wanyu/IteraTeR_v2 huggingface dataset:
Original:  The controller compensates for plant nonlinearities and so can be used for the full envelope of operation.
Revised:  The controller compensates for system nonlinearities and so can be used for the full envelope of operation.
Edit intent: clarity
Original: Caused by biological limits on cognition, information overload has shown to impact behavior of online social media users, affecting behavior in online conversations.
Revised: The inhibiting effects of information overload on the behavior of online social media users, affecting behavior in online conversations.
Edit intent: clarity
Original:  The algorithm is equally applicable to optimization problems in all domains, obtaining accurate energy evaluations with hardware-efficient ansatzes that are up to 18.6\times shallower and use up to 12\times fewer CX gates than results obtained with a unitary coupled cluster ansatz. EVQE demonstrates significant noise-resistant properties, obtaining results in noisy simulation with at least 3.6\times less error than VQE using any tested ansatz configuration.
Revised:  The algorithm is equally applicable to optimization problems in all domains, obtaining accurate energy evaluations with hardware-efficient ansatzes that are up to 18.6\times shallower and use up to 12\times fewer CX gates than those obtained by VQE with a unitary coupled cluster ansatz. EVQE demonstrates significant noise-resistant properties, obtaining results in noisy simulation with at least 3.6\times less error than VQE using any tested ansatz configuration.
Edit intent: clarity
Original:  The algorithm is equally applicable to optimization problems in all domains, obtaining accurate energy evaluations with hardware-efficient ansatzes that are up to 18.6\times shallower and use up to 12\times fewer CX gates than results obtained with a unitary coupled cluster ansatz. EVQE demonstrates significant noise-resistant properties, obtaining results in noisy simulation with at least 3.6\times less error than VQE using any tested ansatz configuration.
Revised:  The algorithm is equally applicable to optimization problems in all domains, obtaining accurate energy evaluations with hardware-efficient ansatzes that are up to 18.6\times shallower and use up to 12\times fewer CX gates than results obtained with a unitary coupled cluster ansatz. EVQE demonstrates significant noise-resistance properties, obtaining results in noisy simulation with at least 3.6\times less error than VQE using any tested ansatz configuration.
Edit intent: fluency
Original:  We design a fine-tuned data path to achieve single us } read/write latency to remote memory, develop decentralized algorithms for cluster-wide memory management, and analyze how to select parameters to mitigate independent and correlated uncertainties.
Revised:  We design a fine-tuned data path to achieve single us }s read/write latency to remote memory, develop decentralized algorithms for cluster-wide memory management, and analyze how to select parameters to mitigate independent and correlated uncertainties.
Edit intent: fluency
Original:  At the same time, it provides performance similar to that of in-memory replication with 1.6 X lower memory overhead  .
Revised:  At the same time, it provides performance similar to that of in-memory replication with 1.6 x lower memory overhead .
Edit intent: fluency
Original:  We formulate a powerful grammar-restricted (syntax-guided) synthesis problem for coherent uninterpreted programs and show the problem to be decidable .
Revised:  We formulate a powerful grammar-restricted (syntax-guided) synthesis problem for coherent uninterpreted programs , and we show the problem to be decidable .
Edit intent: coherence
Original:  We find that the socioconfiguration can be controlled by the parasitically-infected population, under some conditions, even if the healthy population is the majority and of opposite opinion.
Revised:  We find that the socio-configuration can be controlled by the parasitically-infected population, under some conditions, even if the healthy population is the majority and of opposite opinion.
Edit intent: fluency
Original:  In this paper, we show that there is a {\em deterministic} fully polynomial time approximation scheme (FPTAS) for computing \Pr[X_{\rm MAX}\le x] in case the treewidth of G is bounded by a constant k ;
Revised:  In this paper, we show that there is a {\em deterministic} fully polynomial time approximation scheme (FPTAS) for computing \Pr[X_{\rm MAX}\le x] in case the treewidth of G is bounded by a constant k ,
Edit intent: fluency
Original:  where there may be exponentially many source-terminal paths in G. The running time of our algorithm is O( n(\frac{2mn{\epsilon})} {\epsilon})} ^{ k ^2+ 4k }) to achieve a multiplicative approximation ratio 1+\epsilon.
Revised:  where there may be exponentially many source-terminal paths in G. The running time of our algorithm is O( n(\frac{2mn{\epsilon})} {\epsilon})} ^{ 4k ^2+ 4k }) to achieve a multiplicative approximation ratio 1+\epsilon.
Edit intent: fluency
Original:  On the way to show our FPTAS, we show a fundamental formula that represents \Pr[X_{\rm MAX}\le x] by  n-1 repetition of definite integrals.
