wanyu/IteraTeR_full_sent huggingface dataset:
Original:  It has been well known that static inequality is rising in India  , but the assumption has been that while the rich may be benefiting more than proportionally from economic growth, the poor are also better off than before.
Revised:  It has been well known that static inequality is rising in India in the recent past , but the assumption has been that while the rich may be benefiting more than proportionally from economic growth, the poor are also better off than before.
Edit intent: meaning-changed
Original:  The surprising finding from our work is that the nature of income inequality is such that we have moved from a regime of progressive to regressive re-distribution. Essentially, continued impoverishment of the poor is directly spurring multiplicative income growth of the rich.
Revised:  The surprising finding from our work is that the nature of income inequality is such that we have moved from a regime of progressive to regressive redistribution, where continued impoverishment of the poor is directly spurring multiplicative income growth of the rich.
Edit intent: clarity
Original:   We characterize these findings in the context of increasing informality of the workforce in the formal manufacturing and service sectors, as well as the possible evolution of negative net incomes of the agriculture workforce in India.
Revised:  Outcomes from the model suggest that income shares of the bottom decile (~1\%) and bottom percentile (~0.03\%) are at historic lows. We characterize these findings in the context of increasing informality of the workforce in the formal manufacturing and service sectors, as well as the possible evolution of negative net incomes of the agriculture workforce in India.
Edit intent: meaning-changed
Original:  Our Visual Scripting module is capable generating training applications utilizing a node-based scripting system whereas the VR Editor gives the user/developer the ability to customize and populate new VR training scenarios directly from within the virtual environment.
Revised:  Our visual scripting module is capable generating training applications utilizing a node-based scripting system whereas the VR Editor gives the user/developer the ability to customize and populate new VR training scenarios directly from within the virtual environment.
Edit intent: fluency
Original:  Our Visual Scripting module is capable generating training applications utilizing a node-based scripting system whereas the VR Editor gives the user/developer the ability to customize and populate new VR training scenarios directly from within the virtual environment.
Revised:  Our Visual Scripting module is capable to generate training applications utilizing a node-based scripting system whereas the VR Editor gives the user/developer the ability to customize and populate new VR training scenarios directly from within the virtual environment.
Edit intent: fluency
Original:  Our Visual Scripting module is capable generating training applications utilizing a node-based scripting system whereas the VR Editor gives the user/developer the ability to customize and populate new VR training scenarios directly from within the virtual environment.
Revised:  Our Visual Scripting module is capable generating training applications utilizing a node-based scripting system whereas the VR editor gives user/developer the ability to customize and populate new VR training scenarios directly from within the virtual environment.
Edit intent: fluency
Original:  Our Visual Scripting module is capable generating training applications utilizing a node-based scripting system whereas the VR Editor gives the user/developer the ability to customize and populate new VR training scenarios directly from within the virtual environment.
Revised:  Our Visual Scripting module is capable generating training applications utilizing a node-based scripting system whereas the VR Editor gives the user/developer the ability to customize and populate new VR training scenarios directly from the virtual environment.
Edit intent: clarity
Original:  In a addition, we present the  scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum  .
Revised:  In a addition, we present the scenegraph architecture as the main model to represent training scenarios on a modular, dynamic and highly adaptive acyclic graph based on a structured educational curriculum . Finally, a user-based evaluation of the proposed solution indicated that users - regardless of their programming expertise - can effectively use the tools to create and modify training scenarios in VR .
Edit intent: meaning-changed
Original:  We prove here that, many years later, Data Leakage can still be a non negligible overfitting source in deep learning models for digital pathology due to (i) the presence of multiple images for each subject in histology collections;
Revised:  We prove here that, many years later, Data Leakage can still be a non-negligible overfitting source in deep learning models for digital pathology due to (i) the presence of multiple images for each subject in histology collections;
Edit intent: fluency
Original:  We prove here that, many years later, Data Leakage can still be a non negligible overfitting source in deep learning models for digital pathology due to (i) the presence of multiple images for each subject in histology collections;
Revised:  We prove here that, many years later, Data Leakage can still be a non negligible overfitting source in deep learning models for digital pathology . In particular, we evaluate the impact of (i) the presence of multiple images for each subject in histology collections;
Edit intent: meaning-changed
Original:  We quantify that between 1\% and 12\% of accuracy scores may result inflated , even if a well designed 10x5 nested cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Revised:  We verify that accuracy scores may result inflated , even if a well designed 10x5 nested cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Edit intent: clarity
Original:  We quantify that between 1\% and 12\% of accuracy scores may result inflated , even if a well designed 10x5 nested cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Revised:  We quantify that between 1\% and 12\% of accuracy scores may be inflated up to 41\% , even if a well designed 10x5 nested cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Edit intent: meaning-changed
Original:  We quantify that between 1\% and 12\% of accuracy scores may result inflated , even if a well designed 10x5 nested cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Revised:  We quantify that between 1\% and 12\% of accuracy scores may result inflated , even if a well-designed 10x5 nested cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Edit intent: fluency
Original:  We quantify that between 1\% and 12\% of accuracy scores may result inflated , even if a well designed 10x5 nested cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Revised:  We quantify that between 1\% and 12\% of accuracy scores may result inflated , even if a well designed 10x5 iterated cross-validation DAP is applied, unless all images from the same subject are kept together either in the internal training or validation splits.
