wanyu/IteraTeR_v2 huggingface dataset:
Original:  Among these applications, implicit neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality.
Revised:  Among these applications, neural volume rendering has recently been proposed as a new paradigm for view synthesis, achieving photorealistic image quality.
Edit intent: clarity
Original:  For training, we instantiate the computational graph corresponding to the derivative of the implicit neural representation .
Revised:  For training, we instantiate the computational graph corresponding to the derivative of the network .
Edit intent: clarity
Original:  Using this approach , we demonstrate a greater than 10x improvement in computation requirements, enabling fast neural volume rendering .
Revised:  Applying this approach to neural rendering, we improve a tradeoff between rendering speed and image quality: improving render times by greater than 10 times with a tradeoff of slightly reduced image quality .
Edit intent: clarity
Original:  (3) Moreover, we present \textit{Recurrent States Space Visualisations (RSSviz), a method to visualize the structure of latent state space of RNNs, based on dimension reduction (using UMAP).
Revised:  (3) Moreover, we present \textit{Recurrent States Space Visualisations (RSSviz), a method to visualize the structure of latent state space of RNNs, based on dimension reduction (using UMAP).
Edit intent: fluency
Original:  This regime contains many classes of interest, for instance perfect graphs or comparability graphs, for which we obtain an efficient adjacency labelling scheme with labels of n/4+o(n) bits per vertex.
Revised:  This regime contains many classes of interest, for instance perfect graphs or comparability graphs, for which we obtain an adjacency labelling scheme with labels of n/4+o(n) bits per vertex.
Edit intent: clarity
Original:  We also use this result to construct a poset on 2^{n/4+o(n) . All these results are best possible, up to the lower order term , and solve several open problems in the area .
Revised:  We also use this result to construct a poset on 2^{n/4+o(n) . All these results are best possible, up to the lower order term .
Edit intent: coherence
Original:  We employ storytelling and metaphors from fairytales to argue how a new and all-encompassing concept of Privacy Labelling (the 'knight in shining armour') can come to the rescue of Privacy (the 'princess in distress').
Revised:  We employ storytelling and metaphors from fairytales to argue how a new and all-encompassing concept of Privacy Labeling (the 'knight in shining armour') can come to the rescue of Privacy (the 'princess in distress').
Edit intent: fluency
Original:  We employ storytelling and metaphors from fairytales to argue how a new and all-encompassing concept of Privacy Labelling (the 'knight in shining armour') can come to the rescue of Privacy (the 'princess in distress').
Revised:  We employ storytelling and metaphors from fairytales to argue how a new and all-encompassing concept of Privacy Labelling (the 'knight in shining armour') can be a solution to the current state of Privacy (the 'princess in distress').
Edit intent: clarity
Original:  We paint a detailed portrait of Privacy Labelling (PL) that emerges from discussions carried out from seven different perspectives (the 'seven helpers'): Business, Legal, Regulatory, Usability and Human Factors, Educative, Technological, and Multidisciplinary.
Revised:  We give a precise definition of Privacy Labeling (PL) that emerges from discussions carried out from seven different perspectives (the 'seven helpers'): Business, Legal, Regulatory, Usability and Human Factors, Educative, Technological, and Multidisciplinary.
Edit intent: clarity
Original:  We describe a common vision, proposing several important 'traits of character' of PL as well as identifying ' places of improvement ', i.e., open problems on which the community can focus their efforts .
Revised:  We describe a common vision, proposing several important 'traits of character' of PL as well as identifying ' undeveloped potentialities ', i.e., open problems on which the community can focus their efforts .
Edit intent: clarity
Original:  We describe a common vision, proposing several important 'traits of character' of PL as well as identifying ' places of improvement ', i.e., open problems on which the community can focus their efforts .
Revised:  We describe a common vision, proposing several important 'traits of character' of PL as well as identifying ' places of improvement ', i.e., open problems on which the community can focus .
Edit intent: clarity
Original:  More specifically, this position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how the PL should be and look like in order to respond to these needs.
Revised:  More specifically, this position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how PL should be and look like in order to respond to these needs.
Edit intent: fluency
Original:  More specifically, this position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how the PL should be and look like in order to respond to these needs.
Revised:  More specifically, this position paper identifies the stakeholders of the PL and their needs with regard to privacy, describing how the PL should be and look like in order to address these needs.
