wanyu/IteraTeR_v2 huggingface dataset:
Original:  A basic reproduction number is found from the model .
Revised:  A basic reproduction number .
Edit intent: clarity
Original:  The main purpose is to project the \lambda-model D_\inftyof Dana Scott to an extensional \lambda-model with structure of \infty-grupoid under a composition operation between cells.
Revised:  The main purpose is to project the \lambda-model D_\inftyof Dana Scott to an extensional lambda model with an \infty-groupoid structure under a composition operation between cells.
Edit intent: clarity
Original:  This language can be seen as a theory, with certain pre-established axioms and inference rules, which can be represented by models.
Revised:  This language can be seen as a formal theory with certain pre-established axioms and inference rules, which can be represented by models.
Edit intent: clarity
Original:  This language can be seen as a theory, with certain pre-established axioms and inference rules, which can be represented by models.
Revised:  This language can be seen as a theory, with certain pre-established axioms and inference rules, which can be interpreted by models.
Edit intent: clarity
Original:  Dana Scott proposed the first non-trivial model of the extensional lambda calculus, known as D_\infty, in order to represent the \lambda-terms as the typical functions of set theory, where it is not allowed to evaluate a function about itself.
Revised:  Dana Scott proposed the first non-trivial model of the extensional lambda calculus, known as D_\infty, to represent the \lambda-terms as the typical functions of set theory, where it is not allowed to evaluate a function about itself.
Edit intent: coherence
Original:  Dana Scott proposed the first non-trivial model of the extensional lambda calculus, known as D_\infty, in order to represent the \lambda-terms as the typical functions of set theory, where it is not allowed to evaluate a function about itself.
Revised:  Dana Scott proposed the first non-trivial model of the extensional lambda calculus, known as D_\infty, in order to represent the \lambda-terms as the typical functions of set theory, where it is not allowed to apply a function to itself.
Edit intent: clarity
Original:  Here we propose a construction of a \infty-groupoid from any lambda model endowed with a topology , with the purpose of projecting D_\infty, with the Scott topology , to an extensional lambda model with an \infty-groupoid structure under a composition operation between cells .
Revised:  Here we propose a construction of an \infty-groupoid from any lambda model endowed with a topology , with the purpose of projecting D_\infty, with the Scott topology , to an extensional lambda model with an \infty-groupoid structure under a composition operation between cells .
Edit intent: fluency
Original: Generalising prior work on the rank of random matrices over finite fields Coja-Oghlan and Gao 2018%DIFDELCMD < ]%%% , we determine the rank of a random matrix  with prescribed numbers of non-zero entries in each row and column over any field.
Revised:  Coja-Oghlan and Gao 2018%DIFDELCMD < ]%%% , we determine the rank of a random matrix with prescribed numbers of non-zero entries in each row and column over any field.
Edit intent: clarity
Original: Generalising prior work on the rank of random matrices over finite fields Coja-Oghlan and Gao 2018%DIFDELCMD < ]%%% , we determine the rank of a random matrix  with prescribed numbers of non-zero entries in each row and column over any field.
Revised: Generalising prior work on the rank of random matrices over finite fields %DIFDELCMD < ]%%% , we determine the rank of a random matrix with prescribed numbers of non-zero entries in each row and column over any field.
Edit intent: clarity
Original: Generalising prior work on the rank of random matrices over finite fields Coja-Oghlan and Gao 2018%DIFDELCMD < ]%%% , we determine the rank of a random matrix  with prescribed numbers of non-zero entries in each row and column over any field.
Revised: Generalising prior work on the rank of random matrices over finite fields Coja-Oghlan and Gao 2018%DIFDELCMD < ]%%% We determine the rank of a random matrix with prescribed numbers of non-zero entries in each row and column over any field.
Edit intent: fluency
Original:  Furthermore,  our method can  be used when only trajectories without expert actions are available , which can leverage kinestetic or third person demonstration.
Revised:  Furthermore, we show our method can be used when only trajectories without expert actions are available , which can leverage kinestetic or third person demonstration.
Edit intent: coherence
Original:  Furthermore,  our method can  be used when only trajectories without expert actions are available , which can leverage kinestetic or third person demonstration.
Revised:  Furthermore, our method can also be used when only trajectories without expert actions are available , which can leverage kinestetic or third person demonstration.
Edit intent: coherence
Original:  Furthermore,  our method can  be used when only trajectories without expert actions are available , which can leverage kinestetic or third person demonstration.
Revised:  Furthermore, our method can be used when the available expert trajectories do not contain the actions , which can leverage kinestetic or third person demonstration.