Edit intent: clarity
Original:  Results are replicated for 4 classification tasks  on 3 datasets, for a total of 364 subjectsand 538 total slides ( about 27, 000 tiles).
Revised:  Results are replicated for 4 classification tasks in digital pathology on 3 datasets, for a total of 364 subjectsand 538 total slides ( about 27, 000 tiles).
Edit intent: meaning-changed
Original:  Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metrics, which causes obstacles for the discovery of weak and hidden complexes shielded by stronger complexes. Also , protein complexes are not only in different density but also in a large range of scale, making it extremely difficult to be detected.
Revised:  Traditional algorithms simply consider strong protein complexes that can be found in the original network by optimizing some metrics, which causes obstacles for the discovery of weak and hidden complexes that are overshadowed by strong complexes. Additionally , protein complexes are not only in different density but also in a large range of scale, making it extremely difficult to be detected.
Edit intent: clarity
Original:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Revised:  We address these issues and propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Edit intent: coherence
Original:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Revised:  Toward this objective, we propose a hierarchical hidden community detection approach to accurately predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Edit intent: clarity
Original:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Revised:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes of various strengths and scales. We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Edit intent: meaning-changed
Original:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Revised:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a meta-method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Edit intent: meaning-changed
Original:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities .
Revised:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can adopt any standard community detection method as the base algorithm and enable it to discover hierarchical hidden communities .
Edit intent: clarity
Original:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities . It is the first community detection algorithm that can find a hierarchical structure as well as hidden structure .
Revised:  Toward this objective, we propose a hierarchical hidden community approach to predict protein complexes . Results: We propose a method called HirHide (Hierarchical Hidden Community Detection), which can be combined with traditional community detection methods to enable them to discover hierarchical hidden communities as well as boosting the detection on hierarchical strong communities. To our knowledge, this is the first community detection algorithm that can find a hierarchical structure as well as hidden structure .
Edit intent: meaning-changed
Original:   We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to  )-consistent and asymptotically normal  .
Revised:  ~ We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to )-consistent and asymptotically normal .
Edit intent: fluency
Original:   We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to  )-consistent and asymptotically normal  .
Revised:  We prove that, as both the dimension of the panel n and the sample size T diverge to infinity : (i) the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to )-consistent and asymptotically normal .
Edit intent: meaning-changed
Original:   We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to  )-consistent and asymptotically normal  .
Revised:  We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings are \sqrt T-consistent and asymptotically normal if \sqrt T/n\to \to )-consistent and asymptotically normal .
Edit intent: meaning-changed
Original:   We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to  )-consistent and asymptotically normal  .
Revised:  We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to 0; (ii) the estimated factors are \sqrt n-consistent and asymptotically normal if \sqrt n/T\to )-consistent and asymptotically normal .
Edit intent: meaning-changed
Original:   We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to  )-consistent and asymptotically normal  .
Revised:  We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to 0; (iii) the estimated common component is \min(\sqrt T,\sqrt n )-consistent and asymptotically normal .
Edit intent: meaning-changed
Original:   We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to  )-consistent and asymptotically normal  .
Revised:  We prove that, as both the dimension of the panel n and the sample size T diverge to infinity , the estimated loadings , factors , and common components are \min(\sqrt n,\sqrt T\to \to )-consistent and asymptotically normal regardless of the relative rate of divergence of n and T .
Edit intent: meaning-changed
Original:  Moreover, we give conditions under which the derived asymptotic distribution can still be used for inference even in case of mis-specifications. Our results are confirmed by a MonteCarlo simulation exercise where we compare the performance of our estimators with Principal Components .
Revised:  Moreover, the estimated loadings are asymptotically as efficient as those obtained with the Principal Components estimator, whereas numerical results show that the loss in efficiency of the estimated factors becomes negligible as n and T increase.~We then propose robust estimators of the asymptotic covariances, which can be used to conduct inference on the loadings and to compute confidence intervals for the factors and common components. In a MonteCarlo simulation exercise where we compare the performance of our estimators with Principal Components .
Edit intent: meaning-changed
Original:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required  .
Revised:  How the animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required .
Edit intent: clarity
Original:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required  .
Revised:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory ? How temporal information is processed concurrently with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required .
Edit intent: meaning-changed
Original:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required  .
Revised:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required .
Edit intent: coherence
Original:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required  .
Revised:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making ? Why there are strong neuronal temporal signal even when animals performing tasks in which time information is not required .
Edit intent: coherence
Original:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required  .
Revised:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signals in tasks in which time information is not required .
Edit intent: clarity
Original:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required  .
Revised:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which temporal information is not required .
Edit intent: coherence
Original:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required  .
Revised:  However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required ? A systematic understanding of the underlying neural mechanisms is still lacking .
Edit intent: clarity