wanyu/IteraTeR_v2 huggingface dataset:
Original:  The rigorous encoding of our data-aware models, based on a common interpretation of the semantics of Action Languages, Classical Planning, and Model-Checking in terms of transition systems, paired with a first comprehensive assessment of the performances of their tools in computing reachability for data-aware workflow net languages, provide a solid contribution to advancing the state-of-the-art on the concrete exploitation of formal verification techniques on  business processes.
Revised:  The rigorous encoding of our data-aware models, based on a common interpretation of the semantics of Action Languages, Classical Planning, and Model-Checking in terms of transition systems, paired with a first comprehensive assessment of the performances of their tools in computing reachability for data-aware workflow net languages, provide a solid contribution to advancing the state-of-the-art on the concrete exploitation of formal verification techniques on data-aware business processes.
Edit intent: fluency
Original:  Here, we extend the theoretical underpinnings of PSRO by considering an alternative solution concept, \alpha -Rank, which is unique (thus faces no equilibrium selection issues, unlike Nash) and tractable to compute in general-sum, many-player settings.
Revised:  Here, we extend the theoretical underpinnings of PSRO by considering an alternative solution concept, \alpha -Rank, which is unique (thus faces no equilibrium selection issues, unlike Nash) and tractable to compute in general-sum, many-player settings.
Edit intent: fluency
Original:  Here, we extend the theoretical underpinnings of PSRO by considering an alternative solution concept, \alpha -Rank, which is unique (thus faces no equilibrium selection issues, unlike Nash) and tractable to compute in general-sum, many-player settings.
Revised:  Here, we extend the theoretical underpinnings of PSRO by considering an alternative solution concept, \alpha -Rank, which is unique (thus faces no equilibrium selection issues, unlike Nash) and applies readily to general-sum, many-player settings.
Edit intent: clarity
Original:  We establish convergence guarantees in several games classes, and identify links between Nash equilibria and \alpha -Rank.
Revised:  We establish convergence guarantees in several games classes, and identify links between Nash equilibria and \alpha -Rank.
Edit intent: fluency
Original:  We demonstrate the competitive performance of \alpha -Rank-based PSRO against an exact Nash solver-based PSRO in 2-player Kuhn and Leduc Poker.
Revised:  We demonstrate the competitive performance of \alpha -Rank-based PSRO against an exact Nash solver-based PSRO in 2-player Kuhn and Leduc Poker.
Edit intent: fluency
Original:  We then go beyond the reach of prior PSRO applications by considering 3- to 5-player poker games, yielding instances where \alpha -Rank achieves faster convergence than approximate Nash solvers, thus establishing it as a favorable general games solver.
Revised:  We then go beyond the reach of prior PSRO applications by considering 3- to 5-player poker games, yielding instances where \alpha -Rank achieves faster convergence than approximate Nash solvers, thus establishing it as a favorable general games solver.
Edit intent: fluency
Original: Circuit-augmentation algorithms are a generalization of the Simplex method, where in each step one is allowed to move along a  set of directions, called circuits, that is a superset of the edges of a polytope.
Revised: Circuit-augmentation algorithms are generalizations of the Simplex method, where in each step one is allowed to move along a set of directions, called circuits, that is a superset of the edges of a polytope.
Edit intent: fluency
Original: Circuit-augmentation algorithms are a generalization of the Simplex method, where in each step one is allowed to move along a  set of directions, called circuits, that is a superset of the edges of a polytope.
Revised: Circuit-augmentation algorithms are a generalization of the Simplex method, where in each step one is allowed to move along a fixed set of directions, called circuits, that is a superset of the edges of a polytope.
Edit intent: clarity
Original:  We show that in this general context the greatest-improvement and Dantzig pivot rules are NP-hard  . Differently, the steepest-descent pivot rule can be computed in polynomial time  , and the number of  augmentations required to reach an optimal solution according to this rule is strongly-polynomial for 0/ 1 LPs.
Revised:  We show that in this general context the greatest-improvement and Dantzig pivot rules are NP-hard . Differently, the steepest-descent pivot rule can be carried out in polynomial time , and the number of augmentations required to reach an optimal solution according to this rule is strongly-polynomial for 0/ 1 LPs.
Edit intent: clarity
Original:  Interestingly, we show that this more general framework can be exploited also to make conclusions about the  Simplex method itself .
