wanyu/IteraTeR_v2 huggingface dataset:
Original:  The important notion of temporal stability describes the character of fluctuations in population dynamics, driven by intrinsic or extrinsic noise.
Revised:  The important notion of `temporal stability' describes the character of fluctuations in population dynamics, driven by intrinsic or extrinsic noise.
Edit intent: fluency
Original:  Excitingly, we are able to show how different network structures leave distinct signatures in the spectrum of fluctuations, opening the door to a new avenue of research in the age-old complexity/stability debate .
Revised:  We show that different network structures leave distinct signatures in the spectrum of fluctuations, opening the door to a new avenue of research in the age-old complexity/stability debate .
Edit intent: coherence
Original:  By expanding self-adaptation, a novel class of GAs oriented towards artificial life and open ended evolution can emerge .
Revised:  By expanding self-adaptation, a novel class of GAs oriented towards artificial life and AI .
Edit intent: clarity
Original:  This representation further yields the first IPM with o(%DIFDELCMD < \operatorname{rank}%%% (A)) time per iteration when the treewidth is small .
Revised:  This representation further yields the first IPM with o(%DIFDELCMD < \operatorname{rank}%%% .
Edit intent: clarity
Original:  Almost all the decreases occurred between mid-April and mid-June , which unexpectedly corresponds to the time when many state and national lockdowns were released resulting in surges of new cases.
Revised:  Surprisingly, these changes mostly occurred between mid-April and mid-June , which unexpectedly corresponds to the time when many state and national lockdowns were released resulting in surges of new cases.
Edit intent: clarity
Original:  Almost all the decreases occurred between mid-April and mid-June , which unexpectedly corresponds to the time when many state and national lockdowns were released resulting in surges of new cases.
Revised:  Almost all the decreases occurred between mid-April and mid-June -- at a time when lockdown relaxations in many states and countries caused surges of new cases.
Edit intent: clarity
Original: Our compartment model shows that the COVID-19 case mortality rate  decreased by at least 80 percent in most of the US states and at least 90 percent in most European countries.
Revised: Our compartment model shows that the COVID-19 case mortality rate seems to have decreased by at least 80 percent in most of the US states and at least 90 percent in most European countries.
Edit intent: clarity
Original: Our compartment model shows that the COVID-19 case mortality rate  decreased by at least 80 percent in most of the US states and at least 90 percent in most European countries.
Revised: Our compartment model shows that the COVID-19 case mortality rate decreased by at least 80 \% in most of the US states and at least 90 percent in most European countries.
Edit intent: fluency
Original: Our compartment model shows that the COVID-19 case mortality rate  decreased by at least 80 percent in most of the US states and at least 90 percent in most European countries.
Revised: Our compartment model shows that the COVID-19 case mortality rate decreased by at least 80 percent in most of the US and at least 90 percent in most European countries.
Edit intent: clarity
Original:  Surprisingly, these changes mostly occurred between mid-April and mid-June -- at a time when lockdown relaxations in many states and countries caused surges of new cases.
Revised:  Almost all the decreases occurred between mid-April and mid-June -- at a time when lockdown relaxations in many states and countries caused surges of new cases.
Edit intent: clarity
Original:  Surprisingly, these changes mostly occurred between mid-April and mid-June -- at a time when lockdown relaxations in many states and countries caused surges of new cases.
Revised:  Surprisingly, these changes mostly occurred between mid-April and mid-June , which unexpectedly corresponds to the time when many state and national lockdowns were released resulting in surges of new cases.
Edit intent: clarity
Original:  Plausible causes for this drop are discussed , such as improvements in treatments , face mask wearing, a new virus strain, and age demographics of infected patients, but none alone support such large decreases .
Revised:  Several plausible causes for this drop are discussed , such as improvements in treatments , face mask wearing, a new virus strain, and age demographics of infected patients, but none alone support such large decreases .
