wanyu/IteraTeR_full_sent huggingface dataset:
Original: In this paper, we provide an axiomatic approach to general premium priciples giving rise to a decomposition into risk, as a generalization of the expected value, and deviation, as a generalization of the variance.
Revised: In this paper, we provide an axiomatic approach to general premium principles giving rise to a decomposition into risk, as a generalization of the expected value, and deviation, as a generalization of the variance.
Edit intent: fluency
Original: In this paper, we provide an axiomatic approach to general premium principles giving rise to a decomposition into risk , as a generalization of the expected value, and deviation , as a generalization of the variance.
Revised: In this paper, we provide an axiomatic approach to general premium principles in a probability-free setting that allows for Knightian uncertainty. Every premium principle is the sum of a risk measure , as a generalization of the expected value, and deviation , as a generalization of the variance.
Edit intent: meaning-changed
Original: In this paper, we provide an axiomatic approach to general premium principles giving rise to a decomposition into risk , as a generalization of the expected value, and deviation , as a generalization of the variance.
Revised: In this paper, we provide an axiomatic approach to general premium principles giving rise to a decomposition into risk , as a generalization of the expected value, and a deviation measure , as a generalization of the variance.
Edit intent: clarity
Original:  We show that, for every premium principle, there exists a maximal risk measure capturing all risky components covered by the insurance prices. In a second step, we consider dual representations of convex risk measures consistent with the premium principle.
Revised:  We show that, for every premium principle, there exists a maximal risk measure and a minimal deviation measure in such decompositions. We show how previous axiomatizations of premium principles can be embedded into our more general framework. We discuss dual representations of convex risk measures consistent with the premium principle.
Edit intent: meaning-changed
Original:  In a second step, we consider dual representations of convex risk measures consistent with the premium principle. In particular, we show that the convex conjugate of the aforementioned maximal risk measure coincides with the convex conjugate of the premium principle on the set of all finitely additive probability measures. In a last step, we consider insurance prices in the presence of a not neccesarily frictionless market , where insurance claims are traded. In this setup, we discuss premium principles that are consistent with hedging using securization products that are traded in the market .
Revised:  In a second step, we consider dual representations of convex premium principles, and study the consistency of premium principles with a financial market in which insurance contracts are traded .
Edit intent: clarity
Original:  Here, we demonstrate that, in addition to alarming and informing, mobile tracing can detect nearly all individuals that are infected by SARS-CoV-2 , including the notorious asymptomatic infections.
Revised:  Here, we demonstrate that, in addition to alarming and informing, mobile tracing can detect nearly all users that are infected by SARS-CoV-2 , including the notorious asymptomatic infections.
Edit intent: meaning-changed
Original:  Here, we demonstrate that, in addition to alarming and informing, mobile tracing can detect nearly all individuals that are infected by SARS-CoV-2 , including the notorious asymptomatic infections. Besides voluntary reports of the infectious health status, our algorithm requires the location information of an overwhelming part of the population and then guarantees that almost all SARS-CoV-2 infections in that closed population can be detected.
Revised:  Here, we demonstrate that, in addition to alarming and informing, mobile tracing can detect nearly all individuals that are infected by SARS-CoV-2 . Our algorithm BETIS (Bayesian Estimation for Tracing Infection States) makes use of self-reports of the user's health status. Then, BETIS guarantees that almost all SARS-CoV-2 infections in that closed population can be detected.
Edit intent: clarity
Original:  Besides voluntary reports of the infectious health status, our algorithm requires the location information of an overwhelming part of the population and then guarantees that almost all SARS-CoV-2 infections in that closed population can be detected.
Revised:  Besides voluntary reports of the infectious health status, our algorithm requires the location information of an overwhelming part of the population and then guarantees that almost all SARS-CoV-2 infections of the group of users can be detected.
Edit intent: clarity
Original:   Numerical experiments using synthetic data showcase the efficiency of these algorithms, while they also allow to understand the reduction of model-risk by including additional information, in the form of known derivative prices.
