wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In prior work, we have revealed that CRDT is like OT in following the same general transformation approach and CRDT is not natively commutative for concurrent operations in co-editors, which helps to clarify what CRDT really is and is not for co-editors. In this article , we reveal OT and CRDT differences in correctness and complexity by dissecting and examining representative OT and CRDT solutions.
Revised:  In prior work, we have made important discoveries about OT and CRDT, and revealed facts and evidences that refute CRDT claims over OT on all accounts. These discoveries help explain the underlying reasons for the choice between OT and CRDT in the real world. We report these results in a series of three articles. In the second article of this series , we reveal OT and CRDT differences in correctness and complexity by dissecting and examining representative OT and CRDT solutions.
Edit intent: meaning-changed
Original:  In this article , we reveal OT and CRDT differences in correctness and complexity by dissecting and examining representative OT and CRDT solutions. We explore how different basic approaches , i.e. the concurrency-centric approach taken by OT and the content-centric approach taken by CRDT, had resulted in different technical challenges , correctness and complexity issues , and solutions .
Revised:  In this article , we reveal OT and CRDT differences in correctness and complexity by dissecting and examining representative OT and CRDT in their basic approaches to realizing the same general transformation and how such differences had resulted in different technical challenges , correctness and complexity issues , and solutions .
Edit intent: meaning-changed
Original:  We explore how different basic approaches , i.e. the concurrency-centric approach taken by OT and the content-centric approach taken by CRDT, had resulted in different technical challenges , correctness and complexity issues , and solutions .
Revised:  We explore how different basic approaches , i.e. the concurrency-centric approach taken by OT and the content-centric approach taken by CRDT, had resulted in different challenges and consequential correctness and complexity issues , and solutions .
Edit intent: clarity
Original:  We explore how different basic approaches , i.e. the concurrency-centric approach taken by OT and the content-centric approach taken by CRDT, had resulted in different technical challenges , correctness and complexity issues , and solutions .
Revised:  We explore how different basic approaches , i.e. the concurrency-centric approach taken by OT and the content-centric approach taken by CRDT, had resulted in different technical challenges , correctness and complexity issues .
Edit intent: clarity
Original:  Moreover, we reveal hidden  algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness , time and space complexity , and simplicity of OT and CRDT . We present facts and evidences that refute CRDT claimed advantages over OT .
Revised:  Moreover, we reveal hidden complexity and algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness , time and space complexity , and simplicity of OT and CRDT . We present facts and evidences that refute CRDT claimed advantages over OT .
Edit intent: meaning-changed
Original:  Moreover, we reveal hidden  algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness , time and space complexity , and simplicity of OT and CRDT . We present facts and evidences that refute CRDT claimed advantages over OT .
Revised:  Moreover, we reveal hidden algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness and complexity of OT and CRDT . We present facts and evidences that refute CRDT claimed advantages over OT .
Edit intent: clarity
Original:  Moreover, we reveal hidden  algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness , time and space complexity , and simplicity of OT and CRDT . We present facts and evidences that refute CRDT claimed advantages over OT .
Revised:  Moreover, we reveal hidden algorithmic flaws with representative CRDT solutions, and discuss common myths and facts related to correctness , time and space complexity , and simplicity of OT and CRDT .
Edit intent: coherence
Original:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of  product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined.
Revised:  However, most smallholder farmers and agricultural stakeholders are unwilling to pay for the price of weather derivatives (WD) because of product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined.
Edit intent: meaning-changed
Original:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of  product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined.
Revised:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of the presence of basis risks ( product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined.
Edit intent: meaning-changed
Original:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of  product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined.
Revised:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of product-design and geographical ) in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined.
Edit intent: clarity
Original:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of  product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined.
Revised:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of product-design and geographical basis risks in the pricing models . To eliminate product-design basis risks, a machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined.
Edit intent: coherence
Original:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of  product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique for crop yield forecasting and feature importance, the major major weather variable (average temperature) that affects crop yields are empirically determined. This variable (average temperature) is used as the underlying index for WD to eliminate product-design basis risks. A model with time-varying speed of mean reversion, seasonal mean,  local volatility that depends on the average temperature and time for the contract period is proposed.
Revised:  However, most of these stakeholders are unwilling to pay for the price of weather derivatives (WD) because of product-design and geographical basis risks in the pricing models of WD. Using machine learning ensemble technique was used to determine the relationship between maize yield and weather variables. The results revealed that the most significant weather variable that affected the yield of maize was average temperature. A mean-reverting model with a time-varying speed of mean reversion, seasonal mean, local volatility that depends on the average temperature and time for the contract period is proposed.
Edit intent: clarity
Original:  A model with time-varying speed of mean reversion, seasonal mean,  local volatility that depends on the average temperature and time for the contract period is proposed. Based on this model , pricing models for futures, options on futures, and basket futures for cumulative average temperature and growing degree-days are presented.
