wanyu/IteraTeR_full_sent huggingface dataset:
Original:  (i) learning the structure of a chaotic system purely from  time series, (ii) generating new streams of stimuli from a chaotic system, (iii) switching stream generation among multiple learned chaotic systems , either spontaneously or in response to external perturbations , (iv) inferring missing data from sparse observations of the chaotic system, and (v) deciphering superimposed input from different chaotic systems.
Revised:  (i) learning the structure of a chaotic system purely from time series, (ii) generating new streams of stimuli from a chaotic system, (iii) switching stream generation among multiple learned chaotic systems , either spontaneously or driven by external cues , (iv) inferring missing data from sparse observations of the chaotic system, and (v) deciphering superimposed input from different chaotic systems.
Edit intent: clarity
Original:  (i) learning the structure of a chaotic system purely from  time series, (ii) generating new streams of stimuli from a chaotic system, (iii) switching stream generation among multiple learned chaotic systems , either spontaneously or in response to external perturbations , (iv) inferring missing data from sparse observations of the chaotic system, and (v) deciphering superimposed input from different chaotic systems.
Revised:  (i) learning the structure of a chaotic system purely from time series, (ii) generating new streams of stimuli from a chaotic system, (iii) switching stream generation among multiple learned chaotic systems , either spontaneously or in response to external perturbations , (iv) filling-in missing variables from incomplete observations of a learned dynamical system, and (v) deciphering superimposed input from different chaotic systems.
Edit intent: clarity
Original:  (i) learning the structure of a chaotic system purely from  time series, (ii) generating new streams of stimuli from a chaotic system, (iii) switching stream generation among multiple learned chaotic systems , either spontaneously or in response to external perturbations , (iv) inferring missing data from sparse observations of the chaotic system, and (v) deciphering superimposed input from different chaotic systems. Numerically, we show that these phenomena emerge naturally from a recurrent neural network of Erdos-Renyi topology in which the synaptic strengths adapt in a Hebbian-like manner. Broadly, our work blends chaotic theory and artificial neural networks to answer the long standing question of how neural systems can learn the structure underlying temporal sequences of stimuli .
Revised:  (i) learning the structure of a chaotic system purely from time series, (ii) generating new streams of stimuli from a chaotic system, (iii) switching stream generation among multiple learned chaotic systems , either spontaneously or in response to external perturbations , (iv) inferring missing data from sparse observations of the chaotic system, and (v) deciphering superimposed input from different dynamical systems. Collectively, our findings support the notion that artificial and biological neural networks can learn the structure underlying temporal sequences of stimuli .
Edit intent: clarity
Original:  This approach takes advantage of both physicochemical modeling and data-based approaches and allows to construct optimal parameterized reduced dynamics in the number of species , reactions and parameters, while controlling the information loss due to the reduction.
Revised:  This approach takes advantage of both physicochemical modeling and data-based approaches and allows to construct optimal parameterized reduced dynamics in the number of variables , reactions and parameters, while controlling the information loss due to the reduction.
Edit intent: clarity
Original: Recent emergence of cryptocurrencies such as Bitcoin and Ethereum has posed possible alternatives to global payments as well as financial assets around the globe, so measuring their financial risk is crucial for investors and financial regulators . Analysis of price fluctuations in financial markets is often based on the assumption of a Gaussian distribution , which fails to capture the extreme values and leads to the underestimating of the risks .
Revised: Recent emergence of cryptocurrencies such as Bitcoin and Ethereum has posed possible alternatives to global payments as well as financial assets around the globe, making investors and financial regulators . Analysis of price fluctuations in financial markets is often based on the assumption of a Gaussian distribution , which fails to capture the extreme values and leads to the underestimating of the risks .
Edit intent: coherence
Original: Recent emergence of cryptocurrencies such as Bitcoin and Ethereum has posed possible alternatives to global payments as well as financial assets around the globe, so measuring their financial risk is crucial for investors and financial regulators . Analysis of price fluctuations in financial markets is often based on the assumption of a Gaussian distribution , which fails to capture the extreme values and leads to the underestimating of the risks .
Revised: Recent emergence of cryptocurrencies such as Bitcoin and Ethereum has posed possible alternatives to global payments as well as financial assets around the globe, so measuring their financial risk is crucial for investors and financial regulators aware of the importance to modeling them properly. The Levy's stable distribution is one of the attractive distribution that well describes the fat tails and scaling phenomena in economic systems .
