wanyu/IteraTeR_v2 huggingface dataset:
Original:  We derive the optimal consumption policies explicitly in terms of the solution of a nonlinear free-boundary problem, which we analyze in detail.
Revised:  Optimal consumption policies are derived explicitly in terms of the solution of a nonlinear free-boundary problem, which we analyze in detail.
Edit intent: clarity
Original:  Impatient individuals (those with sufficiently large utility discount rates) always consume above the minimum rate;
Revised:  Impatient always consume above the minimum rate;
Edit intent: clarity
Original:  Patient individuals (those with small utility discount rates) consume at the minimum rate if their wealth-to-habit ratio is below a threshold, and above it otherwise.
Revised:  Patient individuals consume at the minimum rate if their wealth-to-habit ratio is below a threshold, and above it otherwise.
Edit intent: clarity
Original:  In particular, it is currently unclear whether or not VAEs can faithfully reproduce the complex multi-residue mutation patterns observed in natural sequences arising due to epistasis.
Revised:  It is currently unclear whether or not VAEs can faithfully reproduce the complex multi-residue mutation patterns observed in natural sequences arising due to epistasis.
Edit intent: coherence
Original:  Another line of prior work addresses the exploration-exploitation trade-off problem with contextual bandit methods, which are  less studied in the industry recently due to the difficulty in extending their flexibility with deep models.
Revised:  Another line of prior work addresses the exploration-exploitation trade-off problem with contextual bandit methods, which are less studied in the industry due to the difficulty in extending their flexibility with deep models.
Edit intent: coherence
Original:  In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn deep CTR models based on Gaussian processes, which can provide efficient uncertainty estimations along with the CTR predictions while maintaining the flexibility of deep neural networks.
Revised:  In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn CTR models based on Gaussian processes, which can provide efficient uncertainty estimations along with the CTR predictions while maintaining the flexibility of deep neural networks.
Edit intent: clarity
Original:  In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn deep CTR models based on Gaussian processes, which can provide efficient uncertainty estimations along with the CTR predictions while maintaining the flexibility of deep neural networks.
Revised:  In this paper, we propose a novel Deep Uncertainty-Aware Learning (DUAL) method to learn deep CTR models based on Gaussian processes, which can provide predictive uncertainty estimations while maintaining the flexibility of deep neural networks.
Edit intent: clarity
Original:   By linking the ability to estimate predictive uncertainties of DUAL to well-known bandit algorithms, we further present DUAL-based Ad-ranking strategies to boost up long-term utilities such as the social welfare in advertising systems.
Revised:  By linking the predictive uncertainty estimation ability of DUAL to well-known bandit algorithms, we further present DUAL-based Ad-ranking strategies to boost up long-term utilities such as the social welfare in advertising systems.
Edit intent: clarity
Original: A point cloud serves as a representation of the surface of a three-dimensional  shape.
Revised: A point cloud serves as a representation of the surface of a three-dimensional (3D) shape.
Edit intent: clarity
Original:  Deep generative models have been adapted to model their variations typically by a map from a ball-like set of latent variables.
Revised:  Deep generative models have been adapted to model their variations typically using a map from a ball-like set of latent variables.
Edit intent: fluency
Original:  However, previous approaches have not paid much attention to the topological structure of a point cloud ;
Revised:  However, previous approaches did not pay much attention to the topological structure of a point cloud ;
Edit intent: coherence
Original:  However, previous approaches have not paid much attention to the topological structure of a point cloud ;
Revised:  However, previous approaches have not paid much attention to the topological structure of a point cloud , despite that
Edit intent: coherence
Original:  a continuous map cannot express the varying number of holes and intersections.
Revised:  a continuous map cannot express the varying numbers of holes and intersections.
Edit intent: fluency
Original:  Moreover, a point cloud is often composed of multiple subparts, and it is also hardly expressed .
Revised:  Moreover, a point cloud is often composed of multiple subparts, and it is also difficult to express .
