wanyu/IteraTeR_v2 huggingface dataset:
Original:   However, existing solutions are plagued by lack of any criteria distinguishing between random formula, matching  literally decimal expansion (i.e. approximation) and probable "exact" (or at least probable ) expression match in the sense of Occam's razor.
Revised:  However, existing solutions are plagued by lack of solid criteria distinguishing between random formula, matching literally decimal expansion (i.e. approximation) and probable "exact" (or at least probable ) expression match in the sense of Occam's razor.
Edit intent: clarity
Original:   However, existing solutions are plagued by lack of any criteria distinguishing between random formula, matching  literally decimal expansion (i.e. approximation) and probable "exact" (or at least probable ) expression match in the sense of Occam's razor.
Revised:  However, existing solutions are plagued by lack of any criteria distinguishing between random formula, matching literally decimal expansion and probable ''exact'' (the best ) expression match in the sense of Occam's razor.
Edit intent: clarity
Original:  In  article, such a criteria, working in statistical sense, are provided.
Revised:  In the article, such a criteria, working in statistical sense, are provided.
Edit intent: fluency
Original:  Recognition process can be viewed as (1) enumeration of all formulas in order of increasing Kolmogorov complexity  (2) random process with appropriate statistical distribution (3) compression of a decimal string.
Revised:  Recognition process can be viewed as (1) enumeration of all formulas in order of increasing Kolmogorov complexity K (2) random process with appropriate statistical distribution (3) compression of a decimal string.
Edit intent: clarity
Original:  Moreover , we discuss the (in)tractability of both problems with respect to special graph classes.
Revised:  Finally , we discuss the (in)tractability of both problems with respect to special graph classes.
Edit intent: coherence
Original:  Using techniques of convex optimisation, we present an efficient polynomial tuning algorithm for multi-parametric combinatorial specifications.
Revised:  Using techniques of convex optimisation, we present an efficient tuning algorithm for multi-parametric combinatorial specifications.
Edit intent: clarity
Original:  For a given combinatorial system of description lengthL with d tuning parametersand target size parameter value n, our algorithm runs in time O(d^{3.5 L \log n)}  . We demonstrate the effectiveness of our method on a series of practical examples, including rational, algebraic, and so-called P\'olya specifications.
Revised:  L \log n)} . We demonstrate the effectiveness of our method on a series of practical examples, including rational, algebraic, and so-called P\'olya specifications.
Edit intent: clarity
Original:  Generalizing the basic idea of Ma et . al. (2015), we argue that the frontier of the LOB (e.g., the best asking price) is the value function of a mean-field stochastic control problem, as the limiting version of a Bertrand-type competition among the liquidity providers.
Revised:  Generalizing the basic idea of Ma et al. (2015), we argue that the frontier of the LOB (e.g., the best asking price) is the value function of a mean-field stochastic control problem, as the limiting version of a Bertrand-type competition among the liquidity providers.
Edit intent: fluency
Original:  We argue that the value function can be used to obtain the equilibrium density function of the LOB, following the idea of Ma et . al. (2015).
Revised:  We argue that the value function can be used to obtain the equilibrium density function of the LOB, following the idea of Ma et al. (2015).
Edit intent: fluency
Original:  Second, it converges at a faster slower rate than the Rayleigh-Schr\"odinger expansion, i.e. fewer iterations are required to reach a given precision.
Revised:  Second, it converges at a faster rate than the Rayleigh-Schr\"odinger expansion, i.e. fewer iterations are required to reach a given precision.
Edit intent: clarity
Original:  Here we introduce a novel iterative algorithm to compute these eigenpairs based on a reformulation of the eigenvalue problem as an algebraic equation in complex projective space.
Revised:  Here we introduce a novel iterative algorithm to compute these eigenpairs based on fixed-point iteration for an algebraic equation in complex projective space.
Edit intent: clarity
Original:  In this work, we consider a wireless federated learning scenario where the nodes communicate to and from the master node via a wireless channel . Current and upcoming technologies such as 5G (and beyond) will operate mostly in a non-orthogonal multiple access (NOMA) mode where transmissions from the users occupy the same bandwidth and interfere at the access point.
Revised:  In this work, we consider a wireless federated learning scenario where K peer nodes communicate with a master node via a wireless channel . Current and upcoming technologies such as 5G (and beyond) will operate mostly in a non-orthogonal multiple access (NOMA) mode where transmissions from the users occupy the same bandwidth and interfere at the access point.
Edit intent: clarity
Original:  This iteration noise introduces a novel set of challenges that have not been previously studied in the literature.
Revised:  This iteration noise introduces a novel set of challenges that have not been previously explored in the literature.
