wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We approximately factorize the diagonal blocks in these preconditioners using a near-linear-complexity multilevel ILUfactorization called HILUCSI, which is significantly more robust and more efficient than ILU(0)  . We apply the  preconditioners in right-preconditioned GMRES to solve the advection-diffusion equation in 3D using finite element and finite difference methods.
Revised:  We approximate the diagonal blocks in these preconditioners using a near-linear-complexity multilevel ILUfactorization called HILUCSI, which is significantly more robust and more efficient than ILU(0) . We apply the preconditioners in right-preconditioned GMRES to solve the advection-diffusion equation in 3D using finite element and finite difference methods.
Edit intent: clarity
Original:  We approximately factorize the diagonal blocks in these preconditioners using a near-linear-complexity multilevel ILUfactorization called HILUCSI, which is significantly more robust and more efficient than ILU(0)  . We apply the  preconditioners in right-preconditioned GMRES to solve the advection-diffusion equation in 3D using finite element and finite difference methods.
Revised:  We approximately factorize the diagonal blocks in these preconditioning techniques using an incomplete factorization with (near) linear complexity, such as multilevel ILU, ILU(0) . We apply the preconditioners in right-preconditioned GMRES to solve the advection-diffusion equation in 3D using finite element and finite difference methods.
Edit intent: clarity
Original:  We approximately factorize the diagonal blocks in these preconditioners using a near-linear-complexity multilevel ILUfactorization called HILUCSI, which is significantly more robust and more efficient than ILU(0)  . We apply the  preconditioners in right-preconditioned GMRES to solve the advection-diffusion equation in 3D using finite element and finite difference methods.
Revised:  We approximately factorize the diagonal blocks in these preconditioners using a near-linear-complexity multilevel ILUfactorization called HILUCSI, which is significantly more robust and more efficient than ILU(0) , or a multigrid method with an ILU-based smoother . We apply the preconditioners in right-preconditioned GMRES to solve the advection-diffusion equation in 3D using finite element and finite difference methods.
Edit intent: meaning-changed
Original:  We approximately factorize the diagonal blocks in these preconditioners using a near-linear-complexity multilevel ILUfactorization called HILUCSI, which is significantly more robust and more efficient than ILU(0)  . We apply the  preconditioners in right-preconditioned GMRES to solve the advection-diffusion equation in 3D using finite element and finite difference methods.
Revised:  We approximately factorize the diagonal blocks in these preconditioners using a near-linear-complexity multilevel ILUfactorization called HILUCSI, which is significantly more robust and more efficient than ILU(0) . We apply the block preconditioners in right-preconditioned GMRES to solve the advection-diffusion equation in 3D using finite element and finite difference methods.
Edit intent: clarity
Original:  We applied the principles of the Projective Consciousness Model (PCM) to derive a unified model of appraisal and social-affective perspective taking, and their role in the motivation of action  .
Revised:  We applied the principles of the Projective Consciousness Model (PCM) to derive a unified model of appraisal and social-affective perspective taking, and their role in the motivation of action , based on the concept of Field of Consciousness, central to the PCM .
Edit intent: meaning-changed
Original:  Using well-developed and understood concepts from simplicial geometry we are able to define a distance based on the persistent homology applied to the simplicial complexes that captures the model structure . In this way we can identify shared topological features of different models.
Revised:  Using well-developed and understood concepts from simplicial geometry we are able to define a distance based on the persistent homology applied to the simplicial complexes . The persistent homology represents the structure of the models, so in this way we can identify shared topological features of different models.
Edit intent: coherence
Original:  We then expand this, and move from a distance between simplicial complexes to studying equivalences between models in order to determine their functional relatedness .
Revised:  We then expand this, and move from a distance between simplicial complexes to studying equivalences between models in order to determine their conceptual similarity. We apply our methodology to demonstrate an equivalence between a positional-information model and a Turing-pattern model from developmental biology, constituting a novel observation for two classes of models that were previously regarded as unrelated .
