wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em   However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Revised:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . This raises the security threats of the released models. In addition to the clean models' unintentional weaknesses,{\em However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Edit intent: meaning-changed
Original:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em   However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Revised:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em i.e., However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Edit intent: meaning-changed
Original:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em   However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Revised:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em adversarial attacks, the poisoned models with malicious intentions are much more dangerous in real life. However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Edit intent: meaning-changed
Original:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em   However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Revised:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em However, most existing works currently focus on the adversarial attacks on NLP models instead of positioning attack , also named backdoor attack .
Edit intent: meaning-changed
Original:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em   However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Revised:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em However, most existing works currently focus on evasion attack on text data instead of positioning attacks , also named backdoor attack .
Edit intent: fluency
Original:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em   However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attack .
Revised:  Many existing studies show the vulnerabilities of machine learning and deep learning modelsagainst adversarial examples . {\em However, most existing works currently focus on evasion attack on text data instead of positioning attack , also named backdoor attacks .
Edit intent: fluency
Original:  In this paper, we systematically study the backdoor attack against models on text data. First, we define the backdoor attack on text data. Then, we propose the different attack  strategies to generate trigger on text data . Next, we propose different types of the triggers based on modification scope, human recognition  and special cases.
Revised:  In this paper, we first propose the strategies to generate trigger on text data . Next, we propose different types of the triggers based on modification scope, human recognition and special cases.
Edit intent: clarity
Original:  Our results provide compelling insights for policy-makers, including the significance of early testing and the emergence of a second wave of infections if NPIs are prematurely withdrawn.
Revised:  We illustrate the significance of the proposed closed-loop framework via extensive simulations and analysis of real, publicly-available testing data for COVID-19. Our results illustrate the significance of early testing and the emergence of a second wave of infections if NPIs are prematurely withdrawn.
Edit intent: meaning-changed
Original: I study a reputation model in which a patient player privately observes a persistent state that  affects his myopic opponents' payoffs, and can be one of the several commitment types that plays the same (possibly mixed ) action over time.
Revised: I study a reputation model in which a patient player privately observes a persistent state that directly affects his myopic opponents' payoffs, and can be one of the several commitment types that plays the same (possibly mixed ) action over time.
Edit intent: meaning-changed
Original: I study a reputation model in which a patient player privately observes a persistent state that  affects his myopic opponents' payoffs, and can be one of the several commitment types that plays the same (possibly mixed ) action over time. The main result is a characterization of the set of environments under which the patient player obtains at least his commitment payoff in all equilibria regardless of his stage-game payoff function.
Revised: I study a reputation model in which a patient player privately observes a persistent state that affects his myopic opponents' payoffs, and can be one of the several commitment types that plays the same mixed action in every period. I characterize the set of environments under which the patient player obtains at least his commitment payoff in all equilibria regardless of his stage-game payoff function.
Edit intent: clarity
Original:  My result implies that small perturbations to a pure commitment action can lead to a discontinuous change in the patient player's equilibrium payoff. The main technical contribution is to use martingale techniques to construct a non-stationary strategy under which the patient player can avoid signaling negative information about the state while at the same time, matching the long-run frequency of his actions to the mixed commitment action and convincing his opponents that his action is close to the commitment action in almost all periods .
Revised:  My result implies that small perturbations to a pure commitment action can lead to a discontinuous change in the patient player's guaranteed equilibrium payoff .
Edit intent: clarity
Original:  Using synthetic and real data, we demonstrate the advantages of our methods. For Eurozone data, we show that our nonparametric approach improves upon commonly used forecasting models and that it produces impulse responses to an uncertainty shock that are consistent with established findings in the literature .
Revised:  We apply our model to two datasets. The first application shows that the BAVART model yields highly competitive forecasts of the US term structure of interest rates. In a second application, we estimate our model using a moderately sized Eurozone dataset to investigate the dynamic effects of uncertainty on the economy .
Edit intent: meaning-changed
Original:  For the space discretization  we introduce and analyze a high-order discontinuous Galerkin method on polygonal and polyhedral meshes, which is then coupled with Newmark-\beta time integration schemes.
Revised:  For the space discretization , we introduce and analyze a high-order discontinuous Galerkin method on polygonal and polyhedral meshes, which is then coupled with Newmark-\beta time integration schemes.
Edit intent: fluency
Original:  The South African case is of immediate interest as the number of confirmed infectious cases does not appear to have peaked yet (at the time of writing), while at the same time the South African government is busy reducing the degree of lockdown . An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios.
Revised:  The South African case is of interest as the number of confirmed infectious cases does not appear to have peaked yet (at the time of writing), while at the same time the South African government is busy reducing the degree of lockdown . An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios.
Edit intent: clarity
Original:  The South African case is of immediate interest as the number of confirmed infectious cases does not appear to have peaked yet (at the time of writing), while at the same time the South African government is busy reducing the degree of lockdown . An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios.
Revised:  The South African case is of immediate interest as the South African government is busy reducing the degree of lockdown . An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios.
