wanyu/IteraTeR_v2 huggingface dataset:
Original: The Finite Element Method (FEM) is widely used to solve discrete Partial Differential Equations (PDEs) and it is a staple in most engineering applications.
Revised: The Finite Element Method (FEM) is widely used to solve discrete Partial Differential Equations (PDEs) in engineering applications.
Edit intent: clarity
Original:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performances have never been systematically studied over a large collection of 3D models .
Revised:  The popularity of FEM led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performances have never been systematically studied over a large collection of 3D models .
Edit intent: clarity
Original:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performances have never been systematically studied over a large collection of 3D models .
Revised:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are understood well , their practical performances have never been systematically studied over a large collection of 3D models .
Edit intent: clarity
Original:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performances have never been systematically studied over a large collection of 3D models .
Revised:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performance have not been systematically studied over a large collection of 3D models .
Edit intent: clarity
Original:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performances have never been systematically studied over a large collection of 3D models .
Revised:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performances have never been systematically studied for large collections of automatically meshed 3D models .
Edit intent: clarity
Original:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performances have never been systematically studied over a large collection of 3D models .
Revised:  The popularity of this approach led to the development of a large family of variants, and, while their theoretical properties (such as convergence rate, stability, etc.) are well studied , their practical performances have never been systematically studied over a large collection of 3D geometry .
Edit intent: clarity
Original:  We introduce a large set of benchmark problems, starting from simple cases with an analytical solution, moving to classical experimental setups, and finally fabricating solutions for thousands of real-world  geometries.
Revised:  We introduce a set of benchmark problems, starting from simple cases with an analytical solution, moving to classical experimental setups, and finally fabricating solutions for thousands of real-world geometries.
Edit intent: clarity
Original:  We introduce a large set of benchmark problems, starting from simple cases with an analytical solution, moving to classical experimental setups, and finally fabricating solutions for thousands of real-world  geometries.
Revised:  We introduce a large set of benchmark problems, starting from simple cases with an analytical solution, moving to commonly used test problem setups, and finally fabricating solutions for thousands of real-world geometries.
Edit intent: clarity
Original:  For all these cases, we use state-of-the-art meshing tools to create both unstructured (tetrahedral) and structured (hexahedral) meshes, and compare the performance of different element types for a wide spectrum of elliptic PDEs ranging from heat convection to fluid propagation.
Revised:  For all these cases, we use state-of-the-art meshing tools to create both unstructured (tetrahedral) and structured (hexahedral) meshes, and compare the performance of different element types for a wide spectrum of elliptic PDEs ranging from heat diffusion to fluid propagation.
Edit intent: clarity
Original:  We observe that, while linear tetrahedral elements perform poorly, often leading to locking artefacts, quadratic tetrahedral elements outperform hexahedral elements in all the settings we tested.
Revised:  We observe that, while linear tetrahedral elements perform poorly, often leading to locking artefacts, quadratic tetrahedral elements outperform hexahedral elements in all settings we tested.
Edit intent: fluency
Original:  This unexpected result suggests that for  static structural analysis, thermal analysis, and low reynolds number flows  it is unnecessary to target automatic hex mesh generation, since superior results can be obtained with unstructured meshes, which can be created robustly and automatically with existing meshing algorithms.
Revised:  This observation suggests that for static structural analysis, thermal analysis, and low reynolds number flows it is unnecessary to target automatic hex mesh generation, since superior results can be obtained with unstructured meshes, which can be created robustly and automatically with existing meshing algorithms.
Edit intent: clarity
Original:  This unexpected result suggests that for  static structural analysis, thermal analysis, and low reynolds number flows  it is unnecessary to target automatic hex mesh generation, since superior results can be obtained with unstructured meshes, which can be created robustly and automatically with existing meshing algorithms.
Revised:  This unexpected result suggests that for most problems in static structural analysis, thermal analysis, and low reynolds number flows it is unnecessary to target automatic hex mesh generation, since superior results can be obtained with unstructured meshes, which can be created robustly and automatically with existing meshing algorithms.