Edit intent: fluency
Original:  Plausible causes for this drop are discussed , such as improvements in treatments , face mask wearing, a new virus strain, and age demographics of infected patients, but none alone support such large decreases .
Revised:  Plausible causes for this drop are examined , such as improvements in treatments , face mask wearing, a new virus strain, and age demographics of infected patients, but none alone support such large decreases .
Edit intent: clarity
Original:  Plausible causes for this drop are discussed , such as improvements in treatments , face mask wearing, a new virus strain, and age demographics of infected patients, but none alone support such large decreases .
Revised:  Plausible causes for this drop are discussed , such as improvements in treatment , face mask wearing, a new virus strain, and age demographics of infected patients, but none alone support such large decreases .
Edit intent: fluency
Original:  Plausible causes for this drop are discussed , such as improvements in treatments , face mask wearing, a new virus strain, and age demographics of infected patients, but none alone support such large decreases .
Revised:  Plausible causes for this drop are discussed , such as improvements in treatments , face mask wearing, a new virus strain, and potentially changing demographics of infected patients, but none alone support such large decreases .
Edit intent: clarity
Original:  However, the basic question of how to sample efficiently from the HCIZ density has remained open.
Revised:  However, the basic question of efficient sampling from the HCIZ density has remained open.
Edit intent: coherence
Original:  However, the basic question of how to sample efficiently from the HCIZ density has remained open.
Revised:  However, the basic question of how to sample efficiently from the HCIZ distribution has remained open.
Edit intent: clarity
Original: We consider discrete linear Chebyshev approximation problems , where the approximation criterion to minimize by fitting the unknown parameters of a linear function takes the form of the least maximum absolute deviation of errors.
Revised: We consider discrete linear Chebyshev approximation problems in which the unknown parameters of a linear function takes the form of the least maximum absolute deviation of errors.
Edit intent: clarity
Original: We consider discrete linear Chebyshev approximation problems , where the approximation criterion to minimize by fitting the unknown parameters of a linear function takes the form of the least maximum absolute deviation of errors.
Revised: We consider discrete linear Chebyshev approximation problems , where the approximation criterion to minimize by fitting the unknown parameters of linear function are fitted by minimizing the least maximum absolute deviation of errors.
Edit intent: clarity
Original:  The least maximum absolute deviation estimator is widely used in regression analysis in statistics when the distribution of errors has bounded support.
Revised:  The least maximum absolute deviation estimator is extensively used in regression analysis in statistics when the distribution of errors has bounded support.
Edit intent: clarity
Original:  We propose an algebraic approach to derive a direct solution of the problem .
Revised:  To derive a direct solution of the problem .
Edit intent: clarity
Original:  Furthermore, we construct a procedure to  multidimensional approximation (multiple linear regression) problems.
Revised:  Furthermore, we develop a procedure to multidimensional approximation (multiple linear regression) problems.
Edit intent: clarity
Original:  Furthermore, we construct a procedure to  multidimensional approximation (multiple linear regression) problems.
Revised:  Furthermore, we construct a procedure to solve multidimensional approximation (multiple linear regression) problems.
Edit intent: clarity
Original:  We estimate computational complexity of the procedure and give an illustrative numerical example .
Revised:  We describe the main components of the procedure and give an illustrative numerical example .
Edit intent: clarity
Original: We consider discrete linear Chebyshev approximation problems in which the unknown parameters of linear function are fitted by minimizing the least maximum absolute deviation of approximation errors.
Revised: We consider discrete linear Chebyshev approximation problems in which the unknown parameters of linear function are fitted by minimizing the least maximum absolute deviation of errors.
Edit intent: fluency
Original:  Such problems find wide application in the solution of overdetermined systems of linear equations that appear in many practical contexts.
Revised:  Such problems find application in the solution of overdetermined systems of linear equations that appear in many practical contexts.
Edit intent: clarity
Original:  The least maximum absolute deviation estimator is extensively used in regression analysis in statistics when the distribution of errors has bounded support.
Revised:  The least maximum absolute deviation estimator is used in regression analysis in statistics when the distribution of errors has bounded support.
