wanyu/IteraTeR_v2 huggingface dataset:
Original: In this paper, we present a supervised machine learning (ML) based computational framework for designing particulate multifunctional composite materials for desired thermal conductivity (TC) . In this framework, the design variables are physical descriptors of the material microstructure to link microstructure to properties for materialdesign.
Revised: In this paper, we present a supervised machine learning (ML) based computational framework for designing particulate multifunctional composite materials with desired thermal conductivity (TC) . In this framework, the design variables are physical descriptors of the material microstructure to link microstructure to properties for materialdesign.
Edit intent: fluency
Original: In this paper, we present a supervised machine learning (ML) based computational framework for designing particulate multifunctional composite materials for desired thermal conductivity (TC) . In this framework, the design variables are physical descriptors of the material microstructure to link microstructure to properties for materialdesign.
Revised: In this paper, we present a supervised machine learning (ML) based computational framework for designing particulate multifunctional composite materials for desired thermal conductivity (TC) is presented. The design variables are physical descriptors of the material microstructure to link microstructure to properties for materialdesign.
Edit intent: coherence
Original: In this paper, we present a supervised machine learning (ML) based computational framework for designing particulate multifunctional composite materials for desired thermal conductivity (TC) . In this framework, the design variables are physical descriptors of the material microstructure to link microstructure to properties for materialdesign.
Revised: In this paper, we present a supervised machine learning (ML) based computational framework for designing particulate multifunctional composite materials for desired thermal conductivity (TC) . In this framework, the design variables are physical descriptors of the material microstructure that directly link microstructure to properties for materialdesign.
Edit intent: clarity
Original:  Microstructures were realized through an efficient , fast packing algorithm, and the TC of microstructures were obtained using our previous Fast Fourier Transform (FFT) homogenization method.
Revised:  Microstructures were realized through an efficient , fast packing algorithm, and the TCs were obtained using our previous Fast Fourier Transform (FFT) homogenization method.
Edit intent: clarity
Original:  Microstructures were realized through an efficient , fast packing algorithm, and the TC of microstructures were obtained using our previous Fast Fourier Transform (FFT) homogenization method.
Revised:  Microstructures were realized through an efficient , fast packing algorithm, and the TC of microstructures were obtained using our previously developed Fast Fourier Transform (FFT) homogenization method.
Edit intent: clarity
Original:  Thereafter, the ML methods constituting a reduced-order model (ROM) was trained over the generated database to establish the complex relationship between the structure and properties.
Revised:  Our optimized ML method is trained over the generated database to establish the complex relationship between the structure and properties.
Edit intent: clarity
Original:  Thereafter, the ML methods constituting a reduced-order model (ROM) was trained over the generated database to establish the complex relationship between the structure and properties.
Revised:  Thereafter, the ML methods constituting a reduced-order model (ROM) was trained over the generated database and establishes the complex relationship between the structure and properties.
Edit intent: coherence
Original:  Finally, the ROM is used for material design through an optimization algorithm to solve the inverse problem of finding the material with desired properties represented by its physical descriptors .
Revised:  Finally, the application of the trained ML model in the inverse design of a new class of composite materials, liquid metal (LM) elastomer, with desired TC is discussed .
Edit intent: clarity
Original:  The results showed that the surrogate model is accurate in predicting the behavior of microstructure with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies.
Revised:  The results show that the surrogate model is accurate in predicting the behavior of microstructure with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies.
Edit intent: fluency
Original:  The results showed that the surrogate model is accurate in predicting the behavior of microstructure with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies.
Revised:  The results showed that the surrogate model is accurate in predicting the microstructure behavior with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies.
Edit intent: clarity
Original:  First, we provide a precise analysis of Optimistic Gradient Descent/Ascent (OGDA) -- an optimistic variant of Gradient Descent/Ascent \mbox{ DBLP:journals/corr/abs-1711-00141 -- for unconstrained bilinear games, extending and strengthening prior results along several directions.
