wanyu/IteraTeR_v2 huggingface dataset:
Original: Many important multi-component crystalline solids undergo mechanochemical spinodal decomposition: a phase transformation in which the compositional redistribution is coupled with structural changes of the crystal, resulting in dynamic and intricate microstructures.
Revised: Many important multi-component crystalline solids undergo mechanochemical spinodal decomposition: a phase transformation in which the compositional redistribution is coupled with structural changes of the crystal, resulting in dynamically evolving microstructures.
Edit intent: clarity
Original:  A further complexity of a hierarchical nature arises if the elastic free energy and its variation with strain is a small scale fluctuation on the dominant trajectory of the total free energy driven by microstructural dynamics.
Revised:  A further complexity of a hierarchical nature arises if the elastic free energy and its variation with strain is a small-scale fluctuation on the dominant trajectory of the total free energy driven by microstructural dynamics.
Edit intent: fluency
Original:  To address these challenges, we present a data-driven approach, which combines advanced neural network (NN) models with DNS to predict the  mechanical free energy and homogenized stress fields on microstructures in a family of two-dimensional multi-component crystalline solids .
Revised:  To address these challenges, we present a data-driven approach, which combines advanced neural network (NN) models with DNS to predict the mechanical free energy and stress fields arising in a family of two-dimensional multi-component crystalline solids .
Edit intent: clarity
Original:  To address these challenges, we present a data-driven approach, which combines advanced neural network (NN) models with DNS to predict the  mechanical free energy and homogenized stress fields on microstructures in a family of two-dimensional multi-component crystalline solids .
Revised:  To address these challenges, we present a data-driven approach, which combines advanced neural network (NN) models with DNS to predict the mechanical free energy and homogenized stress fields on microstructures in a family of multi-component crystalline solids .
Edit intent: clarity
Original:  The hierarchical structure of the free energy's evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs)  or pre-trained convolutional neural networks (CNNs) that describe the dominant feature of the data to fully represent the hierarchichally evolving free energy .
Revised:  The hierarchical structure of the free energy's evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs) , or pre-trained convolutional neural networks (CNNs) that describe the dominant feature of the data to fully represent the hierarchichally evolving free energy .
Edit intent: fluency
Original:  The hierarchical structure of the free energy's evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs)  or pre-trained convolutional neural networks (CNNs) that describe the dominant feature of the data to fully represent the hierarchichally evolving free energy .
Revised:  The hierarchical structure of the free energy's evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs) or pre-trained convolutional neural networks (CNNs) that describe the dominant characteristic of the data to fully represent the hierarchichally evolving free energy .
Edit intent: fluency
Original:  The hierarchical structure of the free energy's evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs)  or pre-trained convolutional neural networks (CNNs) that describe the dominant feature of the data to fully represent the hierarchichally evolving free energy .
Revised:  The hierarchical structure of the free energy's evolution induces a multi-resolution character to the machine learning paradigm: We construct knowledge-based neural networks (KBNNs) with either pre-trained fully connected deep neural networks (DNNs) or pre-trained convolutional neural networks (CNNs) that describe the dominant feature of the data to fully represent the hierarchically evolving free energy .
Edit intent: clarity
Original:  This paper investigates a RBF-FD solution of time-domain acoustic wave propagation in the context of seismic modeling in the Earth's subsurface.
Revised:  This paper investigates an RBF-FD solution of time-domain acoustic wave propagation in the context of seismic modeling in the Earth's subsurface.
Edit intent: fluency
Original:  Through a number of numerical tests, ranging from homogeneous to highly-heterogeneous velocity models  , we demonstrate that the present approach can be further generalized to solve large-scale seismic modeling and full waveform inversion problems in arbitrarily complex models --- enabling more robust interpretations to geophysical observations.
Revised:  Through a number of numerical tests, ranging from homogeneous to highly-heterogeneous velocity models , we demonstrate that the present approach can be further generalized to solve large-scale seismic modeling and full waveform inversion problems in arbitrarily complex models enabling more robust interpretations to geophysical observations.
Edit intent: fluency
Original:  Through a number of numerical tests, ranging from homogeneous to highly-heterogeneous velocity models  , we demonstrate that the present approach can be further generalized to solve large-scale seismic modeling and full waveform inversion problems in arbitrarily complex models --- enabling more robust interpretations to geophysical observations.