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Original: In natural language processing (NLP), cross-lingual transfer learning is as essential as in-domain learning due to the unavailability of annotated resources for low-resource languages.
Revised: Syntactic parsing is a highly linguistic processing task whose parser requires training on treebanks from the expensive human annotation. As it is unlikely to obtain a treebank for every human language, in this work, we propose an effective cross-lingual transfer learning is as essential as in-domain learning due to the unavailability of annotated resources for low-resource languages.
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Original: In natural language processing (NLP), cross-lingual transfer learning is as essential as in-domain learning due to the unavailability of annotated resources for low-resource languages. In this paper, we use the ability of a pre-training task that extracts universal features without supervision. We add two pre-training tasks as the auxiliary task into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and  cross-lingual aspects.
Revised: In natural language processing (NLP), cross-lingual UD parsing framework for transferring parser from only one source monolingual treebank to any other target languages without treebank available. To reach satisfactory parsing accuracy among quite different languages, we introduce two language modeling tasks into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and cross-lingual aspects.
Edit intent: meaning-changed
Original:  We add two pre-training tasks as the auxiliary task into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and  cross-lingual aspects.
Revised:  We add two pre-training tasks as the auxiliary task into dependency parsing as multi-tasking . Assuming only unlabeled data from target languages plus the source treebank can be exploited together, we adopt a self-training strategy for further performance improvement in terms of our multi-task framework. Our proposed cross-lingual parsers are implemented for English, Chinese, and cross-lingual aspects.
Edit intent: meaning-changed
Original:  We add two pre-training tasks as the auxiliary task into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and  cross-lingual aspects.
Revised:  We add two pre-training tasks as the auxiliary task into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and 22 UD treebanks. The empirical study shows that our cross-lingual aspects.
Edit intent: meaning-changed
Original:  We add two pre-training tasks as the auxiliary task into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and  cross-lingual aspects. Moreover, inspired by the usefulness of self-training in cross-domain learning, we combine the traditional self-training and the two pre-training tasks. In this way, we can continuously extract universal features not only in training corpus but also in extra unannotated data and gain further improvement .
Revised:  We add two pre-training tasks as the auxiliary task into dependency parsing as multi-tasking , which improves the performance of the model in both in-domain and cross-lingual parsers yield promising results for all target languages, for the first time, approaching the parser performance which is trained in its own target treebank .
Edit intent: meaning-changed
Original:  instead, we demonstrate a bifurcation in which wealthier Western European countries have managed their mortality rate more successfully.A similar distinction exists in the United States, where Northeastern states have been the most successful in the country  .
Revised:  instead, we demonstrate a bifurcation in which wealthier Western European countries , greater than both Eastern European countries and U.S. states. Some structural similarity is observed between Europe and the United States, where Northeastern states have been the most successful in the country .
Edit intent: meaning-changed
Original:  In this paper, we address the challenge of learning motion policies to generate motions for execution of such tasks. Additionally, to encode multiple motion constraints and their synergies, we enforce structure in our motion policy . Specifically, the structure results from decomposing a motion policy into multiple subtask policies , whereby each subtask policy dictates a particular subtask behavior. By learning the subtask policies together in an end-to-end fashion, our formulation not only learns coordination between subtask behaviors, but also learns how to trade them off against default behaviors that may exist .
Revised:  In this paper, we propose to solve multi-task problems through learning structured policies from human demonstrations. Our structured policy is inspired by RMPflow, a framework for combining subtask policies on different spaces. The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned. We derive an end-to-end fashion, our formulation not only learns coordination between subtask behaviors, but also learns how to trade them off against default behaviors that may exist .
Edit intent: coherence
Original:  Furthermore, due to our choice of parameterization for the constituting subtask policies, our overall structured motion policy is guaranteed to generate stablemotions. To corroborate our theory, we also present qualitative and quantitative evaluations on multiple robotic tasks .
