wanyu/IteraTeR_v2 huggingface dataset:
Original:  Using the obtained results , we get a number of exact solutions of the Black--Scholes equation  .
Revised:  Using the results obtained , we get a number of exact solutions of the Black--Scholes equation .
Edit intent: fluency
Original:  Most of this inability to achieve high throughput is explained by the fundamental constraints involved in guaranteeing ACID --- the addition of cores results in more concurrent transactions accessing the same contended data for , which access must be serialized in order to guarantee isolation.
Revised:  Most of this inability to achieve high throughput is explained by the fundamental constraints involved in guaranteeing ACID --- the addition of cores results in more concurrent transactions accessing the same contended data for which access must be serialized in order to guarantee isolation.
Edit intent: fluency
Original:  In this paper we formalize this question through a novel model called optimization from samples (OPS).
Revised:  We formalize this question through a novel model called optimization from samples (OPS).
Edit intent: clarity
Original:  In this paper we formalize this question through a novel model called optimization from samples (OPS).
Revised:  In this paper we formalize this question through a novel framework called optimization from samples (OPS).
Edit intent: clarity
Original:  In the OPS model , we are given sampled values of a function drawn from some distribution and our objective is to optimize the function under some constraint.
Revised:  In the OPS framework , we are given sampled values of a function drawn from some distribution and our objective is to optimize the function under some constraint.
Edit intent: clarity
Original:  In the OPS model , we are given sampled values of a function drawn from some distribution and our objective is to optimize the function under some constraint.
Revised:  In the OPS model , we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint.
Edit intent: fluency
Original:  Our main interest is in the following question: are functions that are learnable (from samples) and approximable (given oracle access to the function) also optimizable from samples?
Edit intent: coherence
Original:  We show that there are classes of submodular functions which have desirable approximation and learnability guarantees and for which no reasonable approximation for optimizing from samples is achievable.
Revised:  We show that there are classes of submodular functions which have desirable learnability and optimizability guarantees and for which no reasonable approximation for optimizing from samples is achievable.
Edit intent: clarity
Original:  We show that there are classes of submodular functions which have desirable approximation and learnability guarantees and for which no reasonable approximation for optimizing from samples is achievable.
Revised:  We show that there are classes of submodular functions which have desirable approximation and learnability guarantees and for which no reasonable approximation for optimization from samples is achievable.
Edit intent: fluency
Original: In this paper we consider the following question: can we optimize decisions on models learned from data and be guaranteed that we achieve desirable outcomes ?
Revised: In this paper we consider the following question: can we optimize objective functions from the training data we use to learn them ?
Edit intent: clarity
Original:  We formalize this question through a novel framework called optimization from samples (OPS).
Revised:  We formalize this question through a novel framework we call optimization from samples (OPS).
Edit intent: clarity
Original:  In the OPSframework , we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint.
Revised:  In OPS , we are given sampled values of a function drawn from some distribution and the objective is to optimize the function under some constraint.
Edit intent: clarity
Original:   We show that there are classes of functions which have desirable learnability and optimizability guarantees and for which no reasonable approximation for optimization from samples is achievable.
Revised:  We show that there are classes of functions which are statistically learnable and optimizable, but for which no reasonable approximation for optimization from samples is achievable.
Edit intent: clarity
Original:  In particular, our main result shows that even for maximization of coverage functions under a cardinality constraint k, there exists a hypothesis class of functions that cannot be approximated within a factor of n^{-1/4 + \epsilon (for any constant \epsilon > 0) of the optimal solution, from }  samples drawn from the uniform distributionover all sets of size at most k.
Revised:  In particular, our main result shows that even for maximization of coverage functions under a cardinality constraint (for any constant \epsilon > 0) of the optimal solution, from } samples drawn from the uniform distributionover all sets of size at most k.
Edit intent: coherence
Original:  In the general case of monotone submodular functions, we show an n^{-1/3 + \epsilon lower bound and an almost matching }%DIFDELCMD < \Omega%%% (n^{-1/3
Revised:  In the general case of monotone submodular functions, lower bound and an almost matching }%DIFDELCMD < \Omega%%% (n^{-1/3
Edit intent: clarity
Original:  Yet, viruses that cause chronic infections, such as HIV , can survive by evolving as quickly as the adaptive immune system, forming an evolutionary arms race within a host .
Revised:  Yet, viruses such as HIV , can survive by evolving as quickly as the adaptive immune system, forming an evolutionary arms race within a host .
Edit intent: clarity
Original:  Yet, viruses that cause chronic infections, such as HIV , can survive by evolving as quickly as the adaptive immune system, forming an evolutionary arms race within a host .
Revised:  Yet, viruses that cause chronic infections, such as HIV can cause chronic infections by evolving as quickly as the adaptive immune system, forming an evolutionary arms race within a host .