Revised:  Interestingly, we show that this more general framework can be exploited also to make conclusions about the Simplex method itself :
Edit intent: fluency
Original:  In particular, as a byproduct of our  results, we prove that (i) computing the shortest monotone path to an optimal solution on the 1-skeleton of a polytope is NP-hard, and hard to approximate within a factor better than 2, and (ii) for 0/1 polytopes, a monotone path of polynomial length can be constructed using steepest improving edges.
Revised:  In particular, as a byproduct of our results, we prove that (i) computing the shortest (monotone) path to an optimal solution on the 1-skeleton of a polytope is NP-hard, and hard to approximate within a factor better than 2, and (ii) for 0/1 polytopes, a monotone path of polynomial length can be constructed using steepest improving edges.
Edit intent: clarity
Original:  In particular, as a byproduct of our  results, we prove that (i) computing the shortest monotone path to an optimal solution on the 1-skeleton of a polytope is NP-hard, and hard to approximate within a factor better than 2, and (ii) for 0/1 polytopes, a monotone path of polynomial length can be constructed using steepest improving edges.
Revised:  In particular, as a byproduct of our results, we prove that (i) computing the shortest monotone path to an optimal solution on the 1-skeleton of a polytope is NP-hard, and hard to approximate within a factor better than 2, and (ii) for 0/1 polytopes, a monotone path of strongly-polynomial length can be constructed using steepest improving edges.
Edit intent: clarity
Original:  Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social , web and citation network datasets .
Revised:  Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social networks and web graphs .
Edit intent: clarity
Original:  Taking the number of factors, say m, into account  we introduce and study invariant filtrations and their equivariant Hilbert series.
Revised:  Taking the number of factors, say m, into account , we introduce and study invariant filtrations and their equivariant Hilbert series.
Edit intent: fluency
Original: The time fractional ODEs are equivalent to the convolutional Volterra integral equations with a weakly singular kernel.
Revised: The time fractional ODEs are equivalent to convolutional Volterra integral equations with a weakly singular kernel.
Edit intent: fluency
Original:  We therefore introduce the concept of complete monotonicity-preserving (CM-preserving) numerical methods for fractional ODEs, in which the discrete convolutional kernel inherits the CM property as the continuous equations.
Revised:  We therefore introduce the concept of complete monotonicity-preserving (CM-preserving) numerical methods for fractional ODEs, in which the discrete convolutional kernels inherit the CM property as the continuous equations.
Edit intent: fluency
Original:  Three concrete numerical schemes, including the Gr\"{unwald-Letnikov formula, numerical method based on piecewise interpolation formula and convolutional quadrature based on \theta-method, are proved to be }  CM-preserving .
Revised:  nwald-Letnikov formula, numerical method based on piecewise interpolation formula and convolutional quadrature based on \theta-method, are proved to be } CM-preserving .
Edit intent: clarity
Original:  Three concrete numerical schemes, including the Gr\"{unwald-Letnikov formula, numerical method based on piecewise interpolation formula and convolutional quadrature based on \theta-method, are proved to be }  CM-preserving .
Revised:  Three concrete numerical schemes, including the Gr\"{unwald-Letnikov formula, numerical method based on piecewise interpolation formula and convolutional quadrature based on \theta-method, are proved to be } We prove that CM-preserving .
Edit intent: coherence
Original:  The results for fractional ODEs can be extended to CM-preserving numerical methods for Volterra integral equations with general completely monotone kernels.
Revised:  The results for fractional ODEs are extended to CM-preserving numerical methods for Volterra integral equations with general completely monotone kernels.
Edit intent: clarity
Original:  However, there is not much study focusing on different types of online harassment alone attracting natural language processing techniques.
Revised:  However, there is no much study focusing on different types of online harassment alone attracting natural language processing techniques.
Edit intent: fluency
Original:  However, there is not much study focusing on different types of online harassment alone attracting natural language processing techniques.
Revised:  However, there is not much study focusing on different types of online harassment attracting natural language processing techniques.
Edit intent: clarity
Original:  In this work, we present an attention-based approach for the detection of harassment in tweets and the detection of different types of harassment as well .
Revised:  In this work, we present an attention-based approach for the detection of different types of harassment as well .
Edit intent: coherence
Original:  In this work, we present an attention-based approach for the detection of harassment in tweets and the detection of different types of harassment as well .
Revised:  In this work, we present an attention-based approach for the detection of harassment in tweets and the detection of different types of harassment in tweets .
Edit intent: clarity
Original:  It can provide useful insights for studying the interactions between hosts and microbes, infectious disease proliferation, and novel species discovery.
Edit intent: coherence
Original:  One important step in this analysis is the taxonomic classification of those DNA fragments.