Revised:  Through a number of numerical tests, ranging from homogeneous to highly-heterogeneous velocity models , we demonstrate that the present approach can be further generalized to solve large-scale seismic modeling and full waveform inversion problems in arbitrarily complex models --- enabling more robust interpretations of geophysical observations
Edit intent: fluency
Original: Evolutionary-based optimization approaches have recently shown promising results in domains such as Atari and robot locomotion but less so in solving 3D tasks directly from pixels .
Revised: Evolutionary-based optimization approaches have recently shown promising results in domains such as Atari and robot locomotion but less so in solving 3D task .
Edit intent: clarity
Original:  This paper presents a method called Deep Innovation Protection (DIP) that allows training complex world models end-to-end for such 3D environments.
Revised:  This paper presents a method called Deep Innovation Protection (DIP) that allows training complex world models end-to-end for such environments.
Edit intent: clarity
Original:  The main idea behind the approach is to employ multiobjective optimization to temporally reduce the selection pressure on specific components in a world model , allowing other components to adapt.
Revised:  The main idea behind the approach is to employ multiobjective optimization to temporally reduce the selection pressure on specific components in multi-component network , allowing other components to adapt.
Edit intent: clarity
Original: The next frontier in communications is the telemanipulation of remote environments.
Revised: The next frontier in communications is teleoperation -- manipulation and control of remote environments.
Edit intent: clarity
Original:  Compared to conventional network-based applications, telemanipulation poses widely different requirements, which demand a significant redesign of conventional networking techniques as well as their performance metrics.
Revised:  Compared to conventional networked applications, teleoperation poses widely different requirements, which demand a significant redesign of conventional networking techniques as well as their performance metrics.
Edit intent: clarity
Original:  In this paper, we present our first step towards developing a quantitative metric for TI.
Revised:  In this paper, we take the first step towards developing a quantitative metric for TI.
Edit intent: clarity
Original:  We propose a method that characterizes TI in an end-to-end fashion to obtain fine-grained performance  in terms of delay and precision.
Revised:  We propose a method that captures the fine-grained performance in terms of delay and precision.
Edit intent: clarity
Original:  We propose a method that characterizes TI in an end-to-end fashion to obtain fine-grained performance  in terms of delay and precision.
Revised:  We propose a method that characterizes TI in an end-to-end fashion to obtain fine-grained performance of TI in terms of delay and precision.
Edit intent: clarity
Original: The resurgence of measles is largely attributed to the decline in vaccine adoption and the increase in travels .
Revised: The resurgence of measles is largely attributed to the decline in vaccine adoption and the increase in mobility .
Edit intent: clarity
Original:  Although the vaccine for measles is readily available and highly successful, its current adoption is not adequate to prevent epidemic .
Revised:  Although the vaccine for measles is readily available and highly successful, its current adoption is not adequate to prevent epidemics .
Edit intent: fluency
Original:  Vaccine adoption is directly affected by vaccination decisions from human behaviour and has a complex interplay with the spatial spread of disease specific to one's travelling network.
Revised:  Vaccine adoption is directly affected by individual vaccination decisions, and has a complex interplay with the spatial spread of disease specific to one's travelling network.
Edit intent: clarity
Original:  Vaccine adoption is directly affected by vaccination decisions from human behaviour and has a complex interplay with the spatial spread of disease specific to one's travelling network.
Revised:  Vaccine adoption is directly affected by vaccination decisions from human behaviour and has a complex interplay with the spatial spread of disease shaped by an underlying mobility (travelling) network.
Edit intent: clarity
Original:  Our results show a non-linear correlation between epidemic dynamics and  network's assortativity, highlighting that highly disassortative networks are particularly impactful at containing epidemics .
Revised:  Our results show a correlation between the epidemic dynamics and network's assortativity, highlighting that highly disassortative networks are particularly impactful at containing epidemics .
Edit intent: clarity
Original:  Our results show a non-linear correlation between epidemic dynamics and  network's assortativity, highlighting that highly disassortative networks are particularly impactful at containing epidemics .
Revised:  Our results show a non-linear correlation between epidemic dynamics and the network's assortativity, highlighting that highly disassortative networks are particularly impactful at containing epidemics .
Edit intent: fluency
Original:  The main practical conclusion of the model is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor.
Revised:  The main practical conclusion of the proposed explanation is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor.
Edit intent: clarity
Original:  This conclusion, in turn, relies on two more concrete predictions of the model , one of which is tested using publicly available market data without the information about meta-orders .
Revised:  This conclusion, in turn, relies on two more concrete predictions , one of which is tested using publicly available market data without the information about meta-orders .
