wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this work, we consider the S\&P 500 portfolio over the 1958-2015 time horizon weighted using John Tukey's transformational ladder (Tukey2): 1/x^2,\,\, 1/x,\,\, 1/x,\,\, \text{log}(x),\,\, \sqrt{x},\,\, x, \\ \,\,  \,\, } x^2, where x is the market capitalization weighted  portfolio.
Revised:  In this work, we consider the S\&P 500 portfolio over the 1958-2015 time horizon weighted by Tukey's transformational ladder (Tukey2): 1/x^2,\,\, 1/x,\,\, 1/x,\,\, \text{log}(x),\,\, \sqrt{x},\,\, x, \\ \,\, \,\, } x^2, where x is the market capitalization weighted portfolio.
Edit intent: clarity
Original:  In this work, we consider the S\&P 500 portfolio over the 1958-2015 time horizon weighted using John Tukey's transformational ladder (Tukey2): 1/x^2,\,\, 1/x,\,\, 1/x,\,\, \text{log}(x),\,\, \sqrt{x},\,\, x, \\ \,\,  \,\, } x^2, where x is the market capitalization weighted  portfolio.
Revised:  In this work, we consider the S\&P 500 portfolio over the 1958-2015 time horizon weighted using John Tukey's transformational ladder (Tukey2): 1/x^2,\,\, 1/x,\,\, 1/x,\,\, \text{log}(x),\,\, \sqrt{x},\,\, x, \\ \,\, \text{and \,\, } x^2, where x is the market capitalization weighted portfolio.
Edit intent: meaning-changed
Original:  In this work, we consider the S\&P 500 portfolio over the 1958-2015 time horizon weighted using John Tukey's transformational ladder (Tukey2): 1/x^2,\,\, 1/x,\,\, 1/x,\,\, \text{log}(x),\,\, \sqrt{x},\,\, x, \\ \,\,  \,\, } x^2, where x is the market capitalization weighted  portfolio.
Revised:  In this work, we consider the S\&P 500 portfolio over the 1958-2015 time horizon weighted using John Tukey's transformational ladder (Tukey2): 1/x^2,\,\, 1/x,\,\, 1/x,\,\, \text{log}(x),\,\, \sqrt{x},\,\, x, \\ \,\, \,\, } x^2, where x is the market capitalization weighted S\&P 500 portfolio.
Edit intent: fluency
Original:  We find that the 1/x^2 weighting strategy produces cumulative returns which significantly dominates all other portfolios, achieving an annual geometric mean return of 20.889\\%DIF < . Further, the 1/x^2 weighting strategy is superior to a 1/x weighting strategy, which is in turn superior to a 1/\sqrt{x} weighted portfolio, and so forth, culminating with the x^2 transformation, whose cumulative returns are the lowest of the seven transformations of John Tukey's transformational ladder.
Revised:  We find that the 1/x^2 weighting strategy produces cumulative returns that significantly dominates all other portfolios, achieving an annual geometric mean return of 20.889\\%DIF < . Further, the 1/x^2 weighting strategy is superior to a 1/x weighting strategy, which is in turn superior to a 1/\sqrt{x} weighted portfolio, and so forth, culminating with the x^2 transformation, whose cumulative returns are the lowest of the seven transformations of John Tukey's transformational ladder.
Edit intent: fluency
Original:  We find that the 1/x^2 weighting strategy produces cumulative returns which significantly dominates all other portfolios, achieving an annual geometric mean return of 20.889\\%DIF < . Further, the 1/x^2 weighting strategy is superior to a 1/x weighting strategy, which is in turn superior to a 1/\sqrt{x} weighted portfolio, and so forth, culminating with the x^2 transformation, whose cumulative returns are the lowest of the seven transformations of John Tukey's transformational ladder.
Revised:  We find that the 1/x^2 weighting strategy produces cumulative returns which significantly dominates all other portfolios, achieving a compound annual growth rate of 20.889\\%DIF < . Further, the 1/x^2 weighting strategy is superior to a 1/x weighting strategy, which is in turn superior to a 1/\sqrt{x} weighted portfolio, and so forth, culminating with the x^2 transformation, whose cumulative returns are the lowest of the seven transformations of John Tukey's transformational ladder.