Edit intent: clarity
Original:  To derive a direct solution of the approximation problem, we propose an algebraic approach based on a parameter elimination technique.
Revised:  To derive a direct solution of the problem, we propose an algebraic approach based on a parameter elimination technique.
Edit intent: clarity
Original:  As a key component of the approach, an elimination lemma is proved that allows to handle the problem by reducing  to a problem with one unknown parameter eliminated together with a box constraint imposed on this parameter.
Revised:  As a key component of the approach, an elimination lemma is proved to handle the problem by reducing to a problem with one unknown parameter eliminated together with a box constraint imposed on this parameter.
Edit intent: coherence
Original:  As a key component of the approach, an elimination lemma is proved that allows to handle the problem by reducing  to a problem with one unknown parameter eliminated together with a box constraint imposed on this parameter.
Revised:  As a key component of the approach, an elimination lemma is proved that allows to handle the problem by reducing it to a problem with one unknown parameter eliminated together with a box constraint imposed on this parameter.
Edit intent: clarity
Original:  As a key component of the approach, an elimination lemma is proved that allows to handle the problem by reducing  to a problem with one unknown parameter eliminated together with a box constraint imposed on this parameter.
Revised:  As a key component of the approach, an elimination lemma is proved that allows to handle the problem by reducing to a problem with one parameter eliminated, together with a box constraint imposed on this parameter.
Edit intent: clarity
Original:  We apply this result to derive direct solutions of problems of low dimension, formulated as linear regression problems with one and two parameters.
Revised:  We demonstrate the application of the lemma to the direct solution of linear regression problems with one and two parameters.
Edit intent: clarity
Original:  Furthermore, we develop a procedure to solve multidimensional approximation (multiple linear regression) problems  .
Revised:  We develop a procedure to solve multidimensional approximation (multiple linear regression) problems .
Edit intent: coherence
Original:  The procedure is based on a direct solution method that comprises two phases: backward elimination and forward determination (substitution ) of the unknown parameters.
Revised:  The procedure follows a method that comprises two phases: backward elimination and forward determination (substitution ) of the unknown parameters.
Edit intent: clarity
Original:  The procedure is based on a direct solution method that comprises two phases: backward elimination and forward determination (substitution ) of the unknown parameters.
Revised:  The procedure is based on a direct solution method that comprises two phases: backward elimination and forward substitution of parameters.
Edit intent: clarity
Original:  We implement symbolic computations in MATLAB to obtain exact solutions for two illustrative numerical examples.
Revised:  We implement symbolic computations in MATLAB to obtain exact solutions for two numerical examples.
Edit intent: clarity
Original: We consider discrete linear Chebyshev approximation problems in which the unknown parameters of linear function are fitted by minimizing the least maximum absolute deviation of errors.
Revised: We consider discrete linear Chebyshev approximation problems in which the unknown parameters of linear function are fitted by minimizing the maximum absolute deviation of errors.
Edit intent: clarity
Original: This paper proposes a framework for modeling sound change that combines deep convolutional neural networks and iterative learning.
Revised: This paper proposes a framework for modeling sound change that combines deep learning and iterative learning.
Edit intent: clarity
Original:  Acquisition and transmission of speech across generations is modeled by training generations of Generative Adversarial Networks ( Goodfellow et al. arXiv:1406.2661,Donahue et al. arXiv:1705.07904 ) on unannotated raw speech data.
Revised:  Acquisition and transmission of speech is modeled by training generations of Generative Adversarial Networks ( Goodfellow et al. arXiv:1406.2661,Donahue et al. arXiv:1705.07904 ) on unannotated raw speech data.
Edit intent: clarity
Original:  Acquisition and transmission of speech across generations is modeled by training generations of Generative Adversarial Networks ( Goodfellow et al. arXiv:1406.2661,Donahue et al. arXiv:1705.07904 ) on unannotated raw speech data.
