wanyu/IteraTeR_full_sent huggingface dataset:
Original:  One advantage of the proposed mechanisms is their informational robustness as they depend only on certain }  moments of the valuation distributions . In contrast, previous mechanisms proposed to solve this question, such as the second-best mechanism, typically require the knowledge of the entire valuation distribution. Also, our study extends the results to non-binary decision environments with general utility functions. Lastly, we show that if the threshold is instead set equal to the cost, the proposed mechanism can achieve a non-negligible fraction of the optimal profit in the limit, where the fraction is the correlation between the virtual value and the aforementioned transformed value .
Revised:  One advantage of the proposed mechanisms is their informational robustness as they depend only on certain } moments of the valuation distributions .
Edit intent: coherence
Original: An error estimate is presented for a fully discrete semi-implicit finite element method for the two-dimensional Navier--Stokes equations with L^2 initial data in convex polygonal domains, without extra regularity assumptions or CFL conditions, by utilizing the smoothing properties of the Navier--Stokes equations, an appropriate duality argument, and the smallness of the numerical solution in the discrete L^2(0,t_m;H^1) norm when t_m is smaller than some constant.
Revised: First-order convergence in time and space is proved for a fully discrete semi-implicit finite element method for the two-dimensional Navier--Stokes equations with L^2 initial data in convex polygonal domains, without extra regularity assumptions or CFL conditions, by utilizing the smoothing properties of the Navier--Stokes equations, an appropriate duality argument, and the smallness of the numerical solution in the discrete L^2(0,t_m;H^1) norm when t_m is smaller than some constant.
Edit intent: meaning-changed
Original: An error estimate is presented for a fully discrete semi-implicit finite element method for the two-dimensional Navier--Stokes equations with L^2 initial data in convex polygonal domains, without extra regularity assumptions or CFL conditions, by utilizing the smoothing properties of the Navier--Stokes equations, an appropriate duality argument, and the smallness of the numerical solution in the discrete L^2(0,t_m;H^1) norm when t_m is smaller than some constant.
Revised: An error estimate is presented for a fully discrete semi-implicit finite element method for the two-dimensional Navier--Stokes equations with L^2 initial data in convex polygonal domains, without extra regularity assumptions or grid-ratio conditions. The proof utilises the smoothing properties of the Navier--Stokes equations, an appropriate duality argument, and the smallness of the numerical solution in the discrete L^2(0,t_m;H^1) norm when t_m is smaller than some constant.
Edit intent: clarity
Original: Machine learning is becoming a commonplace part of our technological experience. The notion of explainable AI (XAI) is attractive when regulatory or usability considerations necessitate the ability to back decisions with a coherent explanation. A large body of research has addressed algorithmic methods of XAI, but it is still unclear how to determine what is best suited to create human cooperation and adoption of automatic systems. Here we develop an experimental methodology where participants play a web-based game, during which they receive advice from either a human or algorithmic advisor, accompanied with explanations that vary in nature between experimental conditions.
Revised: We study whether receiving advice from either a human or algorithmic advisor, accompanied with explanations that vary in nature between experimental conditions.
Edit intent: clarity
Original:  Here we develop an experimental methodology where participants play a web-based game, during which they receive advice from either a human or algorithmic advisor, accompanied with explanations that vary in nature between experimental conditions. We use a reference-dependent decision-making framework, evaluate the game results over time, and in various key situations, to determine whether the different types of explanations affect the readiness to adopt, willingness to pay and trust a financial AI consultant.
Revised:  Here we develop an experimental methodology where participants play a web-based game, during which they receive advice from either a human or algorithmic advisor, accompanied by five types of Local and Global explanation labelings, has an effect on the readiness to adopt, willingness to pay and trust a financial AI consultant.
Edit intent: meaning-changed
Original:  We use a reference-dependent decision-making framework, evaluate the game results over time, and in various key situations, to determine whether the different types of explanations affect the readiness to adopt, willingness to pay and trust a financial AI consultant.
