wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C are arbitrary, which turns out to be closely related to the 3-dimensional geometric assignment problem).
Revised:  Let P_i be the set of feature points in%DIFDELCMD < {\cal %%% P _i. Here we mainly focus on the two discrete versions when points in%DIFDELCMD < {\cal %%% C could be selected with or without replacement from P_i's. (We will briefly discuss the continuous case, i. e., points in%DIFDELCMD < {\cal %%% C and one continuous version.
Edit intent: meaning-changed
Original:  These positive results hold for any L_p metric when P_i's are point sets of the same size, and also hold for the case when P_i's have different sizes in the L_\infty metric (i.e., for the center persistence diagram problem).
Revised:  These positive results hold for any L_p metric when P_i's are point sets of the same size, and also hold for the case when P_i's have different sizes in the L_\infty metric (i.e., for the Center Persistence Diagram problem).
Edit intent: fluency
Original:  The simpler dual formulation of the robust CVA optimization is derived . Next, some computational experiments are conducted to measure the additional CVA charge due to distributional uncertainty under a variety of portfolio and market configurations.
Revised:  The simpler dual formulations are derived using recent infinite dimensional Lagrangian duality results . Next, some computational experiments are conducted to measure the additional CVA charge due to distributional uncertainty under a variety of portfolio and market configurations.
Edit intent: meaning-changed
Original:  The simpler dual formulation of the robust CVA optimization is derived . Next, some computational experiments are conducted to measure the additional CVA charge due to distributional uncertainty under a variety of portfolio and market configurations.
Revised:  The simpler dual formulation of the robust CVA optimization is derived . Next, some computational experiments are conducted to measure the additional XVA charges due to distributional uncertainty under a variety of portfolio and market configurations.
Edit intent: meaning-changed
Original:  The results provided by these fourteen countries significantly increase the priority of confronting global actions such as climate change mitigation  .
Revised:  The results provided by these fourteen countries significantly increase the priority of confronting global actions such as climate change mitigation . We finally extend the analysis by first allowing for fluctuations of the mean level in the Ornstein-Uhlenbeck model and secondly by considering modified versions of the Feller and lognormal models. In both cases, results remain basically unchanged thus demonstrating the robustness of the results presented .
Edit intent: meaning-changed
Original:  In exchange, the algorithm is not universally applicable : It works for polynomial system with coefficients satisfying certain concavity conditions . More precisely, it requires the given polynomial system to be located in the unbounded components of the complement of the underlying A-discriminant amoeba. A preliminary implementation of an example from the literature suggests practical performance of the algorithm. We plan to work towards a vigorous implementation including a larger scale of examples and a software paper .
Revised:  In exchange, the algorithm is not universally applicable : It works for polynomial system with coefficients satisfying certain concavity conditions .
Edit intent: coherence
Original: We explore a remote collaboration setup , which involves three parties: a local worker, a remote helper and a handheld robot carried by the local worker.
Revised: We explore a remote collaboration setup allows an expert to instruct a remotely located novice user to help them with a task. Advanced solutions exist in the field of remote guidance and telemanipulation, however, we lack a device that combines these two aspects to grant the expert physical access to the work site. We explore a setup that involves three parties: a local worker, a remote helper and a handheld robot carried by the local worker.
Edit intent: meaning-changed
Original:  We show that the handheld robot can mediate the helper's instructions and remote object interactions while the robot's semi-autonomous features improve task performance by 24 \%, reduce the workload for the remote user and decrease required communication bandwidth between both users.
Revised:  We show that the handheld robot can mediate the helper's instructions and remote object interactions while the robot's semi-autonomous features improve task performance by 37 \%, reduce the workload for the remote user and decrease required communication bandwidth between both users.
Edit intent: meaning-changed
Original:  We show that the handheld robot can mediate the helper's instructions and remote object interactions while the robot's semi-autonomous features improve task performance by 24 \%, reduce the workload for the remote user and decrease required communication bandwidth between both users.
Revised:  We show that the handheld robot can mediate the helper's instructions and remote object interactions while the robot's semi-autonomous features improve task performance by 24 \%, reduce the workload for the remote user and decrease required verbal communication bandwidth. Our results demonstrate the effectiveness of task delegation with the remote user choosing an action and the robot autonomously completing it at the local level.