Edit intent: clarity
Original:  This unexpected result suggests that for  static structural analysis, thermal analysis, and low reynolds number flows  it is unnecessary to target automatic hex mesh generation, since superior results can be obtained with unstructured meshes, which can be created robustly and automatically with existing meshing algorithms.
Revised:  This unexpected result suggests that for static structural analysis, thermal analysis, and low reynolds number flows , it is unnecessary to target automatic hex mesh generation, since superior results can be obtained with unstructured meshes, which can be created robustly and automatically with existing meshing algorithms.
Edit intent: fluency
Original:  This unexpected result suggests that for  static structural analysis, thermal analysis, and low reynolds number flows  it is unnecessary to target automatic hex mesh generation, since superior results can be obtained with unstructured meshes, which can be created robustly and automatically with existing meshing algorithms.
Revised:  This unexpected result suggests that for static structural analysis, thermal analysis, and low reynolds number flows it is unnecessary to target automatic hex mesh generation, since high-quality results can be obtained with unstructured meshes, which can be created robustly and automatically with existing meshing algorithms.
Edit intent: clarity
Original:  We release the experimental description , meshes, and reference implementation of our testing infrastructure , providing a standard benchmark .
Revised:  We release the experimental description , meshes, and reference implementation of our testing infrastructure .
Edit intent: clarity
Original:  This enables statistically significant comparisons between different FE methods and discretization , which we believe will provide a guide in the development of new meshing and FEA techniques.
Revised:  This enables statistically significant comparisons between different FE methods , which we believe will provide a guide in the development of new meshing and FEA techniques.
Edit intent: clarity
Original: The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their inner configuration.
Revised: The plasticity property of biological neural networks allows them to perform learning and optimize their behavior by changing their configuration.
Edit intent: clarity
Original:  Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i.e. rules that update synapses based on the neuron activations and a reinforcement signal received from the environment .
Revised:  Inspired by biology, plasticity can be modeled in artificial neural networks by using Hebbian learning rules, i.e. rules that update synapses based on the neuron activations and reinforcement signals .
Edit intent: clarity
Original:  We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse and keep track of the activation of the neurons while the network performs a certain task for an episode . Delayed reinforcement signals are provided after each episode , based on the performance of the network relative to its performance during the previous episode.
Revised:  We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse to keep track of the activation of the neurons while the network performs a certain task for an episode . Delayed reinforcement signals are provided after each episode , based on the performance of the network relative to its performance during the previous episode.
Edit intent: coherence
Original:  We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse and keep track of the activation of the neurons while the network performs a certain task for an episode . Delayed reinforcement signals are provided after each episode , based on the performance of the network relative to its performance during the previous episode.
Revised:  We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse and keep track of the activation of the neurons . Delayed reinforcement signals are provided after each episode , based on the performance of the network relative to its performance during the previous episode.
Edit intent: clarity
Original:  We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse and keep track of the activation of the neurons while the network performs a certain task for an episode . Delayed reinforcement signals are provided after each episode , based on the performance of the network relative to its performance during the previous episode.
Revised:  We propose the use of neuron activation traces (NATs) to provide additional data storage in each synapse and keep track of the activation of the neurons while the network performs a certain task for an episode . Delayed reinforcement signals are provided after each episode relative to the networks' performance during the previous episode.
Edit intent: clarity
Original:  We compare DSP with an analogous hill climbing algorithm which does not incorporate domain knowledge introduced with the NATs  .
Revised:  We compare DSP with an analogous hill climbing algorithm that does not incorporate domain knowledge introduced with the NATs .
Edit intent: fluency
Original: Reinforcement learning algorithms can be used to optimally solve dynamic decision-making and  control problems.
Revised: Reinforcement learning algorithms can solve dynamic decision-making and control problems.
Edit intent: clarity
Original:  Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learned, and they require significant trial and error tuning of their meta-parameters .