Revised:  First, we provide a precise analysis of Optimistic Gradient Descent/Ascent (OGDA) -- an optimistic variant of Gradient Descent/Ascent -- for unconstrained bilinear games, extending and strengthening prior results along several directions.
Edit intent: coherence
Original:  Although our algorithm closely resembles the no-regret projected OGDA dynamics, it surpasses the optimal no-regret convergence rate of \Theta(1/\epsilon) \mbox{ DASKALAKIS2015327 recent work in pursuing last-iterate guarantees in saddle-point problems \mbox{ daskalakis2018lastiterate,mertikopoulos2018optimistic .
Revised:  Our techniques supplement the recent work in pursuing last-iterate guarantees in saddle-point problems \mbox{ daskalakis2018lastiterate,mertikopoulos2018optimistic .
Edit intent: clarity
Original:  Although our algorithm closely resembles the no-regret projected OGDA dynamics, it surpasses the optimal no-regret convergence rate of \Theta(1/\epsilon) \mbox{ DASKALAKIS2015327 recent work in pursuing last-iterate guarantees in saddle-point problems \mbox{ daskalakis2018lastiterate,mertikopoulos2018optimistic .
Revised:  Although our algorithm closely resembles the no-regret projected OGDA dynamics, it surpasses the optimal no-regret convergence rate of \Theta(1/\epsilon) \mbox{ DASKALAKIS2015327 recent work in pursuing last-iterate guarantees in min-max optimization .
Edit intent: clarity
Original: We present a general post-quantum error-correcting technique for quantum annealing, called multi-qubit correction (MQC), that views the evolution in an open-system as a Gibs sampler and reduces a set of (first) excited states to a new synthetic state with lower energy value.
Revised: We present a generic post-quantum error-correcting technique for quantum annealing, called multi-qubit correction (MQC), that views the evolution in an open-system as a Gibs sampler and reduces a set of (first) excited states to a new synthetic state with lower energy value.
Edit intent: fluency
Original: We present a general post-quantum error-correcting technique for quantum annealing, called multi-qubit correction (MQC), that views the evolution in an open-system as a Gibs sampler and reduces a set of (first) excited states to a new synthetic state with lower energy value.
Revised: We present a general post-quantum error-correcting technique for quantum annealing, called multi-qubit correction (MQC), that views the evolution in an open-system as a Gibbs sampler and reduces a set of (first) excited states to a new synthetic state with lower energy value.
Edit intent: fluency
Original:  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels -- i .e., a group of qubits that changing their states simultaneously can result in a new state with lower energy -- and successively converges to the ground state.
Revised:  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels--i .e., a group of qubits that changing their states simultaneously can result in a new state with lower energy -- and successively converges to the ground state.
Edit intent: fluency
Original:  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels -- i .e., a group of qubits that changing their states simultaneously can result in a new state with lower energy -- and successively converges to the ground state.
Revised:  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels -- i .e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value--and successively converges to the ground state.
Edit intent: clarity
Original:  Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and also improves the reproducibility of results , compared to recent hardware/software advances in the realm of quantum annealing  such as reverse quantum annealing, increased inter-sample delay, and classical pre/post-processing methods.
Revised:  Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results , compared to recent hardware/software advances in the realm of quantum annealing such as reverse quantum annealing, increased inter-sample delay, and classical pre/post-processing methods.
Edit intent: clarity
Original:  Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and also improves the reproducibility of results , compared to recent hardware/software advances in the realm of quantum annealing  such as reverse quantum annealing, increased inter-sample delay, and classical pre/post-processing methods.
Revised:  Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and also improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing such as reverse quantum annealing, increased inter-sample delay, and classical pre/post-processing methods.
Edit intent: coherence
Original:  Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and also improves the reproducibility of results , compared to recent hardware/software advances in the realm of quantum annealing  such as reverse quantum annealing, increased inter-sample delay, and classical pre/post-processing methods.
