wanyu/IteraTeR_v2 huggingface dataset:
Original: A recent breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on each side of a random matching market has a profound effect on the expected behavior of the stable matches . Specifically, across all stable matchings, the "long side" (i.e. the side with a greater number of agents) receives significantly worse matches in expectation than the short side.
Revised: A recent breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on either side of a random matching market has a profound effect on the expected behavior of the stable matches . Specifically, across all stable matchings, the "long side" (i.e. the side with a greater number of agents) receives significantly worse matches in expectation than the short side.
Edit intent: fluency
Original: A recent breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on each side of a random matching market has a profound effect on the expected behavior of the stable matches . Specifically, across all stable matchings, the "long side" (i.e. the side with a greater number of agents) receives significantly worse matches in expectation than the short side.
Revised: A recent breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on each side of a random matching market has a profound effect on the expected characteristics of the market . Specifically, across all stable matchings, the "long side" (i.e. the side with a greater number of agents) receives significantly worse matches in expectation than the short side.
Edit intent: clarity
Original:   We provide new intuition and a new proof for preliminary results in the spirit of [AKL17], showing that the "long side " is at a noticeable disadvantage in all stable matchings .
Revised:  We provide new intuition and a new proof for preliminary results in the direction of [AKL17], showing that the "long side " is at a noticeable disadvantage in all stable matchings .
Edit intent: clarity
Original: A recent breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on either side of a random matching market has a profound effect on the expected characteristics of the market .
Revised: A breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on either side of a random matching market has a profound effect on the expected characteristics of the market .
Edit intent: clarity
Original: A recent breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on either side of a random matching market has a profound effect on the expected characteristics of the market .
Revised: A recent breakthrough of Ashlagi, Kanoria, and Leshno [AKL17] found that imbalance in the number of agents on either side of a random matching market has a profound effect on the market's expected characteristics .
Edit intent: clarity
Original:  If matchings are found via the classic one-side proposing deferred acceptance algorithm, this indicates that the difference between the proposing and the receiving side is essentially unimportant compared to the difference between the long and the short side .
Revised:  Intuitively, this occurs because an agent on the long side is essentially unimportant compared to the difference between the long and the short side .
Edit intent: clarity
Original:  We provide new intuition and a new proof for preliminary results in the direction of [AKL17] , namely calculating the expected rank that an agent on the long side has for their optimal stable match .
Revised:  We provide a new and simpler proof for a result of [AKL17] , namely calculating the expected rank that an agent on the long side has for their optimal stable match .
Edit intent: clarity
Original: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Revised: We present a robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Edit intent: clarity
Original: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Revised: We present a comprehensive robust decision support framework with computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Edit intent: clarity
Original: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Revised: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Edit intent: fluency
Original: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Revised: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setups . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Edit intent: fluency
Original: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Revised: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Edit intent: clarity
Original: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient under certain conditions like in presence of a high number of decision alternatives and uncertain outcomes.
Revised: We present a comprehensive robust decision support framework with novel computational algorithms for decision makers in a non-cooperative sequential setup . Existing simulation based approaches such as Monte Carlo methods can be inefficient when there is a large number of feasible decisions and uncertain outcomes.
Edit intent: clarity
Original:  Hence, we provide a novel augmented probability simulation alternative to solve non-cooperative sequential games .
Revised:  Hence, we provide a novel alternative to solve non-cooperative sequential games .
Edit intent: clarity
Original:  We cover approaches to approximate subgame perfect equilibria under common knowledge conditions , assess the robustness of such solutions and, finally, approximate adversarial risk analysis solutions when lacking common knowledge.
Revised:  We cover approaches to approximate subgame perfect equilibria under complete information , assess the robustness of such solutions and, finally, approximate adversarial risk analysis solutions when lacking common knowledge.
Edit intent: clarity
Original:  Successful implementation of the endurance time method heavily relies on the quality of endurance time excitations.