Edit intent: clarity
Original:  It needs to be treated differently from the well-studied setting of noise or corruption in the dataset itself.
Edit intent: coherence
Original:  Finally, we show that inverting the robustness method to select modularity minimizing nodes solves the community deception problem in a scalable manner .
Revised:  Finally, we show that our group-centrality measure can be used as a greedy solution to the community deception problem in a scalable manner .
Edit intent: clarity
Original:  Finally, we show that inverting the robustness method to select modularity minimizing nodes solves the community deception problem in a scalable manner .
Revised:  Finally, we show that inverting the robustness method to select modularity minimizing nodes solves the community deception problem .
Edit intent: clarity
Original: Group-centrality is an emerging area in Network Science which seeks to quantify the importance of nodes within and between communities.
Revised: Community-aware centrality is an emerging area in Network Science which seeks to quantify the importance of nodes within and between communities.
Edit intent: clarity
Original: Group-centrality is an emerging area in Network Science which seeks to quantify the importance of nodes within and between communities.
Revised: Group-centrality is an emerging research area in network science concerned with the importance of nodes within and between communities.
Edit intent: clarity
Original:  We further find that the social media communication network demonstrates extreme robustness to group-centrality attacks, suggesting that user-based interventions to mitigate misinformation diffusion will be ineffective.
Revised:  We further find that the social media communication networks, which exhibit extreme robustness to group-centrality attacks, suggesting that user-based interventions to mitigate misinformation diffusion will be ineffective.
Edit intent: clarity
Original:  Finally, we show that our group-centrality measure can be used as a greedy solution to the community deception problem .
Revised:  Finally, we demonstrate that modularity vitality provides a new approach to community-deception .
Edit intent: clarity
Original:  Perceptual capabilities are therefore heightened  enabling, for instance, the well known "cocktail party" and McGurk effects, i.e.  speech disambiguation from a panoply of sound signals.
Revised:  Perceptual capabilities are therefore heightened , enabling, for instance, the well known "cocktail party" and McGurk effects, i.e. speech disambiguation from a panoply of sound signals.
Edit intent: fluency
Original:  Perceptual capabilities are therefore heightened  enabling, for instance, the well known "cocktail party" and McGurk effects, i.e.  speech disambiguation from a panoply of sound signals.
Revised:  Perceptual capabilities are therefore heightened enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e. speech disambiguation from a panoply of sound signals.
Edit intent: fluency
Original:  Perceptual capabilities are therefore heightened  enabling, for instance, the well known "cocktail party" and McGurk effects, i.e.  speech disambiguation from a panoply of sound signals.
Revised:  Perceptual capabilities are therefore heightened enabling, for instance, the well known "cocktail party" and McGurk effects, i.e. , speech disambiguation from a panoply of sound signals.
Edit intent: fluency
Original:  Furthermore, Neuroscience has successfully identified the superior colliculus region in the brain as the one responsible for this modality fusion, with a handful of biological models having been proposed to approach its underlying neurophysiological process.
Revised:  Furthermore, neuroscience has successfully identified the superior colliculus region in the brain as the one responsible for this modality fusion, with a handful of biological models having been proposed to approach its underlying neurophysiological process.
Edit intent: fluency
Original:  To be specific,  first level tackles high combinatorial action space problem that selects a subset items from the large item space;
Revised:  To be specific, the first level tackles high combinatorial action space problem that selects a subset items from the large item space;
Edit intent: fluency
Original:  Although previous work has typically focused on its normalization component, BatchNorm also adds two per-feature trainable parameters : a coefficient and a bias .
Revised:  Although previous work has typically focused on its normalization component, BatchNorm also adds two per-feature trainable parameters - a coefficient and a bias .
Edit intent: fluency
Original:  However, the role and expressive power of these parameters remains unclear.
Revised:  However, the role and expressive power remain unclear.
Edit intent: clarity
Original:  For example, a sufficiently deep ResNet reaches 83\% accuracy on CIFAR-10  in this configuration .
Revised:  For example, sufficiently deep ResNets reach 82\% ( CIFAR-10 in this configuration .
Edit intent: clarity
Original:  Interestingly, BatchNorm achieves this performance in part by naturally learning to disable around a third of the random features without any changes to the training objective .
Revised:  Interestingly, BatchNorm achieves this performance in part by naturally learning to disable around a third of the random features .
Edit intent: clarity
Original:  To study this question , we investigate the performance achieved when training only these parameters  and freezing all others at their random initializations.
Revised:  To study this question , we investigate the performance achieved when training only these parameters and freezing all weights at their random initializations.
Edit intent: clarity
Original:  We find that doing so leads to surprisingly high performance  .
Revised:  Doing so leads to surprisingly high performance .