Revised:  Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and also improves the reproducibility of results , compared to recent hardware/software advances in the realm of quantum annealing , such as reverse quantum annealing, increased inter-sample delay, and classical pre/post-processing methods.
Edit intent: fluency
Original: We present a generic post-quantum error-correcting technique for quantum annealing, called multi-qubit correction (MQC) , that views the evolution in an open-system as a Gibbs sampler and reduces a set of (first) excited states to a new synthetic state with lower energy value.
Revised: We present multi-qubit correction (MQC) , that views the evolution in an open-system as a Gibbs sampler and reduces a set of (first) excited states to a new synthetic state with lower energy value.
Edit intent: coherence
Original: We present a generic post-quantum error-correcting technique for quantum annealing, called multi-qubit correction (MQC) , that views the evolution in an open-system as a Gibbs sampler and reduces a set of (first) excited states to a new synthetic state with lower energy value.
Revised: We present a generic post-quantum error-correcting technique for quantum annealing, called multi-qubit correction (MQC) , that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value.
Edit intent: coherence
Original:  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels--i.e., a group of qubits that changing their states simultaneously can result in a new state with  lower energy value--and successively converges to the ground state.
Revised:  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels--i.e., a group of qubits that changing their states simultaneously can result in a new state with a lower energy value--and successively converges to the ground state.
Edit intent: fluency
Original:  Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as reverse quantum annealing, increased inter-sample delay , and classical pre/post-processing methods .
Revised:  Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as reverse quantum annealing, increased inter-sample delay between successive measurements .
Edit intent: clarity
Original: We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value.
Revised: We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value.
Edit intent: fluency
Original:  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels--i.e., a group of qubits that changing their states simultaneously can result in a new state with a lower energy value--and successively converges to the ground state.
Revised:  After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels--i.e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value--and successively converges to the ground state.
Edit intent: fluency
Original:  We study opinion dynamics in tie-decay networks , in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions.
Revised:  We study opinion dynamics in tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions.
Edit intent: fluency
Original:  First, we examine the spectral gaps of tie-decay networks from empirical data .
Revised:  First, we compare the spectral gaps of tie-decay networks from empirical data .
Edit intent: clarity
Original:  Intuitively, we expect small tie-decay rates to lead to fast convergence, because the influence of each interaction event between a pair of nodes lasts longer for smaller decay rates.
Revised:  Intuitively, we expect small tie-decay rates to lead to fast convergence, because the influence of each interaction event between two nodes lasts longer for smaller decay rates.
Edit intent: fluency
Original:  We study opinion dynamics in tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions.
Revised:  We study opinion dynamics on tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions.
Edit intent: clarity
Original:  We examine the speed of convergence by studying the spectral gap of combinatorial Laplacian matrices of tie-decay networks.
Revised:  We examine the speed of convergence by studying the spectral gaps of combinatorial Laplacian matrices of tie-decay networks.
Edit intent: fluency
Original:  First, we compare the spectral gaps of  tie-decay networks  from empirical data with those for corresponding randomized and aggregate networks.
Revised:  First, we compare the spectral gaps of tie-decay networks that we construct from empirical data with those for corresponding randomized and aggregate networks.
Edit intent: clarity
Original:  First, we compare the spectral gaps of  tie-decay networks  from empirical data with those for corresponding randomized and aggregate networks.
Revised:  First, we compare the spectral gaps of tie-decay networks from empirical data with the spectral gaps for corresponding randomized and aggregate networks.
Edit intent: clarity
Original:  We find that the spectral gaps of the empirical networks tend to be smaller than those of the randomized and aggregate networks.
Revised:  We find that the spectral gaps for the empirical networks tend to be smaller than those of the randomized and aggregate networks.
Edit intent: clarity
Original:  We find that the spectral gaps of the empirical networks tend to be smaller than those of the randomized and aggregate networks.
Revised:  We find that the spectral gaps of the empirical networks tend to be smaller than those for the randomized and aggregate networks.