Edit intent: coherence
Original:  The South African case is of immediate interest as the number of confirmed infectious cases does not appear to have peaked yet (at the time of writing), while at the same time the South African government is busy reducing the degree of lockdown . An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios.
Revised:  The South African case is of immediate interest as the number of confirmed infectious cases does not appear to have peaked yet (at the time of writing), while at the same time the South African government has defined five distinct levels of lockdown, which serves as a discrete control input . An epidemiological model for the spread of COVID-19 in South Africa was previously developed, and is used in conjunction with a hybrid model predictive controller to optimize lockdown management under different policy scenarios.
Edit intent: meaning-changed
Original:  Scenarios considered include how  flatten the curve to a level that the healthcare system can cope with, how to balance lives and livelihoods, and what impact the compliance of the population to the lockdown measures has on the spread of COVID-19  .
Revised:  Scenarios considered include how to flatten the curve to a level that the healthcare system can cope with, how to balance lives and livelihoods, and what impact the compliance of the population to the lockdown measures has on the spread of COVID-19 .
Edit intent: fluency
Original:  Scenarios considered include how  flatten the curve to a level that the healthcare system can cope with, how to balance lives and livelihoods, and what impact the compliance of the population to the lockdown measures has on the spread of COVID-19  .
Revised:  Scenarios considered include how flatten the curve to a level that the healthcare system can cope with, how to balance lives and livelihoods, and what impact the compliance of the population to the lockdown measures has on the spread of COVID-19 . The main purpose of this paper is to show what the optimal lockdown level should be given the policy that is in place, as determined by the closed-loop feedback controller .
Edit intent: meaning-changed
Original:  In this paper, we propose an approach capable of efficiently approximating node centralities for large networks using Neural Networks and Node Embedding echniques. We thus call our approach the Network Centrality Approximation using Graph Convolutional Networks (NCA-GCN)model.
Revised:  In this paper, we propose an approach capable of efficiently approximating node centralities for large networks using Neural Networks and Graph Embedding techniques. Our proposed model, entitled Network Centrality Approximation using Graph Convolutional Networks (NCA-GCN)model.
Edit intent: coherence
Original:  We thus call our approach the Network Centrality Approximation using Graph Convolutional Networks (NCA-GCN)model.In contrast to recent related work, the NCA-GCN model requires only the degree centrality of each node in order to predict any other centrality . We show that the NCA-GCN model works well in different node centralities for different network sizes  .
Revised:  We thus call our approach the Network Centrality Approximation using Graph Embedding (NCA-GE), receives the adjacency matrix of a graph and the degree of every node as input and computes the approximate desired centrality rank for every node. NCA-GE has a time complexity of O(|E|), E being the set of edges of a graph, making it suitable for large networks. NCA-GE also trains pretty fast, requiring only a set of small synthetic graphs, and it works well for different node centralities for different network sizes .
Edit intent: meaning-changed
Original:  We show that the NCA-GCN model works well in different node centralities for different network sizes  .
Revised:  We show that the NCA-GCN model works well in different node centralities for different network sizes and topologies. Finally, we compare our approach to the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input, where we show that the NCA-GE outperforms the former in a variety of scenarios .
Edit intent: meaning-changed
Original:  Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), receives  the adjacency matrix of a graph and the degree of every node as input and computes the approximate desired centrality rank for every node.
Revised:  Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), receives as input the adjacency matrix of a graph and the degree of every node as input and computes the approximate desired centrality rank for every node.
Edit intent: fluency
Original:  Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), receives  the adjacency matrix of a graph and the degree of every node as input and computes the approximate desired centrality rank for every node.
Revised:  Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), receives the adjacency matrix of a graph and a set of features for each node (here, we use only the degree) and computes the approximate desired centrality rank for every node.
Edit intent: meaning-changed
Original:  NCA-GE also trains pretty fast, requiring only a set of small synthetic graphs , and it works well for different node centralities and different network sizes and topologies.
Revised:  NCA-GE also trains pretty fast, requiring only a set of a thousand small synthetic scale-free graphs (ranging from 100 to 1000 nodes each) , and it works well for different node centralities and different network sizes and topologies.
Edit intent: meaning-changed
Original:  Finally, we compare our approach to the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input , where we show that the NCA-GE outperforms the former in a variety of scenarios .
Revised:  Finally, we show that NCA-GE outperforms, in a variety of scenarios, the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input , where we show that the NCA-GE outperforms the former in a variety of scenarios .
Edit intent: clarity
Original:  Finally, we compare our approach to the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input , where we show that the NCA-GE outperforms the former in a variety of scenarios .
Revised:  Finally, we compare our approach to the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input .
Edit intent: coherence
Original:  Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), receives as input the adjacency matrix of a graph and a set of features for each node (here, we use only the degree)  and computes the approximate desired centrality rank for every node.
Revised:  Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), uses the adjacency matrix of a graph and a set of features for each node (here, we use only the degree) and computes the approximate desired centrality rank for every node.