Revised:  Successful implementation of the endurance time method relies heavily on the quality of endurance time excitations.
Edit intent: fluency
Original:  Different types of endurance time excitations are explained .
Revised:  Different types of endurance time excitations are described .
Edit intent: clarity
Original:  Particular attention was given to different applications of the endurance time method in the field of earthquake engineering.
Revised:  Particular attention is given to different applications of the endurance time method in the field of earthquake engineering.
Edit intent: fluency
Original: We show that the size of the optimal percolation set of a multilayer network is substantially affected by the presence of interlayer degree correlations and edge overlap.
Revised: We show that the size of the critical percolation set of a multilayer network is substantially affected by the presence of interlayer degree correlations and edge overlap.
Edit intent: clarity
Original: We show that the size of the optimal percolation set of a multilayer network is substantially affected by the presence of interlayer degree correlations and edge overlap.
Revised: We show that the size of the optimal percolation set of a multilayer network is substantially affected by the presence of inter-layer degree correlations and edge overlap.
Edit intent: fluency
Original:  We provide extensive numerical evidence to confirm that the state-of-the-art optimal percolation strategies normally fail to identify minimal percolation sets in synthetic and real-world correlated multilayer networks, thus underestimating their fragility .
Revised:  We provide extensive numerical evidence which confirms that the state-of-the-art optimal percolation strategies normally fail to identify minimal percolation sets in synthetic and real-world correlated multilayer networks, thus underestimating their fragility .
Edit intent: clarity
Original:  We provide extensive numerical evidence to confirm that the state-of-the-art optimal percolation strategies normally fail to identify minimal percolation sets in synthetic and real-world correlated multilayer networks, thus underestimating their fragility .
Revised:  We provide extensive numerical evidence to confirm that the state-of-the-art optimal percolation strategies consistently fail to identify minimal percolation sets in synthetic and real-world correlated multilayer networks, thus underestimating their fragility .
Edit intent: clarity
Original:  We provide extensive numerical evidence to confirm that the state-of-the-art optimal percolation strategies normally fail to identify minimal percolation sets in synthetic and real-world correlated multilayer networks, thus underestimating their fragility .
Revised:  We provide extensive numerical evidence to confirm that the state-of-the-art optimal percolation strategies normally fail to identify minimal percolation sets in synthetic and real-world correlated multilayer networks, thus overestimating their robustness .
Edit intent: clarity
Original:  We propose a family of Pareto-efficient strategies for optimal multilayer percolation, that take into account both intralayer and interlayer heuristics, and can be easily extended to multiplex networks with an arbitrary number of layers.
Revised:  We propose a family of Pareto-efficient strategies that take into account both intralayer and interlayer heuristics, and can be easily extended to multiplex networks with an arbitrary number of layers.
Edit intent: clarity
Original:  We propose a family of Pareto-efficient strategies for optimal multilayer percolation, that take into account both intralayer and interlayer heuristics, and can be easily extended to multiplex networks with an arbitrary number of layers.
Revised:  We propose a family of Pareto-efficient strategies for optimal multilayer percolation, that take into account both intra-layer and inter-layer heuristics, and can be easily extended to multiplex networks with an arbitrary number of layers.
Edit intent: fluency
Original: In principle one can reconstruct the past states of a growing network from only its current state .
Revised: One can often make inferences about a growing network from only its current state .
Edit intent: clarity
Original: In principle one can reconstruct the past states of a growing network from only its current state .
Revised: In principle one can reconstruct the past states of a growing network from its current state .
Edit intent: clarity
Original:  In practice, however, the extent to which this can be done is severely limited since existing methods are either inexact, inefficient, or both.
Revised:  In practice, however, the extent to which such problems can be solved is limited by existing techniques, which are often inexact, inefficient, or both.
Edit intent: clarity
Original:  Here we present methods for temporal reconstruction that are both exact and efficient on trees.
Revised:  In this article we derive exact and efficient on trees.