Revised:  A model with time-varying speed of mean reversion, seasonal mean, local volatility that depended on the local average temperature was then proposed. The model was extended to a multi-dimensional model for different but correlated locations. Based on these average temperature models , pricing models for futures, options on futures, and basket futures for cumulative average temperature and growing degree-days are presented.
Edit intent: meaning-changed
Original:  In the 2019 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the yield performances of 2,452 maize hybrids planted in 1,560 locations between 2008 and 2017 and asked participants to classify the corn hybrids as either tolerant or susceptible to heat stress, drought stress, and stress due to the combination of heat and drought .
Revised:  In the 2019 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the yield performances of 2,452 corn hybrids planted in 1,560 locations between 2008 and 2017 and asked participants to classify the corn hybrids as either tolerant or susceptible to heat stress, drought stress, and stress due to the combination of heat and drought .
Edit intent: clarity
Original:  In the 2019 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the yield performances of 2,452 maize hybrids planted in 1,560 locations between 2008 and 2017 and asked participants to classify the corn hybrids as either tolerant or susceptible to heat stress, drought stress, and stress due to the combination of heat and drought .
Revised:  In the 2019 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the yield performances of 2,452 maize hybrids planted in 1,560 locations between 2008 and 2017 and asked participants to classify the corn hybrids as either tolerant or susceptible to drought stress, heat stress, and stress due to the combination of heat and drought .
Edit intent: fluency
Original:  In the 2019 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the yield performances of 2,452 maize hybrids planted in 1,560 locations between 2008 and 2017 and asked participants to classify the corn hybrids as either tolerant or susceptible to heat stress, drought stress, and stress due to the combination of heat and drought .
Revised:  In the 2019 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the yield performances of 2,452 maize hybrids planted in 1,560 locations between 2008 and 2017 and asked participants to classify the corn hybrids as either tolerant or susceptible to heat stress, drought stress, and combined drought and heat stress .
Edit intent: clarity
Original:  As one of the winning teams, we designed a two-step approach to solve this problem in an unsupervised way since no dataset was provided that classified any set of hybrids as tolerant or susceptible to any  stress.
Revised:  As one of the winning teams, we designed a two-step approach to solve this problem in an unsupervised way since no data was provided that classified any set of hybrids as tolerant or susceptible to any stress.
Edit intent: fluency
Original:  As one of the winning teams, we designed a two-step approach to solve this problem in an unsupervised way since no dataset was provided that classified any set of hybrids as tolerant or susceptible to any  stress.
Revised:  As one of the winning teams, we designed a two-step approach to solve this problem in an unsupervised way since no dataset was provided that classified any set of hybrids as tolerant or susceptible to any type of stress.
Edit intent: clarity
Original:  Then, we conducted a linear regression of the yield of hybrids against each stress metric, and classified the hybrids based on the slope  the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress.
Revised:  Then, we conducted a linear regression of the yield of hybrid against each stress metric, and classified the hybrids based on the slope the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress.
Edit intent: fluency
Original:  Then, we conducted a linear regression of the yield of hybrids against each stress metric, and classified the hybrids based on the slope  the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress.
Revised:  Then, we conducted a linear regression of the yield of hybrids against each stress metric, and classified the hybrid based on the slope the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress.
Edit intent: fluency
Original:  Then, we conducted a linear regression of the yield of hybrids against each stress metric, and classified the hybrids based on the slope  the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress.
Revised:  Then, we conducted a linear regression of the yield of hybrids against each stress metric, and classified the hybrids based on the slope of the regression line, since the slope of the regression line showed how sensitive a hybrid was to a specific environmental stress.
Edit intent: fluency
Original:  We first show that  for general \phi-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problems , where the ambiguity radius controls investor's risk preference . Based on this non-robust reformulation, we then show that when a boundedness constraint is added to the investment strategy.
Revised:  We first show that , for general \phi-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problems , where the ambiguity radius controls investor's risk preference . Based on this non-robust reformulation, we then show that when a boundedness constraint is added to the investment strategy.
Edit intent: fluency
Original:  We first show that  for general \phi-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problems , where the ambiguity radius controls investor's risk preference . Based on this non-robust reformulation, we then show that when a boundedness constraint is added to the investment strategy.
Revised:  We first show that for general \phi-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problems . These mean-deviation problems are not subject to uncertain distributions, and the ambiguity radius controls investor's risk preference . Based on this non-robust reformulation, we then show that when a boundedness constraint is added to the investment strategy.
Edit intent: meaning-changed
Original:  We first show that  for general \phi-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problems , where the ambiguity radius controls investor's risk preference . Based on this non-robust reformulation, we then show that when a boundedness constraint is added to the investment strategy. The DRO problem can be cast as a chance-constrained optimization (CCO) problem without distributional uncertainties .
Revised:  We first show that for general \phi-divergences, a DRO problem is asymptotically equivalent to a class of mean-deviation problems , where the ambiguity radius in the original DRO problem now plays the role of controlling the risk preference of the decision-maker. We then demonstrate that a DRO problem can be cast as a chance-constrained optimization (CCO) problem without distributional uncertainties .