Edit intent: clarity
Original:  Throughout the paper, we highlight goals, characteristics, open problems, and starting points for how one could approach what we define as  the ideal PL.
Revised:  Throughout the paper, we highlight goals, characteristics, open problems, and starting points for creating, what we define as the ideal PL.
Edit intent: clarity
Original:  Throughout the paper, we highlight goals, characteristics, open problems, and starting points for how one could approach what we define as  the ideal PL.
Revised:  Throughout the paper, we highlight goals, characteristics, open problems, and starting points for how one could approach what we define as , the ideal PL.
Edit intent: fluency
Original:  In the end we present three approaches to finding PLthrough, self-evaluations, certifications, or community endeavours, and sketch a roadmap for future developments aiming for an Individualised PL .
Revised:  In the end we present three approaches to establish and manage PL, through: self-evaluations, certifications, or community endeavours, and sketch a roadmap for future developments aiming for an Individualised PL .
Edit intent: clarity
Original:  In the end we present three approaches to finding PLthrough, self-evaluations, certifications, or community endeavours, and sketch a roadmap for future developments aiming for an Individualised PL .
Revised:  In the end we present three approaches to finding PLthrough, self-evaluations, certifications, or community endeavors. Based on these, we sketch a roadmap for future developments aiming for an Individualised PL .
Edit intent: clarity
Original:  In the end we present three approaches to finding PLthrough, self-evaluations, certifications, or community endeavours, and sketch a roadmap for future developments aiming for an Individualised PL .
Revised:  In the end we present three approaches to finding PLthrough, self-evaluations, certifications, or community endeavours, and sketch a roadmap for future developments .
Edit intent: clarity
Original: Privacy is currently in 'distress ' and in need of 'rescue' , much like princesses in the all-familiar fairytales.
Revised: Privacy is currently in distress and in need of 'rescue' , much like princesses in the all-familiar fairytales.
Edit intent: fluency
Original: Privacy is currently in 'distress ' and in need of 'rescue' , much like princesses in the all-familiar fairytales.
Revised: Privacy is currently in 'distress ' and in need of rescue , much like princesses in the all-familiar fairytales.
Edit intent: fluency
Original:  We employ storytelling and metaphors from fairytales to make reader-friendly and streamline our arguments about how a complex concept of Privacy Labeling (the 'knight in shining armour ') can be a solution to the current state of Privacy (the 'princess in distress').
Revised:  We employ storytelling and metaphors from fairytales to make reader-friendly and streamline our arguments about how a complex concept of Privacy Labeling (the 'knight in shining armor ') can be a solution to the current state of Privacy (the 'princess in distress').
Edit intent: fluency
Original:  Throughout the paper, we highlight goals, characteristics, open problems, and starting points for creating, what we define as , the ideal PL.
Revised:  Throughout the paper, we highlight goals, characteristics, open problems, and starting points for creating, what we consider to be , the ideal PL.
Edit intent: clarity
Original: Counterfactual Regret Minimization (CFR) has achieved many fascinating results in solving large scale Imperfect Information Games (IIGs).
Revised: Counterfactual Regret Minimization (CFR) has achieved many fascinating results in solving large-scale Imperfect Information Games (IIGs).
Edit intent: fluency
Original:  Neural CFR is one of the promising techniques that can effectively reduce the computation and memory consumption of CFR by generalizing decision information between similar states.
Revised:  Neural CFR is one of the promising techniques that can effectively reduce computation and memory consumption of CFR by generalizing decision information between similar states.
Edit intent: fluency
Original:  Neural CFR is one of the promising techniques that can effectively reduce the computation and memory consumption of CFR by generalizing decision information between similar states.
Revised:  Neural CFR is one of the promising techniques that can effectively reduce the computation and memory consumption by generalizing decision information between similar states.
Edit intent: clarity
Original:  However, current neural CFR algorithms have to approximate the cumulative variables in iterations with neural networks , which usually results in large estimation variance given the huge complexity of IIGs.
Revised:  However, current neural CFR algorithms have to approximate the cumulative regrets with neural networks , which usually results in large estimation variance given the huge complexity of IIGs.