Edit intent: clarity
Original:  Intuitively, we expect small tie-decay rates to lead to fast convergence , because the influence of each interaction event between two nodes lasts longer for smaller decay rates.
Revised:  Intuitively, we expect small tie-decay rates to lead to fast convergence because the influence of each interaction event between two nodes lasts longer for smaller decay rates.
Edit intent: fluency
Original:  Intuitively, we expect small tie-decay rates to lead to fast convergence , because the influence of each interaction event between two nodes lasts longer for smaller decay rates.
Revised:  Intuitively, we expect small tie-decay rates to lead to fast convergence , because the influence of each interaction between two nodes lasts longer for smaller decay rates.
Edit intent: clarity
Original:  Moreover, as time progresses and more interaction events occur, we expect to observe convergenceto proceed .
Revised:  Moreover, as time progresses and more interactions occur, we expect to observe convergenceto proceed .
Edit intent: clarity
Original:  Moreover, as time progresses and more interaction events occur, we expect to observe convergenceto proceed .
Revised:  Moreover, as time progresses and more interaction events occur, we expect eventual convergence .
Edit intent: clarity
Original:  However, we demonstrate that the spectral gaps need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time.
Revised:  However, we demonstrate that the spectral gap need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time.
Edit intent: fluency
Original:  Our results highlight the importance of the interplay between the times that edges arise and decay in temporal networks.
Revised:  Our results highlight the importance of the interplay between the times that edges strengthen and decay in temporal networks.
Edit intent: clarity
Original:  These shortcomings make such methods insufficient for recommending adequate citations when working on manuscripts.
Revised:  These shortcomings make such methods insufficient for recommending adequate citations to academic manuscripts.
Edit intent: clarity
Original:  In this study, we propose a novel approach called dual attention model for citation recommendation (DACR)  to recommend citations during manuscript preparation.
Revised:  In this study, we propose a novel approach called dual attention model for citation recommendation (DACR) " to recommend citations during manuscript preparation.
Edit intent: fluency
Original:  Our method considers three dimensions of information: contextual words , structural contexts, and the section on which a user is working.
Revised:  Our method adapts embedding of three dimensions of information: contextual words , structural contexts, and the section on which a user is working.
Edit intent: clarity
Original:  Our method considers three dimensions of information: contextual words , structural contexts, and the section on which a user is working.
Revised:  Our method considers three dimensions of semantic information: words in the local context , structural contexts, and the section on which a user is working.
Edit intent: clarity
Original:   The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information , and the latter aims to learn the importance of inputs .
Revised:  The core of the neural network is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information , and the latter aims to learn the importance of inputs .
Edit intent: clarity
Original:   The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information , and the latter aims to learn the importance of inputs .
Revised:  The core of the proposed model is composed of self-attention and additive attention, where the former aims to capture the relatedness between input information , and the latter aims to learn the importance of them .
Edit intent: clarity
Original: Recently, some researchers in the community of answer set programming introduced the notions of subjective constraint monotonicity, epistemic splitting, and foundedness  for epistemic logic programs, aiming to use them as main criteria / intuitions to compare different answer set semantics proposed in the literature on how they comply with these intuitions.
Revised: Recently, the notions of subjective constraint monotonicity, epistemic splitting, and foundedness for epistemic logic programs, aiming to use them as main criteria / intuitions to compare different answer set semantics proposed in the literature on how they comply with these intuitions.
Edit intent: clarity
Original: Recently, some researchers in the community of answer set programming introduced the notions of subjective constraint monotonicity, epistemic splitting, and foundedness  for epistemic logic programs, aiming to use them as main criteria / intuitions to compare different answer set semantics proposed in the literature on how they comply with these intuitions.
Revised: Recently, some researchers in the community of answer set programming introduced the notions of subjective constraint monotonicity, epistemic splitting, and foundedness for epistemic logic programs, with the aim to use them as main criteria / intuitions to compare different answer set semantics proposed in the literature on how they comply with these intuitions.
Edit intent: coherence