wanyu/IteraTeR_full_sent huggingface dataset:
Original:  A key finding is that while the test based on the \infty\to1 norm requires relatively new tools from the semidefinite programming literature to implement, it can be substantially more powerful than that based on the 2\to2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.
Revised:  A key finding is that the test based on the \infty\to1 norm requires relatively new tools from the semidefinite programming literature to implement, it can be substantially more powerful than that based on the 2\to2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.
Edit intent: coherence
Original:  A key finding is that while the test based on the \infty\to1 norm requires relatively new tools from the semidefinite programming literature to implement, it can be substantially more powerful than that based on the 2\to2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.
Revised:  A key finding is that while the test based on the \infty\to1 norm can be substantially more powerful than that based on the 2\to2 norm for the kinds of sparse and degree-heterogeneous networks common in economics.
Edit intent: clarity
Original: This paper considers a two-sample goodness-of-fit testing problem for network data  in which the null hypothesis is that the networks are drawn from the same inhomogeneous random graph model.
Revised: How can one determine whether a community-level treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogues of a two-sample goodness-of-fit testing problem for network data in which the null hypothesis is that the networks are drawn from the same inhomogeneous random graph model.
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Original: This paper considers a two-sample goodness-of-fit testing problem for network data  in which the null hypothesis is that the networks are drawn from the same inhomogeneous random graph model.
Revised: This paper considers a two-sample Kolmogorov-Smirnov test, widely used in the literature to test the null hypothesis of "no treatment effects", for network data in which the null hypothesis is that the networks are drawn from the same inhomogeneous random graph model.
Edit intent: meaning-changed
Original: This paper considers a two-sample goodness-of-fit testing problem for network data  in which the null hypothesis is that the networks are drawn from the same inhomogeneous random graph model.
Revised: This paper considers a two-sample goodness-of-fit testing problem for network data . It first specifies a testing problem in which the null hypothesis is that the networks are drawn from the same inhomogeneous random graph model.
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Original:  It first outlines a randomization test for the null hypothesis that controls size in finite samples based on any test statistic. It then focuses on two particular statistics that produce tests powerful against a large class of alternative hypotheses. The statistics are based on the magnitude of the difference between the networks' adjacency matrices as measured by the 2\to2 and \infty\to1 operator norms.
Revised:  It then describes two randomization tests based on the magnitude of the difference between the networks' adjacency matrices as measured by the 2\to2 and \infty\to1 operator norms.
Edit intent: clarity
Original:  Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains . The analysis of multi-dimensional social networks has recently garnered the attention of the mathematics and physics communities. Multilayer networks are ubiquitous and have consequences, so processes giving rise to them are important social phenomena.
Revised:  Some argue that failing to find reciprocal food sharing means that some process other than reciprocity must be occurring, whereas others argue for models that allow reciprocity to span domains in the form of trade.2 Multilayer networks, high-dimensional networks that allow us to consider multiple sets of relationships at the same time, are ubiquitous and have consequences, so processes giving rise to them are important social phenomena.
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Original:   Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions .
Revised:  The analysis of multi-dimensional social networks has recently garnered the attention of the network science community.3 Recent models of these processes show how ignoring layer interdependencies can lead one to miss why a layer formed the way it did, and/or draw erroneous conclusions .
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Original: In this paper we propose an efficient method to compute the price of American basket options, based on Machine Learning and Monte Carlo simulations  . Specifically, the options we consider are written on a basket of assets, each of them following a Black-Scholes dynamics.
Revised: In this paper we propose an efficient method to compute the price of multi-asset American options, based on Machine Learning and Monte Carlo simulations . Specifically, the options we consider are written on a basket of assets, each of them following a Black-Scholes dynamics.
Edit intent: meaning-changed
Original: In this paper we propose an efficient method to compute the price of American basket options, based on Machine Learning and Monte Carlo simulations  . Specifically, the options we consider are written on a basket of assets, each of them following a Black-Scholes dynamics.
Revised: In this paper we propose an efficient method to compute the price of American basket options, based on Machine Learning , Monte Carlo simulations . Specifically, the options we consider are written on a basket of assets, each of them following a Black-Scholes dynamics.
