wanyu/IteraTeR_v2 huggingface dataset:
Original:  We employ an AREPO-type approach, which rapidly rebuilds a new high quality mesh exploiting the previous one, butrearranging the element shapes and neighbors in order to guarantee that the mesh evolution is robust even for vortex flows and for very long computational times.
Revised:  We employ an AREPO-type approach, which rapidly rebuilds a new high quality mesh exploiting the previous one, butrearranging the element shapes and neighbors in order to guarantee that the mesh evolution is robust even for vortex flows and very long simulation times.
Edit intent: clarity
Original:  The old and new Voronoi elements associated to the same generator point are connected in space--time to construct closed space-time control volumes, whose bottom and top faces may be polygons with a different number of sides.
Revised:  The old and new Voronoi elements associated to the same generator are connected to construct closed space-time control volumes, whose bottom and top faces may be polygons with a different number of sides.
Edit intent: clarity
Original:  The old and new Voronoi elements associated to the same generator point are connected in space--time to construct closed space-time control volumes, whose bottom and top faces may be polygons with a different number of sides.
Revised:  The old and new Voronoi elements associated to the same generator point are connected in space--time to construct closed space--time control volumes, whose bottom and top faces may be polygons with a different number of sides.
Edit intent: fluency
Original:  We also need to incorporate some degenerate space--time sliver elements, which are needed in order to fill the space-time holes that arise because of the topology changes.
Revised:  We also incorporate degenerate space--time sliver elements, which are needed in order to fill the space-time holes that arise because of the topology changes.
Edit intent: coherence
Original:  We also need to incorporate some degenerate space--time sliver elements, which are needed in order to fill the space-time holes that arise because of the topology changes.
Revised:  We also need to incorporate some degenerate space--time sliver elements, needed to fill the space-time holes that arise because of the topology changes.
Edit intent: coherence
Original:  We also need to incorporate some degenerate space--time sliver elements, which are needed in order to fill the space-time holes that arise because of the topology changes.
Revised:  We also need to incorporate some degenerate space--time sliver elements, which are needed in order to fill the space--time holes that arise because of the topology changes.
Edit intent: fluency
Original:  We also need to incorporate some degenerate space--time sliver elements, which are needed in order to fill the space-time holes that arise because of the topology changes.
Revised:  We also need to incorporate some degenerate space--time sliver elements, which are needed in order to fill the space-time holes that arise because of topology changes.
Edit intent: fluency
Original:  The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Revised:  The final ALE FV-DG scheme is obtained by a redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Edit intent: clarity
Original:  The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Revised:  The final ALE FV-DG scheme is obtained by a novel redesign of the fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Edit intent: clarity
Original:  The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Revised:  The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, extended here to general moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Edit intent: clarity
Original:  The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Revised:  The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Edit intent: clarity
Original:  The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements , and it is based on the integration over arbitrary shaped closed space-time control volumes .
Revised:  The final ALE FV-DG scheme is obtained by a novel redesign of the high order accurate fully discrete direct ALE schemes of Boscheri and Dumbser, which have been extended here to general moving Voronoi meshes and space--time sliver elements . Our new numerical scheme is based on the integration over arbitrary shaped closed space-time control volumes .
Edit intent: coherence
Original:  Numerical convergence studies as well as a large set of benchmark problems for hydrodynamics and magnetohydrodynamics (MHD) demonstrate the accuracy and robustness of the  method.
Revised:  Numerical convergence studies as well as a large set of benchmarks for hydrodynamics and magnetohydrodynamics (MHD) demonstrate the accuracy and robustness of the method.
Edit intent: clarity
Original:  Numerical convergence studies as well as a large set of benchmark problems for hydrodynamics and magnetohydrodynamics (MHD) demonstrate the accuracy and robustness of the  method.
Revised:  Numerical convergence studies as well as a large set of benchmark problems for hydrodynamics and magnetohydrodynamics (MHD) demonstrate the accuracy and robustness of the proposed method.
Edit intent: clarity
Original:  Our numerical results clearly show that the new combination of very high order schemes with regenerated meshes that allow topology changes in each time step lead to substantial improvements over the existing state of the art in direct ALE methods  .
Revised:  Our numerical results clearly show that the new combination of very high order schemes with regenerated meshes with topology changes lead to substantial improvements over the existing state of the art in direct ALE methods .
Edit intent: clarity
Original:  Our numerical results clearly show that the new combination of very high order schemes with regenerated meshes that allow topology changes in each time step lead to substantial improvements over the existing state of the art in direct ALE methods  .
Revised:  Our numerical results clearly show that the new combination of very high order schemes with regenerated meshes that allow topology changes in each time step lead to substantial improvements compared to direct ALE methods .
Edit intent: clarity
Original:  Some will describe the weather and features within it, others will use a holistic descriptor like ' park', and others still will use unique identifiers such as cities or names.