Revised:  Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering little insight into the mappings learned, and they require significant trial and error tuning of their meta-parameters .
Edit intent: clarity
Original:  Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learned, and they require significant trial and error tuning of their meta-parameters .
Revised:  Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learned, and they require extensive trial and error tuning of their meta-parameters .
Edit intent: clarity
Original:  Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learned, and they require significant trial and error tuning of their meta-parameters .
Revised:  Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learned, and they require significant trial and error tuning of their hyper-parameters .
Edit intent: clarity
Original:  We introduce three off-line methods for finding value functions based on a state transition model: symbolic value iteration, symbolic policy iteration, and a direct solution of the Bellman equation.
Revised:  We introduce three off-line methods for finding value functions based on a state-transition model: symbolic value iteration, symbolic policy iteration, and a direct solution of the Bellman equation.
Edit intent: fluency
Original:  The results show that the value functions not only yield well-performing policies , but also are compact, human-readable and mathematically tractable .
Revised:  The results show that the value functions yield well-performing policies , but also are compact, human-readable and mathematically tractable .
Edit intent: clarity
Original:  The results show that the value functions not only yield well-performing policies , but also are compact, human-readable and mathematically tractable .
Revised:  The results show that the value functions not only yield well-performing policies and are compact, human-readable and mathematically tractable .
Edit intent: fluency
Original:  A comparison with alternative approaches using neural networks shows that our method constructs well-performing value functions with substantially fewer parameters .
Revised:  A comparison with an alternative approach using neural networks shows that our method constructs well-performing value functions with substantially fewer parameters .
Edit intent: fluency
Original:  A comparison with alternative approaches using neural networks shows that our method constructs well-performing value functions with substantially fewer parameters .
Revised:  A comparison with alternative approaches using neural networks shows that our method outperforms the neural network-based one .
Edit intent: clarity
Original:  Then, we give a new MPC algorithm for an arbitrary acyclic join with load O ({\IN \over p} + \sqrt{{\IN \cdot \OUT \over p }\over  ), where \IN,\OUT are the input and output sizes of the join, and p is the number of servers in the MPC model.
Revised:  Then, we give a new MPC algorithm for an arbitrary acyclic join with load O ({\IN \over p} + \sqrt{\IN \cdot \OUT }\over ), where \IN,\OUT are the input and output sizes of the join, and p is the number of servers in the MPC model.
Edit intent: fluency
Original:  Then, we give a new MPC algorithm for an arbitrary acyclic join with load O ({\IN \over p} + \sqrt{{\IN \cdot \OUT \over p }\over  ), where \IN,\OUT are the input and output sizes of the join, and p is the number of servers in the MPC model.
Revised:  Then, we give a new MPC algorithm for an arbitrary acyclic join with load O ({\IN \over p} + \sqrt{{\IN \cdot \OUT \over p }\over p ), where \IN,\OUT are the input and output sizes of the join, and p is the number of servers in the MPC model.
Edit intent: fluency
Original:  When no attack is present, training with GCE also outperforms cross-entropy in terms of model accuracy .
Revised:  When no attack is present, training with even better performance compared to the commonly used cross-entropy in terms of model accuracy .
Edit intent: clarity
Original:  We also show that our method can be used orthogonally to adversarial training across well-known methods with noticeable robustness gain.
Revised:  We also show that our method can be used orthogonal to adversarial training across well-known methods with noticeable robustness gain.
Edit intent: fluency
Original:  As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosofies .
Revised:  As of today, there are many different implementations of cryptocurrencies working over a blockchain, with different approaches and philosophies .
Edit intent: fluency
Original:  This proof-of-work scheme often consists on the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force.
Revised:  This proof-of-work scheme often consists in the resolution of a cryptography problem, most commonly breaking a hash value, which can only be achieved through brute-force.
Edit intent: fluency
Original:  Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing the access to artificial intelligence.
Revised:  Additionally, this paper presents a proof-of-storage scheme for rewarding users that provide storage for the deep learning models, as well as a theoretical dissertation on how the mechanics of the system could be articulated with the ultimate goal of democratizing access to artificial intelligence.