Revised:  We use a reference-dependent decision-making framework, evaluate the game results over time, and in various key situations, to determine whether the different types of explanations affect the readiness to adopt, willingness to pay , and trust in a financial AI consultant.
Edit intent: clarity
Original:  We find that the types of explanations that promotes adoption during first encounter differ from those that are most successful following failureor when cost is involved .
Revised:  We compare the differences over time and in various key situations using a unique experimental framework where participants play a web-based game with real monetary consequences. We observed that accuracy-based explanations of the model in initial phases leads to higher adoption rates. When the performance of the model is immaculate, there is less importance associated with the kind of explanation for adoption. Using more elaborate feature-based or accuracy-based explanations helps substantially in reducing the adoption drop upon model failure .
Edit intent: meaning-changed
Original:  With its use in the Holland Bloorview Kids Rehabilitation Hospital's BCI clinic, EmoconLite will bridge the gap between research and clinical practice, providing children with access to BCI technology and supporting BCI-enabled play.
Revised:  Through its deployment in the Holland Bloorview Kids Rehabilitation Hospital's BCI clinic, EmoconLite will bridge the gap between research and clinical practice, providing children with access to BCI technology and supporting BCI-enabled play.
Edit intent: clarity
Original:  With its use in the Holland Bloorview Kids Rehabilitation Hospital's BCI clinic, EmoconLite will bridge the gap between research and clinical practice, providing children with access to BCI technology and supporting BCI-enabled play.
Revised:  With its use in the Holland Bloorview Kids Rehabilitation Hospital's BCI clinic, EmoconLite is bridging the gap between research and clinical practice, providing children with access to BCI technology and supporting BCI-enabled play.
Edit intent: clarity
Original:  Using linear programming, we can control the motion of  non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -33.6 um, 74 +/ -44.4 um , and 67.2 +/ -38.6 um, respectively.
Revised:  Using linear programming, we can control the motion of the non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -33.6 um, 74 +/ -44.4 um , and 67.2 +/ -38.6 um, respectively.
Edit intent: fluency
Original:  Using linear programming, we can control the motion of  non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -33.6 um, 74 +/ -44.4 um , and 67.2 +/ -38.6 um, respectively.
Revised:  Using linear programming, we can control the motion of non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 25.1 +/ -33.6 um, 74 +/ -44.4 um , and 67.2 +/ -38.6 um, respectively.
Edit intent: meaning-changed
Original:  Using linear programming, we can control the motion of  non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -33.6 um, 74 +/ -44.4 um , and 67.2 +/ -38.6 um, respectively.
Revised:  Using linear programming, we can control the motion of non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -19.5 um, 34.4 +/ -44.4 um , and 67.2 +/ -38.6 um, respectively.
Edit intent: meaning-changed
Original:  Using linear programming, we can control the motion of  non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -33.6 um, 74 +/ -44.4 um , and 67.2 +/ -38.6 um, respectively.
Revised:  Using linear programming, we can control the motion of non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -33.6 um, 74 +/ -28.4 um and 33.4 +/ -38.6 um, respectively.
Edit intent: meaning-changed
Original:  Using linear programming, we can control the motion of  non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -33.6 um, 74 +/ -44.4 um , and 67.2 +/ -38.6 um, respectively.
Revised:  Using linear programming, we can control the motion of non-magnetic particles with a predefined trajectory of a line, square, and circle with a precision of 57.4 +/ -33.6 um, 74 +/ -44.4 um , and 67.2 +/ -26.6 um, respectively.
Edit intent: meaning-changed
Original:  Nevertheless, the challenging problem of estimating the BTD model structure, namely the number of block terms  and their individual ranks,  has only recently started to attract significant attention  .
Revised:  Nevertheless, the challenging problem of estimating the BTD model structure, namely the number of block terms and their individual ranks, has only recently started to attract significant attention , mainly through regularization-based approaches which entail the need to tune the regularization parameter(s) .
Edit intent: meaning-changed
Original:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns  of  non-negligible energy.