Edit intent: clarity
Original:  However, current neural CFR algorithms have to approximate the cumulative variables in iterations with neural networks , which usually results in large estimation variance given the huge complexity of IIGs.
Revised:  However, current neural CFR algorithms have to approximate the cumulative variables in iterations with neural networks . This usually results in large estimation variance given the huge complexity of IIGs.
Edit intent: coherence
Original:  Then there is no need to track or approximate the cumulative variables any more . Based on the RSV, the new neural CFR algorithm is model-free and has higher training efficiency . Experimental results show that the new algorithm can match the state-of-the-art neural CFR algorithms and with less training cost .
Revised:  Then there is no need to track or approximate the cumulative variables any more . Based on the RSV, the new neural CFR algorithm is model-free and has higher training efficiency . Experimental results show that the new algorithm can match the state-of-the-art neural CFR algorithms but with less training cost .
Edit intent: coherence
Original:  Then there is no need to track or approximate the cumulative variables any more . Based on the RSV, the new neural CFR algorithm is model-free and has higher training efficiency . Experimental results show that the new algorithm can match the state-of-the-art neural CFR algorithms and with less training cost .
Revised:  Then there is no need to track or approximate the cumulative variables any more . Based on the RSV, the new neural CFR algorithm is model-free and has higher training efficiency . Experimental results show that the new algorithm can match the state-of-the-art neural CFR algorithms and with less training overhead .
Edit intent: clarity
Original: Large-scale testing is considered key to assessing the state of the current COVID-19 pandemic , yet interpreting such data remains elusive.
Revised: Large-scale testing is considered key to assess the state of the current COVID-19 pandemic , yet interpreting such data remains elusive.
Edit intent: fluency
Original: Large-scale testing is considered key to assessing the state of the current COVID-19 pandemic , yet interpreting such data remains elusive.
Revised: Large-scale testing is considered key to assessing the state of the current COVID-19 pandemic . Yet, the link between the reported case numbers and the true state of the pandemic remains elusive.
Edit intent: clarity
Original:  We modeled competing hypotheses regarding the underlying testing mechanisms , thereby providing different prevalence estimates based on case numbers, and used them to predict SARS-CoV-2-attributed death rate trajectories.
Revised:  We modeled competing hypotheses regarding the underlying testing mechanisms , thereby providing different prevalence estimates based on case numbers, and validate them by predicting SARS-CoV-2-attributed death rate trajectories.
Edit intent: clarity
Original:  Assuming that individuals were tested based solely on a predefined risk of being infectious implied the absolute case numbers reflected prevalence, but turned out to be a poor predictor  .
Revised:  Assuming that individuals were tested based solely on a predefined risk of being infectious implies the absolute case numbers reflected prevalence, but turned out to be a poor predictor .
Edit intent: fluency
Original:  Assuming that individuals were tested based solely on a predefined risk of being infectious implied the absolute case numbers reflected prevalence, but turned out to be a poor predictor  .
Revised:  Assuming that individuals were tested based solely on a predefined risk of being infectious implied the absolute case numbers reflect the prevalence, but turned out to be a poor predictor .
Edit intent: fluency
Original:  In contrast, models accounting for testing capacity , limiting the pool of tested individuals, performed better.
Revised:  In contrast, assuming that testing capacity is fully exploited performs better.
Edit intent: clarity
Original:  This puts forward the percentage of positive tests as a robust indicator of epidemic dynamics in absence of country-specific information.
Revised:  This leads to using the percent-positive rate as a more robust indicator of epidemic dynamics in absence of country-specific information.
Edit intent: clarity
Original: We present a machine learning based COVID-19 cough classifier which is able to discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone.
Revised: We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone.
Edit intent: clarity
Original:  This type of screening is non-contact and easily applied, and could help reduce workload in testing centers as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19.
Revised:  This type of screening is non-contact , easy to apply, and can reduce the workload in testing centers as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19.
Edit intent: coherence
Original:  This type of screening is non-contact and easily applied, and could help reduce workload in testing centers as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19.
Revised:  This type of screening is non-contact and easily applied, and could help reduce workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19.
Edit intent: fluency
Original:  The two dataset used in this study include subjects from all six continents and contain both forced and natural coughs  .
Revised:  The datasets used in this study include subjects from all six continents and contain both forced and natural coughs .