Edit intent: clarity
Original:  Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), receives as input the adjacency matrix of a graph and a set of features for each node (here, we use only the degree)  and computes the approximate desired centrality rank for every node.
Revised:  Our proposed model, entitled Network Centrality Approximation using Graph Embedding (NCA-GE), receives as input the adjacency matrix of a graph and a set of features for each node (here, we use only the degree) as input and computes the approximate desired centrality rank for every node.
Edit intent: meaning-changed
Original:  Finally, we show that NCA-GE outperforms, in a variety of scenarios, the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input  .
Revised:  Finally, we show that NCA-GE outperforms, in a variety of scenarios, the state-of-the-art method that approximates centrality ranks using the degree and eigenvector centralities as input , where we show that the NCA-GE outperforms the former in a variety of scenarios .
Edit intent: meaning-changed
Original: An enhanced finite-difference time-domain (FDTD) algorithm is built to solve the transverse electric 2D Maxwell's equations with inhomogeneous dielectric media where the electric fields are discontinuous across the dielectric interface.
Revised: An efficient finite-difference time-domain (FDTD) algorithm is built to solve the transverse electric 2D Maxwell's equations with inhomogeneous dielectric media where the electric fields are discontinuous across the dielectric interface.
Edit intent: clarity
Original:  To resolve the instability issue of Yee's scheme (staircasing) caused by discontinuous permittivity across the interface, our algorithm revises the permittivities and makes some corrections to the scheme for the cells around the interface. It is also an improvement over the contour-path effective permittivity algorithm by including some extra terms in the formulas.
Revised:  It is an improvement over the contour-path effective permittivity algorithm by including some extra terms in the formulas.
Edit intent: coherence
Original:  The scheme is validated in solving the scattering of a dielectric cylinder with exact solution from Mie theory and is then compared with the above contour-path method, the usual staircasing and the volume-average method.
Revised:  The scheme is validated in solving the scattering of a dielectric cylinder with exact solution from Mie theory and is then compared with the above contour-path method, the usual staircase and the volume-average method.
Edit intent: fluency
Original: Conventional algorithms for finding low-rank canonical polyadic (CP) tensor decompositions are unwieldy for large sparse tensors. The CP decomposition can be computed by solving a sequence of overdetermined least problems with special Khatri-Rao structure.
Revised: Conventional algorithms for finding low-rank canonical polyadic tensor decomposition is useful in data analysis and can be computed by solving a sequence of overdetermined least problems with special Khatri-Rao structure.
Edit intent: meaning-changed
Original:  The CP decomposition can be computed by solving a sequence of overdetermined least problems with special Khatri-Rao structure. In this work, we present an application of randomized numerical linear algebra to fitting the CP decomposition of sparse tensors, solving a significantly smaller sampled least squares problem at each iteration with probabilistic guarantees on the approximation errors.
Revised:  The CP decomposition can be computed by solving a sequence of overdetermined least squares subproblems. Motivated by consideration of sparse tensors, solving a significantly smaller sampled least squares problem at each iteration with probabilistic guarantees on the approximation errors.
Edit intent: meaning-changed
Original:  In this work, we present an application of randomized numerical linear algebra to fitting the CP decomposition of sparse tensors, solving a significantly smaller sampled least squares problem at each iteration with probabilistic guarantees on the approximation errors.
Revised:  In this work, we present an application of randomized numerical linear algebra to fitting the CP decomposition of sparse tensors, we propose sketching each subproblem using leverage scores to select a subset of the rows, with probabilistic guarantees on the approximation errors.
Edit intent: meaning-changed
Original:  In this work, we present an application of randomized numerical linear algebra to fitting the CP decomposition of sparse tensors, solving a significantly smaller sampled least squares problem at each iteration with probabilistic guarantees on the approximation errors. Prior work has shown that sketching is effective in the dense case, but the prior approach cannot be applied to the sparse case because a fast Johnson-Lindenstrauss transform (e.g., using a fast Fourier transform) must be applied in each mode, causing the sparse tensor to become dense. Instead, we perform sketching through leverage score sampling, crucially relying on the fact that the structure of the Khatri-Rao product allows sampling from overestimates of the leverage scores without forming the full product or the corresponding probabilities.
Revised:  In this work, we present an application of randomized numerical linear algebra to fitting the CP decomposition of sparse tensors, solving a significantly smaller sampled least squares problem at each iteration with probabilistic guarantees of the solution accuracy. We randomly sample rows proportional to leverage score upper bounds that can be efficiently computed using the special Khatri-Rao product allows sampling from overestimates of the leverage scores without forming the full product or the corresponding probabilities.
Edit intent: meaning-changed
Original:  Instead, we perform sketching through leverage score sampling, crucially relying on the fact that the structure of the Khatri-Rao product allows sampling from overestimates of the leverage scores without forming the full product or the corresponding probabilities.
Revised:  Instead, we perform sketching through leverage score sampling, crucially relying on the fact that the structure of the Khatri-Rao subproblem structure inherent in tensor decomposition. Crucially, the leverage scores without forming the full product or the corresponding probabilities.
Edit intent: meaning-changed