Revised:  In this work, jointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns of non-negligible energy.
Edit intent: clarity
Original:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns  of  non-negligible energy.
Revised:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsity on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns of non-negligible energy.
Edit intent: coherence
Original:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns  of  non-negligible energy.
Revised:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly we build on ideas of sparse Bayesian learning (SBL) and put forward a fully automated Bayesian approach. Through a suitably crafted multi-level hierarchical manner and estimating the ranks as the numbers of factor columns of non-negligible energy.
Edit intent: meaning-changed
Original:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns  of  non-negligible energy.
Revised:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical probabilistic model, which gives rise to heavy-tailed prior distributions for the BTD factors, structured sparsity is of non-negligible energy.
Edit intent: meaning-changed
Original:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns  of  non-negligible energy.
Revised:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columnsjointly of non-negligible energy.
Edit intent: fluency
Original:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns  of  non-negligible energy.
Revised:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns imposed. Ranks are then estimated from the numbers of non-negligible energy.
Edit intent: clarity
Original:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns  of  non-negligible energy.
Revised:  In this work, a Bayesian approach is taken to addressing the problem of rank-(L_r,L_r,1) BTD model selection and computation, based on the idea of imposing column sparsityjointly on the factors and in a hierarchical manner and estimating the ranks as the numbers of factor columns of blocks (R) and columns (L_r) of non-negligible energy.
Edit intent: meaning-changed
Original:  Using variational inference in the proposed probabilistic model results in an iterative algorithm that comprises closed-form updates. Its Bayesian nature completely avoids the ubiquitous in regularization-based methods task of hyper-parameter tuning.
Revised:  Approximate posterior inference is implemented, within the variational inference framework. The resulting iterative algorithm completely avoids hyperparameter tuning, which is a significant defect of regularization-based methods task of hyper-parameter tuning.
Edit intent: clarity
Original:  Its Bayesian nature completely avoids the ubiquitous in regularization-based methods task of hyper-parameter tuning. Simulation results with synthetic data are reported , which demonstrate the effectiveness of the proposed scheme in terms of both rank estimation and model fitting  .
Revised:  Its Bayesian nature completely avoids the ubiquitous in regularization-based methods . Alternative probabilistic models are also explored and the connections with their regularization-based counterparts are brought to light with the aid of the associated maximum a-posteriori (MAP) estimators. We report simulation results with both synthetic and real-word data , which demonstrate the effectiveness of the proposed scheme in terms of both rank estimation and model fitting .
Edit intent: meaning-changed
Original:  Novel Coronavirus officially called COVID-19, which has become a pandemic and has created a crisis in human history , is one such topic that has attracted a lot of attention on Twitter in recent times .
Revised:  One such subject that has gained a lot of coverage on Twitter is the Novel Coronavirus, officially known as COVID-19, which has become a pandemic and has created a crisis in human history , is one such topic that has attracted a lot of attention on Twitter in recent times .
Edit intent: meaning-changed
Original:  We group these leaders in four clusters, namely research, news, health and politics. Our analysis shows that i) all clusters show a similar amount of fear in their tweets, ii) researchers and news cluster display more sadness compared to othersand, iii) URLanizations and politicians try to gain public trust.
Revised:  We group these leaders in four clusters, namely research, news, health , and politics, with each cluster containing Twitter handles (accounts) of individual users URLanizations. E.g., the health cluster includes the World Health Organization (@WHO), the Director-General of WHO (@DrTedros), and so on. The emotion analysis reveals that ( i) all clusters show a similar amount of fear in their tweets, ii) researchers and news cluster display more sadness compared to othersand, iii) URLanizations and politicians try to gain public trust.
Edit intent: meaning-changed
Original:  Our analysis shows that i) all clusters show a similar amount of fear in their tweets, ii) researchers and news cluster display more sadness compared to othersand, iii) URLanizations and politicians try to gain public trust.
Revised:  Our analysis shows that i) all clusters show an equal amount of fear in their tweets, ii) researchers and news cluster display more sadness compared to othersand, iii) URLanizations and politicians try to gain public trust.
