wanyu/IteraTeR_full_sent huggingface dataset:
Original:  As a rough magnitude estimate, we obtain that a relative change in R_0, with variations in pollution levels observed in the USA, is typically ~30 \%, going up to 70 \%, which further underscores the importance of pollution in COVID-19 transmissibility.
Revised:  As a rough magnitude estimate, we obtain that a relative change in R_0, with variations in pollution levels observed in the USA, is typically ~30 \%, which further underscores the importance of pollution in COVID-19 transmissibility.
Edit intent: clarity
Original:  Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution beyond the diffraction limit , but also enhances suppressed visual details between the imaging planes and removes imaging artifacts.
Revised:  Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution , but also enhances suppressed visual details between the imaging planes and removes imaging artifacts.
Edit intent: clarity
Original:  Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution beyond the diffraction limit , but also enhances suppressed visual details between the imaging planes and removes imaging artifacts.
Revised:  Using fluorescence confocal microscopy and light-sheet microscopy, we demonstrate that the trained network not only enhances axial resolution beyond the diffraction limit , but also restores suppressed visual details between the imaging planes and removes imaging artifacts.
Edit intent: clarity
Original:  Other properties of interest include local optima, level sets, integrals, or graph-structured information induced by f. Often, we can find an algorithm A to compute the desired property, but it may require far more than T queries to execute.
Revised:  Other properties of interest include local optima, level sets, integrals, or graph-structured information induced by f. Often, we can find an algorithm \mathcal{A to compute the desired property, but it may require far more than T queries to execute.
Edit intent: meaning-changed
Original:  In this paper, we perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between January 2015 and December 2020.
Revised:  In this paper, we perform a longitudinal study of the migration to public clouds among universities in the U.S. and Europe, as well as institutions listed in the Times Higher Education (THE) Top100 between January 2015 and June 2021.
Edit intent: meaning-changed
Original:  QGANs exploit the properties of quaternion algebra, e.g., the Hamilton product for convolutions. This allows to process channels as a single entity and capture internal latent relations, while reducing by a factor of 4 the overall number of parameters.
Revised:  QGANs exploit the properties of quaternion algebra, e.g., the Hamilton product , that allows to process channels as a single entity and capture internal latent relations, while reducing by a factor of 4 the overall number of parameters.
Edit intent: coherence
Original:  Results show that QGANs are able to generate visually pleasing images and to obtain better FID scores with respect to their real-valued GANs  . Furthermore, QGANs save up to 75\% of the training parameters.
Revised:  Results show that QGANs are able to obtain better FID scores with respect to their real-valued GANs . Furthermore, QGANs save up to 75\% of the training parameters.
Edit intent: clarity
Original:  Results show that QGANs are able to generate visually pleasing images and to obtain better FID scores with respect to their real-valued GANs  . Furthermore, QGANs save up to 75\% of the training parameters.
Revised:  Results show that QGANs are able to generate visually pleasing images and to obtain better FID scores than real-valued GANs . Furthermore, QGANs save up to 75\% of the training parameters.
Edit intent: clarity
Original:  Results show that QGANs are able to generate visually pleasing images and to obtain better FID scores with respect to their real-valued GANs  . Furthermore, QGANs save up to 75\% of the training parameters.
Revised:  Results show that QGANs are able to generate visually pleasing images and to obtain better FID scores with respect to their real-valued GANs and to generate visually pleasing images . Furthermore, QGANs save up to 75\% of the training parameters.
Edit intent: coherence
Original:  In particular, an attacker can disrupt the integrity and availability of a model by simply reordering training batches, with no knowledge about either the model or the dataset. Indeed, the attacks presented here are not specific to the model or dataset, but rather target the stochastic nature of modern learning procedures. We extensively evaluate our attacks to find that the adversary can disrupt model training and even introduce backdoors. For integrity we find that the attacker can either stop the model from learning, or poison it to learn behaviours specified by the attacker.
Revised:  In particular, we find that the attacker can either stop the model from learning, or poison it to learn behaviours specified by the attacker.
Edit intent: coherence
Original:  Such attacks have a long-term impact in that they decrease model performance hundreds of epochs after the attack took place. Reordering is a very powerful adversarial paradigm in that it removes the assumption that an adversary must inject adversarial data points or perturbations to perform training-time attacks . It reminds us that stochastic gradient descent relies on the assumption that data are sampled at random. If this randomness is compromised, then all bets are off .
Revised:  Indeed, the attacks presented here are not specific to the model or dataset, but rather target the stochastic nature of modern learning procedures. We extensively evaluate our attacks on computer vision and natural language benchmarks to find that the adversary can disrupt model training and even introduce backdoors .