Edit intent: clarity
Original:  In this work, we consider the S\&P 500 portfolio over the 1958-2015 time horizon weighted by Tukey's transformational ladder (Tukey2): 1/x^2,\,\, 1/x,\,\, 1/x,\,\, log(x),\,\, x,\,\, x, %DIFDELCMD < \\%%% \,\, and \,\, x^2, where x is  the market capitalization weighted S\&P 500 portfolio.
Revised:  In this work, we consider the S\&P 500 portfolio over the 1958-2015 time horizon weighted by Tukey's transformational ladder (Tukey2): 1/x^2,\,\, 1/x,\,\, 1/x,\,\, log(x),\,\, x,\,\, x, %DIFDELCMD < \\%%% \,\, and \,\, x^2, where x is defined as the market capitalization weighted S\&P 500 portfolio.
Edit intent: clarity
Original:  We find that the 1/x^2 weighting strategy produces cumulative returns that significantly dominates all other portfolios, achieving a compound annual growth rate of 20.889 \\%DIF < over the 1958-2015 horizon.
Revised:  Accounting for dividends and transaction fees, we find that the 1/x^2 weighting strategy produces cumulative returns that significantly dominates all other portfolios, achieving a compound annual growth rate of 20.889 \\%DIF < over the 1958-2015 horizon.
Edit intent: meaning-changed
Original:  We find that the 1/x^2 weighting strategy produces cumulative returns that significantly dominates all other portfolios, achieving a compound annual growth rate of 20.889 \\%DIF < over the 1958-2015 horizon.
Revised:  We find that the 1/x^2 weighting strategy produces cumulative returns that significantly dominates all other portfolios, achieving a compound annual growth rate of 18 \\%DIF < over the 1958-2015 horizon.
Edit intent: meaning-changed
Original:  The problem stems from the optimal liquidation of an asset position in a financial market with multiplicative price impact with stochastic resilience .
Revised:  The problem stems from the optimal liquidation of an asset position in a financial market with multiplicative and transient price impact. Liquidity is stochastic in that the volume effect process, which determines the inter-temporal resilience of the market in spirit of Predoiu, Shaikhet and Shreve (2011), is taken to be stochastic, being driven by own random noise .
Edit intent: meaning-changed
Original:  To solve the  variational inequality and prove optimality, we show new results of independent interest on constructive approximations and Laplace transforms of the inverse local times for diffusions reflected at elastic boundaries.
Revised:  To solve the HJB variational inequality and prove optimality, we show new results of independent interest on constructive approximations and Laplace transforms of the inverse local times for diffusions reflected at elastic boundaries.
Edit intent: meaning-changed
Original:  To solve the  variational inequality and prove optimality, we show new results of independent interest on constructive approximations and Laplace transforms of the inverse local times for diffusions reflected at elastic boundaries.
Revised:  To solve the variational inequality and prove optimality, we need a combination of probabilistic arguments and calculus of variations methods, involving Laplace transforms of the inverse local times for diffusions reflected at elastic boundaries.
Edit intent: meaning-changed
Original:  These equations represent analogs of Hamilton-Jacobi-Bellman equations for the value functions of optimal control problems in non-Markovian setting arising in financial modelling.
Revised:  These equations represent analogs of Hamilton-Jacobi-Bellman equations for the value functions of optimal control problems with unspecified dynamics where the underlying processes do not necessarily satisfy stochastic differential equations of a known kind with a given structure. The problems considered arise in financial modelling.
Edit intent: meaning-changed
Original:  As a step in this direction, we apply a reaction-diffusion model to study autoinducer signaling dynamics in a growing bacterial community as a function of the density of metapopulations, or spatially dispersed colonies , in the total system.
Revised:  As a step in this direction, we apply a reaction-diffusion model to study autoinducer signaling dynamics in a single-species community as a function of the density of metapopulations, or spatially dispersed colonies , in the total system.
Edit intent: clarity
Original:  As a step in this direction, we apply a reaction-diffusion model to study autoinducer signaling dynamics in a growing bacterial community as a function of the density of metapopulations, or spatially dispersed colonies , in the total system.
Revised:  As a step in this direction, we apply a reaction-diffusion model to study autoinducer signaling dynamics in a growing bacterial community as a function of the spatial distribution of colonies in the system.
Edit intent: clarity
Original:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being governed by the collective metapopulation density.