Edit intent: coherence
Original: In this paper we propose an efficient method to compute the price of American basket options, based on Machine Learning and Monte Carlo simulations  . Specifically, the options we consider are written on a basket of assets, each of them following a Black-Scholes dynamics.
Revised: In this paper we propose an efficient method to compute the price of American basket options, based on Machine Learning and Monte Carlo simulations and variance reduction technique . Specifically, the options we consider are written on a basket of assets, each of them following a Black-Scholes dynamics.
Edit intent: meaning-changed
Original:  On these dates, the value of the option is computed as the maximum between the exercise value and the continuation value, which is approximated via Gaussian Process Regression. Specifically, we consider a finite number of points, each of them representing the values reached by the underlying at a certain time. First of all, we compute the continuation value only for these points by means of Monte Carlo simulations and then we employ Gaussian Process Regression to approximate the whole continuation value function .
Revised:  On these dates, the value of the option is computed as the maximum between the exercise value and the continuation value, which is obtained by means of Gaussian process regression technique and Monte Carlo simulations and then we employ Gaussian Process Regression to approximate the whole continuation value function .
Edit intent: clarity
Original:  First of all, we compute the continuation value only for these points by means of Monte Carlo simulations and then we employ Gaussian Process Regression to approximate the whole continuation value function .
Revised:  First of all, we compute the continuation value only for these points by means of Monte Carlo simulations . Such a method performs well for low dimension baskets but it is not accurate for very high dimension baskets. In order to improve the dimension range, we employ the European option price as a control variate, which allows us to treat very large baskets and moreover to reduce the variance of price estimators .
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Original: The challenge in controlling stochastic systems in which random events can set the system on catastrophic trajectories is to develop a robust ability to respond to such events without significantly compromising the optimality of the baseline control policy.
Revised: The challenge in controlling stochastic systems in which low-probability events can set the system on catastrophic trajectories is to develop a robust ability to respond to such events without significantly compromising the optimality of the baseline control policy.
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Original:  Dynamic feedback dosage control holds promise in combatting this phenomenon, but cell population evolutionary dynamics are complex, stochastic, and often high-dimensional, posing significant challenges to system control. This paper presents CelluDose, a deep reinforcement learning closed-loop dynamic control prototype for automated precision drug dosing targeting stochastic and heterogeneous cell proliferation. Developing optimal dosing schedules for preventing therapy-induced drug resistance involves a tradeoff between the effective suppression of emergent resistant cell subpopulations and the use of conservative dosages and a preference for first-line drugs. CelluDose is trained on model simulations of cell population evolutionary dynamics that combine a system of stochastic differential equations and the additional occurrence of random perturbing events. Both the single-drug and combination therapy policies obtained in training exhibit a 100\% success rate at suppressing simulated heterogeneous harmful cell growth and responding to diverse system fluctuations and perturbations within the alloted time and using conservative dosing.
Revised:  Dynamic feedback dosage control holds promise in combatting this phenomenon, but the application of traditional control approaches to such systems is fraught with challenges due to the complexity of cell dynamics, uncertainty in model parameters, and the need in medical applications for a robust controller that can be trusted to properly handle unexpected outcomes. Here, training on a sample biological scenario identified single-drug and combination therapy policies obtained in training exhibit a 100\% success rate at suppressing simulated heterogeneous harmful cell growth and responding to diverse system fluctuations and perturbations within the alloted time and using conservative dosing.
Edit intent: clarity
Original:  Both the single-drug and combination therapy policies obtained in training exhibit a 100\% success rate at suppressing simulated heterogeneous harmful cell growth and responding to diverse system fluctuations and perturbations within the alloted time and using conservative dosing.
Revised:  Both the single-drug and combination therapy policies that exhibit a 100\% success rate at suppressing simulated heterogeneous harmful cell growth and responding to diverse system fluctuations and perturbations within the alloted time and using conservative dosing.
Edit intent: clarity
Original:  Both the single-drug and combination therapy policies obtained in training exhibit a 100\% success rate at suppressing simulated heterogeneous harmful cell growth and responding to diverse system fluctuations and perturbations within the alloted time and using conservative dosing.
Revised:  Both the single-drug and combination therapy policies obtained in training exhibit a 100\% success rate at suppressing cell proliferation and responding to diverse system fluctuations and perturbations within the alloted time and using conservative dosing.