Edit intent: clarity
Original:  Yet, viruses that cause chronic infections, such as HIV , can survive by evolving as quickly as the adaptive immune system, forming an evolutionary arms race within a host .
Revised:  Yet, viruses that cause chronic infections, such as HIV , can survive by evolving as quickly as the adaptive immune system, forming an evolutionary arms race .
Edit intent: clarity
Original:  Here we introduce a mathematical framework to study the co-evolutionary dynamics of antibodies with antigens within a patient .
Revised:  Here we introduce a mathematical framework to study the coevolutionary dynamics of antibodies with antigens within a patient .
Edit intent: fluency
Original:  Here we introduce a mathematical framework to study the co-evolutionary dynamics of antibodies with antigens within a patient .
Revised:  Here we introduce a mathematical framework to study the co-evolutionary dynamics of antibodies with antigens within a host .
Edit intent: clarity
Original:  We also identify definitive signatures of co-evolution that measure the reciprocal response between the antibody and viruses, and we introduce experimentally measurable quantities that quantify the extent of adaptation during continual co-evolution of the two opposing populations.
Revised:  We also identify definitive signatures of coevolution that measure the reciprocal response between the antibody and viruses, and we introduce experimentally measurable quantities that quantify the extent of adaptation during continual co-evolution of the two opposing populations.
Edit intent: fluency
Original:  We also identify definitive signatures of co-evolution that measure the reciprocal response between the antibody and viruses, and we introduce experimentally measurable quantities that quantify the extent of adaptation during continual co-evolution of the two opposing populations.
Revised:  We also identify definitive signatures of co-evolution that measure the reciprocal response between antibodies and viruses, and we introduce experimentally measurable quantities that quantify the extent of adaptation during continual co-evolution of the two opposing populations.
Edit intent: fluency
Original:  We also identify definitive signatures of co-evolution that measure the reciprocal response between the antibody and viruses, and we introduce experimentally measurable quantities that quantify the extent of adaptation during continual co-evolution of the two opposing populations.
Revised:  We also identify definitive signatures of co-evolution that measure the reciprocal response between the antibody and viruses, and we introduce experimentally measurable quantities that quantify the extent of adaptation during continual coevolution of the two opposing populations.
Edit intent: fluency
Original:   Finally, we analyze competition between clonal lineages of antibodies and characterize the fate of a given lineage dependent on the state of the antibody and viral populations.
Revised:  Finally, we analyze competition between clonal lineages of antibodies and characterize the fate of a given lineage in terms of the state of the antibody and viral populations.
Edit intent: clarity
Original:  In particular, we derive the conditions that favor the emergence of broadly neutralizing antibodies, which may be used in designing a vaccine against HIV.
Revised:  In particular, we derive the conditions that favor the emergence of broadly neutralizing antibodies, which may be useful in designing a vaccine against HIV.
Edit intent: clarity
Original:  In cases where the graph is of minimum degree 2 and its girth g is at least 12, the lower bound can be further improved to \max{\{\sqrt{n, \frac{\lfloor \frac{g{3} \rfloor-1}{\lfloor g{3} \rfloor}m}\}}} , \frac{\lfloor \frac{g{3} \rfloor-1}{3}m}\}}} .
Revised:  In cases where the graph is of minimum degree 2 and its girth g is at least 12, the lower bound can be further improved to \max{\{\sqrt{n, \frac{\lfloor \frac{g{3} \rfloor-1}{\lfloor g{3} \rfloor}m}\}}} \max{\{\sqrt{n, \frac{\lfloor \frac{g{3} \rfloor-1}{3}m}\}}} .
Edit intent: fluency
Original:  Typically, two- or three-factor versions allow  to capture the dependence structure between yields with different times to maturity in an appropriate way.
Revised:  Typically, two- or three-factor versions allow one to capture the dependence structure between yields with different times to maturity in an appropriate way.
Edit intent: clarity
Original:  In practice, re-calibration of the model to the prevailing market conditions leads to model parameters which change over time.
Revised:  In practice, re-calibration of the model to the prevailing market conditions leads to model parameters that change over time.
Edit intent: fluency
Original:  Therefore, the model parameters should be understood as being time-dependent , or even stochastic.
Revised:  Therefore, the model parameters should be understood as being time-dependent or even stochastic.
Edit intent: fluency
Original:  As a numerical example  we fit Swiss interest rates using CRC multifactor Vasicek models.
Revised:  As a numerical example , we fit Swiss interest rates using CRC multifactor Vasicek models.
Edit intent: fluency
Original:  Finally, we compute the persistent homology of two real-world networks and one network constructed by a common dynamical systems model, and we compare the results with the three classes to obtain a better understanding of those networks 
Revised:  Finally, we compute the persistent homology of two real-world networks and one network constructed by a common dynamical systems model, and we compare the results with the three classes to obtain a better understanding of those networks .