Revised:  One important step in this analysis is the taxonomic classification of the DNA fragments.
Edit intent: clarity
Original:  Of particular interest is the determination of the distribution of the taxa of microbes in metagenomic samples.
Revised:  Of particular interest is abundance estimation in metagenomic samples.
Edit intent: clarity
Original:  Recent attempts using deep learning focus on architectures that classify single DNA reads independently from each other  .
Revised:  Current attempts rely on classifying single DNA reads independently from each other .
Edit intent: clarity
Original:  We illustrate that our architecture can exploit the co-occurrence of species in metagenomic read sets and outperforms the single-read architectures in predicting the distribution over the taxa at higher taxonomic ranks.
Revised:  We illustrate that our architectures can exploit the co-occurrence of species in metagenomic read sets and outperforms the single-read architectures in predicting the distribution over the taxa at higher taxonomic ranks.
Edit intent: fluency
Original:  We illustrate that our architecture can exploit the co-occurrence of species in metagenomic read sets and outperforms the single-read architectures in predicting the distribution over the taxa at higher taxonomic ranks.
Revised:  We illustrate that our architecture can exploit the co-occurrence of species in metagenomic read sets and outperform the single-read architectures in predicting the distribution over the taxa at higher taxonomic ranks.
Edit intent: fluency
Original:  We illustrate that our architecture can exploit the co-occurrence of species in metagenomic read sets and outperforms the single-read architectures in predicting the distribution over the taxa at higher taxonomic ranks.
Revised:  We illustrate that our architecture can exploit the co-occurrence of species in metagenomic read sets and outperforms the single-read architectures in predicting the distribution over taxa at higher taxonomic ranks.
Edit intent: fluency
Original: The use of mixed variational formulations in mathematical modeling, as well as their numerical implementation, are well known areas of mathematical and numerical analysis that have been well-investigated in the literature.
Edit intent: coherence
Original:  We also analyze the associated inverse problem and we show how to solve it by means of an extension of the Generalized Collage Theorem which extends previous results by the same authors . Numerical examples show how the method works in a practical context.
Revised:  We also analyze the associated inverse problem and we show how to solve it by means of the mentioned Generalized Collage Theorem which extends previous results by the same authors . Numerical examples show how the method works in a practical context.
Edit intent: coherence
Original: Many of the complex systems we study in their representation as networks are growing objects, evolving by the addition of nodes and links over time .
Revised: Discriminating between competing explanatory models as to which is more likely responsible for the growth of a network is a problem of fundamental importance for network science .
Edit intent: clarity
Original:  We propose an energy functional method and the total variational regularization combining with the quadratic stabilizing term to tackle the identification problem  .
Revised:  We propose an energy functional method and the total variational regularization combining with the quadratic stabilizing term to formulate the identification problem .
Edit intent: clarity
Original:  We show the stability of the proposed regularization method and the convergence of the finite element regularized solutions to the identification in the Lebesgue spaces and in the sense of the Bregman distance with respect to the total variation semi-norm.
Revised:  We show the stability of the proposed regularization method and the convergence of the finite element regularized solutions to the identification in the Lebesgue norms and in the sense of the Bregman distance with respect to the total variation semi-norm.
Edit intent: clarity
Original: Targeted clean-label  poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference.
Revised: Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference.
Edit intent: fluency
Original: Targeted clean-label  poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference.
Revised: Targeted clean-label poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference.
Edit intent: fluency
Original: Targeted clean-label  poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference.
Revised: Targeted clean-label poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data , causing a model to misclassify a particular test sample during inference.
Edit intent: clarity
Original:  Although defenses have been proposed for general poisoning attacks (those which aim to reduce overall test accuracy) , no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and their realistic use cases .
Revised:  Although defenses have been proposed for general poisoning attacks , no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and their realistic use cases .
Edit intent: clarity
Original:  Although defenses have been proposed for general poisoning attacks (those which aim to reduce overall test accuracy) , no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and their realistic use cases .
Revised:  Although defenses have been proposed for general poisoning attacks (those which aim to reduce overall test accuracy) , no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and realistic applications .
Edit intent: clarity
Original:  In this work, we propose a set of simple, yet highly-effective defenses against these attacks.
Revised:  In this work, we propose a simple, yet highly-effective defenses against these attacks.
Edit intent: clarity
Original:  We test our proposed approach against two recently published clean-label poisoning attacks , both of which use the CIFAR-10 dataset.
Revised:  We test our proposed approach against two recently published clean-label attacks on the CIFAR-10 dataset.