Edit intent: clarity
Original:  Both the single-drug and combination therapy policies obtained in training exhibit a 100\% success rate at suppressing simulated heterogeneous harmful cell growth and responding to diverse system fluctuations and perturbations within the alloted time and using conservative dosing. The policies obtained were found to be highly robust to model parameter changes and fluctuations not introduced during training .
Revised:  Both the single-drug and combination therapy policies obtained in training exhibit a 100\% success rate at suppressing simulated heterogeneous harmful cell growth and responding to diverse system perturbations while establishing low-dose no-event baselines. These policies were found to be highly robust to model parameter changes and fluctuations not introduced during training .
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Original:  In such context, the traditional economic literature has been attempting to frame all the data-driven economy features. Such features, although being able to bring about a more efficient matching of people and relevant purchase opportunities, also result into distortions and disequilibria, up to market failures. Data-economy market disequilibria can be decrypted by leveraging on some of the known network properties, thus obtaining general results suitable for building a new theoretical framework for economic phenomena. Starting from the hypothesis that a digital company can always benefit from an underlying network of consumers or items related to its market, their representation can indeed provide significant competitive advantages, also enhancing the  platforms' capacity to implement discriminatory practices by means of an increased ability to estimate individuals' preferences.
Revised:  In the platforms' capacity to implement discriminatory practices by means of an increased ability to estimate individuals' preferences.
Edit intent: meaning-changed
Original:  Starting from the hypothesis that a digital company can always benefit from an underlying network of consumers or items related to its market, their representation can indeed provide significant competitive advantages, also enhancing the  platforms' capacity to implement discriminatory practices by means of an increased ability to estimate individuals' preferences.
Revised:  Starting from the hypothesis that a digital company can always benefit from an underlying network of consumers or items related to its market, their representation can indeed provide significant competitive advantages, also enhancing the present paper, we show how platforms' performances still appear astonishing in relation to some unexplored data and networks properties, capable to enhance the platforms' capacity to implement discriminatory practices by means of an increased ability to estimate individuals' preferences.
Edit intent: meaning-changed
Original:  Starting from the hypothesis that a digital company can always benefit from an underlying network of consumers or items related to its market, their representation can indeed provide significant competitive advantages, also enhancing the  platforms' capacity to implement discriminatory practices by means of an increased ability to estimate individuals' preferences.
Revised:  Starting from the hypothesis that a digital company can always benefit from an underlying network of consumers or items related to its market, their representation can indeed provide significant competitive advantages, also enhancing the platforms' capacity to implement steering practices by means of an increased ability to estimate individuals' preferences.
Edit intent: fluency
Original:  In the present paper, we propose a measure called  Information Patrimony, considering the amount of information available within the system and we look into how platforms may exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages.
Revised:  To this end, we rely on network science whose analytical tools allow data representations capable of highlighting relationships between subjects and/or items, extracting a great amount of information. We therefore propose a measure called Information Patrimony, considering the amount of information available within the system and we look into how platforms may exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages.
Edit intent: meaning-changed
Original:  In the present paper, we propose a measure called  Information Patrimony, considering the amount of information available within the system and we look into how platforms may exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages.
Revised:  In the present paper, we propose a measure called Network Information Patrimony, considering the amount of information available within the system and we look into how platforms may exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages.
Edit intent: clarity
Original:  In the present paper, we propose a measure called  Information Patrimony, considering the amount of information available within the system and we look into how platforms may exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages.
Revised:  In the present paper, we propose a measure called Information Patrimony, considering the amount of information available within the system and we look into how platforms could exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages.
Edit intent: clarity
Original:  Such information flow may eventually allow to envisage the emergence of a new hybrid price discrimination pattern, through which platforms may influence and steer individuals' purchase choices, as well as to apply different prices to different customers .
Revised:  Our measure takes into account the quality of the connections among nodes as the one of a hypothetical user in relation to its neighbourhood, detecting how users with a good neighbourhood -- hence of a superior connections set -- obtain better information. We tested our measures on Amazons' instances, obtaining evidence which confirm the relevance of information extracted from nodes' neighbourhood in order to steer targeted users .