Revised:  Moreover, these algorithms can be used to detect arbitrage opportunities and identify the corresponding arbitrage strategies. Numerical experiments using synthetic data showcase the efficiency of these algorithms, while they also allow to understand the reduction of model-risk by including additional information, in the form of known derivative prices.
Edit intent: meaning-changed
Original:  However, it is not yet clear how to efficiently train GNNs on temporal graphs , where new vertices, edges, and even classes appear over time. We face two challenges : First, shifts in the label distribution (including the appearance of new labels), which require adapting the model .
Revised:  However, real-world graphs are often evolving over time and even new classes may arise. We model these challenges as an instance of lifelong learning, in which a learner faces a sequence of tasks and may take over knowledge acquired in past tasks. Such knowledge may be stored explicitly as historic data or implicitly within model parameters. In this work, we systematically analyze the influence of implicit and explicit knowledge. Therefore, we present an incremental training method for lifelong learning on graphs and introduce a new measure based on k-neighborhood time differences to address variances in the historic data. We apply our training method to five representative GNN architectures and evaluate them on three new lifelong node classification datasets .
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Original:  Our results show that both GNN types provide good results even for a window size of just 1 time step. With window sizes of 3 to 4 time steps, GNNs achieve at least 95\% accuracy compared to using the entire timeline of the graph .
Revised:  Our results show that no more than 50\% of the GNN's receptive field is necessary to retain at least 95\% accuracy compared to using the entire timeline of the graph .
Edit intent: meaning-changed
Original:  With window sizes of 3 to 4 time steps, GNNs achieve at least 95\% accuracy compared to using the entire timeline of the graph . With window sizes of 6 or 8, at least 99\% accuracy could be retained. These discoveries have direct consequences for training GNNs over temporal graphs. We provide the code URL and the newly compiled datasets URL for reproducibility and reuse .
Revised:  With window sizes of 3 to 4 time steps, GNNs achieve at least 95\% accuracy compared to using the entire timeline of the graph data. Furthermore, our experiments confirm that implicit knowledge becomes more important when fewer explicit knowledge is available .
Edit intent: meaning-changed
Original:  With respect to the recent outbreak of  COVID-19  , online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact.
Revised:  With respect to the recent outbreak of the Coronavirus disease 2019 ( COVID-19 , online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact.
Edit intent: meaning-changed
Original:  It is linked to the asymptotic behavior in measure of differentially private estimators. We demonstrate that particular instances of regression discontinuity design and average treatment effect may be problematic for inference with differential privacy because their estimators can only be ensured to converge weakly with their asymptotic limit remaining random and, thus, may not be estimated consistently.
Revised:  It is naturally characterized by the concepts from the random sets theory. We show that particular instances of regression discontinuity design and average treatment effect may be problematic for inference with differential privacy because their estimators can only be ensured to converge weakly with their asymptotic limit remaining random and, thus, may not be estimated consistently.
Edit intent: meaning-changed
Original:  We demonstrate that particular instances of regression discontinuity design and average treatment effect may be problematic for inference with differential privacy because their estimators can only be ensured to converge weakly with their asymptotic limit remaining random and, thus, may not be estimated consistently.
Revised:  We demonstrate that particular instances of regression discontinuity design may be problematic for inference with differential privacy because their estimators can only be ensured to converge weakly with their asymptotic limit remaining random and, thus, may not be estimated consistently.
Edit intent: clarity
Original:  We demonstrate that particular instances of regression discontinuity design and average treatment effect may be problematic for inference with differential privacy because their estimators can only be ensured to converge weakly with their asymptotic limit remaining random and, thus, may not be estimated consistently. This result is clearly supported by our simulation evidence.
Revised:  We demonstrate that particular instances of regression discontinuity design and average treatment effect may be problematic for inference with differential privacy as parameters turn out to be neither point nor partially identified. The set of differentially private estimators converges weakly to a random set.
Edit intent: meaning-changed
Original:  Our analysis suggests that many other estimators that rely on nuisance parameters may have similar properties with the requirement of differential privacy  .