Edit intent: clarity
Original:  After reproducing their experiments, we demonstrate that our defenses are able to detect over 99\% of poisoning examples in both attacks and remove them without any compromise on model performance.
Revised:  We demonstrate that our defenses are able to detect over 99\% of poisoning examples in both attacks and remove them without any compromise on model performance.
Edit intent: clarity
Original:  After reproducing their experiments, we demonstrate that our defenses are able to detect over 99\% of poisoning examples in both attacks and remove them without any compromise on model performance.
Revised:  After reproducing their experiments, we demonstrate that our proposed strategy is able to detect over 99\% of poisoning examples in both attacks and remove them without any compromise on model performance.
Edit intent: clarity
Original:  After reproducing their experiments, we demonstrate that our defenses are able to detect over 99\% of poisoning examples in both attacks and remove them without any compromise on model performance.
Revised:  After reproducing their experiments, we demonstrate that our defenses are able to detect over 99\% of poisoned examples in both attacks and remove them without any compromise on model performance.
Edit intent: fluency
Original:  After reproducing their experiments, we demonstrate that our defenses are able to detect over 99\% of poisoning examples in both attacks and remove them without any compromise on model performance.
Revised:  After reproducing their experiments, we demonstrate that our defenses are able to detect over 99\% of poisoning examples in both attacks and remove them without compromising model performance.
Edit intent: clarity
Original:  Our simple defenses show that current clean-label poisoning attack strategies can be annulled, and serve as strong but simple-to-implement baseline defense for which to test future clean-label poisoning attacks. 
Revised:  Our simple defenses show that current clean-label poisoning attack strategies can be annulled, and serves as a strong yet simple-to-implement baseline defense for which to test future clean-label poisoning attacks. 
Edit intent: fluency
Original:  Our simple defenses show that current clean-label poisoning attack strategies can be annulled, and serve as strong but simple-to-implement baseline defense for which to test future clean-label poisoning attacks. 
Revised:  Our simple defenses show that current clean-label poisoning attack strategies can be annulled, and serve as strong but simple-to-implement baseline defense to test future clean-label poisoning attacks. 
Edit intent: clarity
Original: For a planar simplicial complex Delta contained in R^2, Alfeld-Schumaker proved that the dimension of the space C^r_k(Delta) of planar splines of smoothness r and polynomial degree at most k on Delta is given by a polynomial P_Delta(r,k)  when k >= 3r +1.
Revised: For a planar simplicial complex Delta contained in R^2, Schumaker proved that a lower bound on the dimension of the space C^r_k(Delta) of planar splines of smoothness r and polynomial degree at most k on Delta is given by a polynomial P_Delta(r,k) when k >= 3r +1.
Edit intent: clarity
Original: Limited data access is a substantial barrier to data-driven networking research and development .
Revised: Limited data access is a longstanding barrier to data-driven networking research and development .
Edit intent: clarity
Original: Limited data access is a substantial barrier to data-driven networking research and development .
Revised: Limited data access is a substantial barrier to data-driven research and development .
Edit intent: clarity
Original:  In this work, we present DoppelGANger, a synthetic data generation framework based on generative adversarial networks (GANs) . DoppelGANger is designed to work on time series datasets with both continuous features (e.g. traffic measurements ) and discrete ones (e.g., protocol name) .
Revised:  In this work, we present DoppelGANger, a synthetic data generation framework based on generative adversarial networks (GANs) . DoppelGANger is designed to work on time series datasets with metadata (e.g. traffic measurements ) and discrete ones (e.g., protocol name) .
Edit intent: clarity
Original:  However, there is still a performance gap between the successful full-precision neural network with ReLU activation and BNNs.
Revised:  However, there is still a performance gap between a successful full-precision neural network with ReLU activation and BNNs.
Edit intent: coherence
Original:  We analyze the behaviour of the full-precision neural network with ReLU activation and compare it with its binarized counterpart .
Revised:  We start with analyzing full-precision neural network with ReLU activation and compare it with its binarized counterpart .
Edit intent: clarity
Original:  We analyze the behaviour of the full-precision neural network with ReLU activation and compare it with its binarized counterpart .
Revised:  We analyze the behaviour of the full-precision neural networks with ReLU activation and compare it with its binarized counterpart .
Edit intent: fluency
Original:  We analyze the behaviour of the full-precision neural network with ReLU activation and compare it with its binarized counterpart .
Revised:  We analyze the behaviour of the full-precision neural network with ReLU activation and compare it with its binarized version .
Edit intent: clarity