Revised:  Furthermore, due to our choice of parameterization for the constituting subtask policies, our overall structured motion policy is guaranteed to be stable. We validate the effectiveness of our proposed learning framework through qualitative and quantitative evaluations on multiple robotic tasks .
Edit intent: clarity
Original:  Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model.
Revised:  Experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model.
Edit intent: clarity
Original:  Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model.
Revised:  Preliminary experiments on text classification show promising results: the CKB imports knowledge from a single model and then exports the knowledge to a new model, achieving comparable performance with the original model. More interesting, we import the knowledge from multiple models to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model.
Edit intent: meaning-changed
Original:  Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model.
Revised:  Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to a single model, achieving a higher classification accuracy than the original RNN model.
Edit intent: clarity
Original:  Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model.
Revised:  Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher accuracy than the original RNN model.
Edit intent: fluency
Original:  Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original RNN model.
Revised:  Preliminary experiments on text classification show promising results: we first import the knowledge encoded in an RNN model and a CNN model to the knowledge base, from which the fused knowledge is exported back to the RNN model, achieving a higher classification accuracy than the original model.
Edit intent: fluency
Original: In this paper, we design and analyze  new methods based on Additive Average Schwarz--AAS Method introduced in 3 . The new methods are designed for elliptic problems with highly heterogeneous coefficients.
Revised: In this paper, we design and analyze two new methods based on Additive Average Schwarz--AAS Method introduced in 3 . The new methods are designed for elliptic problems with highly heterogeneous coefficients.
Edit intent: meaning-changed
Original: In this paper, we design and analyze  new methods based on Additive Average Schwarz--AAS Method introduced in 3 . The new methods are designed for elliptic problems with highly heterogeneous coefficients.
Revised: In this paper, we design and analyze new methods based on Additive Average Schwarz--AAS Method introduced in additive average Schwarz -- AAS method introduced in \mbox{%DIFAUXCMD MR1943457 . The new methods are designed for elliptic problems with highly heterogeneous coefficients.
Edit intent: meaning-changed
Original: In this paper, we design and analyze  new methods based on Additive Average Schwarz--AAS Method introduced in 3 . The new methods are designed for elliptic problems with highly heterogeneous coefficients.
Revised: In this paper, we design and analyze new methods based on Additive Average Schwarz--AAS Method introduced in 3 . The new methods design for elliptic problems with highly heterogeneous coefficients.
Edit intent: coherence
Original:  We prove that the condition number of NOSAS methods do not depend on the coefficients , and with a specific choice of the threshold of the eigenvalues, we can obtain condition numbers for the preconditioned system similar to the AAS method with constant coefficients .
Revised:  To achieve the low-rank, we solve a generalized eigenvalue problem in each subdomain. NOSAS have the minimum energy for a given rank of the coarse space. The condition number of NOSAS methods do not depend on the coefficients , and with a specific choice of the threshold of the eigenvalues, we can obtain condition numbers for the preconditioned system similar to the AAS method with constant coefficients .
Edit intent: meaning-changed
Original:  We prove that the condition number of NOSAS methods do not depend on the coefficients , and with a specific choice of the threshold of the eigenvalues, we can obtain condition numbers for the preconditioned system similar to the AAS method with constant coefficients .
Revised:  We prove that the condition number of the NOSAS methods does not depend on the coefficients , and with a specific choice of the threshold of the eigenvalues, we can obtain condition numbers for the preconditioned system similar to the AAS method with constant coefficients .
Edit intent: fluency
Original:  Additionally, the NOSAS methods have good parallelization properties , and the size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of islands or channels with high coefficient that touch the boundary of the subdomains .
Revised:  Additionally, the NOSAS methods have good parallelization properties . The size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of islands or channels with high coefficient that touch the boundary of the subdomains .
Edit intent: coherence
Original:  Additionally, the NOSAS methods have good parallelization properties , and the size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of islands or channels with high coefficient that touch the boundary of the subdomains .
Revised:  Additionally, the NOSAS methods have good parallelization properties , and the size of the global problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of islands or channels with high coefficient that touch the boundary of the subdomains .