Revised:  Some will describe the weather and features within it, others will use a holistic descriptor like ` park', and others still will use unique identifiers such as cities or names.
Edit intent: fluency
Original:  In this problem each each city has recordings from multiple scenes.
Revised:  In this problem each city has recordings from multiple scenes.
Edit intent: fluency
Original:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data ), with a simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Revised:  We test a series of methods for this novel task and show that a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data ), with a simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Edit intent: coherence
Original:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data ), with a simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Revised:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% . This is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data ), with a simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Edit intent: fluency
Original:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data ), with a simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Revised:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge on the same data ), with a simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Edit intent: fluency
Original:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data ), with a simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Revised:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data . A simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Edit intent: coherence
Original:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data ), with a simple adaptation to the class labels to use paired city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Revised:  We test a series of methods for this novel task and show that whilst a simple convolutional neural network (CNN) can achieve accuracy of 50\% , which is less than the acoustic scene classification task baseline in the DCASE 2018 ASC challenge ( on the same data ), with a simple adaptation to the class labels of pairing city labels with grouped scenes, accuracy increases to 52\%, closer to the simpler scene classification task.
Edit intent: coherence
Original: The first part of this paper is devoted to the approximative solution of linear and Hermitian eigenvalue problems where the differential operator satisfies a Garding inequality.
Revised: The first part of this paper is devoted to the approximative solution of linear and Hermitian eigenvalue problem, which satisfies a Garding inequality.
Edit intent: coherence
Original:  For this, known iterative schemes for the matrix case such as the inverse power or  Arnoldi method are extended to the infinite-dimensional case.
Revised:  For this, known iterative schemes for the matrix case such as the inverse power or the Arnoldi method are extended to the infinite-dimensional case.
Edit intent: fluency
Original:  Then we resolve the mobility-aware association problem to predictively consider handover effects caused by the mobility.
Revised:  Then we facilitate the mobility-aware association problem to predictively consider handover effects caused by the mobility.
Edit intent: clarity
Original:  Then we resolve the mobility-aware association problem to predictively consider handover effects caused by the mobility.
Revised:  Then we resolve the mobility-aware anticipatory network association to predictively consider handover effects caused by the mobility.
Edit intent: clarity
Original:  Based on the Markov decision processes (MDP) and Lyapunov optimization, the two-stage online proactive network association (TOPNA) decision algorithm is proposed without the probability distribution knowledge of random events.
Revised:  Based on the Markov decision processes (MDP) and Lyapunov optimization, the two-stage online proactive network association (TOPNA) decision algorithm is proposed without the knowledge nor distribution of random events.
Edit intent: clarity
Original: Sequence-to-sequence ASR models require large quantities of data to attain high performance.
Revised: Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance.
Edit intent: clarity
Original:  This work builds upon recent results showing notable improvements in self-supervised training using cycle-consistency and related techniques.
Revised:  This work builds upon recent results showing notable improvements in semi-supervised training using cycle-consistency and related techniques.
Edit intent: clarity
Original:  Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with text-to-speech (TTS) models.
Revised:  Such techniques derive training procedures and losses able to leverage unpaired speech and/or text data by combining ASR with Text-to-Speech (TTS) models.
Edit intent: fluency
Original:  In particular, this work proposes a new self-supervised loss combining an end-to-end differentiable ASR\rightarrowTTS loss with a point estimate TTS\rightarrowASR loss.
Revised:  In particular, this work proposes a new semi-supervised loss combining an end-to-end differentiable ASR\rightarrowTTS loss with a point estimate TTS\rightarrowASR loss.
Edit intent: clarity
Original:  In particular, this work proposes a new self-supervised loss combining an end-to-end differentiable ASR\rightarrowTTS loss with a point estimate TTS\rightarrowASR loss.
Revised:  In particular, this work proposes a new self-supervised loss combining an end-to-end differentiable ASR\rightarrowTTS loss with TTS\rightarrowASR loss.
Edit intent: clarity
Original:  We provide  results analyzing the impact of data quantity and speech and text modalities and show consistent gains across WSJ and Librispeech corpora.
Revised:  We provide extensive results analyzing the impact of data quantity and speech and text modalities and show consistent gains across WSJ and Librispeech corpora.
Edit intent: clarity
Original:  Here, we address both these issues : we show how to extend the Impagliazzo-Kabanets algorithm to real-valued random variables , and we improve the corresponding concentration bound by a constant factor.
Revised:  Herein, we revisit both these issues : we show how to extend the Impagliazzo-Kabanets algorithm to real-valued random variables , and we improve the corresponding concentration bound by a constant factor.
Edit intent: clarity
Original:  Here, we address both these issues : we show how to extend the Impagliazzo-Kabanets algorithm to real-valued random variables , and we improve the corresponding concentration bound by a constant factor.