Edit intent: fluency
Original:  However, previous research has found it difficult to statistically model these factors using observational data due to the inability to separate the effects of network formation from those of network influence.
Revised:  However, previous research has found it difficult to statistically model these factors from observational data due to the inability to separate the effects of network formation from those of network influence.
Edit intent: coherence
Original:  However, previous research has found it difficult to statistically model these factors using observational data due to the inability to separate the effects of network formation from those of network influence.
Revised:  However, previous research has found it difficult to statistically model these factors using observational data due to the inability to separately assess the effects of network formation from those of network influence.
Edit intent: clarity
Original:  However, previous research has found it difficult to statistically model these factors using observational data due to the inability to separate the effects of network formation from those of network influence.
Revised:  However, previous research has found it difficult to statistically model these factors using observational data due to the inability to separate the effects of network formation and network influence.
Edit intent: clarity
Original:  The inability to successfully separate these two mechanisms makes it difficult to interpret whether the observed behavior is a result of peer influence or merely indicative of a selection bias due to homophily.
Edit intent: clarity
Original:  In this paper, we propose an actor-oriented continuous-time model to jointly estimate the co-evolution of the users' social network structure and their content production behavior using a Markov Chain Monte Carlo (MCMC)  based simulation approach.
Revised:  In this paper, we propose an actor-oriented continuous-time model to jointly estimate the co-evolution of the users' social network structure and their content production behavior using a Markov Chain Monte Carlo (MCMC) - based simulation approach.
Edit intent: fluency
Original:  Leveraging a unique dataset contributed by Facebook , we apply our model to data on university students across six months to find that  users tend to connect with others that have similar posting behavior .
Revised:  Leveraging a unique dataset from a large social network site , we apply our model to data on university students across six months to find that users tend to connect with others that have similar posting behavior .
Edit intent: clarity
Original: We study the problem of estimating the quality of data sources in data fusionsettings.
Revised: We revisit data fusion, i.e., the problem of estimating the quality of data sources in data fusionsettings.
Edit intent: clarity
Original:  We evaluate FUSE on real data\model and show that it outperforms feature-less baselines, and can yield reductions of more than 30%DIFDELCMD < \\%%% %DIF < in the source accuracy estimation error and improvements of more than 10%DIFDELCMD < \\%%% %DIF < in the F1-score when resolving conflicts across sources.abstract 
Revised:  We evaluate FUSE on real data\model and show that it %DIFDELCMD < \\%%% %DIF < in the source accuracy estimation error and improvements of more than 10%DIFDELCMD < \\%%% %DIF < in the F1-score when resolving conflicts across sources.abstract 
Edit intent: clarity
Original:  We evaluate FUSE on real data\model and show that it outperforms feature-less baselines, and can yield reductions of more than 30%DIFDELCMD < \\%%% %DIF < in the source accuracy estimation error and improvements of more than 10%DIFDELCMD < \\%%% %DIF < in the F1-score when resolving conflicts across sources.abstract 
Revised:  We evaluate FUSE on real data\model and show that it outperforms feature-less baselines, and can yield reductions of more than 30%DIFDELCMD < \\%%% %DIF < in the source accuracy estimation error and improvements of more than %DIFDELCMD < \\%%% %DIF < in the F1-score when resolving conflicts across sources.abstract 
Edit intent: coherence
Original: We revisit data fusion, i.e., the problem of integrating noisy data from multiple sources by estimating the source accuracies , and show that the simple model of logistic regression can capture most existing approaches for solving data fusion.
Revised: We focus on data fusion, i.e., the problem of integrating noisy data from multiple sources by estimating the source accuracies , and show that the simple model of logistic regression can capture most existing approaches for solving data fusion.
Edit intent: clarity
Original: We revisit data fusion, i.e., the problem of integrating noisy data from multiple sources by estimating the source accuracies , and show that the simple model of logistic regression can capture most existing approaches for solving data fusion.
Revised: We revisit data fusion, i.e., the problem of unifying conflicting data from data sources into a single representation by estimating the source accuracies , and show that the simple model of logistic regression can capture most existing approaches for solving data fusion.
Edit intent: clarity
Original:  Expanding on logistic regression, we introduce%DIFDELCMD < \model%%% , a framework that converts data fusion to a learning and inference problem over discriminative probabilistic models  .
Revised:  Expanding on logistic regression, we introduce%DIFDELCMD < \model%%% , a framework that expresses data fusion as a statistical learning problem over discriminative probabilistic models .
Edit intent: clarity
Original:  In contrast to previous approaches that rely on complex generative models, discriminative models  allow us to decouple the specification of a data fusion model from the algorithm used to learn the model's parameters.