Edit intent: meaning-changed
Original: We present a self-improving, neural tree expansion method for multi-robot online planning in non-cooperative environments, where each robot tries to maximize its cumulative reward while interacting with other self-interested robots.
Revised: We present a self-improving, Neural Tree Expansion (NTE) method for multi-robot online planning in non-cooperative environments, where each robot tries to maximize its cumulative reward while interacting with other self-interested robots.
Edit intent: meaning-changed
Original:  Our method has three interacting components: (i) a centralized, perfect-information `expert' Monte Carlo Tree Search (MCTS) with large computation resources that provides expert demonstrations, (ii) a decentralized, partial-information `learner' MCTS with small computation resources that runs in real-time and provides self-play examples, and (iii) policy & value neural networks that are trained with the expert demonstrations and bias both the expert and the learner tree growth.
Revised:  Our method has three interacting components: (i) a centralized, perfect-information "expert" Monte Carlo Tree Search (MCTS) with large computation resources that provides expert demonstrations, (ii) a decentralized, partial-information `learner' MCTS with small computation resources that runs in real-time and provides self-play examples, and (iii) policy & value neural networks that are trained with the expert demonstrations and bias both the expert and the learner tree growth.
Edit intent: fluency
Original:  Our method has three interacting components: (i) a centralized, perfect-information `expert' Monte Carlo Tree Search (MCTS) with large computation resources that provides expert demonstrations, (ii) a decentralized, partial-information `learner' MCTS with small computation resources that runs in real-time and provides self-play examples, and (iii) policy & value neural networks that are trained with the expert demonstrations and bias both the expert and the learner tree growth.
Revised:  Our method has three interacting components: (i) a centralized, perfect-information `expert' Monte Carlo Tree Search (MCTS) with large computation resources that provides expert demonstrations, (ii) a decentralized, partial-information "learner" MCTS with small computation resources that runs in real-time and provides self-play examples, and (iii) policy & value neural networks that are trained with the expert demonstrations and bias both the expert and the learner tree growth.
Edit intent: fluency
Original: We investigate the solution landscapes of the Onsager free-energy functional with different potential kernels, including the dipolar potential, the Maier--Saupe potential, the coupled dipolar/ Maier--Saupe potential, and the Onsager potential.
Revised: We investigate critical points of the Onsager free-energy functional with different potential kernels, including the dipolar potential, the Maier--Saupe potential, the coupled dipolar/ Maier--Saupe potential, and the Onsager potential.
Edit intent: clarity
Original: We investigate the solution landscapes of the Onsager free-energy functional with different potential kernels, including the dipolar potential, the Maier--Saupe potential, the coupled dipolar/ Maier--Saupe potential, and the Onsager potential.
Revised: We investigate the solution landscapes of the Onsager free-energy model on a sphere with different potential kernels, including the dipolar potential, the Maier--Saupe potential, the coupled dipolar/ Maier--Saupe potential, and the Onsager potential.
Edit intent: clarity
Original: We investigate the solution landscapes of the Onsager free-energy functional with different potential kernels, including the dipolar potential, the Maier--Saupe potential, the coupled dipolar/ Maier--Saupe potential, and the Onsager potential.
Revised: We investigate the solution landscapes of the Onsager free-energy functional with different potential kernels, including the dipolar potential, the Maier-Saupe potential, the coupled dipolar/ Maier--Saupe potential, and the Onsager potential.
Edit intent: fluency
Original: We investigate the solution landscapes of the Onsager free-energy functional with different potential kernels, including the dipolar potential, the Maier--Saupe potential, the coupled dipolar/ Maier--Saupe potential, and the Onsager potential.
Revised: We investigate the solution landscapes of the Onsager free-energy functional with different potential kernels, including the dipolar potential, the Maier--Saupe potential, the coupled dipolar/ Maier-Saupe potential, and the Onsager potential.
Edit intent: fluency
Original:  A uniform sampling method is implemented for the discretization of the Onsager functional, and the solution landscape of the Onsager functional is constructed by using the saddle dynamics coupled with downward/upward search algorithms.
Revised:  A uniform sampling method is implemented for the discretization of the Onsager model, and solution landscapes of the Onsager functional is constructed by using the saddle dynamics coupled with downward/upward search algorithms.
Edit intent: clarity
Original:  A uniform sampling method is implemented for the discretization of the Onsager functional, and the solution landscape of the Onsager functional is constructed by using the saddle dynamics coupled with downward/upward search algorithms.
Revised:  A uniform sampling method is implemented for the discretization of the Onsager functional, and the solution landscape of the Onsager model are constructed using saddle dynamics coupled with downward/upward search algorithms.