Edit intent: clarity
Original:  This conclusion, in turn, relies on two more concrete predictions of the model , one of which is tested using publicly available market data without the information about meta-orders .
Revised:  This conclusion, in turn, relies on two more concrete predictions of the model , one of which is tested using publicly available market data .
Edit intent: clarity
Original: `` What is an algorithm?
Revised: " What is an algorithm?
Edit intent: fluency
Original:  '' is a fundamental question of computer science.
Revised:  " is a fundamental question of computer science.
Edit intent: fluency
Original:  Gurevich's behavioural theory of sequential algorithms (aka the sequential ASM thesis) gives an abstract answer without referring to a particular language or programming style, and shows that sequential algorithms are captured by  sequential Abstract State Machines .
Revised:  Gurevich's behavioural theory of sequential algorithms (aka the sequential ASM thesis) gives an abstract answer without referring to a particular machine model or programming language, and showing that they are captured by sequential Abstract State Machines .
Edit intent: clarity
Original:  Moschovakis criticised that recursive algorithms such as mergesort are not covered by this theory , but can be expressed by recursors .
Revised:  Moschovakis criticised that recursive algorithms such as mergesort are not covered by this theory .
Edit intent: coherence
Original:  Furthermore, we show that recursive algorithms are behaviorally equivalent to finitely composed concurrent algorithms all of their runs are partial-order runs.
Revised:  Furthermore, we show that recursive algorithms are behaviorally equivalent to finitely composed concurrent algorithms all of whose concurrent runs are partial-order runs.
Edit intent: clarity
Original: Spatial networks are ubiquitous in social, geographic , physical, and biological applications.
Revised: Spatial networks are ubiquitous in social, geographical , physical, and biological applications.
Edit intent: fluency
Original:  To understand their large-scale structure  , it is important to develop methods that allow one to directly probe the effects of space on structure and dynamics.
Revised:  To understand the large-scale structure , it is important to develop methods that allow one to directly probe the effects of space on structure and dynamics.
Edit intent: fluency
Original:  In this paper, we study a variety of spatial networks --- including both synthetic and natural ones --- using novel topological methods that we recently developed specifically for analyzing spatial networks.
Revised:  In this paper, we study a variety of spatial networks -- including both synthetic and natural ones --- using novel topological methods that we recently developed specifically for analyzing spatial networks.
Edit intent: fluency
Original:  In this paper, we study a variety of spatial networks --- including both synthetic and natural ones --- using novel topological methods that we recently developed specifically for analyzing spatial networks.
Revised:  In this paper, we study a variety of spatial networks --- including both synthetic and natural ones -- using novel topological methods that we recently developed specifically for analyzing spatial networks.
Edit intent: fluency
Original:  In this paper, we study a variety of spatial networks --- including both synthetic and natural ones --- using novel topological methods that we recently developed specifically for analyzing spatial networks.
Revised:  In this paper, we study a variety of spatial networks --- including both synthetic and natural ones --- using novel topological methods that we recently developed for analyzing spatial networks.
Edit intent: clarity
Original: Received Signal Strength (RSS) fingerprint-based localization  attracted a lot of research effort as well as cultivating many commercial applications of location-based services due to its low cost and ease of implementation.
Revised: Received Signal Strength (RSS) fingerprint-based localization has attracted a lot of research effort as well as cultivating many commercial applications of location-based services due to its low cost and ease of implementation.
Edit intent: fluency
Original: Received Signal Strength (RSS) fingerprint-based localization  attracted a lot of research effort as well as cultivating many commercial applications of location-based services due to its low cost and ease of implementation.
Revised: Received Signal Strength (RSS) fingerprint-based localization attracted a lot of research effort and cultivated many commercial applications of location-based services due to its low cost and ease of implementation.
Edit intent: coherence
Original:  These problems require frequent retraining of machine learning models with new data gathered by crowdsourcing .
Revised:  These problems require frequent retraining of DL models with vast amounts of measurements .
Edit intent: clarity
Original:  Although crowdsourcing is a good way to collect data, it has a privacy problem due to collection of raw data from participants.
Revised:  Although crowdsourcing is an excellent way to gather immense amounts of data, it has a privacy problem due to collection of raw data from participants.
Edit intent: clarity
Original:  Federated learning enables to preserving privacy of the crowdsourcing participants by performing model training at the edge devices in a decentralized manner , data of participants is not exposed to centralized entity .
Revised:  Federated learning enables to preserving privacy of the crowdsourcing participants by performing model training at the edge devices in a decentralized manner ; the participants do not expose their data anymore to a centralized server .