Edit intent: clarity
Original:  Furthermore,  our method can  be used when only trajectories without expert actions are available , which can leverage kinestetic or third person demonstration.
Revised:  Furthermore, our method can be used when only trajectories without expert actions are available , which can leverage kinesthetic or third person demonstration.
Edit intent: fluency
Original:  The code is available at URL .
Revised:  The code is available at URL 
Edit intent: fluency
Original: Recent works on implicit regularization have shown that gradient descent converges to the max-margin direction for logistic regression with one-layer or multi-layer linear networks.
Edit intent: coherence
Original:  In particular, we study the gradient flow ( gradient descent with infinitesimal step size) optimizing the logistic loss or cross-entropy loss of any homogeneous model (possibly non-smooth), and show that if the training loss decreases below a certain threshold, then we can define a smoothed version of the normalized margin which increases over time.
Revised:  In particular, we study the gradient descent or gradient flow (i.e., gradient descent with infinitesimal step size) optimizing the logistic loss or cross-entropy loss of any homogeneous model (possibly non-smooth), and show that if the training loss decreases below a certain threshold, then we can define a smoothed version of the normalized margin which increases over time.
Edit intent: clarity
Original:  The value processes and the volatility processes are all correlated through systemic Brownian motions  .
Revised:  The value processes and the volatility processes are correlated through systemic Brownian motions .
Edit intent: clarity
Original:  Other studies that compare standard node-link representations with map-like visualizations show that map-like visualizations are superior in terms of task performance, memorization and engagement  .
Revised:  Other studies that compare standard node-link representations with map-like visualizations are as good or better than standard node-link representations of graphs, in terms of task performance, memorization and engagement .
Edit intent: clarity
Original:  ( 4 ) All nodes at the current level and higher levels are labeled and there are no label overlaps;
Revised:  ( 5 ) All nodes at the current level and higher levels are labeled and there are no label overlaps;
Edit intent: coherence
Original: Rectangular treemaps are often the method of choice for visualizing large hierarchical datasets.
Revised: Rectangular treemaps are often the method of choice to visualize large hierarchical datasets.
Edit intent: fluency
Original:  Nowadays such datasets are available over time, and hence there is a need for (a) treemapping algorithms that can handle time-dependent data, and (b) corresponding quality criteria and evaluations .
Revised:  Nowadays such datasets are available over time, hence there is a need for (a) treemapping algorithms that can handle time-dependent data, and (b) corresponding quality criteria and evaluations .
Edit intent: coherence
Original:  Nowadays such datasets are available over time, and hence there is a need for (a) treemapping algorithms that can handle time-dependent data, and (b) corresponding quality criteria and evaluations .
Revised:  Nowadays such datasets are available over time, and hence there is a need for (a) treemaps that can handle time-dependent data, and (b) corresponding quality criteria and evaluations .
Edit intent: clarity
Original:  In recent years a wide variety of treemapping algorithms and corresponding quality criteria have been proposed, each with their own advantages and limitations , which are often hard to judge and compare .
Revised:  In recent years a wide variety of treemapping algorithms and corresponding quality criteria have been proposed, with various advantages and limitations , which are often hard to judge and compare .
Edit intent: clarity
Original:  In recent years a wide variety of treemapping algorithms and corresponding quality criteria have been proposed, each with their own advantages and limitations , which are often hard to judge and compare .
Revised:  In recent years a wide variety of treemapping algorithms and corresponding quality criteria have been proposed, each with their own advantages and limitations .
Edit intent: coherence
Original:  We aim to provide insights to allow researchers and practitioners to  make an informed choice when selecting a treemapping algorithm for their specific application and their data.
Revised:  We aim to provide insights to researchers and practitioners to make an informed choice when selecting a treemapping algorithm for their specific application and their data.
Edit intent: fluency
Original:  For this , we perform an extensive quantitative evaluation of rectangular treemapping algorithms for time-dependent data.
Revised:  For this , we perform an extensive quantitative evaluation of rectangular treemaps for time-dependent data.
Edit intent: clarity
Original:  Exhaustive experiments on real-world datasets reveal that our dynamic keyword dictionary needs up to 68\% less space than the existing smallest ones  .
Revised:  Experiments on real-world datasets reveal that our dynamic keyword dictionary needs up to 68\% less space than the existing smallest ones .
Edit intent: clarity
Original: In adversarial machine learning, there are a huge number of attacks of various types which makes the evaluation of robustness for new models and defenses a daunting task.
Revised: In adversarial machine learning, there are a huge number of attacks of various types which evaluates robustness for new models and defenses a daunting task.
