wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In contrast, individual-no-envy with three or more families  and individual-egalitarian-equivalence with two or more familiesare typically incompatible with efficiency, unlike the situation in an economy of individuals.
Revised:  In contrast, individual-no-envy with three or more families , unanimously envy-free efficient allocations always exist in economies with just two families. The unanimous fair share guarantee is easy to achieve: Under generic conditions the set of efficient allocations with the fair share guarantee contains some collectively envy-free and some collectively egalitarian equivalent allocations. We use modified versions of the traditional market equilibrium approach and individual-egalitarian-equivalence with two or more familiesare typically incompatible with efficiency, unlike the situation in an economy of individuals.
Edit intent: meaning-changed
Original:  In contrast, individual-no-envy with three or more families  and individual-egalitarian-equivalence with two or more familiesare typically incompatible with efficiency, unlike the situation in an economy of individuals. The common market equilibrium approach to fairness is of limited use in economies with families. In contrast, the leximin approach is broadly applicable: it yields an efficient, individual-fair-share, and family-egalitarian-equivalent allocation .
Revised:  In contrast, individual-no-envy with three or more families and lexicographic optimization to establish our results .
Edit intent: clarity
Original: This article introduces a discrete false discovery rate (DFRD+/-) controlling method for data snooping testing. We investigate with DFRD+/- the performance of dynamic portfolios constructed upon over 21,000 technical trading rules on 12 categorical and country-specific markets over the study period 2004-2017.
Revised: We investigate the performance of dynamic portfolios constructed upon over 21,000 technical trading rules on 12 categorical and country-specific markets over the study period 2004-2017.
Edit intent: clarity
Original:  We investigate with DFRD+/- the performance of dynamic portfolios constructed upon over 21,000 technical trading rules on 12 categorical and country-specific markets over the study period 2004-2017.
Revised:  We investigate with DFRD+/- the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the study period 2004-2017.
Edit intent: clarity
Original:  We investigate with DFRD+/- the performance of dynamic portfolios constructed upon over 21,000 technical trading rules on 12 categorical and country-specific markets over the study period 2004-2017.
Revised:  We investigate with DFRD+/- the performance of dynamic portfolios constructed upon over 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for controlling data snooping bias. Compared to the existing methods, DFRD+/- is adaptive and more powerful, and accommodates for discrete p-values.
Edit intent: meaning-changed
Original:  We then show that information complexity of treasure hunt for the class of arbitrary terrains (even for non-convex polygons without obstacles, and even for those with only horizontal or vertical sides) is exponentially larger than for regular terrains.
Revised:  We then show that advice complexity of treasure hunt for the class of arbitrary terrains (even for non-convex polygons without obstacles, and even for those with only horizontal or vertical sides) is exponentially larger than for regular terrains.
Edit intent: fluency
Original:  Specifically, for prescribed parameters \vartheta,\varepsilon \in (0,1), the computed spanner G has O (n \bigl( \varepsilon^{-c} \vartheta^{-6} \log^{c\bigr) edges, where c= O(d).
Revised:  Specifically, for prescribed parameters \vartheta,\varepsilon \in (0,1), the computed spanner G has O \bigl( \varepsilon^{-c} \vartheta^{-6} \log^{c\bigr) edges, where c= O(d).
Edit intent: meaning-changed
Original:  Specifically, for prescribed parameters \vartheta,\varepsilon \in (0,1), the computed spanner G has O (n \bigl( \varepsilon^{-c} \vartheta^{-6} \log^{c\bigr) edges, where c= O(d).
Revised:  Specifically, for prescribed parameters \vartheta,\varepsilon \in (0,1), the computed spanner G has O (n \bigl( \varepsilon^{-c} \vartheta^{-6} n \log n (\log\log n)^6\bigr) edges, where c= O(d).
Edit intent: meaning-changed
Original:  No previous constructions, beyond the trivial clique with O(n^2) edges, were known such that only a tiny additional fraction (i.e., \vartheta) lose their distance preserving connectivity  .
Revised:  No previous constructions, beyond the trivial clique with O(n^2) edges, were known such that only a tiny additional fraction (i.e., \vartheta) lose their distance preserving connectivity . Our construction works by first solving the exact problem in one dimension, and then showing a surprisingly simple and elegant construction in higher dimensions, that uses the one-dimensional construction in a black box fashion .
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Original:  Here we study the origin of stochasticity qualitatively in a network of two genes using stochastic simulation method and argued that stochasticity in gene expression may be a choice of cellular system for optimum output with minimum energy cost.
Revised:  Here we study the role of stochasticity qualitatively in a network of two genes using stochastic simulation method and argued that stochasticity in gene expression may be a choice of cellular system for optimum output with minimum energy cost.