Edit intent: fluency
Original:  In our paper  we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems.
Revised:  In our paper , we propose an advanced pipeline model for the multi-task objective of determining product complementarity, similarity and sales prediction using deep neural models applied to big-data sequential transaction systems.
Edit intent: fluency
Original:  Our highly parallelized hybrid  pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively.
Revised:  Our highly parallelized hybrid model pipeline consists of both unsupervised and supervised models, used for the objectives of generating semantic product embeddings and predicting sales, respectively.
Edit intent: clarity
Original:  Our experimentation and benchmarking  have been done using very large pharma-industry retailer Big Data stream .
Revised:  Our experimentation and benchmarking processes have been done using very large pharma-industry retailer Big Data stream .
Edit intent: clarity
Original: Selectivity estimation - the problem of estimating the result size of queries - is a fundamental yet challenging problem in databases.
Revised: Selectivity estimation - the problem of estimating the result size of queries - is a fundamental problem in databases.
Edit intent: clarity
Original:  In this paper, we investigate the feasibility of using deep learning based approaches for challenging scenarios such as queries involving multiple predicates and /or low selectivity.
Revised:  We investigate the feasibility of using deep learning based approaches for challenging scenarios such as queries involving multiple predicates and /or low selectivity.
Edit intent: clarity
Original:  Our extensive experiments with a special emphasis on queries with a large number of predicates and/or small result sizes demonstrates that deep learning based techniques are a promising research avenue for selectivity estimation worthy of further investigation .
Revised:  Our extensive experiments with a special emphasis on queries with a large number of predicates and/or small result sizes demonstrates that our proposed techniques provide fast and accurate selective estimates with minimal space overhead .
Edit intent: clarity
Original:  One viable solution that was pioneered by the database community is Approximate Query Processing (AQP) .
Revised:  The database community has pioneered many novel techniques for Approximate Query Processing (AQP) .
Edit intent: clarity
Original:  In this work, we explore a novel approach for AQP utilizing deep learning (DL)  . We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully in a compact manner (typically few hundred KBs).
Revised:  In this work, we explore the usage of deep learning (DL) . We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully in a compact manner (typically few hundred KBs).
Edit intent: clarity
Original:  While we specifically focus on variational autoencoders (VAE), we demonstrate how our approach could also be used for other popular DL models such as generative adversarial networks (GAN) and deep Bayesian networks (DBN) . Our other contributions include (a) identifying model bias and minimizing it through a rejection sampling based approach (b) An algorithm to build model ensembles for AQP for improved accuracy and (c) an analysis of VAE latent space to understand its suitability to AQP .
Revised:  While we specifically focus on variational autoencoders (VAE), we demonstrate how our approach could also be used for other popular DL models such as generative adversarial networks (GAN) and deep Bayesian networks (DBN) . Our other contributions include identifying model bias and minimizing it through a rejection sampling based approach (b) An algorithm to build model ensembles for AQP for improved accuracy and (c) an analysis of VAE latent space to understand its suitability to AQP .
Edit intent: fluency
Original:  While we specifically focus on variational autoencoders (VAE), we demonstrate how our approach could also be used for other popular DL models such as generative adversarial networks (GAN) and deep Bayesian networks (DBN) . Our other contributions include (a) identifying model bias and minimizing it through a rejection sampling based approach (b) An algorithm to build model ensembles for AQP for improved accuracy and (c) an analysis of VAE latent space to understand its suitability to AQP .
Revised:  While we specifically focus on variational autoencoders (VAE), we demonstrate how our approach could also be used for other popular DL models such as generative adversarial networks (GAN) and deep Bayesian networks (DBN) . Our other contributions include (a) identifying model bias and minimizing it through a rejection sampling based approach and an algorithm to build model ensembles for AQP for improved accuracy and (c) an analysis of VAE latent space to understand its suitability to AQP .
Edit intent: coherence