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Original: A new approach to obtaining market--directional information, based on a non--stationary solution to the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented.
Revised: A new approach to obtaining market--directional information, based on a non-stationary solution to the dynamic equation "future price tends to the value that maximizes the number of shares traded per unit time" [1] is presented.
Edit intent: fluency
Original: We address the problem of optimally exercising American options based on the assumption that the underlying stock's price follows a Brownian bridge whose final value coincides with the strike price. In order to do so, we solve the discounted optimal stopping problem endowed with the gain function G(x) = (S - x)^+ and a Brownian bridge whose final value equals S.
Revised: We address the problem of optimally exercising American options based on the assumption that the knowledge of the holder is restricted to the price history of the underlying asset, we allow for the disclosure of future information about the terminal price of the asset by modeling it as a Brownian bridge whose final value equals S.
Edit intent: meaning-changed
Original:  In order to do so, we solve the discounted optimal stopping problem endowed with the gain function G(x) = (S - x)^+ and a Brownian bridge whose final value equals S. These settings came up as a first approach of optimally exercising an option within the so-called " stock pinning " scenario.
Revised:  In order to do so, we solve the discounted optimal stopping problem endowed with the gain function G(x) = (S - x)^+ and a Brownian bridge . This model may be used under special market conditions, in particular we focus on what in the literature is known as the " stock pinning " scenario.
Edit intent: clarity
Original:  These settings came up as a first approach of optimally exercising an option within the so-called " stock pinning " scenario. The optimal stopping boundary for this problem isproved to be the unique solution , up to certain regularity conditions, of an integral equation, which is then numerically solved by an algorithm hereby exposed. We face the case where the volatility is unspecified by providing an estimated optimal stopping boundary that, alongside with pointwise confidence intervals, provide alternative stopping rules .
Revised:  These settings came up as a first approach of optimally exercising an option within the so-called " stock pinning " , that is, when the price of the asset approaches the strike price of a highly-traded option close to its expiration date. Our main mathematical contribution is in characterizing the solution to the optimal stopping problem when the gain function includes the discount factor. We show how to numerically compute the solution and we analyze the effect of the volatility estimation on the strategy by computing the confidence curves around the optimal stopping boundary that, alongside with pointwise confidence intervals, provide alternative stopping rules .
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Original:  Finally, we demonstrate the usefulness of our method within the stock pinning scenario through a comparison with the optimal exercise time based on a geometric Brownian motion . We base our comparison on the contingent claims and the 5-minutes intraday stock price data of Apple and IBM for the period 2011-2018. Supplementary materials with the main proofs and auxiliary lemmas are available online .
Revised:  Finally, we demonstrate the usefulness of our method within the stock pinning scenario through a comparison with the optimal exercise time based on a geometric Brownian motion by using real data exhibiting pinning .
Edit intent: clarity
Original:  We observe that lineage distance between neurons , rather than the physical distance between their cell bodies, strongly influences the probability of a connection between them. In addition, the length of neuronal processes, as well as, their birth times determine connectivity. Together, these results help connect the physical location and morphology of neurons with the URLanization of the network.
Revised:  We observe that neurons with certain shared characteristics - such as, neural process lengths, birth time cohort, lineage and bilateral symmetry - exhibit a preference for connecting to each other. Recognizing the existence of such homophily helps in connecting the physical location and morphology of neurons with the URLanization of the network.
Edit intent: clarity
Original:  Further, neurons coordinating activity across distinct neuronal clusters are seen to appear early which relates the temporal hierarchy imposed by development to the functional role of neurons. By providing a framework for a mechanistic understanding of how developmentshapes a nervous system, our results complement interpretive analysis of how efficiency and optimality govern its design . Providing crucial insights for understanding principles that may be common across URLanisms, our study shows how the trajectory in the developmental landscape constrains the eventual spatial and network URLanization of a nervous system.
Revised:  Further, neurons coordinating activity across distinct neuronal clusters are seen to appear early which relates the temporal hierarchy of their appearance during the course of development . Providing crucial insights for understanding principles that may be common across URLanisms, our study shows how the trajectory in the developmental landscape constrains the eventual spatial and network URLanization of a nervous system.
Edit intent: clarity