Edit intent: clarity
Original:  This paper presents a novel method  to improve the reliability and the robustness of RSS fingerprint-based localization while preserving  privacy of the participants and the users by employing federated learning.
Revised:  This paper presents a novel method utilizing federated learning to improve the reliability and the robustness of RSS fingerprint-based localization while preserving privacy of the participants and the users by employing federated learning.
Edit intent: clarity
Original:  This paper presents a novel method  to improve the reliability and the robustness of RSS fingerprint-based localization while preserving  privacy of the participants and the users by employing federated learning.
Revised:  This paper presents a novel method to improve the accuracy of RSS fingerprint-based localization while preserving privacy of the participants and the users by employing federated learning.
Edit intent: clarity
Original:  This paper presents a novel method  to improve the reliability and the robustness of RSS fingerprint-based localization while preserving  privacy of the participants and the users by employing federated learning.
Revised:  This paper presents a novel method to improve the reliability and the robustness of RSS fingerprint-based localization while preserving the privacy of the participants and the users by employing federated learning.
Edit intent: fluency
Original:  The cell volume fraction, cell velocity - fluid pressure system, and nutrient concentration are the model variables.
Revised:  The cell volume fraction, cell velocity--fluid pressure system, and nutrient concentration are the model variables.
Edit intent: fluency
Original:  A coupled system of a hyperbolic conservation law, a viscoelastic system \cred{a viscous fluid model} , and a parabolic diffusion equation governs the dynamics of the model variables.
Revised:  A coupled system of a hyperbolic conservation law, \cred{a viscous fluid model} , and a parabolic diffusion equation governs the dynamics of the model variables.
Edit intent: clarity
Original:  The tumour boundary moves with the normal velocity of the outermost layer of cells, and this time-dependence is a challenge in designing and implementing a stable and fast numerical scheme.
Revised:  The tumour boundary moves with the normal velocity of the outermost layer of cells, and this time--dependence is a challenge in designing and implementing a stable and fast numerical scheme.
Edit intent: fluency
Original:  This procedure eliminates the need to track the tumour boundary explicitly and the computationally expensive re-meshing of the time-dependent domains.
Revised:  This procedure eliminates the need to track the tumour boundary explicitly and the computationally expensive re--meshing of the time--dependent domains.
Edit intent: fluency
Original:  A numerical scheme based on finite volume methods for the hyperbolic conservation law, Lagrange P_2 - P_1 Taylor-Hood finite element method for the viscoelastic system, and mass-lumped finite element method for the parabolic equations is implemented in two spatial dimensions, and several cases are studied.
Revised:  A numerical scheme based on finite volume methods for the hyperbolic conservation law, Lagrange P_2 - P_1 Taylor--Hood finite element method for the viscoelastic system, and mass-lumped finite element method for the parabolic equations is implemented in two spatial dimensions, and several cases are studied.
Edit intent: fluency
Original:  A numerical scheme based on finite volume methods for the hyperbolic conservation law, Lagrange P_2 - P_1 Taylor-Hood finite element method for the viscoelastic system, and mass-lumped finite element method for the parabolic equations is implemented in two spatial dimensions, and several cases are studied.
Revised:  A numerical scheme based on finite volume methods for the hyperbolic conservation law, Lagrange P_2 - P_1 Taylor-Hood finite element method for the system, and mass-lumped finite element method for the parabolic equations is implemented in two spatial dimensions, and several cases are studied.
Edit intent: clarity
Original:  A numerical scheme based on finite volume methods for the hyperbolic conservation law, Lagrange P_2 - P_1 Taylor-Hood finite element method for the viscoelastic system, and mass-lumped finite element method for the parabolic equations is implemented in two spatial dimensions, and several cases are studied.
Revised:  A numerical scheme based on finite volume methods for the hyperbolic conservation law, Lagrange P_2 - P_1 Taylor-Hood finite element method for the viscoelastic system, and mass--lumped finite element method for the parabolic equations is implemented in two spatial dimensions, and several cases are studied.
Edit intent: fluency
Original: The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, gives the distribution function of the Just Noticeable Difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one.
Revised: The satisfied user ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, gives the distribution function of the Just Noticeable Difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one.
Edit intent: fluency
Original: The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, gives the distribution function of the Just Noticeable Difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one.
Revised: The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, gives the distribution function of the just noticeable difference (JND), the smallest distortion level that can be perceived by a subject when a reference image is compared to a distorted one.
Edit intent: fluency