Revised:  On the way to show our FPTAS, we show a fundamental formula that represents \Pr[X_{\rm MAX}\le x] by at most n-1 repetition of definite integrals.
Edit intent: clarity
Original:  Especially, when the edge lengths are mutually independent and uniformly distributed, the problem of computing the distribution function \Pr[X_{\rm MAX}\le x] is known to be \# P-hard even in case G is a directed path.
Revised:  Especially, when the edge lengths are mutually independent and uniformly distributed, the problem of computing the distribution function \Pr[X_{\rm MAX}\le x] is known to be P-hard even in case G is a directed path.
Edit intent: coherence
Original:  We experimentally observe that if two nodes transmit the same number of data packets, the amount of energy consumption of the nodes is approximately  same even if the payload lengths of the transmitted packets are different.
Revised:  We experimentally observe that if two nodes transmit same number of data packets, the amount of energy consumption of the nodes is approximately same even if the payload lengths of the transmitted packets are different.
Edit intent: fluency
Original:  We experimentally observe that if two nodes transmit the same number of data packets, the amount of energy consumption of the nodes is approximately  same even if the payload lengths of the transmitted packets are different.
Revised:  We experimentally observe that if two nodes transmit the same number of data packets, the amount of energy consumption of the nodes is approximately the same even if the payload lengths of the transmitted packets are different.
Edit intent: fluency
Original:  The major energy consumption during a packet transmission arises from radio start-up and medium access control overhead.
Revised:  This is because the major energy consumption during a packet transmission arises from radio start-up and medium access control overhead.
Edit intent: coherence
Original:  Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets  in contrast to prior works, which consider  data units (amount of sensor data generated by a node)  . Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work.
Revised:  Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets transmitted/ received, in contrast to prior works, which consider data units (amount of sensor data generated by a node) . Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work.
Edit intent: clarity
Original:  Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets  in contrast to prior works, which consider  data units (amount of sensor data generated by a node)  . Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work.
Revised:  Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets in contrast to prior works, which consider the number of data units (amount of sensor data generated by a node) . Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work.
Edit intent: fluency
Original:  Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets  in contrast to prior works, which consider  data units (amount of sensor data generated by a node)  . Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work.
Revised:  Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/ receptions in terms of the number of data packets in contrast to prior works, which consider data units (amount of sensor data generated by a node) transmitted/ received . Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work.
Edit intent: clarity
Original:  We prove that this problem is NP-complete by reducing the set cover problem to it and propose an algorithm to solve it.
Revised:  We prove that this problem is NP-complete and propose an algorithm to solve it.
Edit intent: coherence
Original:  The performance of the proposed algorithm is experimentally evaluated using Jain's fairness index as a metric by implementing it on an actual testbed consisting of 20 sensor nodes and compared with those of the widely used shortest path tree  and random data collection tree  algorithms.
Revised:  The performance of the proposed algorithm is experimentally evaluated using Jain's fairness index as a metric by implementing it on an actual testbed consisting of 20 sensor nodes and compared with those of the widely used shortest path tree (SPT) and random data collection tree algorithms.
Edit intent: clarity
Original:  The performance of the proposed algorithm is experimentally evaluated using Jain's fairness index as a metric by implementing it on an actual testbed consisting of 20 sensor nodes and compared with those of the widely used shortest path tree  and random data collection tree  algorithms.
Revised:  The performance of the proposed algorithm is experimentally evaluated using Jain's fairness index as a metric by implementing it on an actual testbed consisting of 20 sensor nodes and compared with those of the widely used shortest path tree and random data collection tree (RDCT) algorithms.
Edit intent: clarity
Original:  Hence, the proposed algorithm provides a  higher network lifetime than that under the other two algorithms .
Revised:  Hence, the proposed algorithm provides a higher network lifetime compared to all the other considered data collection approaches .
Edit intent: clarity
Original:  However, many more modern techniques -- for example, adaptive mesh refinement and the use of higher order geometry approximation methods -- really do need information about the underlying geometry to realize their full potential.
Revised:  However, many modern techniques -- for example, adaptive mesh refinement and the use of higher order geometry approximation methods -- really do need information about the underlying geometry to realize their full potential.
Edit intent: coherence
Original:  Our study shows that all geometry needs inside the simulators can be satisfied by just two ``primitives'' : elementary queries posed by the simulation software to the geometry description.
Revised:  Our study shows that nearly all geometry-related needs inside the simulators can be satisfied by just two ``primitives'' : elementary queries posed by the simulation software to the geometry description.
Edit intent: clarity
Original:  Our study shows that all geometry needs inside the simulators can be satisfied by just two ``primitives'' : elementary queries posed by the simulation software to the geometry description.