Edit intent: meaning-changed
Original:  In this paper we first show that the behaviors of price fluctuations of cryptocurrencies can also be characterized by the fat-tail Levy's stable distribution by our parameter estimation method. After confirming that price returns of cryptocurrencies follow Levy's stable distribution, we discuss the recent market instability by focusing on one of the parameters of the distribution, which can characterize the fat-tailed behavior of cryptocurrency price returns. Our analysis shows that the fluctuations of estimated tail index parameter could be a candidate for the measure to capture extreme price behaviors of recently emerging cryptocurrencies and the tail index can be applicable for risk management and their financial modeling.
Revised:  In this paper we first show that the behaviors of price fluctuations of cryptocurrencies can also be characterized by the fat-tail Levy's stable distribution under certain conditions of time intervals ranging from 30 minutes to 4 hours. The arguments are developed under the theoretical background of the General Central Limit Theorem (GCLT) and quantitative valuation defined as a distance function using the Parseval's relation. Our approach can generally be extended for further analysis of statistical properties and applications for financial modeling.
Edit intent: meaning-changed
Original: In this paper, we show that the behaviors of price fluctuations in  cryptocurrency markets can also be characterized by a Levy's stable distribution  under certain conditions of time intervals ranging from 30 minutes to 4 hours.
Revised: In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can also be characterized by a Levy's stable distribution under certain conditions of time intervals ranging from 30 minutes to 4 hours.
Edit intent: meaning-changed
Original: In this paper, we show that the behaviors of price fluctuations in  cryptocurrency markets can also be characterized by a Levy's stable distribution  under certain conditions of time intervals ranging from 30 minutes to 4 hours.
Revised: In this paper, we show that the behaviors of price fluctuations in cryptocurrency markets can be characterized fairly well by a Levy's stable distribution under certain conditions of time intervals ranging from 30 minutes to 4 hours.
Edit intent: meaning-changed
Original: In this paper, we show that the behaviors of price fluctuations in  cryptocurrency markets can also be characterized by a Levy's stable distribution  under certain conditions of time intervals ranging from 30 minutes to 4 hours.
Revised: In this paper, we show that the behaviors of price fluctuations in cryptocurrency markets can also be characterized by a Levy's stable distribution with alpha = 1.4, which indicates fatter tails compared to a Gaussian distribution. Moreover, we have come to realize the limitations of the stable model especially under certain conditions of time intervals ranging from 30 minutes to 4 hours.
Edit intent: meaning-changed
Original: In this paper, we show that the behaviors of price fluctuations in  cryptocurrency markets can also be characterized by a Levy's stable distribution  under certain conditions of time intervals ranging from 30 minutes to 4 hours.
Revised: In this paper, we show that the behaviors of price fluctuations in cryptocurrency markets can also be characterized by a Levy's stable distribution under certain conditions outside time intervals ranging from 30 minutes to 4 hours.
Edit intent: clarity
Original:  In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized fairly well by a Levy's stable distribution with alpha = 1.4 , which indicates fatter tails compared to a Gaussian distribution.
Revised:  In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized by a non-Gaussian Levy's stable distribution with alpha = 1.4 , which indicates fatter tails compared to a Gaussian distribution.
Edit intent: clarity
Original:  In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized fairly well by a Levy's stable distribution with alpha = 1.4 , which indicates fatter tails compared to a Gaussian distribution.
Revised:  In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized fairly well by a Levy's stable distribution with \alpha \simeq 1.4 , which indicates fatter tails compared to a Gaussian distribution.
Edit intent: meaning-changed
Original:  In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized fairly well by a Levy's stable distribution with alpha = 1.4 , which indicates fatter tails compared to a Gaussian distribution. Moreover, we have come to realize the limitations of the stable model especially under certain conditions outside time intervals ranging  from 30 minutes to 4 hours.
Revised:  In this paper, we show that the behaviors of price fluctuations in emerging cryptocurrency markets can be characterized fairly well by a Levy's stable distribution with alpha = 1.4 under certain conditions outside time intervals ranging from 30 minutes to 4 hours.
Edit intent: clarity
Original:  The arguments are developed under the theoretical background of the General Central Limit Theorem (GCLT) and quantitative valuation defined as a distance function using the Parseval's relation .