Edit intent: clarity
Original:  Additionally, the NOSAS methods have good parallelization properties , and the size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of islands or channels with high coefficient that touch the boundary of the subdomains .
Revised:  Additionally, the NOSAS methods have good parallelization properties , and the size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain . It is only related to the number of islands or channels with high coefficient that touch the boundary of the subdomains .
Edit intent: coherence
Original:  Additionally, the NOSAS methods have good parallelization properties , and the size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of islands or channels with high coefficient that touch the boundary of the subdomains .
Revised:  Additionally, the NOSAS methods have good parallelization properties , and the size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of high-permeable islands that touch the boundary of the subdomains .
Edit intent: clarity
Original:  Additionally, the NOSAS methods have good parallelization properties , and the size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of islands or channels with high coefficient that touch the boundary of the subdomains .
Revised:  Additionally, the NOSAS methods have good parallelization properties , and the size of the coarse problem is equal to the total number of eigenvalues chosen in each subdomain and which might be related to the number of islands or channels with high coefficient that touch the subdomains' interface .
Edit intent: clarity
Original:  However, most of the existing driving simulators only contain a fixed set of maps and a limited number of configurations. As a result the deep models are prone to overfitting training scenarios. Furthermore it is difficult to assess how well the trained models generalize to unseen scenarios.
Revised:  However, most of the existing driving simulators only contain a fixed set of maps and a limited number of configurable settings. That might easily cause the overfitting issue for the learning-based driving systems as well as the lack of their generalization ability to unseen scenarios.
Edit intent: clarity
Original:  To better evaluate and improve the generalization of end-to-end driving, we introduce an open-ended and highly configurable driving simulator called PGDrive  . PGDrive first defines multiple basic road blockssuch as ramp, fork, and roundabout with configurable settings.
Revised:  To better evaluate and improve the generalization of end-to-end driving, we introduce an open-ended and highly configurable driving simulator called PGDrive , following a key feature of procedural generation. Diverse road networks are first generated by the proposed generation algorithm via sampling from elementary road blocks . PGDrive first defines multiple basic road blockssuch as ramp, fork, and roundabout with configurable settings.
Edit intent: meaning-changed
Original:  To better evaluate and improve the generalization of end-to-end driving, we introduce an open-ended and highly configurable driving simulator called PGDrive  . PGDrive first defines multiple basic road blockssuch as ramp, fork, and roundabout with configurable settings. Then a range of diverse maps can be assembled from those blocks with procedural generation, which are further turned into interactive environments.
Revised:  To better evaluate and improve the generalization of end-to-end driving, we introduce an open-ended and highly configurable driving simulator called PGDrive . Then they are turned into interactive environments.
Edit intent: coherence
Original:  Then a range of diverse maps can be assembled from those blocks with procedural generation, which are further turned into interactive environments. The experiments show that the driving agent trained by reinforcement learning on a small fixed set of maps generalizes poorly to unseen maps. We further validate that training with the increasing number of procedurally generated maps significantly improves the generalization of the agent across scenarios of different traffic densities and map structures.
Revised:  Then a range of diverse maps can be assembled from those blocks with procedural generation, which are further turned into interactive training environments where traffic flows of nearby vehicles with realistic kinematics are rendered. We validate that training with the increasing number of procedurally generated maps significantly improves the generalization of the agent across scenarios of different traffic densities and map structures.
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Original:  We further validate that training with the increasing number of procedurally generated maps significantly improves the generalization of the agent across scenarios of different traffic densities and map structures. Code is available at : URL
Revised:  We further validate that training with the increasing number of procedurally generated maps significantly improves the generalization of the agent across scenarios of different traffic densities and road networks. Many applications such as multi-agent traffic simulation and safe driving benchmark can be further built upon the simulator. To facilitate the joint research effort of end-to-end driving, we release the simulator and pretrained models at URL
Edit intent: meaning-changed