Edit intent: meaning-changed
Original:  Our analysis shows that i) all clusters show a similar amount of fear in their tweets, ii) researchers and news cluster display more sadness compared to othersand, iii) URLanizations and politicians try to gain public trust.
Revised:  Our analysis shows that i) all clusters show a similar amount of fear in their tweets, (ii) research and news clusters display more sadness compared to othersand, iii) URLanizations and politicians try to gain public trust.
Edit intent: fluency
Original:  Our analysis shows that i) all clusters show a similar amount of fear in their tweets, ii) researchers and news cluster display more sadness compared to othersand, iii) URLanizations and politicians try to gain public trust.
Revised:  Our analysis shows that i) all clusters show a similar amount of fear in their tweets, ii) researchers and news cluster display more sadness than others, and (iii) health and politics clusters are attempting to win public trust.
Edit intent: clarity
Original:  The text analysisshows that the researchers are more concerned about understanding symptoms and developing vaccination; news and politicians are mainly discussing about traveland hygiene; and URLanizations focuses on hygiene. Our descriptive analysis helps us to extract various features that we used to classify tweets among the four clusters with an accuracy of 96\% AUC ROC.
Revised:  According to the text analysis, the (i) research cluster is more concerned with recognizing symptoms and the development of vaccination; (ii) news and politics clusters are mostly concerned with travel. We then show that we can use our findings to classify tweets among the four clusters with an accuracy of 96\% AUC ROC.
Edit intent: clarity
Original:  Our descriptive analysis of the social network and node classification experiments illustrate that Twitch Gamers is suitable for assessing the predictive performance of novel proximity-preserving and structural role-based node embedding algorithms.
Revised:  Our analysis of the social network and node classification experiments illustrate that Twitch Gamers is suitable for assessing the predictive performance of novel proximity-preserving and structural role-based node embedding algorithms.
Edit intent: clarity
Original:  Our descriptive analysis of the social network and node classification experiments illustrate that Twitch Gamers is suitable for assessing the predictive performance of novel proximity-preserving and structural role-based node embedding algorithms.
Revised:  Our descriptive analysis of the social network and node classification experiments illustrate that Twitch Gamers is suitable for assessing the predictive performance of novel proximity preserving and structural role-based node embedding algorithms.
Edit intent: fluency
Original:  That is, given a  set T of merge trees, we would like to find a  basis set S such that each tree in T can be approximately reconstructed from a linear combination of merge trees in S. A set of high-dimensional vectors can be sketched via matrix sketching techniques such as principal component analysis and column subset selection.
Revised:  That is, given a large set T of merge trees, we would like to find a basis set S such that each tree in T can be approximately reconstructed from a linear combination of merge trees in S. A set of high-dimensional vectors can be sketched via matrix sketching techniques such as principal component analysis and column subset selection.
Edit intent: meaning-changed
Original:  That is, given a  set T of merge trees, we would like to find a  basis set S such that each tree in T can be approximately reconstructed from a linear combination of merge trees in S. A set of high-dimensional vectors can be sketched via matrix sketching techniques such as principal component analysis and column subset selection.
Revised:  That is, given a set T of merge trees, we would like to find a much smaller basis set S such that each tree in T can be approximately reconstructed from a linear combination of merge trees in S. A set of high-dimensional vectors can be sketched via matrix sketching techniques such as principal component analysis and column subset selection.
Edit intent: meaning-changed
Original:  We demonstrate the applications of our framework in sketching merge trees that arise from data ensembles in scientific simulations .
Revised:  We demonstrate the applications of our framework in sketching merge trees that arise from time-varying scientific simulations. Specifically, our framework obtains a much smaller representation of a large set of merge trees for downstream analysis and visualization. It is shown to be useful in identifying good representatives and outliers with respect to a chosen basis. Finally, our work shows a promising direction of utilizing randomized linear algebra within scientific visualization .
Edit intent: meaning-changed