Revised:  The arguments are developed under quantitative valuation defined as a distance function using the Parseval's relation in addition to the theoretical background of the General Central Limit Theorem (GCLT) and quantitative valuation defined as a distance function using the Parseval's relation .
Edit intent: coherence
Original:  The arguments are developed under the theoretical background of the General Central Limit Theorem (GCLT) and quantitative valuation defined as a distance function using the Parseval's relation .
Revised:  The arguments are developed under the theoretical background of the General Central Limit Theorem (GCLT) . We also discuss the fit with the Levy's stable distribution compared to the fit with other distributions by employing the method based on likelihood ratios .
Edit intent: meaning-changed
Original:  We present a series of crowdsourcing experiments that reveal true contextuality in human decision making. Their analysis is based on the Contextuality-by-Default (CbD) theory , one that allows one to separate true contextuality from direct influences in the overall context-dependence.
Revised:  The Contextuality-by-Default (CbD) theory , one that allows one to separate true contextuality from direct influences in the overall context-dependence.
Edit intent: coherence
Original:  The design of our  experiments is an elaboration of one introduced in the "Snow Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the first time demonstrated unequivocally .
Revised:  The CbD analysis of numerous previous attempts to demonstrate contextuality in human judgments shows that all context-dependence in them can be accounted for by direct influences, with no true contextuality present. However, contextual systems in human behavior can be found. In this paper we present a series of crowdsourcing experiments that exhibit true contextuality in simple decision making. experiments is an elaboration of one introduced in the "Snow Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the first time demonstrated unequivocally .
Edit intent: meaning-changed
Original:  The design of our  experiments is an elaboration of one introduced in the "Snow Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the first time demonstrated unequivocally .
Revised:  The design of our The design of these experiments is an elaboration of one introduced in the "Snow Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the first time demonstrated unequivocally .
Edit intent: clarity
Original:  The design of our  experiments is an elaboration of one introduced in the "Snow Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the first time demonstrated unequivocally . The CbD analysis of many previous attempts to demonstrate contextuality in human judgments shows that they were unsuccessful because direct influences in them were all one needed to account for context-dependence .
Revised:  The design of our experiments is an elaboration of one introduced in the "Snow Queen" experiment (Decision 5, 193-204, 2018), where contextuality was for the first time demonstrated unequivocally .
Edit intent: coherence
Original:  In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates one state to periods with average positive returns and the other state to periods with average negative returns, therefore discovering spontaneously the common classification of `bull' and `bear' markets.
Revised:  In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates states prevalently to pre- and post- crisis periods with one state gathering periods with average positive returns and the other state to periods with average negative returns, therefore discovering spontaneously the common classification of `bull' and `bear' markets.
Edit intent: meaning-changed
Original:  In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates one state to periods with average positive returns and the other state to periods with average negative returns, therefore discovering spontaneously the common classification of `bull' and `bear' markets.
Revised:  In particular, we describe an experiment with one hundred log-returns and two states in which the methodology automatically associates one state to periods with average positive returns and the other state periods with average negative returns, therefore discovering spontaneously the common classification of `bull' and `bear' markets.
Edit intent: fluency
Original:  We consider a setting common to modern macroeconomic and financial models where many counties/regions/ macro-variables/ assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous micro-parameters estimated separately for each entity.
Revised:  We consider a setting where many counties/regions/ macro-variables/ assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous micro-parameters estimated separately for each entity.
Edit intent: clarity
Original:  We consider a setting common to modern macroeconomic and financial models where many counties/regions/ macro-variables/ assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous micro-parameters estimated separately for each entity.
Revised:  We consider a setting common to modern macroeconomic and financial models where many counties/regions/ assets are observed for many time periods, and when estimation of a global parameter includes aggregation of a cross-section of heterogeneous micro-parameters estimated separately for each entity.
Edit intent: clarity
Original:  We also study sufficient conditions when one can consistently estimate the asymptotic variance . These results are useful for making inferences in two-step estimation procedures related to factor models .
Revised:  We also study sufficient conditions when one can consistently estimate the asymptotic variance , and proposes a bootstrap scheme for cases when one cannot. A small simulation study illustrates performance of the asymptotic and bootstrap procedures. The results are useful for making inferences in two-step estimation procedures related to factor models .