Edit intent: fluency
Original: In adversarial machine learning, there are a huge number of attacks of various types which makes the evaluation of robustness for new models and defenses a daunting task.
Revised: In adversarial machine learning, there are a huge number of attacks of various types which makes the evaluation of robustness for new models and defences a daunting task.
Edit intent: fluency
Original:  To make matters worse, there is an inherent bias in attacks and defenses .
Revised:  To make matters worse, there is an inherent bias in attacks and defences .
Edit intent: fluency
Original:  Here, we organize the problems faced (model dependence, insufficient evaluation, unreliable adversarial samples and perturbation dependent results) and propose a dual quality assessment method together with the concept of robustness levels to tackle them.
Revised:  Here, we organize the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a dual quality assessment method together with the concept of robustness levels to tackle them.
Edit intent: clarity
Original:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defenses proposed at ICLR 2018 as well as the widely known adversarial training, showing that current models and defenses are vulnerable in all levels of robustness.
Revised:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defences proposed at ICLR 2018 as well as the widely known adversarial training, showing that current models and defenses are vulnerable in all levels of robustness.
Edit intent: fluency
Original:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defenses proposed at ICLR 2018 as well as the widely known adversarial training, showing that current models and defenses are vulnerable in all levels of robustness.
Revised:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defenses proposed at ICLR 2018 and the widely known adversarial training, showing that current models and defenses are vulnerable in all levels of robustness.
Edit intent: coherence
Original:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defenses proposed at ICLR 2018 as well as the widely known adversarial training, showing that current models and defenses are vulnerable in all levels of robustness.
Revised:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defenses proposed at ICLR 2018 as well as the widely known adversarial training, showing that current models and defences are vulnerable in all levels of robustness.
Edit intent: fluency
Original:  Moreover, we show that robustness to L_0 and L_\inftyattacks differ greatly and therefore duality should be taken into account for a correct assessment.
Revised:  The robustness assessment show that depending on the metric used (i.e., L_0 or L_\infty) the robustness may change significantly and therefore duality should be taken into account for a correct assessment.
Edit intent: clarity
Original:  Thus, this paper elucidates the problems of robustness evaluation, proposes a dual quality assessment to tackle them as well as analyze the robustness of current models and defenses.
Revised:  Thus, this paper elucidates the problems of robustness evaluation, proposes a dual quality assessment to tackle them as well as survey the robustness of current models and defenses.
Edit intent: clarity
Original:  Code available at : URL
Revised:  Code available at URL
Edit intent: fluency
Original: In adversarial machine learning, there are a huge number of attacks of various types which evaluates robustness for new models and defences a daunting task.
Revised: There exists a vast number of adversarial attacks and defences for machine learning algorithms of various types which evaluates robustness for new models and defences a daunting task.
Edit intent: clarity
Original: In adversarial machine learning, there are a huge number of attacks of various types which evaluates robustness for new models and defences a daunting task.
Revised: In adversarial machine learning, there are a huge number of attacks of various types which makes assessing the robustness of algorithms a daunting task.
Edit intent: clarity
Original:  To make matters worse, there is an inherent bias in attacks and defences .
Revised:  To make matters worse, there is an intrinsic bias in these adversarial algorithms .
Edit intent: clarity
Original:  Here, we organize the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a model agnostic dual (L_0 and L_\infty) quality assessment method  together with the concept of robustness levels to tackle them.
Revised:  Here, we organise the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a model agnostic dual (L_0 and L_\infty) quality assessment method together with the concept of robustness levels to tackle them.
Edit intent: fluency
Original:  Here, we organize the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a model agnostic dual (L_0 and L_\infty) quality assessment method  together with the concept of robustness levels to tackle them.
Revised:  Here, we organize the problems faced : a) Model Dependence, b) Insufficient Evaluation, c) False Adversarial Samples, and d) Perturbation Dependent Results). Based on this, we propose a model agnostic dual (L_0 and L_\infty) quality assessment method together with the concept of robustness levels to tackle them.
Edit intent: coherence
Original:  Here, we organize the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a model agnostic dual (L_0 and L_\infty) quality assessment method  together with the concept of robustness levels to tackle them.
Revised:  Here, we organize the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a model agnostic dual quality assessment method together with the concept of robustness levels to tackle them.
Edit intent: coherence
Original:  Here, we organize the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a model agnostic dual (L_0 and L_\infty) quality assessment method  together with the concept of robustness levels to tackle them.
Revised:  Here, we organize the problems faced (model dependence, insufficient evaluation, false adversarial samples and perturbation dependent results) and propose a model agnostic dual (L_0 and L_\infty) quality assessment method , together with the concept of robustness levels to tackle them.