Revised:  Acquisition and transmission of speech across generations is modeled by training generations of Generative Adversarial Networks ( GANs ) on unannotated raw speech data.
Edit intent: clarity
Original:  Four generations of Generative Adversarial Networks were trained on an allophonic distribution in English where voiceless stops are aspirated word-initially before stressed vowels except if preceded by s . The first generation of networks is trained on the relevant sequences in human speech from the TIMITdatabase .
Revised:  s . The first generation of networks is trained on the relevant sequences in human speech from the TIMITdatabase .
Edit intent: coherence
Original:  Four generations of Generative Adversarial Networks were trained on an allophonic distribution in English where voiceless stops are aspirated word-initially before stressed vowels except if preceded by s . The first generation of networks is trained on the relevant sequences in human speech from the TIMITdatabase .
Revised:  Four generations of Generative Adversarial Networks were trained on an allophonic distribution in English where voiceless stops are aspirated word-initially before stressed vowels except if preceded by s . The first generation of networks is trained on the relevant sequences in human speech from TIMIT .
Edit intent: clarity
Original:  The initial allophonic distribution is progressively being lost with each generation, likely due to pressures from the global distribution of aspiration in the training data that resembles phonological pressures in natural language .
Revised:  The initial allophonic distribution is progressively being lost with each generation, likely due to pressures from the global distribution of aspiration in the training data .
Edit intent: clarity
Original:  At endpoints, the networks' outputs superficially resemble a phonological change -- rule loss -- driven by imperfect learning.
Revised:  At endpoints, the outputs superficially resemble a phonological change -- rule loss -- driven by imperfect learning.
Edit intent: clarity
Original:  These methods often work by augmenting a solution subspace generated iteratively by a known algorithm with a fixed subspace of vectors which are ``useful ' for solving the problem.
Revised:  These methods often work by augmenting a solution subspace generated iteratively by a known algorithm with a fixed subspace of vectors which are ``useful '' for solving the problem.
Edit intent: fluency
Original:  In this paper, we consider subspace augmentation-type iterative schemes applied to a linear ill-posed problems in a continuous Hilbert space setting, based on a recently developed framework describing these methods.
Revised:  In this paper, we consider subspace augmentation-type iterative schemes applied to linear ill-posed problems in a continuous Hilbert space setting, based on a recently developed framework describing these methods.
Edit intent: fluency
Original:  At the same time,  objective functions are often reported to be black-box and since many problem properties are unknown the design of realistic benchmarks is difficult .
Revised:  At the same time, the design of realistic benchmarks is difficult, because objective functions are often reported to be black-box and since many problem properties are unknown the design of realistic benchmarks is difficult .
Edit intent: coherence
Original:  At the same time,  objective functions are often reported to be black-box and since many problem properties are unknown the design of realistic benchmarks is difficult .
Revised:  At the same time, objective functions are often reported to be black-box and many problem properties are unknown the design of realistic benchmarks is difficult .
Edit intent: coherence
Original:  At the same time,  objective functions are often reported to be black-box and since many problem properties are unknown the design of realistic benchmarks is difficult .
Revised:  At the same time, objective functions are often reported to be black-box and since many problem properties are unknown .
Edit intent: clarity
Original: An image is worth a thousand words, conveying information that goes beyond the mere visual content therein.
Revised: An image is worth a thousand words, conveying information that goes beyond the physical visual content therein.
Edit intent: clarity
Original:  In this paper, we study the intent behind social media images with an aim to analyze how visual information can facilitate recognition of human intent.
Revised:  In this paper, we study the intent behind social media images with an aim to analyze how visual information can help the recognition of human intent.
Edit intent: clarity
Original:  These images are manually annotated with 28 intent categories  derived from a social psychology taxonomy.
Revised:  These images are manually annotated with 28 intent categories that are derived from a social psychology taxonomy.
Edit intent: coherence
Original: Towards characterizing the optimization landscape of games, this paper analyzes the stability and spectrum of gradient-based dynamics near fixed points of two-player continuous games.