Edit intent: coherence
Original:  For example, sufficiently deep ResNets reach 82\% (CIFAR-10) and 32\% (ImageNet, top-5) accuracy in this configuration, far higher than when training an equivalent number of randomly chosen parameters from elsewhere in the network.
Revised:  For example, sufficiently deep ResNets reach 82\% (CIFAR-10) and 32\% (ImageNet, top-5) accuracy in this configuration, far higher than when training an equivalent number of randomly chosen parameters elsewhere in the network.
Edit intent: clarity
Original:  Not only do these results highlight the under-appreciated role of the affine parameters in BatchNorm , but - in a broader sense - they characterize the expressive power of neural networks constructed simply by shifting and rescaling random features.
Revised:  Not only do these results highlight the expressive power of affine parameters in BatchNorm , but - in a broader sense - they characterize the expressive power of neural networks constructed simply by shifting and rescaling random features.
Edit intent: clarity
Original:  Not only do these results highlight the under-appreciated role of the affine parameters in BatchNorm , but - in a broader sense - they characterize the expressive power of neural networks constructed simply by shifting and rescaling random features.
Revised:  Not only do these results highlight the under-appreciated role of the affine parameters in deep learning , but - in a broader sense - they characterize the expressive power of neural networks constructed simply by shifting and rescaling random features.
Edit intent: clarity
Original:  The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019.
Revised: Motivation: The coronavirus disease 2019 (COVID-19) caused by a new type of coronavirus has been emerging from China and led to thousands of death globally since December 2019.
Edit intent: coherence
Original:  Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target -ligand interactions represents a key challenge .
Revised:  Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target-ligand interactions represents a key challenge .
Edit intent: fluency
Original:  Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target -ligand interactions represents a key challenge .
Revised:  Despite many groups have engaged in studying the newly emerged virus and searching for the treatment of COVID-19, the understanding of the COVID-19 target -ligand interactions represents a key chal-lenge .
Edit intent: fluency
Original: Reinforcement learningmethods that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches.
Revised: In reinforcement learning, agents that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches.
Edit intent: clarity
Original:  We introduce a novel deep mixture model formulation for learning a state-dependent prior over source task dynamics that matches the target dynamics using only state trajectories obtained while learning the target policy .
Revised:  We introduce a novel deep mixture model formulation for learning state-dependent prior over source task dynamics that matches the target dynamics using only state trajectories obtained while learning the target policy .
Edit intent: fluency
Original:  We introduce a novel deep mixture model formulation for learning a state-dependent prior over source task dynamics that matches the target dynamics using only state trajectories obtained while learning the target policy .
Revised:  We introduce a novel deep mixture model formulation for learning a state-dependent beliefs over source task dynamics that matches the target dynamics using only state trajectories obtained while learning the target policy .
Edit intent: clarity
Original:  We introduce a novel deep mixture model formulation for learning a state-dependent prior over source task dynamics that matches the target dynamics using only state trajectories obtained while learning the target policy .
Revised:  We introduce a novel deep mixture model formulation for learning a state-dependent prior over source task dynamics that match the target dynamics using only state trajectories obtained while learning the target policy .
Edit intent: fluency
Original:  We introduce a novel deep mixture model formulation for learning a state-dependent prior over source task dynamics that matches the target dynamics using only state trajectories obtained while learning the target policy .
Revised:  We introduce a novel deep mixture model formulation for learning a state-dependent prior over source task dynamics that matches the target dynamics using state trajectories collected from the target task .
Edit intent: clarity
Original:  The mixture model is easy to train and interpret, is compatible with most reinforcement learning algorithms, and complements existing work by leveraging knowledge of source dynamics rather than Q-values .
Revised:  The mixture model is easy to train and interpret, is compatible with most learning algorithms .
Edit intent: coherence
Original:  We then show how the trained mixture model can be incorporated into standard policy reuse frameworks, and demonstrate its effectiveness on benchmarks from OpenAI-Gym.
Revised:  We then show how this model can be incorporated into standard policy reuse frameworks, and demonstrate its effectiveness on benchmarks from OpenAI-Gym.
Edit intent: clarity
Original:  The asymptotics of the Black-Scholes implied volatility for fixed-maturity, large-strike and fixed-maturity, small-strike regimes are  studied.
Revised:  The asymptotics of the Black-Scholes implied volatility for fixed-maturity, large-strike and fixed-maturity, small-strike regimes are also studied.
Edit intent: clarity
Original: Association Rule Mining is a data mining method for discovering the interesting relations between  attributes in a huge transaction database.
Revised: Association Rule Mining is a data mining method for discovering the interesting relations between the attributes in a huge transaction database.