Edit intent: fluency
Original:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defences proposed at ICLR 2018 and the widely known adversarial training, showing that current models and defences are vulnerable in all levels of robustness.
Revised:  We validate the dual quality assessment on state-of-the-art neural networks (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defences proposed at ICLR 2018 and the widely known adversarial training, showing that current models and defences are vulnerable in all levels of robustness.
Edit intent: clarity
Original:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defences proposed at ICLR 2018 and the widely known adversarial training, showing that current models and defences are vulnerable in all levels of robustness.
Revised:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as adversarial defences for image classification problem. We further show that current networks and defences are vulnerable in all levels of robustness.
Edit intent: clarity
Original:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defences proposed at ICLR 2018 and the widely known adversarial training, showing that current models and defences are vulnerable in all levels of robustness.
Revised:  We validate the dual quality assessment on state-of-the-art models (WideResNet, ResNet, AllConv, DenseNet, NIN, LeNet and CapsNet) as well as the current hardest defences proposed at ICLR 2018 and the widely known adversarial training, showing that current models and defences are vulnerable at all levels of robustness.
Edit intent: clarity
Original:  The robustness assessment show that depending on the metric used (i.e., L_0 or L_\infty)  the robustness may change significantlyand therefore duality should be taken into account for a correct assessment .
Revised:  The proposed robustness assessment reveals that depending on the metric used (i.e., L_0 or L_\infty) the robustness may change significantlyand therefore duality should be taken into account for a correct assessment .
Edit intent: clarity
Original:  The robustness assessment show that depending on the metric used (i.e., L_0 or L_\infty)  the robustness may change significantlyand therefore duality should be taken into account for a correct assessment .
Revised:  The robustness assessment show that depending on the metric used (i.e., L_0 or L_\infty) , the robustness may change significantlyand therefore duality should be taken into account for a correct assessment .
Edit intent: fluency
Original:  The robustness assessment show that depending on the metric used (i.e., L_0 or L_\infty)  the robustness may change significantlyand therefore duality should be taken into account for a correct assessment .
Revised:  The robustness assessment show that depending on the metric used (i.e., L_0 or L_\infty) the robustness may vary significantly. Hence, the duality should be taken into account for a correct assessment .
Edit intent: clarity
Original:  The robustness assessment show that depending on the metric used (i.e., L_0 or L_\infty)  the robustness may change significantlyand therefore duality should be taken into account for a correct assessment .
Revised:  The robustness assessment show that depending on the metric used (i.e., L_0 or L_\infty) the robustness may change significantlyand therefore duality should be taken into account for a correct evaluation .
Edit intent: clarity
Original:  Moreover, a mathematical derivation, as well as a counterexample , suggest that L_1 and L_2 metrics alone are not enough to avoid false adversarial samples.
Revised:  Moreover, a mathematical derivation, as well as a counter-example , suggest that L_1 and L_2 metrics alone are not enough to avoid false adversarial samples.
Edit intent: fluency
Original:  Moreover, a mathematical derivation, as well as a counterexample , suggest that L_1 and L_2 metrics alone are not enough to avoid false adversarial samples.
Revised:  Moreover, a mathematical derivation, as well as a counterexample , suggest that L_1 and L_2 metrics alone are not sufficient to avoid spurious adversarial samples.
Edit intent: clarity
Original:  Interestingly, a by-product of the assessment proposed is a novel L_\infty black-box  method which requires even less perturbation than the One-Pixel Attack (only 12\\%DIF < of One-Pixel Attack's amount of perturbation) to %DIF > of One-Pixel Attack's  achieve similar results.
Revised:  Interestingly, a by-product of the assessment proposed is a novel L_\infty black-box adversarial method which requires even less perturbation than the One-Pixel Attack (only 12\\%DIF < of One-Pixel Attack's amount of perturbation) to %DIF > of One-Pixel Attack's achieve similar results.
Edit intent: clarity
Original:  Interestingly, a by-product of the assessment proposed is a novel L_\infty black-box  method which requires even less perturbation than the One-Pixel Attack (only 12\\%DIF < of One-Pixel Attack's amount of perturbation) to %DIF > of One-Pixel Attack's  achieve similar results.
Revised:  Interestingly, a by-product of the assessment proposed is a novel L_\infty black-box method which requires even less perturbation than the One-Pixel Attack (only 12\\%DIF < of One-Pixel Attack's amount of perturbation) to %DIF > of One-Pixel Attack's amount of perturbation) to achieve similar results.
Edit intent: fluency