Revised:  Our study shows that all geometry needs inside the simulators can be satisfied by just two "primitives" : elementary queries posed by the simulation software to the geometry description.
Edit intent: fluency
Original:  We then show that it is possible to provide these primitives in all of the commonly used ways in which geometries are described in common industrial workflows .
Revised:  We then show that it is possible to provide these primitives in all of the frequently used ways in which geometries are described in common industrial workflows .
Edit intent: fluency
Original:  We illustrate our solutions using examples from adaptive mesh refinement for complex geometries .
Revised:  We illustrate our solutions using a number of examples .
Edit intent: clarity
Original: In this paper, we propose two distributed algorithms, named Distributed Voting with Beeps (DVB), for multi-choice voting in beep model where each node can communicate with its neighbors merely by sending beep signals.
Revised: We consider the problem of distributed multi-choice voting in beep model where each node can communicate with its neighbors merely by sending beep signals.
Edit intent: clarity
Original:  We  show that the probability of success of the first algorithm goes to one as the percentage of nodes choosing the majority vote as their initial values gets close to one.
Revised:  We show that the success probability of the first algorithm goes to one as the percentage of nodes choosing the majority vote as their initial values gets close to one.
Edit intent: clarity
Original:  We  show that the probability of success of the first algorithm goes to one as the percentage of nodes choosing the majority vote as their initial values gets close to one.
Revised:  We show that the probability of success of the first algorithm tends to one as the percentage of nodes choosing the majority vote as their initial values gets close to one.
Edit intent: clarity
Original:  The second algorithm returns the correct vote with high probability.
Revised:  The second algorithm returns the correct output with high probability.
Edit intent: clarity
Original:  We show how these changes are related to and improve on alternative recently proposed gating mechanisms such as chrono-initialization and Ordered Neurons.
Revised:  We show how these changes are related to and improve on alternative recently proposed gating mechanisms such as chrono initialization and Ordered Neurons.
Edit intent: fluency
Original:  However, building a good speech recognition system usually requires significant amounts of transcribed data  which is expensive to collect.
Revised:  However, building a good speech recognition system usually requires significant amounts of transcribed data , which is expensive to collect.
Edit intent: fluency
Original:  To tackle this problem, we propose a novel unsupervised pre-training method called masked predictive coding , which can be applied for unsupervised pre-training with  Transformer based model.
Revised:  To tackle this problem, an unsupervised pre-training method called masked predictive coding , which can be applied for unsupervised pre-training with Transformer based model.
Edit intent: clarity
Original:  To tackle this problem, we propose a novel unsupervised pre-training method called masked predictive coding , which can be applied for unsupervised pre-training with  Transformer based model.
Revised:  To tackle this problem, we propose a novel unsupervised pre-training method called Masked Predictive Coding is proposed , which can be applied for unsupervised pre-training with Transformer based model.
Edit intent: coherence
Original:  However, building a good speech recognition system usually requires significant amounts of transcribed data, which is expensive to collect.
Revised:  However, building a good speech recognition system usually requires large amounts of transcribed data, which is expensive to collect.
Edit intent: clarity
Original:  To tackle this problem, an unsupervised pre-training method called Masked Predictive Coding is proposed, which can be applied for unsupervised pre-training with state-of-the-arts Transformer based model.
Revised:  To tackle this problem, an unsupervised pre-training method called Masked Predictive Coding is proposed, which can be applied for unsupervised pre-training with Transformer based model.
Edit intent: clarity
Original:  Experiments on HKUST show that using the same training data and other open source Mandarin data , we can achieve a CER of 22.9, or a 3.8\%relative improvements over a strong Transformer baseline .
Revised:  Experiments on HKUST show that using the same training data , we can achieve a CER of 22.9, or a 3.8\%relative improvements over a strong Transformer baseline .
Edit intent: clarity
Original:  With more pre-training data, we can further reduce the CER to 21.0  , or a 11.8\% relative CER reduction over baseline.
Revised:  With more pre-training data, we can further reduce the CER to 21.0 \% , or a 11.8\% relative CER reduction over baseline.
Edit intent: fluency
Original:  Acoustic scenes can be shaped by occurred acoustic events which can provide useful information in training ASC tasks .
Revised:  Acoustic events can provide useful information in training ASC tasks .
Edit intent: clarity
Original:  Acoustic scenes can be shaped by occurred acoustic events which can provide useful information in training ASC tasks .
Revised:  Acoustic scenes can be shaped by occurred acoustic events which can provide useful information for recognizing acoustic scenes .
Edit intent: clarity
Original:  Therefore, We explored cross-task pre-training mechanism to utilize acoustic event information extracted from the pre-trained model to optimize the ASC task.