Edit intent: clarity
Original:  In this paper , we propose ChartPointFlow, which is a flow-based generative model with multiple latent labels .
Revised:  In this study , we propose ChartPointFlow, which is a flow-based generative model with multiple latent labels .
Edit intent: clarity
Original:  In this paper , we propose ChartPointFlow, which is a flow-based generative model with multiple latent labels .
Revised:  In this paper , we propose ChartPointFlow, a flow-based generative model with multiple latent labels .
Edit intent: coherence
Original:  By maximizing the mutual information , a map conditioned by a label is assigned to a continuous subset of a given point cloud, like a chart of a manifold.
Revised:  By maximizing the mutual information , a map conditioned on a label is assigned to a continuous subset of a given point cloud, like a chart of a manifold.
Edit intent: clarity
Original:  By maximizing the mutual information , a map conditioned by a label is assigned to a continuous subset of a given point cloud, like a chart of a manifold.
Revised:  By maximizing the mutual information , a map conditioned by a label is assigned to a continuous subset of a point cloud, like a chart of a manifold.
Edit intent: clarity
Original:  By maximizing the mutual information , a map conditioned by a label is assigned to a continuous subset of a given point cloud, like a chart of a manifold.
Revised:  By maximizing the mutual information , a map conditioned by a label is assigned to a continuous subset of a given point cloud, similar to a chart of a manifold.
Edit intent: fluency
Original:  This enables our proposed model to preserve the topological structure with clear boundaries, while previous approaches tend to suffer from blurs and to fail in generating holes.
Revised:  This enables our proposed model to preserve the topological structure with clear boundaries, whereas previous approaches tend to suffer from blurs and to fail in generating holes.
Edit intent: fluency
Original:  Experimental results demonstrate that ChartPointFlow achieves the state-of-the-art performance in  generation and reconstruction among sampling-based point cloud generators  .
Revised:  Experimental results demonstrate that ChartPointFlow achieves state-of-the-art performance in generation and reconstruction among sampling-based point cloud generators .
Edit intent: coherence
Original:  Experimental results demonstrate that ChartPointFlow achieves the state-of-the-art performance in  generation and reconstruction among sampling-based point cloud generators  .
Revised:  Experimental results demonstrate that ChartPointFlow achieves the state-of-the-art performance in terms of generation and reconstruction among sampling-based point cloud generators .
Edit intent: clarity
Original:  Experimental results demonstrate that ChartPointFlow achieves the state-of-the-art performance in  generation and reconstruction among sampling-based point cloud generators  .
Revised:  Experimental results demonstrate that ChartPointFlow achieves the state-of-the-art performance in generation and reconstruction compared with other point cloud generators .
Edit intent: clarity
Original:  The first is the %DIFDELCMD < {\sc %DIFDELCMD < %%% Rich Lines problem, which asks for the set of all lines that each covers at least \lambda points from S, for a given integer parameter \lambda \geq 2;
Revised:  The first is the %DIFDELCMD < {\sc %DIFDELCMD < %%% Rich Lines problem, which asks for the set of all lines that each covers at least \lambda points from S, for a given integer parameter \lambda \geq 2;
Edit intent: fluency
Original:  In particular, the %DIFDELCMD < {\sc %%% Rich Lines problem is a fundamental problem whose solution serves as a building block for several algorithms in computational geometry.
Revised:  In particular, the %DIFDELCMD < {\sc %%% Rich Lines problem is a fundamental problem whose solution serves as a building block for several algorithms in computational geometry.
Edit intent: fluency
Original:  We derive lower-bound results showing that, for \lambda =\Omega(n \log n), the upper bound on the running time of this randomized algorithm matches the lower bound that we derive on the time complexity of %DIFDELCMD < {\sc %%% Rich Lines in the algebraic computation trees model.