Edit intent: clarity
Original:  Dataset skew was addressed by applying  synthetic minority oversampling  (SMOTE) and leave-p-out cross validation was used to train and evaluate classifiers.
Revised:  Dataset skew was addressed by applying the synthetic minority oversampling (SMOTE) and leave-p-out cross validation was used to train and evaluate classifiers.
Edit intent: fluency
Original:  Dataset skew was addressed by applying  synthetic minority oversampling  (SMOTE) and leave-p-out cross validation was used to train and evaluate classifiers.
Revised:  Dataset skew was addressed by applying synthetic minority oversampling technique (SMOTE) and leave-p-out cross validation was used to train and evaluate classifiers.
Edit intent: clarity
Original:  Dataset skew was addressed by applying  synthetic minority oversampling  (SMOTE) and leave-p-out cross validation was used to train and evaluate classifiers.
Revised:  Dataset skew was addressed by applying synthetic minority oversampling (SMOTE) . A leave-p-out cross validation was used to train and evaluate classifiers.
Edit intent: coherence
Original:  Dataset skew was addressed by applying  synthetic minority oversampling  (SMOTE) and leave-p-out cross validation was used to train and evaluate classifiers.
Revised:  Dataset skew was addressed by applying synthetic minority oversampling (SMOTE) and leave-p-out cross-validation scheme was used to train and evaluate classifiers.
Edit intent: clarity
Original:  Logistic regression (LR), support vector machines (SVM), multilayer perceptrons (MLP), convolutional neural networks (CNN), long-short term memory (LSTM ) and a residual-based neural network architecture ( Resnet50 ) were considered as classifiers .
Revised:  Logistic regression (LR), support vector machines (SVM), multilayer perceptrons (MLP), convolutional neural networks (CNN), long-short term memory (LSTM ) and a residual-based neural network architecture ( Resnet50 .
Edit intent: clarity
Original:  Our results show that the Resnet50 classifier  was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs  with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features selected by sequential forward search (SFS).
Revised:  Our results show that the Resnet50 classifier , which was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features selected by sequential forward search (SFS).
Edit intent: clarity
Original:  Our results show that the Resnet50 classifier  was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs  with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features selected by sequential forward search (SFS).
Revised:  Our results show that the Resnet50 classifier was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 . An LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features selected by sequential forward search (SFS).
Edit intent: coherence
Original:  Our results show that the Resnet50 classifier  was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs  with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features selected by sequential forward search (SFS).
Revised:  Our results show that the Resnet50 classifier was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs , with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features selected by sequential forward search (SFS).
Edit intent: fluency
Original:  Our results show that the Resnet50 classifier  was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs  with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features selected by sequential forward search (SFS).
Revised:  Our results show that the Resnet50 classifier was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs with an AUC of 0.94 after selecting the best 13 features selected by sequential forward search (SFS).
Edit intent: clarity
Original:  Our results show that the Resnet50 classifier  was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs  with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features selected by sequential forward search (SFS).
Revised:  Our results show that the Resnet50 classifier was best able to discriminate between the COVID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98 while a LSTM classifier was best able to discriminate between the COVID-19 positive and COVID-19 negative coughs with an AUC of 0.94 . The LSTM classifier achieved these results using 13 features from a sequential forward selection (SFS).
Edit intent: clarity
Original:  Since it can be implemented on a smartphone, cough audio classification is cost-effective and easy to apply and deploy, and therefore is potentially a useful and viable means of non-contact COVID-19 screening.
Revised:  Since this type of cough audio classification is cost-effective and easy to apply and deploy, and therefore is potentially a useful and viable means of non-contact COVID-19 screening.
Edit intent: clarity
Original:  Since it can be implemented on a smartphone, cough audio classification is cost-effective and easy to apply and deploy, and therefore is potentially a useful and viable means of non-contact COVID-19 screening.
Revised:  Since it can be implemented on a smartphone, cough audio classification is cost-effective and easy to deploy, it is potentially a useful and viable means of non-contact COVID-19 screening.
Edit intent: coherence
Original:  Nevertheless, NPI efficacy varies enormously between and within countries, mainly because of population and behavioral heterogeneity.
Revised:  Nevertheless, NPI efficacy varies enormously between and within countries, mainly because of population and behavioural heterogeneity.
Edit intent: fluency