Edit intent: clarity
Original:  Here we study the origin of stochasticity qualitatively in a network of two genes using stochastic simulation method and argued that stochasticity in gene expression may be a choice of cellular system for optimum output with minimum energy cost.
Revised:  Here we study the origin of stochasticity qualitatively in a network of two genes using stochastic simulation method and approximately measure the energy consumption for the gene expression process. We find that the noise in gene expression process reduces the energy cost of protein synthesis. Therefore, we argued that the stochasticity in gene expression may be a choice of cellular system for optimum output with minimum energy cost.
Edit intent: meaning-changed
Original:  Here we study the origin of stochasticity qualitatively in a network of two genes using stochastic simulation method and argued that stochasticity in gene expression may be a choice of cellular system for optimum output with minimum energy cost.
Revised:  Here we study the origin of stochasticity qualitatively in a network of two genes using stochastic simulation method and argued that stochasticity in gene expression may be a choice of cellular system for protein synthesis with minimum energy cost.
Edit intent: clarity
Original:  While the rationale for framing culture as an evolutionary process is correct, it does not follow that culture is a Darwinian or selectionist process, or that population genetics and phylogenetics provide viable starting points for modeling cultural change.
Revised:  While the rationale for framing culture as an evolutionary process is correct, it does not follow that culture is a Darwinian or selectionist process, or that population genetics provides viable starting points for modeling cultural change.
Edit intent: clarity
Original:  This paper lays out step-by-step arguments as to why this approach is ill-conceived , focusing on the lack of randomness  and lack of a self-assembly code in cultural evolution, and summarizes an alternative approach .
Revised:  This paper lays out step-by-step arguments as to why a selectionist approach to cultural evolution is inappropriate , focusing on the lack of randomness and lack of a self-assembly code in cultural evolution, and summarizes an alternative approach .
Edit intent: meaning-changed
Original:  This paper lays out step-by-step arguments as to why this approach is ill-conceived , focusing on the lack of randomness  and lack of a self-assembly code in cultural evolution, and summarizes an alternative approach .
Revised:  This paper lays out step-by-step arguments as to why this approach is ill-conceived , focusing on the lack of randomness , and lack of a self-assembly code in cultural evolution, and summarizes an alternative approach .
Edit intent: fluency
Original:  This paper lays out step-by-step arguments as to why this approach is ill-conceived , focusing on the lack of randomness  and lack of a self-assembly code in cultural evolution, and summarizes an alternative approach .
Revised:  This paper lays out step-by-step arguments as to why this approach is ill-conceived , focusing on the lack of randomness and lack of a self-assembly code . It summarizes an alternative approach .
Edit intent: clarity
Original:  This paper lays out step-by-step arguments as to why this approach is ill-conceived , focusing on the lack of randomness  and lack of a self-assembly code in cultural evolution, and summarizes an alternative approach .
Revised:  This paper lays out step-by-step arguments as to why this approach is ill-conceived , focusing on the lack of randomness and lack of a self-assembly code in cultural evolution, and summarizes an alternative evolutionary approach to culture: self-other URLanization via context-driven actualization of potential .
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Original: In this paper, we study a threshold-kernel estimation method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance.
Revised: In this paper, we study a threshold-kernel jump-detection method for jump-diffusion processes, which iteratively applies thresholding and kernel methods in an approximately optimal way to achieve improved finite-sample performance.
Edit intent: meaning-changed
Original:  As in Figueroa-L\'opez and Nisen (2013), we use the expected number of jump misclassification as the objective function to optimally select the threshold parameter of the jump detection scheme.
Revised:  As in Figueroa-L\'opez and Nisen (2013), we use the expected number of jump misclassifications as the objective function to optimally select the threshold parameter of the jump detection scheme.
Edit intent: fluency
Original:  The estimation methods for these quantities are then developed, where the spot volatility is estimated by a kernel estimator with a threshold and the value of the jump density at the origin is estimated by a density kernel estimator applied to those increments deemed to contains jumps by the chosen thresholding criterion.
Revised:  Estimation methods for these quantities are then developed, where the spot volatility is estimated by a kernel estimator with a threshold and the value of the jump density at the origin is estimated by a density kernel estimator applied to those increments deemed to contains jumps by the chosen thresholding criterion.
Edit intent: clarity
Original:  The estimation methods for these quantities are then developed, where the spot volatility is estimated by a kernel estimator with a threshold and the value of the jump density at the origin is estimated by a density kernel estimator applied to those increments deemed to contains jumps by the chosen thresholding criterion.
Revised:  The estimation methods for these quantities are then developed, where the spot volatility is estimated by a kernel estimator with thresholding and the value of the jump density at the origin is estimated by a density kernel estimator applied to those increments deemed to contains jumps by the chosen thresholding criterion.
Edit intent: fluency
Original:  Simulation studies  show that it is not only feasible to implement the higher-order local optimal threshold scheme but also that this is superior to those based only on the first order approximation and/or on average values of the parameters over the estimation time period.