Revised:  We derive lower-bound results showing that, for \lambda =\Omega(n \log n), the upper bound on the running time of this randomized algorithm matches the lower bound that we derive on the time complexity of %DIFDELCMD < {\sc %%% Rich Lines in the algebraic computation trees model.
Edit intent: fluency
Original:  For %DIFDELCMD < {\sc %%% Line Cover , we present two kernelization algorithms: a randomized Monte Carlo algorithm and a deterministic algorithm.
Revised:  For %DIFDELCMD < {\sc %%% Line Cover , we present two kernelization algorithms: a randomized Monte Carlo algorithm and a deterministic algorithm.
Edit intent: fluency
Original:  Both algorithms improve the running time of existing kernelization algorithms for %DIFDELCMD < {\sc %%% Line Cover .
Revised:  Both algorithms improve the running time of existing kernelization algorithms for %DIFDELCMD < {\sc %%% Line Cover .
Edit intent: fluency
Original: In early 2020, the Corona Virus Disease 2019 (COVID-19) epidemic swept the world.
Revised: In early 2020, the Corona Virus Disease 2019 (COVID-19) pandemic swept the world.
Edit intent: clarity
Original:  Moreover, online rumors during  COVID-19 epidemic increased people's panic about public health and social stability.
Revised:  Moreover, online rumors during the COVID-19 epidemic increased people's panic about public health and social stability.
Edit intent: fluency
Original:  Moreover, online rumors during  COVID-19 epidemic increased people's panic about public health and social stability.
Revised:  Moreover, online rumors during COVID-19 pandemic increased people's panic about public health and social stability.
Edit intent: fluency
Original:  Understanding and curbing the spread of online rumor is an urgent task at present .
Revised:  At present, understanding and curbing the spread of online rumor is an urgent task at present .
Edit intent: coherence
Original:  Understanding and curbing the spread of online rumor is an urgent task at present .
Revised:  Understanding and curbing the spread of online rumors is an urgent task at present .
Edit intent: fluency
Original:  Understanding and curbing the spread of online rumor is an urgent task at present .
Revised:  Understanding and curbing the spread of online rumor is an urgent task .
Edit intent: clarity
Original:  Therefore, we analyzed the rumor spread mechanism and proposed a method to quantify the rumor influence by the speed of new insiders.
Revised:  Therefore, we analyzed the rumor spreading mechanism and propose a method to quantify the rumor influence by the speed of new insiders.
Edit intent: fluency
Original:  Therefore, we analyzed the rumor spread mechanism and proposed a method to quantify the rumor influence by the speed of new insiders.
Revised:  Therefore, we analyzed the rumor spread mechanism and proposed a method to quantify a rumors' influence by the speed of new insiders.
Edit intent: fluency
Original:  We use the search frequency of rumor as an observation variable of new insiders.
Revised:  The search frequency of rumor as an observation variable of new insiders.
Edit intent: coherence
Original:  We use the search frequency of rumor as an observation variable of new insiders.
Revised:  We use the search frequency of the rumor is used as an observation variable of new insiders.
Edit intent: clarity
Original:  We calculated the peak coefficient and attenuation coefficient for the search frequency, which conform to the exponential distribution.
Revised:  The peak coefficient and attenuation coefficient for the search frequency, which conform to the exponential distribution.
Edit intent: clarity
Original:  We calculated the peak coefficient and attenuation coefficient for the search frequency, which conform to the exponential distribution.
Revised:  We calculated the peak coefficient and the attenuation coefficient are calculated for the search frequency, which conform to the exponential distribution.
Edit intent: clarity
Original:  We calculated the peak coefficient and attenuation coefficient for the search frequency, which conform to the exponential distribution.
Revised:  We calculated the peak coefficient and attenuation coefficient for the search frequency, which conforms to the exponential distribution.
Edit intent: fluency
Original:  Then we designed several rumor features and used the above two coefficients as predictable labels.
Revised:  We designed several rumor features and used the above two coefficients as predictable labels.