Edit intent: clarity
Original:  We demonstrate the use of these methods with a series of applications: seed finding, network interpolation, full history reconstruction, and model fitting.
Revised:  We demonstrate the use of these methods with a series of applications: network interpolation, full history reconstruction, and model fitting.
Edit intent: clarity
Original:  We demonstrate the use of these methods with a series of applications: seed finding, network interpolation, full history reconstruction, and model fitting.
Revised:  We demonstrate the use of these methods with a series of applications: seed finding, network interpolation, history reconstruction, and model fitting.
Edit intent: clarity
Original:  For case use, these models were applied for the annotation tasks in clinician-oriented (cMDX)  reports for prostatectomy specimens.
Revised:  For case use, these models were applied for the annotation tasks in clinician-oriented (cMDX) pathology reports for prostatectomy specimens.
Edit intent: clarity
Original:  These images spanned a variety of image qualities, origins ( Whole-slide, Tissue micro array, Whole mount, Internet), scanning machines, timestamps, H&E staining protocols, and institutions.
Revised:  These images spanned a variety of image qualities, origins ( whole slide, tissue micro array, Whole mount, Internet), scanning machines, timestamps, H&E staining protocols, and institutions.
Edit intent: fluency
Original:  These images spanned a variety of image qualities, origins ( Whole-slide, Tissue micro array, Whole mount, Internet), scanning machines, timestamps, H&E staining protocols, and institutions.
Revised:  These images spanned a variety of image qualities, origins ( Whole-slide, Tissue micro array, whole mount, Internet), scanning machines, timestamps, H&E staining protocols, and institutions.
Edit intent: fluency
Original:  For real case use , these models were applied for the annotation tasks in clinician-oriented (cMDX) pathology reports for prostatectomy specimens.
Revised:  For case usage , these models were applied for the annotation tasks in clinician-oriented (cMDX) pathology reports for prostatectomy specimens.
Edit intent: clarity
Original:  For real case use , these models were applied for the annotation tasks in clinician-oriented (cMDX) pathology reports for prostatectomy specimens.
Revised:  For real case use , these models were applied for the annotation tasks in clinician-oriented pathology reports for prostatectomy specimens.
Edit intent: clarity
Original:  The true positive rate (TPR) for slides with prostate cancer was 99.7\%  . The F1-scores of Gleason patterns reported in cMDX reports range between 0.795 and 1.0 at case level (n=55) .
Revised:  The true positive rate (TPR) for slides with prostate cancer was 99.7\% . The F1-scores of Gleason patterns reported in cMDX reports range between 0.795 to 1.0 at case level (n=55) .
Edit intent: fluency
Original:  The true positive rate (TPR) for slides with prostate cancer was 99.7\%  . The F1-scores of Gleason patterns reported in cMDX reports range between 0.795 and 1.0 at case level (n=55) .
Revised:  The true positive rate (TPR) for slides with prostate cancer was 99.7\% . The F1-scores of Gleason patterns reported in cMDX reports range between 0.795 and 1.0 at the case level .
Edit intent: clarity
Original:  The R-squared for the relative tumor volume was 0.987 between the ground truth and the prediction  .
Revised:  The correlation between the ground truth and the prediction .
Edit intent: clarity
Original:  Our models cover the major  prostate pathology and successfully accomplish the annotation tasks with high accuracy .
Revised:  Our models cover the major components of prostate pathology and successfully accomplish the annotation tasks with high accuracy .
Edit intent: clarity
Original:  Our models cover the major  prostate pathology and successfully accomplish the annotation tasks with high accuracy .
Revised:  Our models cover the major prostate pathology and successfully accomplish the annotation tasks .
Edit intent: clarity
Original: We present a simple and efficient algorithm for the batched stochastic multi-armed bandit problem .
Revised: We present simple and efficient algorithm for the batched stochastic multi-armed bandit problem .
Edit intent: fluency
Original: We present a simple and efficient algorithm for the batched stochastic multi-armed bandit problem .