Revised:  Our analysis suggests that many other estimators that rely on nuisance parameters may have similar properties with the requirement of differential privacy . We show that identification becomes possible if the target parameter can be deterministically located within the random set. In that case, a full exploration of the random set of the weak limits of differentially private estimators can allow the data curator to select a sequence of instances of differentially private estimators converging to the target parameter in probability .
Edit intent: meaning-changed
Original: How does the human brain learn new concepts from raw sensory experience , without explicit instruction ? We still do not have a widely-accepted answer to this central question. Here, we propose a detailed biological mechanism for the widely-embraced idea that learning is based on the differences between predictions and actual outcomes (i.e., predictive error-driven learning).
Revised: How does the human brain learn new concepts from raw sensory experience , without explicit instruction . We propose a detailed biological mechanism for the widely-embraced idea that learning is based on the differences between predictions and actual outcomes (i.e., predictive error-driven learning).
Edit intent: coherence
Original: We develop general tools to construct fully-discrete, conservative numerical methods and apply them to several nonlinear dispersive wave equations: Benjamin-Bona-Mahony (BBM), Fornberg-Whitham, Camassa-Holm, Degasperis-Procesi, Holm-Hone, and the BBM-BBM system.
Revised: We develop a general framework for designing conservative numerical methods and apply them to several nonlinear dispersive wave equations: Benjamin-Bona-Mahony (BBM), Fornberg-Whitham, Camassa-Holm, Degasperis-Procesi, Holm-Hone, and the BBM-BBM system.
Edit intent: clarity
Original: We develop general tools to construct fully-discrete, conservative numerical methods and apply them to several nonlinear dispersive wave equations: Benjamin-Bona-Mahony (BBM), Fornberg-Whitham, Camassa-Holm, Degasperis-Procesi, Holm-Hone, and the BBM-BBM system.
Revised: We develop general tools to construct fully-discrete, conservative numerical methods based on summation by parts operators and split forms in space, combined with relaxation Runge-Kutta methods in time. We apply this framework to create new classes of fully-discrete conservative methods for several nonlinear dispersive wave equations: Benjamin-Bona-Mahony (BBM), Fornberg-Whitham, Camassa-Holm, Degasperis-Procesi, Holm-Hone, and the BBM-BBM system.
Edit intent: meaning-changed
Original: We study the computational complexity of approximating the partition function of the ferromagnetic Ising model in the Lee-Yang circle of zeros given by |\lambda|=1, where \lambda is the external field of the model . Complex-valued parameters for the Ising model are relevant for quantum circuit computations and phase transitions in statistical physics, but have also been key in the recent deterministic approximation scheme for all |\lambda|\neq 1 by Liu, Sinclair, and Srivastava.
Revised: We study the computational complexity of approximating the partition function of the ferromagnetic Ising model with the external field parameter \lambda on the unit circle in the complex plane . Complex-valued parameters for the Ising model are relevant for quantum circuit computations and phase transitions in statistical physics, but have also been key in the recent deterministic approximation scheme for all |\lambda|\neq 1 by Liu, Sinclair, and Srivastava.
Edit intent: meaning-changed
Original:  Here, we focus on the unresolved complexity picture on the unit circle, and on the tantalising question of what happens in the circular arc around \lambda=1, where on one hand the classical algorithm of Jerrum and Sinclair gives a randomised approximation scheme on the real axis suggesting tractability, and on the other hand the presence of Lee-Yang zeros alludes to computational hardness.
Revised:  Here, we focus on the unresolved complexity picture on the unit circle, and on the tantalising question of what happens around \lambda=1, where on one hand the classical algorithm of Jerrum and Sinclair gives a randomised approximation scheme on the real axis suggesting tractability, and on the other hand the presence of Lee-Yang zeros alludes to computational hardness.
Edit intent: clarity
Original:  Our main result establishes a sharp computational transition at the point \lambda=1 ; in fact, our techniques apply more generally to the whole unit circle|\lambda|=1. We show #P-hardness for approximating the partition function on graphs of maximum degree \Delta when b, the edge-interaction parameter, is in the interval (0,\frac{\Delta-2{\Delta}}%DIFDELCMD < ] %%% and \lambda is a non-real on the unit circle .
