper combines a continuous interpretation of logical constraints derived by a simple program transformation and static analysis of TypeScript code, with natural constraints obtained from a deep learning model, which learns naming conventions for types from a large codebase.
Revised:  OptTyper combines a continuous interpretation of logical constraints derived by classical static analysis of TypeScript code, with natural constraints obtained from a deep learning model, which learns naming conventions for types from a large codebase.
Edit intent: clarity
Original:  Thermodynamic models that are used for secondary structure prediction report a large number of structures in a limited energy range, often failing in identifying the correct native structure unless complemented by auxiliary experimental data.
Revised:  Thermodynamic models currently used for secondary structure prediction report a large number of structures in a limited energy range, often failing in identifying the correct native structure unless complemented by auxiliary experimental data.
Edit intent: clarity
Original:  Thermodynamic models that are used for secondary structure prediction report a large number of structures in a limited energy range, often failing in identifying the correct native structure unless complemented by auxiliary experimental data.
Revised:  Thermodynamic models that are used for secondary structure prediction report a large number of structures unless complemented by auxiliary experimental data.
Edit intent: clarity
Original:  The most transferable model is chosen with a cross-validation strategy that allows to automatically optimize the relative importance of heterogenous input datasets.
Revised:  The most transferable model is selected through a cross-validation strategy that allows to automatically optimize the relative importance of heterogenous input datasets.
Edit intent: clarity
Original:  By using the selected model , we obtain enhanced populations for reference native structures and more sensitive and precise predicted structures in an independent validation set not seen during training .
Revised:  By using the selected model we obtain increased ensemble populations for native structures and more sensitive and precise predicted structures in an independent validation set not seen during training .
Edit intent: clarity
Original:  By using the selected model , we obtain enhanced populations for reference native structures and more sensitive and precise predicted structures in an independent validation set not seen during training .
Revised:  By using the selected model , we obtain enhanced populations for reference native structures and more accurate predictions in an independent validation set not seen during training .
Edit intent: clarity
Original:  By using the selected model , we obtain enhanced populations for reference native structures and more sensitive and precise predicted structures in an independent validation set not seen during training .
Revised:  By using the selected model , we obtain enhanced populations for reference native structures and more sensitive and precise predicted structures in an independent validation set .
Edit intent: clarity
Original:  Furthermore, we elaborate on how artificial intelligence (AI ) can address various constraints introduced by  real-time deployment of SDMs , particularly in terms of latency, storage, energy efficiency, and computation.
Revised:  Furthermore, we elaborate on how artificial intelligence (AI ) can address various constraints introduced by the real-time deployment of SDMs , particularly in terms of latency, storage, energy efficiency, and computation.
Edit intent: fluency
Original:  Italy, that is widely considered one of the main epicenters of the pandemic, registers the highest COVID-2019 death rates and death toll in the world, to the present day.
Revised:  Italy, that is widely considered one of the main epicenters of the pandemic, has registered the highest COVID-2019 death rates and death toll in the world, to the present day.
Edit intent: style
Original: However, many changes are discontinuous due to sudden interventions, such as city lockdown and opening of field hospitals .
Revised: However, many changes are discontinuous due to sudden interventions, such as city lockdowns, the opening of field hospitals .
Edit intent: fluency
Original:  To recover the ground truth dynamic, this study designs a novel yet straightforward optimization algorithm, dubbed iterative Nelder-Mead , which repeatedly applies the Nelder-Mead algorithm.
Revised:  To recover the ground truth dynamic, this study designs a novel yet straightforward optimization algorithm, dubbed Iterated Nelder--Mead , which repeatedly applies the Nelder-Mead algorithm.
Edit intent: clarity
Original:  To recover the ground truth dynamic, this study designs a novel yet straightforward optimization algorithm, dubbed iterative Nelder-Mead , which repeatedly applies the Nelder-Mead algorithm.
Revised:  To recover the ground truth dynamic, this study designs a novel yet straightforward optimization algorithm, dubbed iterative Nelder-Mead , which repeatedly applies the Nelder--Mead algorithm.
Edit intent: fluency
Original:  Experiments  on the simulated data show that the proposed approach can qualitatively reproduce the discontinuities of the underlying dynamics.
Revised:  Experiments conducted on the simulated data show that the proposed approach can qualitatively reproduce the discontinuities of the underlying dynamics.
Edit intent: clarity
Original:  The delay in diagnosis, limited hospital resources , and other treatment resources lead to the rapid spread of COVID-19.
Revised:  The delay in diagnosis, limited hospital resources and other treatment resources lead to the rapid spread of COVID-19.
Edit intent: fluency