Revised:  Simulation studies for a prototypical stochastic volatility model show that it is not only feasible to implement the higher-order local optimal threshold scheme but also that this is superior to those based only on the first order approximation and/or on average values of the parameters over the estimation time period.
Edit intent: meaning-changed
Original:  Here we show that a broad class of high-dimensional linear networks of recurrently connected units exhibits the properties needed for transient coding. We first identify a general criterion that distinguishes two classes of networks depending on properties of the connectivity matrix : networks in which all inputs lead to weak, decaying transients, and networks in which specific inputs elicit strongly amplified transient responses and are mapped onto orthogonal output states during the dynamics.
Revised:  Here we show that a broad class of high-dimensional linear networks of recurrently connected units . We start by reviewing a well-known result that leads to a distinction between two classes of networks depending on properties of the connectivity matrix : networks in which all inputs lead to weak, decaying transients, and networks in which specific inputs elicit strongly amplified transient responses and are mapped onto orthogonal output states during the dynamics.
Edit intent: clarity
Original:  We first identify a general criterion that distinguishes two classes of networks depending on properties of the connectivity matrix : networks in which all inputs lead to weak, decaying transients, and networks in which specific inputs elicit strongly amplified transient responses and are mapped onto orthogonal output states during the dynamics.
Revised:  We first identify a general criterion that distinguishes two classes of networks : networks in which all inputs lead to weak, decaying transients, and networks in which specific inputs elicit strongly amplified transient responses and are mapped onto orthogonal output states during the dynamics.
Edit intent: clarity
Original:   For the second class of networks,  we provide a procedure to identify transiently amplified inputs and the corresponding readouts for arbitrary connectivity matrices. We then exploit our approach to build the minimalconnectivity which robustly implements trajectories that map a specific input onto a specific output state , and we demonstrate that the capacity of the obtained networks increases proportionally with their size.
Revised:  For the second class of networks, we provide a procedure to identify transiently amplified inputs and the corresponding readouts . We first apply these results to standard randomly-connected and two-population networks. We then build minimal, low-rank networks that robustly implement trajectories mapping a specific input onto a specific output state , and we demonstrate that the capacity of the obtained networks increases proportionally with their size.
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Original:  We then exploit our approach to build the minimalconnectivity which robustly implements trajectories that map a specific input onto a specific output state , and we demonstrate that the capacity of the obtained networks increases proportionally with their size.
Revised:  We then exploit our approach to build the minimalconnectivity which robustly implements trajectories that map a specific input onto a specific output state . Finally, we demonstrate that the capacity of the obtained networks increases proportionally with their size.
Edit intent: coherence
Original: In this paper we propose a two-stage least squares (2SLS) estimator whose first stage is based on the equal-weight average over a complete subset . We derive the approximate mean squared error (MSE) that depends on the size of the complete subset and characterize the proposed estimator based on the approximate MSE.
Revised: In this paper we propose a two-stage least squares (2SLS) estimator whose first stage is based on the equal-weight average over a complete subset with k instruments among K available, which we call the approximate mean squared error (MSE) that depends on the size of the complete subset and characterize the proposed estimator based on the approximate MSE.
Edit intent: meaning-changed
Original:  We derive the approximate mean squared error (MSE) that depends on the size of the complete subset and characterize the proposed estimator based on the approximate MSE. The size of the complete subset is chosen by minimizing the sample counterpart of the approximate MSE.
Revised:  We derive the approximate mean squared error (MSE) is derived as a function of the subset size k by the Nagar (1959) expansion. The CSA-2SLS estimator is obtained by choosing k minimizing the sample counterpart of the approximate MSE.
Edit intent: meaning-changed
Original:  To deal with weak or irrelevant instruments, we generalize the approximate MSE under the presence of a possibly growing set of irrelevant instruments, which provides useful guidance under weak IV environments. The Monte Carlo simulation results show that the proposed estimator outperforms alternative methods when instruments are correlated with each other and there exists high endogeneity.
Revised:  To deal with weak or irrelevant instruments, we generalize the approximate MSE under the presence of a possibly growing set of irrelevant instruments, which suggests to choose a smaller k than otherwise. An extensive simulation experiment shows potentially huge improvement in the bias and the MSE by using the CSA-2SLS when instruments are correlated with each other and there exists high endogeneity.
Edit intent: meaning-changed
Original: We propose a two-stage least squares (2SLS) estimator whose first stage is the equal-weight average over a complete subset with k instruments among K available, which we call the complete subset averaging (CSA) 2SLS . The approximate mean squared error (MSE) is derived as a function of the subset size k by the Nagar (1959) expansion.