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Original:  Experiments on two real-world DDI datasets demonstrated that %DIFDELCMD < \mname %%%  is superior to many baselines without consideration of link type correlations and achieved 13.77\\% and 5.01\\% PR-AUC improvement on the two datasets, respectively.
Revised:  Experiments on two real-world DDI datasets demonstrated that %DIFDELCMD < \mname %%% GENN is superior to many baselines without consideration of link type correlations and achieved 13.77\\% and 5.01\\% PR-AUC improvement on the two datasets, respectively.
Edit intent: meaning-changed
Original: The Credit Networkis a model for transactions across a network of agents based on bilateral trust between agents . Credit Networks capture many aspects of traditional currencies as well as new virtual currencies and payment mechanisms.
Revised: In order to scale transaction rates for deployment across the global web, many cryptocurrencies have deployed so-called "Layer-2" networks of private payment channels. An idealized payment network behaves like a Credit Network, a model for transactions across a network of agents based on bilateral trust between agents . Credit Networks capture many aspects of traditional currencies as well as new virtual currencies and payment mechanisms.
Edit intent: meaning-changed
Original:  In this paper, we introduce constraints that bound the total amount of loss that the rest of the network can suffer if an agent (or a set of agents) were to default  .
Revised:  In this paper, we introduce constraints that bound the total amount of loss that the rest of the network can suffer if an agent (or a set of agents) were to default - equivalently, how the network changes if agents can support limited solvency guarantees .
Edit intent: meaning-changed
Original:  We show that these constraints preserve two important aspects of credit networks. The first is route independence: if there are multiple trust-paths over which a transaction can clear, then it does not matter which one is used. The second pertains to liquidity (i.e. the fraction of transactions that succeed): given a symmetric transaction matrix, any achievable vector of net "credit balances" of the agents is equally likely. This technical property allows us to extend the original analysis of liquidity in credit networks to the constrained case. Surprisingly, aggregate borrowing constraints greatly simplify the analysis and achieve the optimal tradeoff between liquidity and the number of trust-edges .
Revised:  We show that these constraints preserve the analytical structure of a credit network. Furthermore, we show that aggregate borrowing constraints greatly simplify the analysis and achieve the optimal tradeoff between liquidity and the number of trust-edges .
Edit intent: clarity
Original:  By comparing our continuous-time Nash equilibrium model to the otherwise identical competitive Radner equilibrium model, we show that our Nash equilibrium model with price-impact can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock volatility puzzle.
Revised:  By comparing our continuous-time price-impact equilibrium model to the otherwise identical competitive Radner equilibrium model, we show that our Nash equilibrium model with price-impact can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock volatility puzzle.
Edit intent: clarity
Original:  By comparing our continuous-time Nash equilibrium model to the otherwise identical competitive Radner equilibrium model, we show that our Nash equilibrium model with price-impact can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock volatility puzzle.
Revised:  By comparing our continuous-time Nash equilibrium model to the analogous Pareto efficient equilibrium model, we show that our Nash equilibrium model with price-impact can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock volatility puzzle.
Edit intent: meaning-changed
Original:  By comparing our continuous-time Nash equilibrium model to the otherwise identical competitive Radner equilibrium model, we show that our Nash equilibrium model with price-impact can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock volatility puzzle.
Revised:  By comparing our continuous-time Nash equilibrium model to the otherwise identical competitive Radner equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock volatility puzzle.
Edit intent: clarity
Original:  By comparing our continuous-time Nash equilibrium model to the otherwise identical competitive Radner equilibrium model, we show that our Nash equilibrium model with price-impact can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock volatility puzzle.
Revised:  By comparing our continuous-time Nash equilibrium model to the otherwise identical competitive Radner equilibrium model, we show that our Nash equilibrium model with price-impact can simultaneously help resolve the interest rate puzzle, the equity premium puzzle, and the stock-price volatility puzzle.
Edit intent: clarity
Original:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility  puzzle.
Revised:  Compared to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility puzzle.