Revised:  We predict a dynamical transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being governed by the collective metapopulation density.
Edit intent: clarity
Original:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being governed by the collective metapopulation density.
Revised:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime , with the AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being governed by the collective metapopulation density.
Edit intent: fluency
Original:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being governed by the collective metapopulation density.
Revised:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population densities of individual colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being governed by the collective metapopulation density.
Edit intent: clarity
Original:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being governed by the collective metapopulation density.
Revised:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime , with the dynamics being governed by the collective metapopulation density.
Edit intent: clarity
Original:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being governed by the collective metapopulation density. In addition, we propose an observable order parameter for this system, termed the Neighbor Interference Fraction (NIF), which accounts for the ratio of neighbor-produced to self-produced signal at a colony. The transition between LQS to GQS is intimately connected to a tradeoff between the signaling network's latency, or speed of activation, and its throughput, or the total spatial range over which all the components of the system communicate .
Revised:  We predict a non-equilibrium phase transition between a local quorum sensing (LQS) regime at low dispersal, with AI signaling dynamics primarily controlled by the local population density of colonies, and a global quorum sensing (GQS) regime at high dispersal , with the dynamics being dependent on collective inter-colony diffusive interactions. The crossover between LQS to GQS is intimately connected to a tradeoff between the signaling network's latency, or speed of activation, and its throughput, or the total spatial range over which all the components of the system communicate .
Edit intent: clarity
Original:  The transition between LQS to GQS is intimately connected to a tradeoff between the signaling network's latency, or speed of activation, and its throughput, or the total spatial range over which all the components of the system communicate . Levels of dispersal near the phase boundary provide an optimal compromise that enables simultaneously high latency and throughput in a given environment .
Revised:  The transition between LQS to GQS is intimately connected to a tradeoff between the signaling network's latency, or speed of activation, and its throughput, or the total spatial range over which all the components of the system communicate .
Edit intent: clarity
Original:  In this paper, we present a comprehensive theoretical analysis on delay-optimal scheduling in queueing systemswith replications. In particular, low-complexity replication policies are designed , and are rigorously proven to be delay-optimal or near delay-optimal among all non-preemptive and causal policies.
Revised:  In this paper, we present a comprehensive theoretical analysis on delay-optimal scheduling of replications in both centralized and distributed multi-server systems. Low-complexity scheduling policies are designed , and are rigorously proven to be delay-optimal or near delay-optimal among all non-preemptive and causal policies.
Edit intent: meaning-changed
Original:  In particular, low-complexity replication policies are designed , and are rigorously proven to be delay-optimal or near delay-optimal among all non-preemptive and causal policies.
Revised:  In particular, low-complexity replication policies are designed , and are rigorously proven to be delay-optimal or near delay-optimal in stochastic ordering among all causal and non-preemptive and causal policies.
Edit intent: meaning-changed
Original:  In order to prove these results , novel sufficient conditions are developed for sample-path delay optimality and near delay optimality, which can be applied to any queueing system and are not limited to the study of replications .
Revised:  Novel sample-path tools are developed to prove these results , novel sufficient conditions are developed for sample-path delay optimality and near delay optimality, which can be applied to any queueing system and are not limited to the study of replications .
Edit intent: clarity
Original: We present an explicit and parsimonious probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena, having for sole parameter the median "true" p- value . P-values are extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon  .
Revised: We present an explicit and parsimonious probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena, having for sole parameter the median "true" p-value, as well as the distribution of the minimum p-value among m independents tests . P-values are extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon .
Edit intent: meaning-changed
Original: We present an explicit and parsimonious probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena, having for sole parameter the median "true" p- value . P-values are extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon  .
Revised: We present an explicit and parsimonious probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena, having for sole parameter the median "true" p- value . P-values are extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon ; such volatility makes the minimum p value diverge significantly from the "true" one .
Edit intent: meaning-changed
Original: We present an explicit and parsimonious probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena, having for sole parameter the median "true" p-value, as well as the distribution of the minimum p-value among m independents tests.
Revised: We present an exact probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena, having for sole parameter the median "true" p-value, as well as the distribution of the minimum p-value among m independents tests.
Edit intent: clarity
Original: We present an explicit and parsimonious probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena, having for sole parameter the median "true" p-value, as well as the distribution of the minimum p-value among m independents tests.