Revised:  Here, we address both these issues : we show how to extend the Impagliazzo-Kabanets algorithm to real-valued random variables and to improve the corresponding concentration bound by a constant factor.
Edit intent: coherence
Original: In this paper, we present a novel method for analysis and segmentation of laminar structure of the cortex based on tissue characteristics whose change across the gray matter facilitates distinction between cortical layers.
Revised: In this paper, we present a novel method for analysis and segmentation of laminar structure of the cortex based on tissue characteristics whose change across the gray matter underlies distinctive between cortical layers.
Edit intent: clarity
Original:  The most accurate classification results were obtained by training three models separately and creating another ensemble by combining probability outputs for final neuron layer classification.
Revised:  Final neuron layer classifications were obtained by training three models separately and creating another ensemble by combining probability outputs for final neuron layer classification.
Edit intent: clarity
Original:  Measurement of importances of developed neuron features on both global model level and individual prediction level are obtained .
Revised:  Measurement of importances of developed neuron features on both global model level and individual prediction level are presented and discussed .
Edit intent: clarity
Original:  Even though the worst-case time per operation of all the fully dynamic algorithms we evaluate is at least linear in the number of edges in the graph (as is to be expected given the conditional lower bounds) we show in our extensive experimental evaluation that their performance differs greatly, both on random as well as on real-world instances.
Revised:  Even though the worst-case time per operation of all the fully dynamic algorithms we evaluate is at least linear in the number of edges in the graph (as is to be expected given the conditional lower bounds) we show in our extensive experimental evaluation that their performance differs greatly, both on generated as well as on real-world instances.
Edit intent: clarity
Original:  Lastly, through a mix of synthetic and real-data experiments we demonstrate that our model builds reasonable  trajectories between snapshots, as measured through various graph statistics.
Revised:  Lastly, through a mix of synthetic and real-world data experiments we demonstrate that our model builds reasonable trajectories between snapshots, as measured through various graph statistics.
Edit intent: clarity
Original:  Lastly, through a mix of synthetic and real-data experiments we demonstrate that our model builds reasonable  trajectories between snapshots, as measured through various graph statistics.
Revised:  Lastly, through a mix of synthetic and real-data experiments we demonstrate that our model builds reasonable graph trajectories between snapshots, as measured through various graph statistics.
Edit intent: fluency
Original:  We also extend our model to include stochastic fluctuations, which we find to decrease uptake times, to expand uptake to parameters regions where uptake is not possible in the deterministic model, and to reverse the order in which particles of different shapes are taken up fastest .
Revised:  We also extend our model to include stochastic fluctuations, which facilitate uptake and lead to corresponding changes in the phase diagrams .
Edit intent: clarity
Original:  Despite being introduced for single-agent Reinforcement Learning , recent studies show that it can also be applied to multi-agent scenarios with promising empirical results.
Revised:  Despite being introduced for single-agent RL originally , recent studies show that it can also be applied to multi-agent scenarios with promising empirical results.
Edit intent: coherence
Original:  In this paper, we propose heuristics-based action advising techniques in cooperative decentralised MARL, using a non-linear function approximation based task-level policy.
Revised:  In this paper, we propose heuristics-based action advising techniques in cooperative decentralised MARL, using a nonlinear function approximation based task-level policy.
Edit intent: fluency
Original:  By adopting a random network distillation technique, we devise a measurement for agents to assess their knowledge in any given state and be able to initiate the teacher-student dynamics with no prior role assumptions.
Revised:  By adopting Random Network Distillation technique, we devise a measurement for agents to assess their knowledge in any given state and be able to initiate the teacher-student dynamics with no prior role assumptions.
Edit intent: fluency
Original:  Experimental results in a gridworld environment show that such  approach may indeed be useful and needs to be further investigated.
Revised:  Experimental results in a gridworld environment show that such an approach may indeed be useful and needs to be further investigated.
Edit intent: fluency
Original:  Using an adaptive time-stepper based on a second-order variable step-size, semi-implicit, backward differentiation formula (VSSBDF2), we observe that when the underlying dynamics is one that would have the solutions converge to a steady state solution, the adaptive time-stepper produces solutions that ``nearly'' converge to the steady state and that, simultaneously, the time-step sizes stabilize at a limiting size dt_\infty.
Revised:  Using an adaptive time-stepper based on a second-order variable step-size, semi-implicit, backward differentiation formula (VSSBDF2), we observe that when the underlying dynamics is one that would have the solutions converge to a steady state solution, the adaptive time-stepper produces solutions that "nearly" converge to the steady state and that, simultaneously, the time-step sizes stabilize at a limiting size dt_\infty.
Edit intent: fluency
Original: The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of  automated methods for neural architecture optimization .
Revised: The growing interest in both the automation of machine learning and deep learning has inevitably led to the development of automated methods for neural architecture search .
Edit intent: clarity