Edit intent: clarity
Original:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility  puzzle.
Revised:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto-efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility puzzle.
Edit intent: fluency
Original:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility  puzzle.
Revised:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, price-impact has an amplification effect on risk-sharing distortions that helps resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility puzzle.
Edit intent: meaning-changed
Original:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility  puzzle.
Revised:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle and the stock-price volatility puzzle.
Edit intent: clarity
Original:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility  puzzle.
Revised:  By comparing our continuous-time price-impact equilibrium model to the analogous Pareto efficient equilibrium model, we show that our model can simultaneously help resolve the interest rate puzzle , the equity premium puzzle, and the stock-price volatility puzzle and, to a lesser extent, affects the equity premium puzzle.
Edit intent: meaning-changed
Original:  A class of successful strategies of direct reciprocity were recently found for the iterated prisoner's dilemma Yi et al., J. Theor. Biol. (2017) and for the three-person iterated public goods game Murase and Baek, J. Theor.
Revised:  A class of successful strategies of direct reciprocity were recently found for the iterated prisoner's dilemma and for the three-person iterated public goods game Murase and Baek, J. Theor.
Edit intent: clarity
Original:  Biol. (2017) and for the three-person iterated public goods game Murase and Baek, J. Theor. Biol. (2018)%DIFDELCMD < ]%%% . A successful strategyforms a cooperative Nash equilibrium in the presence of implementation errorwhile assuring that the long-term payoff never becomes less than the co-players' regardless of their strategies, when the error rate is small.
Revised:  Biol. (2017) and for the three-person iterated public goods game %DIFDELCMD < ]%%% . A successful strategyforms a cooperative Nash equilibrium in the presence of implementation errorwhile assuring that the long-term payoff never becomes less than the co-players' regardless of their strategies, when the error rate is small.
Edit intent: coherence
Original:  Biol. (2018)%DIFDELCMD < ]%%% . A successful strategyforms a cooperative Nash equilibrium in the presence of implementation errorwhile assuring that the long-term payoff never becomes less than the co-players' regardless of their strategies, when the error rate is small.
Revised:  Biol. (2018)%DIFDELCMD < ]%%% iterated three-person public-goods game: By a successful strategy, we mean that it constitutes a cooperative Nash equilibrium in the presence of implementation errorwhile assuring that the long-term payoff never becomes less than the co-players' regardless of their strategies, when the error rate is small.
Edit intent: meaning-changed
Original:  Biol. (2018)%DIFDELCMD < ]%%% . A successful strategyforms a cooperative Nash equilibrium in the presence of implementation errorwhile assuring that the long-term payoff never becomes less than the co-players' regardless of their strategies, when the error rate is small.
Revised:  Biol. (2018)%DIFDELCMD < ]%%% . A successful strategyforms a cooperative Nash equilibrium under implementation error, with assuring that the long-term payoff never becomes less than the co-players' regardless of their strategies, when the error rate is small.
Edit intent: clarity
Original:  Each of the states becomes interpretable in this representation, whereby one can gain insight into the underlying mechanism .
Revised:  Our main finding is that a successful strategy for the iterated three-person public-goods game can be represented as a 10-state automaton by this method. In this automaton, each state can be interpreted as the player's internal judgement of the situation, such as trustworthiness of the co-players and the need to redeem oneself after defection. This result thus suggests a comprehensible way to choose an appropriate action at each step towards cooperation based on a situational judgement, which is mapped from the history of interactions .
Edit intent: meaning-changed
Original:  This makes it hard for domain stakeholders to understand algorithms and deploy them in a way that respects their goalsand values. We created an interactive interface to help stakeholders explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their values .
Revised:  This makes it hard for domain stakeholders to understand algorithms and deploy them in a way that respects users' goals, values, and needs. We proposed a method to help designers and users explore algorithms, visualize their trade-offs, and select algorithms with trade-offs consistent with their values .
Edit intent: clarity
Original:  We evaluated our interface on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts.
Revised:  We evaluated our method on the problem of predicting criminal defendants' likelihood to re-offend through (i) a large-scale Amazon Mechanical Turk experiment, and (ii) in-depth interviews with domain experts.