Revised: We present an explicit and parsimonious probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena, as well as the distribution of the minimum p-value among m independents tests.
Edit intent: clarity
Original:   P-values are  extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon;
Revised:  We derive the distribution for small samples 2<n \leq n^*\approx 30 as well as the limiting one as the sample size n becomes large. We also look at the properties of the "power" of a test through the distribution of its inverse for a given p-value and parametrization. P-values are extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon;
Edit intent: meaning-changed
Original: We present  an exact probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena , as well as the distribution of the minimum p-value among m independents tests .
Revised: We present the expected values from p-value hacking as a choice of the minimum p-value among m independents tests, which can be considerably lower than the "true" p-value, even with a single trial, owing to the extreme skewness of the meta-distribution. We first present an exact probability distribution (meta-distribution) for p-values across ensembles of statistically identical phenomena , as well as the distribution of the minimum p-value among m independents tests .
Edit intent: meaning-changed
Original:   P-values are shown to be extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon;
Revised:  The formulas allow the investigation of the stability of the reproduction of results and "p-hacking" and other aspects of meta-analysis. P-values are shown to be extremely skewed and volatile, regardless of the sample size n, and vary greatly across repetitions of exactly same protocols under identical stochastic copies of the phenomenon;
Edit intent: meaning-changed
Original:  Setting the power is shown to offer little remedy unless sample size is increased markedly or the p-value is lowered by at least one order of magnitude . The formulas allow the investigation of the stability of the reproduction of results and "p-hacking" and other aspects of meta-analysis. From a probabilistic standpoint, neither a p-value of .05 nor a "power" at .9 appear to make the slightest sense .
Revised:  Setting the power is shown to offer little remedy unless sample size is increased markedly or the p-value is lowered by at least one order of magnitude .
Edit intent: coherence
Original:  The distorted presentation of data supporting the concept of the great divergence shows that most regions follow the gently-increasing trajectories describing the growth of income per capita but mathematical analysis of  data and even their undistorted presentations show that these trajectories are now increasing approximately vertically with time.
Revised:  The distorted presentation of data supporting the concept of the great divergence shows that economic growth is now developing along moderately-increasing trajectories but mathematical analysis of data and even their undistorted presentations show that these trajectories are now increasing approximately vertically with time.
Edit intent: clarity
Original:  The distorted presentation of data supporting the concept of the great divergence shows that most regions follow the gently-increasing trajectories describing the growth of income per capita but mathematical analysis of  data and even their undistorted presentations show that these trajectories are now increasing approximately vertically with time.
Revised:  The distorted presentation of data supporting the concept of the great divergence shows that most regions follow the gently-increasing trajectories describing the growth of income per capita but mathematical analysis of the same data and even their undistorted presentations show that these trajectories are now increasing approximately vertically with time.
Edit intent: meaning-changed
Original:  The distorted presentation of data supporting the concept of the great divergence shows that most regions follow the gently-increasing trajectories describing the growth of income per capita but mathematical analysis of  data and even their undistorted presentations show that these trajectories are now increasing approximately vertically with time.
Revised:  The distorted presentation of data supporting the concept of the great divergence shows that most regions follow the gently-increasing trajectories describing the growth of income per capita but mathematical analysis of data and even their undistorted presentation shows that these trajectories are now increasing approximately vertically with time.
Edit intent: fluency
Original:  So, while the distorted presentation of data used in the Unified Growth Theory suggests  sustainable and secure economic growth, the undistorted presentation of data demonstrates that the growth is unsustainable and insecure.
Revised:  So, while the distorted presentation of data used in the Unified Growth Theory suggests generally sustainable and secure economic growth, the undistorted presentation of data demonstrates that the growth is unsustainable and insecure.
Edit intent: coherence
Original:  The concept of takeoffs from stagnation to the sustained-growth regime promoted in the Unified Growth Theory is also dangerously misleading because it suggests a sustainable and prosperous future while the mathematical analysis of data shows that the current economic growth is dangerously insecure and unsustainable.
Revised:  The concept of takeoffs from stagnation to the sustained-growth regime promoted in the Unified Growth Theory is also dangerously misleading because it suggests a sustainable and prosperous future while the mathematical analysis of data shows that the current economic growth is insecure and unsustainable.
Edit intent: fluency