Revised: We present a simple and efficient algorithms for the batched stochastic multi-armed bandit problem .
Edit intent: fluency
Original:  We prove a bound for its expected regret that improves over the best-known regret bound, for any number of batches.
Revised:  We prove bounds for their expected regrets that improve over the best-known regret bound, for any number of batches.
Edit intent: fluency
Original:  We prove a bound for its expected regret that improves over the best-known regret bound, for any number of batches.
Revised:  We prove a bound for its expected regret that improves over the best-known regret bounds for any number of batches.
Edit intent: clarity
Original:  In particular, our algorithm achieves the optimal expected regret by using only a logarithmic number of batches  .
Revised:  In particular, our algorithm achieves the optimal expected regrets by using only a logarithmic number of batches .
Edit intent: fluency
Original: Guo and Zhu (2017) recently proposed an equal-risk pricing approach to the valuation of contingent claims when short selling is completely banned and two elegant pricing formulae are derived in some special cases.
Revised: This paper studies the valuation of contingent claims when short selling is completely banned and two elegant pricing formulae are derived in some special cases.
Edit intent: clarity
Original:  The main contribution of our framework is that it not only recovers the analytical pricing formula derived by Guo and Zhu (2017) when the payoff is monotonic, but also numerically produces equal-risk prices for contingent claims with non-monotonic payoffs , a task which has not been accomplished before.
Revised:  The results of Guo and Zhu (2017) when the payoff is monotonic, but also numerically produces equal-risk prices for contingent claims with non-monotonic payoffs , a task which has not been accomplished before.
Edit intent: clarity
Original:  Furthermore, we demonstrate how a short selling ban affects the valuation of contingent claims by comparing equal-risk prices with Black-Scholes prices.
Revised:  Furthermore, we demonstrate how short selling bans can affect the valuation of contingent claims by comparing equal-risk prices with Black-Scholes prices.
Edit intent: clarity
Original: Recently, more and more works have proposed to drive evolutionary algorithms using machine learning models.
Revised: Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models.
Edit intent: clarity
Original:  In this paper, we provide a quantitative analysis of the expressivity from dynamic models, where Hilbert space is employed to analyze its convergence and criticality.
Revised:  In this paper, we provide a quantitative analysis of the expressivity from dynamic models, where Hilbert space is employed to analyze the convergence and criticality.
Edit intent: fluency
Original:  From the feature mapping of several widely used activation functions made by Hermite polynomials, We found sharp declines or even saddle points in the feature space, which stagnate the information transfer in deep neural networks, then present an activation function design based on the Hermite polynomials for better utilization of spatial representation.
Revised:  We study the feature mapping of several widely used activation functions made by Hermite polynomials, We found sharp declines or even saddle points in the feature space, which stagnate the information transfer in deep neural networks, then present an activation function design based on the Hermite polynomials for better utilization of spatial representation.
Edit intent: coherence
Original:  From the feature mapping of several widely used activation functions made by Hermite polynomials, We found sharp declines or even saddle points in the feature space, which stagnate the information transfer in deep neural networks, then present an activation function design based on the Hermite polynomials for better utilization of spatial representation.
Revised:  From the feature mapping of several widely used activation functions obtained by Hermite polynomials, We found sharp declines or even saddle points in the feature space, which stagnate the information transfer in deep neural networks, then present an activation function design based on the Hermite polynomials for better utilization of spatial representation.
Edit intent: clarity
Original:  From the feature mapping of several widely used activation functions made by Hermite polynomials, We found sharp declines or even saddle points in the feature space, which stagnate the information transfer in deep neural networks, then present an activation function design based on the Hermite polynomials for better utilization of spatial representation.
Revised:  From the feature mapping of several widely used activation functions made by Hermite polynomials, and find sharp declines or even saddle points in the feature space, which stagnate the information transfer in deep neural networks, then present an activation function design based on the Hermite polynomials for better utilization of spatial representation.
Edit intent: coherence