Edit intent: clarity
Original:  The interface proved effective at communicating algorithm trade-offs and supporting human decision making and also affected participants' trust in algorithmically aided decision making. Our results have implications for the deployment of intelligent algorithms and suggest important directions for future work .
Revised:  Our evaluations show that our method can help designers and users of these systems better understand and navigate algorithmic trade-offs. This paper contributes a new way of providing designers the ability to understand and control the outcomes of algorithmic systems they are creating .
Edit intent: clarity
Original: This note strengthens the main result in Lagziel and Lehrer (2019) `` A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior '' .
Revised: This note strengthens the main result of Lagziel and Lehrer (2019) `` A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior '' .
Edit intent: fluency
Original: This note strengthens the main result in Lagziel and Lehrer (2019) `` A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior '' .
Revised: This note strengthens the main result in Lagziel and Lehrer (2019) (LL) " A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior '' .
Edit intent: meaning-changed
Original: This note strengthens the main result in Lagziel and Lehrer (2019) `` A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior '' .
Revised: This note strengthens the main result in Lagziel and Lehrer (2019) `` A bias in screening " using Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior '' .
Edit intent: clarity
Original: This note strengthens the main result in Lagziel and Lehrer (2019) `` A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior '' .
Revised: This note strengthens the main result in Lagziel and Lehrer (2019) `` A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) (CH) " Reversals of signal-posterior monotonicity for any bounded prior '' .
Edit intent: meaning-changed
Original: This note strengthens the main result in Lagziel and Lehrer (2019) `` A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior '' .
Revised: This note strengthens the main result in Lagziel and Lehrer (2019) `` A bias in screening '' by deriving it as a corollary of Chambers Healy (2011) `` Reversals of signal-posterior monotonicity for any bounded prior ". LL show that the conditional expectation of an unobserved variable of interest, given that a noisy signal of it exceeds a cutoff, may decrease in the cutoff. CH prove that the distribution of a variable conditional on a lower signal may first order stochastically dominate the distribution conditional on a higher signal. This note transforms the density of a modified CH signal into the cumulative distribution of the LL signal .
Edit intent: meaning-changed
Original: We  extend the scope of the vol-of-vol expansion given in 27%DIFDELCMD < ] %%% from finite dimensional stochastic volatility models to infinite dimensional (rough) forward variance modelsand provide newexplicit representations of the  push-down Malliavin weights that simplifies the computations and provides new insights into their structure.
Revised: We introduce an asymptotic small noise expansion, a so called vol-of-vol expansion, for potentially infinite dimensional and rough stochastic volatility models. Thereby we extend the scope of the vol-of-vol expansion given in 27%DIFDELCMD < ] %%% from finite dimensional stochastic volatility models to infinite dimensional (rough) forward variance modelsand provide newexplicit representations of the push-down Malliavin weights that simplifies the computations and provides new insights into their structure.
Edit intent: meaning-changed
Original: We  extend the scope of the vol-of-vol expansion given in 27%DIFDELCMD < ] %%% from finite dimensional stochastic volatility models to infinite dimensional (rough) forward variance modelsand provide newexplicit representations of the  push-down Malliavin weights that simplifies the computations and provides new insights into their structure.
Revised: We extend the scope of 27%DIFDELCMD < ] %%% from finite dimensional stochastic volatility models to infinite dimensional (rough) forward variance modelsand provide newexplicit representations of the push-down Malliavin weights that simplifies the computations and provides new insights into their structure.
Edit intent: clarity
Original: We  extend the scope of the vol-of-vol expansion given in 27%DIFDELCMD < ] %%% from finite dimensional stochastic volatility models to infinite dimensional (rough) forward variance modelsand provide newexplicit representations of the  push-down Malliavin weights that simplifies the computations and provides new insights into their structure.
Revised: We extend the scope of the vol-of-vol expansion given in %DIFDELCMD < ] %%% from finite dimensional stochastic volatility models to infinite dimensional (rough) forward variance modelsand provide newexplicit representations of the push-down Malliavin weights that simplifies the computations and provides new insights into their structure.
Edit intent: clarity