Revised: We propose a two-stage least squares (2SLS) estimator whose first stage is the equal-weight average over a complete subset with k instruments among K available, which we call the complete subset averaging (CSA) 2SLS . The approximate mean squared error (MSE) is derived as a function of the subset size k by the Nagar (1959) expansion.
Edit intent: fluency
Original:  To deal with averaging over  irrelevant instruments, we generalize the approximate MSE under the presence of a possibly growing set of irrelevant instruments, which suggests to choose a smaller k than otherwise.
Revised:  To deal with averaging over a growing set of irrelevant instruments, we generalize the approximate MSE under the presence of a possibly growing set of irrelevant instruments, which suggests to choose a smaller k than otherwise.
Edit intent: fluency
Original:  An extensive simulation experiment shows potentially huge improvement in the bias and the MSE by using the CSA-2SLS  when instruments are correlated with each other and there exists large endogeneity .
Revised:  An extensive simulation experiment shows potentially huge improvement in the bias and the MSE by using the CSA-2SLS estimator outperforms the alternative estimators when instruments are correlated with each other and there exists large endogeneity .
Edit intent: meaning-changed
Original:  As an empirical illustration, we estimate the logistic demand function in Berry, Levinsohn, and Pakes (1995) and find the estimated coefficient value is better supported by economic theory than other IV estimators .
Revised:  As an empirical illustration, we estimate the logistic demand function in Berry, Levinsohn, and Pakes (1995) and find the CSA-2SLS estimate is better supported by economic theory than other IV estimators .
Edit intent: meaning-changed
Original:  As an empirical illustration, we estimate the logistic demand function in Berry, Levinsohn, and Pakes (1995) and find the estimated coefficient value is better supported by economic theory than other IV estimators .
Revised:  As an empirical illustration, we estimate the logistic demand function in Berry, Levinsohn, and Pakes (1995) and find the estimated coefficient value is better supported by economic theory than the alternative estimates .
Edit intent: clarity
Original: We propose a two-stage least squares (2SLS) estimator whose first stage is the equal-weight average over a complete subset with k instruments among K available, which we call the complete subset averaging (CSA) 2SLS.
Revised: We propose a two-stage least squares (2SLS) estimator whose first stage is the equal-weighted average over a complete subset with k instruments among K available, which we call the complete subset averaging (CSA) 2SLS.
Edit intent: fluency
Original: Lack of accurate intuition is often cited as a scientific challenge, especially when interpreting probabilistic and statistical research. A popular technique for developing statistical intuition involves imagining a game of chance with well-defined financial outcomes. In 1956 Kelly used this technique to propose an intuitive interpretation of relative entropy 1%DIFDELCMD < ]%%% . He considered a growth-optimizing investor in a game with mutually exclusive outcomes (a "horse race") and showed that the rate of return expected by such an investor is equal to the relative entropy which measures disagreement between the investor's believed probabilities and the official odds.
Revised:  1%DIFDELCMD < ]%%% . He considered a growth-optimizing investor in a game with mutually exclusive outcomes (a "horse race") and showed that the rate of return expected by such an investor is equal to the relative entropy which measures disagreement between the investor's believed probabilities and the official odds.
Edit intent: coherence
Original:  In 1956 Kelly used this technique to propose an intuitive interpretation of relative entropy 1%DIFDELCMD < ]%%% . He considered a growth-optimizing investor in a game with mutually exclusive outcomes (a "horse race") and showed that the rate of return expected by such an investor is equal to the relative entropy which measures disagreement between the investor's believed probabilities and the official odds.
Revised:  In 1956 Kelly used this technique to propose an intuitive interpretation of relative entropy %DIFDELCMD < ]%%% . He considered a growth-optimizing investor in a game with mutually exclusive outcomes (a "horse race") and showed that the rate of return expected by such an investor is equal to the relative entropy which measures disagreement between the investor's believed probabilities and the official odds.
Edit intent: meaning-changed
Original:  In 1956 Kelly used this technique to propose an intuitive interpretation of relative entropy 1%DIFDELCMD < ]%%% . He considered a growth-optimizing investor in a game with mutually exclusive outcomes (a "horse race") and showed that the rate of return expected by such an investor is equal to the relative entropy which measures disagreement between the investor's believed probabilities and the official odds. Effectively, Kelly showed that a growth-optimizing investor would expect on average to convert 1 bit of additional information into a 100\% return on investment. Notwithstanding the mathematics, this interpretation has been vigorously criticized by Samuelson as dangerous on the grounds that most people are not growth-optimizing 2,3%DIFDELCMD < ]%%% . Here we show that a deeper connection between information and expected returns is in fact true.
Revised:  In 1956 Kelly used this technique to propose an intuitive interpretation of relative entropy 1%DIFDELCMD < ]%%% 2,3%DIFDELCMD < ]%%% . Here we show that a deeper connection between information and expected returns is in fact true.
Edit intent: meaning-changed