Edit intent: clarity
Original:  For the coupled dipolar/ Maier--Saupe potential, the solution landscape shows a novel non-axisymmetric critical point, named as tennis, which exists for a wide range of parameters.
Revised:  For the coupled dipolar/ Maier-Saupe potential, the solution landscape shows a novel non-axisymmetric critical point, named as tennis, which exists for a wide range of parameters.
Edit intent: fluency
Original:  For the coupled dipolar/ Maier--Saupe potential, the solution landscape shows a novel non-axisymmetric critical point, named as tennis, which exists for a wide range of parameters.
Revised:  For the coupled dipolar/ Maier--Saupe potential, the solution landscape shows a novel non-axisymmetric critical point, named tennis, which exists for a wide range of parameters.
Edit intent: fluency
Original:  We further demonstrate various non-axisymmetric critical points in the Onsager functional with the Onsager potential, including square, hexagon, octahedral, cubic, quarter, icosahedral, and dodecahedral states.
Revised:  We further demonstrate various non-axisymmetric critical points in the Onsager model with the Onsager potential, including square, hexagon, octahedral, cubic, quarter, icosahedral, and dodecahedral states.
Edit intent: clarity
Original:  The  solution landscape provides an efficient approach to show the global structures as well as the bifurcations of critical points, which can not only verify the previous analytic results but also propose several conjectures based on the numerical findings.
Revised:  The bifurcation diagram is presented to show the primary and secondary bifurcations of the isotropic state and reveal the emergence of the critical points. The solution landscape provides an efficient approach to show the global structures as well as the bifurcations of critical points, which can not only verify the previous analytic results but also propose several conjectures based on the numerical findings.
Edit intent: meaning-changed
Original:  The  solution landscape provides an efficient approach to show the global structures as well as the bifurcations of critical points, which can not only verify the previous analytic results but also propose several conjectures based on the numerical findings.
Revised:  The solution landscape provides an efficient approach to show the global structure of the model system as well as the bifurcations of critical points, which can not only verify the previous analytic results but also propose several conjectures based on the numerical findings.
Edit intent: clarity
Original: The aim of this work is to present a brief report concerning the various aspects of the Reduced Basis Method within Fluid-Structure Interaction problems. The idea is to adopt two different proceduresand apply them to the same test case .
Revised: The aim of this work is to present a brief report concerning the various aspects of the Reduced Basis Method within Fluid-Structure Interaction (FSI) problems, namely a monolithic and a partitioned approach. We provide the details of implementation of two reduction procedures, and we then apply them to the same test case .
Edit intent: meaning-changed
Original:  The idea is to adopt two different proceduresand apply them to the same test case . First we preform a reduction technique that is based on a monolithic procedure , where we therefore solve all at once the fluid and the solid problem.
Revised:  The idea is to adopt two different proceduresand apply them to the same test case of interest. We first implement a reduction technique that is based on a monolithic procedure , where we therefore solve all at once the fluid and the solid problem.
Edit intent: clarity
Original:  First we preform a reduction technique that is based on a monolithic procedure , where we therefore solve all at once the fluid and the solid problem. Then we present an alternative reduction technique , that is based on a partitioned (or segregated) procedure instead: here the fluid and the solid problem are solved separately and then coupled through a fixed point strategy.
Revised:  First we preform a reduction technique that is based on a monolithic procedure , where we therefore solve all at once the fluid and the solid problems all at once. We then present another reduction technique that is based on a partitioned (or segregated) procedure instead: here the fluid and the solid problem are solved separately and then coupled through a fixed point strategy.
Edit intent: clarity
Original:  The toy problem that we consider is  the Turek-Hron benchmark test case, with a fluid Reynolds number Re = 100, which is known to lead to the formation of Karman vortexes in the fluid, and a periodically oscillating behaviour in the structure.
Revised:  The toy problem that we consider is based on the Turek-Hron benchmark test case, with a fluid Reynolds number Re = 100, which is known to lead to the formation of Karman vortexes in the fluid, and a periodically oscillating behaviour in the structure.
Edit intent: clarity
Original:  The toy problem that we consider is  the Turek-Hron benchmark test case, with a fluid Reynolds number Re = 100, which is known to lead to the formation of Karman vortexes in the fluid, and a periodically oscillating behaviour in the structure.
Revised:  The toy problem that we consider is the Turek-Hron benchmark test case, with a fluid Reynolds number Re = 100.
Edit intent: coherence
Original:  Our analysis points out the role of individual strategy-proofness in deriving the preference reversal property, giving back to the latter its original individual nature (cfr. Eliaz \mbox{%DIFAUXCMD E Berga and Moreno \mbox{%DIFAUXCMD BM , have recently derived preference reversal from group strategy-proofness of social choice functions on strict preferences domains if the range has no more than three elements.