Revised:  To utilize the acoustic event information to improve the performance of ASC tasks, we present the cross-task pre-training mechanism to utilize acoustic event information extracted from the pre-trained model to optimize the ASC task.
Edit intent: clarity
Original:  Therefore, We explored cross-task pre-training mechanism to utilize acoustic event information extracted from the pre-trained model to optimize the ASC task.
Revised:  Therefore, We explored cross-task pre-training mechanism which utilizes acoustic event information extracted from the pre-trained model to optimize the ASC task.
Edit intent: coherence
Original:  Therefore, We explored cross-task pre-training mechanism to utilize acoustic event information extracted from the pre-trained model to optimize the ASC task.
Revised:  Therefore, We explored cross-task pre-training mechanism to utilize acoustic event information from the pre-trained model to optimize the ASC task.
Edit intent: clarity
Original:  We present three cross-task pre-training architectures and evaluated them in feature-based and fine-tuning strategies on two datasets respectively : TAU Urban Acoustic Scenes 2019 dataset and TUT Acoustic Scenes 2017 dataset.
Revised:  We present three cross-task pre-training architectures and evaluated them in feature-based and fine-tuning strategies on two datasets : TAU Urban Acoustic Scenes 2019 dataset and TUT Acoustic Scenes 2017 dataset.
Edit intent: coherence
Original:  Results have shown that cross-task pre-training mechanism can significantly improve the performance of ASC tasks and the performance of our best model improved relatively 9.5\% in the TAU Urban Acoustic Scenes 2019 dataset, and also improved 10\% in the TUT Acoustic Scenes 2017 dataset compared with the official baseline  .
Revised:  Results have shown that cross-task pre-training mechanism can significantly improve the performance of ASC tasks . The performance of our best model improved relatively 9.5\% in the TAU Urban Acoustic Scenes 2019 dataset, and also improved 10\% in the TUT Acoustic Scenes 2017 dataset compared with the official baseline .
Edit intent: coherence
Original:  Oftentimes, the underlying relationships involve more than two entities at a time , and hypergraphs are a more faithful model .
Revised:  Oftentimes, the underlying relationships involve more than two nodes at a time , and hypergraphs are a more faithful model .
Edit intent: clarity
Original:  Oftentimes, the underlying relationships involve more than two entities at a time , and hypergraphs are a more faithful model .
Revised:  Oftentimes, the underlying relationships involve more than two entities at a time .
Edit intent: clarity
Original: Matrix factorization (MF) techniques have been shown to be effective for rating predictions (RPs) in personalized recommender systems . Existing MF methods use the same item embeddings and the same RP model for all users, while ignoring the possibility that different usersmay have different views about the same item and may favor different RPmodels.
Revised: Matrix factorization (MF) techniques have been shown to be effective for rating predictions (RPs) in personalized recommender systems . 
Edit intent: clarity
Original:  We introduce a novel MF framework, named meta matrix factorization (MetaMF) , that generates private item embeddings and RP models . Given a vector representing a user, we first obtain a collaborative vector by collecting useful information from all users with a collaborative memory (CM) module.
Revised:  We introduce a federated matrix factorization (MF) framework, named meta matrix factorization (MetaMF) , that generates private item embeddings and RP models . Given a vector representing a user, we first obtain a collaborative vector by collecting useful information from all users with a collaborative memory (CM) module.
Edit intent: clarity
Original:  We introduce a novel MF framework, named meta matrix factorization (MetaMF) , that generates private item embeddings and RP models . Given a vector representing a user, we first obtain a collaborative vector by collecting useful information from all users with a collaborative memory (CM) module.
Revised:  We introduce a novel MF framework, named meta matrix factorization (MetaMF) . Given a vector representing a user, we first obtain a collaborative vector by collecting useful information from all users with a collaborative memory (CM) module.
Edit intent: clarity
Original:  We introduce a novel MF framework, named meta matrix factorization (MetaMF) , that generates private item embeddings and RP models . Given a vector representing a user, we first obtain a collaborative vector by collecting useful information from all users with a collaborative memory (CM) module.
Revised:  We introduce a novel MF framework, named meta matrix factorization (MetaMF) , that generates private item embeddings and RP models . Given a user, we first obtain a collaborative vector by collecting useful information from all users with a collaborative memory (CM) module.
Edit intent: clarity
Original:  We introduce a novel MF framework, named meta matrix factorization (MetaMF) , that generates private item embeddings and RP models . Given a vector representing a user, we first obtain a collaborative vector by collecting useful information from all users with a collaborative memory (CM) module.
Revised:  We introduce a novel MF framework, named meta matrix factorization (MetaMF) , that generates private item embeddings and RP models . Given a vector representing a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory (CM) module.
Edit intent: clarity