Edit intent: clarity
Original:  Besides the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, our results apply to divergence measures that accommodate well heavy tail distribution such as the student t-distribution and the lognormal distribution . Comprehensive testings on synthetic data and real data are provided .
Revised:  Thanks to our results apply to divergence measures that accommodate well heavy tail distribution such as the student t-distribution and the lognormal distribution . Comprehensive testings on synthetic data and real data are provided .
Edit intent: coherence
Original:  Besides the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, our results apply to divergence measures that accommodate well heavy tail distribution such as the student t-distribution and the lognormal distribution . Comprehensive testings on synthetic data and real data are provided .
Revised:  Besides the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, our high-order expansion result, a notable feature of our analysis is that it applies to divergence measures that accommodate well heavy tail distribution such as the student t-distribution and the lognormal distribution . Comprehensive testings on synthetic data and real data are provided .
Edit intent: meaning-changed
Original:  Besides the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, our results apply to divergence measures that accommodate well heavy tail distribution such as the student t-distribution and the lognormal distribution . Comprehensive testings on synthetic data and real data are provided .
Revised:  Besides the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, our results apply to divergence measures that accommodate well heavy tail distributions such as the student t-distribution and the lognormal distribution . Comprehensive testings on synthetic data and real data are provided .
Edit intent: fluency
Original:  Besides the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, our results apply to divergence measures that accommodate well heavy tail distribution such as the student t-distribution and the lognormal distribution . Comprehensive testings on synthetic data and real data are provided .
Revised:  Besides the widely-used Kullback-Leibler (KL) divergence which requires the distribution of the objective function to be exponentially bounded, our results apply to divergence measures that accommodate well heavy tail distribution such as the student t-distribution and the lognormal distribution , besides the widely-used Kullback-Leibler (KL) divergence, which requires the distribution of the objective function to be exponentially bounded. Using the portfolio selection problem as an example, our comprehensive testings on multivariate heavy-tail datasets, both synthetic and real-world, shows that this business-interpretation approach is indeed useful and insightful .
Edit intent: meaning-changed
Original:  The model consists of ordinary differential equations ( ODE) resembling chemostats or continuous stirred tank reactors (CSTRs), an ODE for an extracellular mediator involved in electron transfer , and an algebraic constraint for electric current and hydrogen production.
Revised:  The model resembles a chemostat with ordinary differential equations ( ODE) resembling chemostats or continuous stirred tank reactors (CSTRs), an ODE for an extracellular mediator involved in electron transfer , and an algebraic constraint for electric current and hydrogen production.
Edit intent: clarity
Original:  The model consists of ordinary differential equations ( ODE) resembling chemostats or continuous stirred tank reactors (CSTRs), an ODE for an extracellular mediator involved in electron transfer , and an algebraic constraint for electric current and hydrogen production.
Revised:  The model consists of ordinary differential equations ( ODEs) for concentrations of substrate, URLanisms, and an extracellular mediator involved in electron transfer , and an algebraic constraint for electric current and hydrogen production.
Edit intent: meaning-changed
Original:  The model consists of ordinary differential equations ( ODE) resembling chemostats or continuous stirred tank reactors (CSTRs), an ODE for an extracellular mediator involved in electron transfer , and an algebraic constraint for electric current and hydrogen production.
Revised:  The model consists of ordinary differential equations ( ODE) resembling chemostats or continuous stirred tank reactors (CSTRs), an ODE for an extracellular mediator involved in electron transfer . There is also an algebraic constraint for electric current and hydrogen production.
Edit intent: coherence
Original:  Atypically to standard ANN applications, practitioners equally use option pricing models to validate market prices and to infer unobserved prices. Importantly, models need to generate realistic arbitrage-free  prices, meaning that no option portfolio can lead to risk-free profits.
Revised:  Atypically to standard ANN applications, practitioners equally use option pricing models to validate market prices and to value other financial instruments. In other words, low training losses are as important as generalization capabilities. Importantly, IVS models need to generate realistic arbitrage-free prices, meaning that no option portfolio can lead to risk-free profits.
Edit intent: meaning-changed
Original:  The absence of arbitrage opportunities is guaranteed by penalizing the loss using soft constraints on an extended grid of input values. ANNs can be pre-trained by first calibrating a standard option pricing model, and then training an ANN to a larger synthetic dataset generated from the calibrated model. The parameters transfer as well as the non-arbitrage constraints appear to be particularly useful when only sparse or erroneous data are available.
Revised:  The absence of arbitrage opportunities is guaranteed by penalizing the loss using soft constraints . Furthermore, our method can be combined with standard IVS models in quantitative finance, thus providing a NN-based correction when such models fail at replicating observed market prices. This lets practitioners use our approach as a plug-in on top of classical methods. Empirical results show that this approach is particularly useful when only sparse or erroneous data are available.
Edit intent: meaning-changed