Revised:  Our main result establishes a sharp computational transition at the point \lambda=1 , and more generally on the entire unit circle. For an integer \Delta\geq 3 and edge interaction parameter b\in (0,1) we show #P-hardness for approximating the partition function on graphs of maximum degree \Delta when b, the edge-interaction parameter, is in the interval (0,\frac{\Delta-2{\Delta}}%DIFDELCMD < ] %%% and \lambda is a non-real on the unit circle .
Edit intent: meaning-changed
Original:  We show #P-hardness for approximating the partition function on graphs of maximum degree \Delta when b, the edge-interaction parameter, is in the interval (0,\frac{\Delta-2{\Delta}}%DIFDELCMD < ] %%% and \lambda is a non-real on the unit circle .
Revised:  We show #P-hardness for approximating the partition function on graphs of maximum degree \Delta {\Delta}}%DIFDELCMD < ] %%% and \lambda is a non-real on the unit circle .
Edit intent: meaning-changed
Original:  We show #P-hardness for approximating the partition function on graphs of maximum degree \Delta when b, the edge-interaction parameter, is in the interval (0,\frac{\Delta-2{\Delta}}%DIFDELCMD < ] %%% and \lambda is a non-real on the unit circle .
Revised:  We show #P-hardness for approximating the partition function on graphs of maximum degree \Delta when b, the edge-interaction parameter, is in the interval (0,\frac{\Delta-2{\Delta}}%DIFDELCMD < ] %%% on the arc of the unit circle where the Lee-Yang zeros are dense .
Edit intent: meaning-changed
Original:  This result contrasts with known approximation algorithms when |\lambda|\neq 1 or b\in (\frac{\Delta-2{\Delta},}  1 ), and shows that the Lee-Yang circleof zeros is computationally intractable, even on bounded-degree graphs.
Revised:  This result contrasts with known approximation algorithms when |\lambda|\neq 1 or {\Delta},} 1 ), and shows that the Lee-Yang circleof zeros is computationally intractable, even on bounded-degree graphs.
Edit intent: meaning-changed
Original:  This result contrasts with known approximation algorithms when |\lambda|\neq 1 or b\in (\frac{\Delta-2{\Delta},}  1 ), and shows that the Lee-Yang circleof zeros is computationally intractable, even on bounded-degree graphs.
Revised:  This result contrasts with known approximation algorithms when |\lambda|\neq 1 or b\in (\frac{\Delta-2{\Delta},} when \lambda is in the complementary arc around 1 ), and shows that the Lee-Yang circleof zeros is computationally intractable, even on bounded-degree graphs.
Edit intent: meaning-changed
Original:  This result contrasts with known approximation algorithms when |\lambda|\neq 1 or b\in (\frac{\Delta-2{\Delta},}  1 ), and shows that the Lee-Yang circleof zeros is computationally intractable, even on bounded-degree graphs.
Revised:  This result contrasts with known approximation algorithms when |\lambda|\neq 1 or b\in (\frac{\Delta-2{\Delta},} 1 of the unit circle. Our work thus gives a direct connection between the presence/absence of Lee-Yang zeros and the tractability of efficiently approximating the partition function on bounded-degree graphs.
Edit intent: meaning-changed
Original:  While much is known about these effects individually , little is known about whether one of them "dominates" another . Knowledge of any such dominance is important for effective choice architecture and for accurate descriptive modelling.
Revised:  While much is known about these effects individually , little is known about whether one of them "dominates" the other when both can potentially be present . Knowledge of any such dominance is important for effective choice architecture and for accurate descriptive modelling.
Edit intent: meaning-changed
Original:  We initiate this empirical investigation with a lab experiment on choice under risk that was designed to test for dominance between *status quo bias* and the *decoy effect*.