Edit intent: coherence
Original:  Our feature analysis shows that precursor features are the most important for the outbreak coefficient, while location information and rumor entity information are the most important for the attenuation coefficient.
Revised:  Our feature analysis showed that precursor features are the most important for the outbreak coefficient, while location information and rumor entity information are the most important for the attenuation coefficient.
Edit intent: fluency
Original:  Our feature analysis shows that precursor features are the most important for the outbreak coefficient, while location information and rumor entity information are the most important for the attenuation coefficient.
Revised:  Our feature analysis shows that precursor features were the most important for the outbreak coefficient, while location information and rumor entity information are the most important for the attenuation coefficient.
Edit intent: fluency
Original:  Our feature analysis shows that precursor features are the most important for the outbreak coefficient, while location information and rumor entity information are the most important for the attenuation coefficient.
Revised:  Our feature analysis shows that precursor features are the most important for the outbreak coefficient, while location information and rumor entity information were the most important for the attenuation coefficient.
Edit intent: fluency
Original:  Meanwhile, features which are conducive to the outbreak are usually harmful to the continued spread of rumors.
Revised:  Meanwhile, features that were conducive to the outbreak are usually harmful to the continued spread of rumors.
Edit intent: fluency
Original:  Meanwhile, features which are conducive to the outbreak are usually harmful to the continued spread of rumors.
Revised:  Meanwhile, features which are conducive to the outbreak were usually harmful to the continued spread of rumors.
Edit intent: fluency
Original:  At the same time, anxiety is a crucial rumor-causing factor.
Revised:  At the same time, anxiety was a crucial rumor causing factor.
Edit intent: fluency
Original:  Finally, we discussed how to use deep learning technology to reduce  forecast loss by use BERT model.
Revised:  Finally, we discuss how to use deep learning technology to reduce forecast loss by use BERT model.
Edit intent: fluency
Original:  Finally, we discussed how to use deep learning technology to reduce  forecast loss by use BERT model.
Revised:  Finally, we discussed how to use deep learning technology to reduce the forecast loss by use BERT model.
Edit intent: fluency
Original: Can AI help automate human-easy but computer-hard data preparation tasks (for example, data cleaning, data integration, and information extraction), which currently heavily involve data scientists, practitioners, and crowd workers?
Revised: Can AI help automate human-easy but computer-hard data preparation tasks that burden data scientists, practitioners, and crowd workers?
Edit intent: clarity
Original:  Last but not least , we identify activities that will unleash a series of research opportunities to push forward the field of data preparation.
Revised:  Last but not least , we identify a series of research opportunities to push forward the field of data preparation.
Edit intent: clarity
Original:  Last but not least , we identify activities that will unleash a series of research opportunities to push forward the field of data preparation.
Revised:  Last but not least , we identify activities that will unleash a series of research opportunities to advance the field of data preparation.
Edit intent: clarity
Original: This analytical essay contributes to the ongoing deliberation about the so-called digital sovereignty, as pursued particularly in the European Union (EU).
Revised: This short theoretical essay contributes to the ongoing deliberation about the so-called digital sovereignty, as pursued particularly in the European Union (EU).
Edit intent: clarity
Original:   With this viewpoint, the essay further advances another, broader, and more pressing debate on politics and democracy in the digital era 
Revised:  With this viewpoint, the essay further advances another, broader, and more pressing debate on politics and democracy in the digital era .
Edit intent: fluency
Original: This short theoretical  essay contributes to the ongoing deliberation about the so-called digital sovereignty, as pursued particularly in the European Union (EU).
Revised: This short theoretical and argumentative essay contributes to the ongoing deliberation about the so-called digital sovereignty, as pursued particularly in the European Union (EU).
Edit intent: clarity
Original:  These inequalities imply bounds on the strength of selection, which can be measured from the statistics of traits or divisions along lineages.
Revised:  These inequalities imply bounds on the strength of selection, which can be measured from the statistics of trait values and divisions along lineages.