Edit intent: fluency
Original:  Typically, algorithms for association rule mining generate a huge number of association rules, from which it is hard to extract structured knowledge and automatically present this in a form that would be suitable for the user.
Revised:  Typically, algorithms for Association Rule Mining generate a huge number of association rules, from which it is hard to extract structured knowledge and automatically present this in a form that would be suitable for the user.
Edit intent: fluency
Original:  Typically, algorithms for association rule mining generate a huge number of association rules, from which it is hard to extract structured knowledge and automatically present this in a form that would be suitable for the user.
Revised:  Typically, algorithms for association rule mining generate a huge number of association rules, from which it is hard to extract structured knowledge and present this automatically in a form that would be suitable for the user.
Edit intent: fluency
Original:  In the hope of widening its applicability domain, the aim of this study is to develop a method for the automatic creation of metro maps of information obtained by association rule mining .
Revised:  In the hope of widening its applicability domain, the aim of this study is to develop a method for automatic creation of metro maps of information obtained by association rule mining .
Edit intent: fluency
Original:  Visualizing the resulted metro maps not only justifies the fact this is a suitable tool for presenting structured knowledge hidden in data, but also that they can even tell stories to users.
Revised:  Visualizing the resulted metro maps not only justifies that this is a suitable tool for presenting structured knowledge hidden in data, but also that they can even tell stories to users.
Edit intent: coherence
Original:  Visualizing the resulted metro maps not only justifies the fact this is a suitable tool for presenting structured knowledge hidden in data, but also that they can even tell stories to users.
Revised:  Visualizing the resulted metro maps not only justifies the fact this is a suitable tool for presenting structured knowledge hidden in data, but also that they can tell stories to users.
Edit intent: coherence
Original:  Key ingredients of the study are to manage cases considering the usually computationally " forbidden" combination of poorly conditioned equation system matrices due to challenging geometries, optimal control searches with iterative methods, slow convergence to system solutions on deterministic and non--deterministic level, and expensive remeshing due to geometrical changes.
Revised:  Key ingredients of the study are to manage cases considering the usually computationally `` forbidden" combination of poorly conditioned equation system matrices due to challenging geometries, optimal control searches with iterative methods, slow convergence to system solutions on deterministic and non--deterministic level, and expensive remeshing due to geometrical changes.
Edit intent: fluency
Original:  The variational discretization concept is adopted, optimal error estimates for the state, adjoint state  and control are derived that confirm the efficiency of the cut finite element method in challenging geometries.
Revised:  The variational discretization concept is adopted, optimal error estimates for the state, adjoint state , and control are derived that confirm the efficiency of the cut finite element method in challenging geometries.
Edit intent: fluency
Original: A new algorithm for deciding the satisfiability of polynomial formulas over the real is proposed.
Revised: A new algorithm for deciding the satisfiability of polynomial formulas over the reals is proposed.
Edit intent: fluency
Original:  Recently, due to the capability in feature extraction, a series of deep learning methods have brought important crucial improvement for SISR. However, we observe that no matter how deeper the networks are designed, they usually do not have good generalization ability, which leads to the fact that almost all of existing SR methods have poor performances on restoration of the week texture details.
Revised:  Recently, due to the capability in feature extraction, a series of deep learning methods have brought important crucial improvement for SISR. However, we observe that no matter how deeper the networks are designed, they usually do not have good generalization ability, which leads to the fact that almost all of existing SR methods have poor performances on restoration of the weak texture details.
Edit intent: fluency
Original:  To solve these problems, we propose a week texture information map guided image super-resolution with deep residual networks.
Revised:  To solve these problems, we propose a weak texture information map guided image super-resolution with deep residual networks.
Edit intent: fluency
Original:  The daily data must be compared with the obtained fits  to verify if the spreading is under control.
Revised:  The daily data must be compared with the obtained fits , to verify if the spreading is under control.
Edit intent: fluency
Original:  From our analysis it appears that the spreading is reaching saturation in China and Singapore , due to the strong containment policy of the national governments.
Revised:  From our analysis it appears that the spreading is reaching saturation in China , due to the strong containment policy of the national governments.
Edit intent: coherence
Original:   For South Korea and Italy, instead, the data of the first week of March on the new infections , will be crucial to understand if the saturation will be reached for lower or higher numbers of infected individuals .
Revised:  For South Korea and Italy, instead, the next data on new infections will be crucial to understand if the saturation will be reached for lower or higher numbers of infected individuals .
Edit intent: clarity
Original:  From our analysis it appears that the spreading is reaching saturation in China, due to the strong containment policy of the national governments .
Revised:  From our analysis it appears that the spreading reached saturation in China, due to the strong containment policy of the national governments .
Edit intent: clarity