Revised: Towards characterizing the optimization landscape of games, this paper analyzes the stability of gradient-based dynamics near fixed points of two-player continuous games.
Edit intent: clarity
Original:  We introduce the quadratic numerical range as a method to bound the spectrum of game dynamics linearized about local equilibria.
Revised:  We introduce the quadratic numerical range as a method to characterize the spectrum of game dynamics linearized about local equilibria.
Edit intent: clarity
Original:  Our results show that by decomposing the game Jacobian into symmetric and anti-symmetric components, we can assess the contribution of  vector field's potential and rotational components to the stability of the equilibrium.
Revised:  By decomposing the game Jacobian into symmetric and anti-symmetric components, we can assess the contribution of vector field's potential and rotational components to the stability of the equilibrium.
Edit intent: coherence
Original:  Our results show that by decomposing the game Jacobian into symmetric and anti-symmetric components, we can assess the contribution of  vector field's potential and rotational components to the stability of the equilibrium.
Revised:  Our results show that by decomposing the game Jacobian into symmetric and anti-symmetric components, we assess the contribution of vector field's potential and rotational components to the stability of the equilibrium.
Edit intent: clarity
Original:  Our results show that by decomposing the game Jacobian into symmetric and anti-symmetric components, we can assess the contribution of  vector field's potential and rotational components to the stability of the equilibrium.
Revised:  Our results show that by decomposing the game Jacobian into symmetric and anti-symmetric components, we can assess the contribution of a vector field's potential and rotational components to the stability of the equilibrium.
Edit intent: fluency
Original:  In zero-sum games, all differential Nash equilibria are stable ;
Revised:  In zero-sum games, all Nash are stable and robust ;
Edit intent: clarity
Original:  We conclude with a numerical example that investigates how players with different learning rates can take advantage of rotational components of the game to converge faster .
Revised:  We conclude with a numerical example in which learning with timescale separation results in faster convergence .
Edit intent: clarity
Original: Inspired by the success of deep learning, recent industrial Click-Through Rate (CTR) prediction models have made the transition from traditional shallow approaches to deep approaches.
Revised: Inspired by the success of deep learning, recent industrial Click-Through Rate (CTR) prediction tasks. In recent years,
Edit intent: clarity
Original:  The non-linear interaction may be hard to capture and explicitly model theco-action of raw feature is beneficial for CTR prediction.
Revised:  The non-linear interaction may be hard to capture and explicitly model the of raw feature is beneficial for CTR prediction.
Edit intent: fluency
Original:  The non-linear interaction may be hard to capture and explicitly model theco-action of raw feature is beneficial for CTR prediction.
Revised:  The non-linear interaction may be hard to capture and explicitly model theco-action 
Edit intent: coherence
Original: Co-action refers to the collective effects of features toward final prediction.
Revised:  refers to the collective effects of features toward final prediction.
Edit intent: clarity
Original:  Motivated by our observation ,  we propose feature Co-Action Network (CAN) to explore the potential of feature co-action.
Revised:  Motivated by our observation , we propose a Co-Action Network (CAN) to explore the potential of feature co-action.
Edit intent: fluency
Original:  Up to now , CAN has been deployed in the Alibaba display advertisement system , obtaining averaging 12\\% improvement on CTR and 8\\% on RPM .
Revised:  Up to now , CAN has been deployed in the display advertisement system , obtaining averaging 12\\% improvement on CTR and 8\\% on RPM .
Edit intent: clarity
Original:  As a result, we not only prove an optimal convergence between the true solution and the numerical solution, but also get a supercloseness result between the Lagrange interpolant and the numerical solution.
Revised:  As a result, we not only prove an optimal convergence between the true solution and the numerical solution, but also get a supercloseness result between the Lagrange interpolation and the numerical solution.
Edit intent: fluency
Original:  More importantly, our arguments can be easily generalized to more general situations .
Revised:  Numerical tests confirm our theoretical results .
Edit intent: clarity