Edit intent: clarity
Original:  The upper bound is related to recent generalizations of linear response relations in Stochastic Thermodynamics, and is reminiscent of the fundamental theorem of Natural selectionof R.
Revised:  The upper bound is related to recent generalizations of linear response relations in Stochastic Thermodynamics, and shares common features with Fisher's fundamental theorem of Natural selectionof R.
Edit intent: clarity
Original:  To proof compactness of the latter we combine a suitable parabolic Caccioppoli inequality with the compactness theorem of Aubin-Lions.
Revised:  To prove compactness of the latter we combine a suitable parabolic Caccioppoli inequality with the compactness theorem of Aubin-Lions.
Edit intent: fluency
Original:  We next show a new relation between the persistent Laplacian and the so-called Schur complement of a matrix .
Revised:  We exhibit a novel relationship between the persistent Laplacian and the so-called Schur complement of a matrix .
Edit intent: clarity
Original:  We next show a new relation between the persistent Laplacian and the so-called Schur complement of a matrix .
Revised:  We next show a new relation between the persistent Laplacian and the notion of Schur complement of a matrix .
Edit intent: clarity
Original:  For example, in the graph case, this uncovers a relation with the notion of effective resistance , as well as a persistent version of the Cheeger inequality.
Revised:  For example, in the graph case, it both uncovers a link with the notion of effective resistance , as well as a persistent version of the Cheeger inequality.
Edit intent: clarity
Original:  For example, in the graph case, this uncovers a relation with the notion of effective resistance , as well as a persistent version of the Cheeger inequality.
Revised:  For example, in the graph case, this uncovers a relation with the notion of effective resistance and leads to a persistent version of the Cheeger inequality.
Edit intent: clarity
Original:  This also gives a second , very simple algorithm to compute the q-th persistent Laplacian .
Revised:  This relationship also yields an additional , very simple algorithm to compute the q-th persistent Laplacian .
Edit intent: clarity
Original:  This in turn leads to a new algorithm to compute the q-th persistent Betti number for a pair of spaces which can be significantly more efficient than existing algorithmsunder some conditions .
Revised:  This in turn leads to a new algorithm to compute the q-th persistent Betti number for a pair of spaces which can be significantly more efficient than standard algorithms .
Edit intent: clarity
Original:  Finally, we also study persistent Laplacians for a filtration of simplicial complexes, and present interesting stability results for their eigenvalues  .
Revised:  Finally, we study persistent Laplacians for a filtration of simplicial complexes, and present interesting stability results for their eigenvalues .
Edit intent: clarity
Original:  Finally, we also study persistent Laplacians for a filtration of simplicial complexes, and present interesting stability results for their eigenvalues  .
Revised:  Finally, we also study persistent Laplacians for simplicial filtrations and present novel stability results for their eigenvalues .
Edit intent: clarity
Original:  The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the outstanding predictive power of artificial intelligence, triggered the application of deep learning to human mobility.
Revised:  The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility.
Edit intent: clarity
Original:  (iii) a description of deep learning models and (iv) a discussion about relevant open challenges .
Revised:  (iii) a description of the most relevant solutions to the mobility tasks described above and the relevant challenges for the future .
Edit intent: clarity
Original:  Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, and trajectory generation.
Revised:  Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation.
Edit intent: clarity
Original: Despite an increasing reliance on fully-automated algorithmic decision making in our day-to-day lives, human beings still make highly consequential decisions.
Revised: Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-day lives, human beings still make highly consequential decisions.
Edit intent: fluency
Original:  As frequently seen in business, healthcare, and public policy, recommendations produced by algorithms are provided to human decision-makers in order to guide their decisions.
Revised:  As frequently seen in business, healthcare, and public policy, recommendations produced by algorithms are provided to human decision-makers to guide their decisions.
Edit intent: clarity
Original:  We also show how to examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decisions under various settings.
Revised:  We also show how to examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decision rules under various settings.
Edit intent: clarity