Revised:  Interestingly, Berga and Moreno \mbox{%DIFAUXCMD BM , have recently derived preference reversal from group strategy-proofness of social choice functions on strict preferences domains if the range has no more than three elements.
Edit intent: coherence
Original:  Our analysis points out the role of individual strategy-proofness in deriving the preference reversal property, giving back to the latter its original individual nature (cfr. Eliaz \mbox{%DIFAUXCMD E Berga and Moreno \mbox{%DIFAUXCMD BM , have recently derived preference reversal from group strategy-proofness of social choice functions on strict preferences domains if the range has no more than three elements.
Revised:  Our analysis points out the role of individual strategy-proofness in deriving the preference reversal property, giving back to the latter its original individual nature (cfr. Eliaz \mbox{%DIFAUXCMD E Berga and Moreno (2020) , have recently derived preference reversal from group strategy-proofness of social choice functions on strict preferences domains if the range has no more than three elements.
Edit intent: meaning-changed
Original:  We extend  and at the same time simplify this result. Moreover we show that the difficulties they highlighted relaxing the assumption on the cardinality of the range, disappear under a proper assumption on the domain.
Revised:  We extend and at the same time simplify it. Our analysis points out the role of individual strategy-proofness in deriving the preference reversal property, giving back to the latter its original individual nature (cfr. Eliaz, 2004). Moreover, we show that the difficulties they highlighted relaxing the assumption on the cardinality of the range, disappear under a proper assumption on the domain.
Edit intent: meaning-changed
Original: To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximization problem presenting the first-order adversaries embedded within the outer minimization of the training loss.
Revised: To tackle the susceptibility of deep neural networks to examples, the adversarial training has been proposed which provides a notion of security through an inner maximization problem presenting the first-order adversaries embedded within the outer minimization of the training loss.
Edit intent: fluency
Original: To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximization problem presenting the first-order adversaries embedded within the outer minimization of the training loss.
Revised: To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of robust through an inner maximization problem presenting the first-order adversaries embedded within the outer minimization of the training loss.
Edit intent: clarity
Original: To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximization problem presenting the first-order adversaries embedded within the outer minimization of the training loss.
Revised: To tackle the susceptibility of deep neural networks to adversarial examples, the adversarial training has been proposed which provides a notion of security through an inner maximization problem presenting the first-order embedded within the outer minimization of the training loss.
Edit intent: clarity
Original:  To generalize the adversarial robustness over different perturbation types, the adversarial training method has been augmented with the improved inner maximization presenting a union of multiple perturbations e.g., various \ell_p norm-bounded perturbations . However, the improved inner maximization only enjoys limited flexibility in terms of the allowable perturbation types. In this work, through a gating mechanism, we assemble a set of expert networks, each one either adversarially trained to deal with a particular perturbation type or normally trained for boosting accuracy on clean data. The gating module assigns weights dynamically to each expert to achieve superior accuracy under various data types e.g., adversarial examples, adverse weather perturbations, and clean input. In order to deal with the obfuscated gradients issue, the training of the gating module is conducted together with fine-tuning of the last fully connected layers of expert networks through adversarial training approach. Using extensive experiments, we show that our Mixture of Robust Experts (MoRE) approach enables flexible integration of a broad range of robust experts with superior performance .
Revised:  To generalize the adversarial robustness over different perturbation types, the adversarial training method has been augmented with the improved inner maximization presenting a union of multiple perturbations e.g., various \ell_p norm-bounded perturbations .
Edit intent: meaning-changed
Original:  We find a negative relationship between recent levels of misinformation and daily vaccination rates. Consistently, negative opinions toward vaccines are correlated with misinformation . Vaccine hesitancy is also strongly associated with Republican vote share, but the association between vaccine outcomes and misinformation remains significant when accounting for political as well as demographic and socioeconomic factors.
Revised:  We find a negative relationship between misinformation and vaccination uptake rates. Online misinformation is also correlated with vaccine hesitancy rates taken from survey data. Associations between vaccine outcomes and misinformation remains significant when accounting for political as well as demographic and socioeconomic factors.
Edit intent: meaning-changed
Original:  Consistently, negative opinions toward vaccines are correlated with misinformation . Vaccine hesitancy is also strongly associated with Republican vote share, but the association between vaccine outcomes and misinformation remains significant when accounting for political as well as demographic and socioeconomic factors.
Revised:  Consistently, negative opinions toward vaccines are correlated with misinformation . Vaccine hesitancy is also strongly associated with Republican vote share, but the association between vaccine outcomes and misinformation remain significant when accounting for political as well as demographic and socioeconomic factors.
Edit intent: fluency