Revised:  We initiate this empirical investigation with a between-subjects lab experiment that featured a single decision over two or three money lotteries. Our experiment was designed to test for dominance between *status quo bias* and the *decoy effect*.
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Original:   The observed reversal in risk attitudes is explainable by a large class of Kozsegi-Rabin (2006) reference-dependent preferences.
Revised:  Survey evidence suggests that this is due to subjects focusing on the maximum possible amount when the risky lottery is the default and on the probability of winning more than the smallest amount when there is no default lottery. The observed reversal in risk attitudes is explainable by a large class of Kozsegi-Rabin (2006) reference-dependent preferences.
Edit intent: meaning-changed
Original:  We provide revealed preference tests of three models of choice bracketing: broad, narrow, and partial-narrow. We apply these tests in three experiments to determine how subjects bracket in portfolio allocation under risk, social allocation, and induced-utility shopping experiments . 40-44 \\%DIF < of our subjects are consistent with narrow bracketing, while only 0-15\\% are consistent with broad bracketing.
Revised:  We provide revealed preference tests that determine how each subject brackets her choices. In separate portfolio allocation under risk, social allocation, and induced-utility shopping experiments . 40-44 \\%DIF < of our subjects are consistent with narrow bracketing, while only 0-15\\% are consistent with broad bracketing.
Edit intent: clarity
Original:  We apply these tests in three experiments to determine how subjects bracket in portfolio allocation under risk, social allocation, and induced-utility shopping experiments . 40-44 \\%DIF < of our subjects are consistent with narrow bracketing, while only 0-15\\% are consistent with broad bracketing.
Revised:  We apply these tests in three experiments to determine how subjects bracket in portfolio allocation under risk, social allocation, and induced-utility shopping experiments , we find that 40-43 \\%DIF < of our subjects are consistent with narrow bracketing, while only 0-15\\% are consistent with broad bracketing.
Edit intent: meaning-changed
Original: In this paper, we first establish well-posedness results of McKean-Vlasov stochastic differential equations (SDEs) and related particle systems with a  drift coefficient that is discontinuous in the spatial component .
Revised: In this paper, we first establish well-posedness results for one-dimensional McKean-Vlasov stochastic differential equations (SDEs) and related particle systems with a drift coefficient that is discontinuous in the spatial component .
Edit intent: meaning-changed
Original: In this paper, we first establish well-posedness results of McKean-Vlasov stochastic differential equations (SDEs) and related particle systems with a  drift coefficient that is discontinuous in the spatial component .
Revised: In this paper, we first establish well-posedness results of McKean-Vlasov stochastic differential equations (SDEs) and related particle systems with a measure-dependent drift coefficient that is discontinuous in the spatial component .
Edit intent: meaning-changed
Original: In this paper, we first establish well-posedness results of McKean-Vlasov stochastic differential equations (SDEs) and related particle systems with a  drift coefficient that is discontinuous in the spatial component . The analysis is restricted to the one-dimensional case, and we only require a fairly mild condition on the diffusion coefficient, namely to be non-zero in a point of discontinuity  , while we need to impose certain structural assumptions on the measure-dependence of the drift.
Revised: In this paper, we first establish well-posedness results of McKean-Vlasov stochastic differential equations (SDEs) and related particle systems with a drift coefficient that is discontinuous in the spatial component , and a diffusion coefficient which is a Lipschitz function of the state only. We only require a fairly mild condition on the diffusion coefficient, namely to be non-zero in a point of discontinuity , while we need to impose certain structural assumptions on the measure-dependence of the drift.
Edit intent: meaning-changed
Original:  The analysis is restricted to the one-dimensional case, and we only require a fairly mild condition on the diffusion coefficient, namely to be non-zero in a point of discontinuity  , while we need to impose certain structural assumptions on the measure-dependence of the drift.
Revised:  The analysis is restricted to the one-dimensional case, and we only require a fairly mild condition on the diffusion coefficient, namely to be non-zero in a point of discontinuity of the drift , while we need to impose certain structural assumptions on the measure-dependence of the drift.
Edit intent: fluency