Revised:  In contrast to previous approaches that use complex generative models, discriminative models allow us to decouple the specification of a data fusion model from the algorithm used to learn the model's parameters.
Edit intent: clarity
Original:  We also\\%DIF > over  design an optimizer to automatically select the best algorithm for learning the model 's parameters.
Revised:  We also\\%DIF > over design an optimizer that obviates the need for users to manually select an algorithm for learning the model 's parameters.
Edit intent: clarity
Original:  We validate our optimizer on multiple real datasets and show that it chooses the best algorithm for learning in almost all cases .
Revised:  We validate our optimizer on multiple real-world datasets and show that it chooses the best algorithm for learning in almost all cases .
Edit intent: clarity
Original:  We isolate why this is the case and propose  alternate measures for information flow.
Revised:  We isolate why this is the case and propose several avenues to alternate measures for information flow.
Edit intent: clarity
Original:  An auxiliary consequencereveals that the proliferation of networks as a now-common theoretical model for large-scale systems  in concert with the use of transfer-like entropies  has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems , despite the occurrence of polyadic dependencies.
Revised:  We also address an auxiliary consequence: The proliferation of networks as a now-common theoretical model for large-scale systems in concert with the use of transfer-like entropies has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems , despite the occurrence of polyadic dependencies.
Edit intent: clarity
Original:  An auxiliary consequencereveals that the proliferation of networks as a now-common theoretical model for large-scale systems  in concert with the use of transfer-like entropies  has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems , despite the occurrence of polyadic dependencies.
Revised:  An auxiliary consequencereveals that the proliferation of networks as a now-common theoretical model for large-scale systems , in concert with the use of transfer-like entropies has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems , despite the occurrence of polyadic dependencies.
Edit intent: fluency
Original:  An auxiliary consequencereveals that the proliferation of networks as a now-common theoretical model for large-scale systems  in concert with the use of transfer-like entropies  has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems , despite the occurrence of polyadic dependencies.
Revised:  An auxiliary consequencereveals that the proliferation of networks as a now-common theoretical model for large-scale systems in concert with the use of transfer-like entropies , has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems , despite the occurrence of polyadic dependencies.
Edit intent: fluency
Original:  An auxiliary consequencereveals that the proliferation of networks as a now-common theoretical model for large-scale systems  in concert with the use of transfer-like entropies  has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems , despite the occurrence of polyadic dependencies.
Revised:  An auxiliary consequencereveals that the proliferation of networks as a now-common theoretical model for large-scale systems in concert with the use of transfer-like entropies has shoehorned dyadic relationships into our structural interpretation of the organization and behavior of complex systems . This interpretation thus fails to include the effects of polyadic dependencies.
Edit intent: clarity
Original:  The net result is that much of the sophisticated organization of complex systems goes undetected.
Revised:  The net result is that much of the sophisticated organization of complex systems may go undetected.
Edit intent: clarity
Original:  Unfortunately, existing methods could not solve this problem.
Revised:  Unfortunately, existing approaches could not solve this problem.
Edit intent: clarity
Original:  TSP instances where the points lie in the plane and the distance  between two points is the l_1 distance.
Revised:  TSP instances where the points lie in the plane and the distance considered between two points is the l_1 distance.
Edit intent: clarity
Original:  We propose a fixed-parameter algorithm for the Rectilinear TSP running in O\left(nh7^h\right) where h \leq n denotes the number of horizontal lines containing the points of P.
Revised:  In this paper, a fixed-parameter algorithm for the Rectilinear TSP running in O\left(nh7^h\right) where h \leq n denotes the number of horizontal lines containing the points of P.
Edit intent: clarity
Original:  Our approach can be directly applied to the problem of finding a shortest rectilinear Steiner tree that interconnects the points of P . It provides a O\left(nh5^h\right) algorithm which improves significantly over the best known existing fixed-parameter algorithm for rectilinear Steiner tree .
Revised:  The same technique can be directly applied to the problem of finding a shortest rectilinear Steiner tree that interconnects the points of P . It provides a O\left(nh5^h\right) algorithm which improves significantly over the best known existing fixed-parameter algorithm for rectilinear Steiner tree .
Edit intent: clarity
Original:  Our approach can be directly applied to the problem of finding a shortest rectilinear Steiner tree that interconnects the points of P . It provides a O\left(nh5^h\right) algorithm which improves significantly over the best known existing fixed-parameter algorithm for rectilinear Steiner tree .
Revised:  Our approach can be directly applied to the problem of finding a shortest rectilinear Steiner tree that interconnects the points of P providing a O\left(nh5^h\right) algorithm which improves significantly over the best known existing fixed-parameter algorithm for rectilinear Steiner tree .
Edit intent: coherence