Edit intent: meaning-changed
Original: We ask the following question: Given two random variables, X and Y, under what conditions is it possible to find a random variable Z, independent from X and Y, so that X+Z first-order stochastically dominates Y+Z ? We show that such a Z exists whenever X has higher expectation than Y.In addition, if X and Y have equal mean, but the first has lower variance, then Z can be chosen so that X + Z dominates Y + Z in terms of second-order stochastic dominance.
Revised: Stochastic dominance is a crucial tool for the analysis of choice under risk. It is typically analyzed as a property of two gambles that are taken in isolation. We study how additional independent sources of risk (e.g. uninsurable labor risk, house price risk, etc.) can affect the ordering of gambles. We show that, perhaps surprisingly, background risk can be strong enough to render lotteries that are ranked by their expectation ranked in terms of second-order stochastic dominance.
Edit intent: meaning-changed
Original: In addition, if X and Y have equal mean, but the first has lower variance, then Z can be chosen so that X + Z dominates Y + Z in terms of second-order stochastic dominance.
Revised: In addition, if X and Y have equal mean, but the first has lower variance, then Z can be chosen so that X + Z dominates Y + Z in terms of first-order stochastic dominance.
Edit intent: meaning-changed
Original:  We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract the visual desirability of neighborhoods as proxy variables that are both of interest in their own right, and could be used as inputs to other econometric methods.
Revised:  We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract proxy variables for visual desirability of neighborhoods as proxy variables that are both of interest in their own right, and could be used as inputs to other econometric methods.
Edit intent: clarity
Original:  We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract the visual desirability of neighborhoods as proxy variables that are both of interest in their own right, and could be used as inputs to other econometric methods.
Revised:  We explore the use of non-linear vs. linear methods to fuse these cues with conventional models of house pricing, and show how the interpretability of linear models allows us to directly extract the visual desirability of neighborhoods that are both of interest in their own right, and could be used as inputs to other econometric methods.
Edit intent: clarity
Original:  a test given annually to all Australian primary school students in grades 3, 5, 7, and 9. Students who perform in the `below standard' band constitute approximately 3\% of the sample, with the remainder performing at or above standard, requiring that a proposed classifier be robust to imbalanced classes.
Revised:  a test given annually to all Australian school students in grades 3, 5, 7, and 9. Students who perform in the `below standard' band constitute approximately 3\% of the sample, with the remainder performing at or above standard, requiring that a proposed classifier be robust to imbalanced classes.
Edit intent: clarity
Original:  a test given annually to all Australian primary school students in grades 3, 5, 7, and 9. Students who perform in the `below standard' band constitute approximately 3\% of the sample, with the remainder performing at or above standard, requiring that a proposed classifier be robust to imbalanced classes. Observations for students in grades 5, 7, and 9 contain data on previous achievement in NAPLAN.
Revised:  a test given annually to all Australian primary school students in grades 3, 5, 7, and 9. 
Edit intent: coherence
Original:  The best classifiers for grades 5 and above achieve an area under the ROC curve of approximately 95\%, and for grade 3 achieve an AUC of approximately 80\%. Our results suggest that it is feasible for schools to  screen a large number of students for their riskof obtaining below standard achievement a full two years before they are identified as achieving below standard on their next NAPLAN test .
Revised:  The best classifiers for grades 5 and above achieve an area under the ROC curve , suggesting that it is feasible for schools to screen a large number of students for their riskof obtaining below standard achievement a full two years before they are identified as achieving below standard on their next NAPLAN test .
Edit intent: coherence
Original:  Our results suggest that it is feasible for schools to  screen a large number of students for their riskof obtaining below standard achievement a full two years before they are identified as achieving below standard on their next NAPLAN test .
Revised:  Our results suggest that it is feasible for schools to more accurately screen a large number of students for their riskof obtaining below standard achievement a full two years before they are identified as achieving below standard on their next NAPLAN test .
Edit intent: clarity
Original: We aim to predict whether a primary school student will perform in the `below standard' band of a national standardized test. We exploit a data set containing test performance on the National Assessment Program - Literacy and Numeracy (NAPLAN) ;
Revised: Machine learning methods tend to outperform traditional statistical models at prediction. In the prediction of academic achievement, ML models have not shown substantial improvement over linear and logistic regression. So far, these results have almost entirely focused on college achievement, due to the availability of administrative datasets, and have contained relatively small sample sizes by ML standards. In this article we apply popular machine learning models to a large dataset (n=2.2 million) containing primary and middle school performance on NAPLAN,
Edit intent: meaning-changed