wanyu/IteraTeR_v2 huggingface dataset:
Original:  and r = \mathrm{max}}%DIFDELCMD < \left(%%% 1,\frac{1+\rho{1+\delta}}%DIFDELCMD < \right)%%% . Third, the upper and lower bounds (essentially the same as above)
Revised:  and r = \mathrm{max}}%DIFDELCMD < \left(%%% {1+\delta}}%DIFDELCMD < \right)%%% . Third, the upper and lower bounds (essentially the same as above)
Edit intent: coherence
Original: The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA)  has been shown to be a top performing EA in several domains, including Genetic Programming (GP).
Revised: The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) has been shown to perform well in several domains, including Genetic Programming (GP).
Edit intent: clarity
Original:  Differently from traditional EAs where variation acts randomly , GOMEA learns a model of interdependencies within the genotype, i.e., the linkage, to estimate what patterns to propagate.
Revised:  Differently from traditional EAs where variation acts blindly , GOMEA learns a model of interdependencies within the genotype, i.e., the linkage, to estimate what patterns to propagate.
Edit intent: clarity
Original:  Furthermore, if the discrete convex mesh functions interpolate a Dirichlet boundary data and the Monge-Ampere measure of the uniform limit is finite, the latter satisfies the boundary condition strongly.
Revised:  Furthermore, if the discrete convex mesh functions interpolate boundary data of a continuous convex function and their Monge-Ampere measure of the uniform limit is finite, the latter satisfies the boundary condition strongly.
Edit intent: clarity
Original:  Furthermore, if the discrete convex mesh functions interpolate a Dirichlet boundary data and the Monge-Ampere measure of the uniform limit is finite, the latter satisfies the boundary condition strongly.
Revised:  Furthermore, if the discrete convex mesh functions interpolate a Dirichlet boundary data and the Monge-Ampere masses are uniformly bounded, the limit function satisfies the boundary condition strongly.
Edit intent: clarity
Original:  Our framework highlights how to combine data types in dynamical models for multiple species , and may be extended to other biotic interactions for which data on both interaction rates and population counts are available .
Revised:  Our framework highlights how to combine data types in dynamical models for multiple species . These results may extend to other biotic interactions for which data on both interaction rates and population counts are available .
Edit intent: clarity
Original:  Our framework highlights how to combine data types in dynamical models for multiple species , and may be extended to other biotic interactions for which data on both interaction rates and population counts are available .
Revised:  Our framework highlights how to combine data types in dynamical models for multiple species , and may be extended to other biotic interactions for which data on both interaction rates and population counts could be developed .
Edit intent: clarity
Original: Unique prime factorization of integers is taught in every high school.
Edit intent: coherence
Original:  We  establish a novel connection between counting constraint satisfaction problems and counting weighted Eulerian orientation problems that is global in nature , and is based on the determination of half-weighted affine linear subspaces _2, which is proved using M\"obius inversion} .
Revised:  We establish a novel connection between #CSP and counting weighted Eulerian orientation problems that is global in nature , and is based on the determination of half-weighted affine linear subspaces _2, which is proved using M\"obius inversion} .
Edit intent: clarity
Original:  We  establish a novel connection between counting constraint satisfaction problems and counting weighted Eulerian orientation problems that is global in nature , and is based on the determination of half-weighted affine linear subspaces _2, which is proved using M\"obius inversion} .
Revised:  We establish a novel connection between counting constraint satisfaction problems and counting weighted Eulerian orientation problems that is global in nature . This connection is based on the determination of half-weighted affine linear subspaces _2, which is proved using M\"obius inversion} .
Edit intent: coherence
Original: As one of the classic models that describe the belief dynamics over social networks,  non-Bayesian social learning model assumes that members in the network are possessing accurate signal knowledge through the process of Bayesian inference.
Revised: As one of the classic models that describe the belief dynamics over social networks, a non-Bayesian social learning model assumes that members in the network are possessing accurate signal knowledge through the process of Bayesian inference.
Edit intent: fluency
Original: As one of the classic models that describe the belief dynamics over social networks,  non-Bayesian social learning model assumes that members in the network are possessing accurate signal knowledge through the process of Bayesian inference.
Revised: As one of the classic models that describe the belief dynamics over social networks, non-Bayesian social learning model assumes that members in the network possess accurate signal knowledge through the process of Bayesian inference.
Edit intent: clarity
Original:  In order to make the non-Bayesian social learning model more applicable to human and animal societies, this paper extended this model by assuming  existence of private signal structure bias : each social member in each time step uses an imperfect signal knowledge to form its Bayesian part belief , then incorporates its neighbors' beliefs into this Bayesian part belief to form a new belief report.
Revised:  In order to make the non-Bayesian social learning model more applicable to human and animal societies, this paper extended this model by assuming the existence of private signal structure bias : each social member in each time step uses an imperfect signal knowledge to form its Bayesian part belief , then incorporates its neighbors' beliefs into this Bayesian part belief to form a new belief report.
Edit intent: fluency
Original:  In order to make the non-Bayesian social learning model more applicable to human and animal societies, this paper extended this model by assuming  existence of private signal structure bias : each social member in each time step uses an imperfect signal knowledge to form its Bayesian part belief , then incorporates its neighbors' beliefs into this Bayesian part belief to form a new belief report.
Revised:  In order to make the non-Bayesian social learning model more applicable to human and animal societies, this paper extended this model by assuming existence of private signal structure bias . Each social member in each time step uses an imperfect signal knowledge to form its Bayesian part belief , then incorporates its neighbors' beliefs into this Bayesian part belief to form a new belief report.
Edit intent: fluency
Original:  In order to make the non-Bayesian social learning model more applicable to human and animal societies, this paper extended this model by assuming  existence of private signal structure bias : each social member in each time step uses an imperfect signal knowledge to form its Bayesian part belief , then incorporates its neighbors' beliefs into this Bayesian part belief to form a new belief report.
Revised:  In order to make the non-Bayesian social learning model more applicable to human and animal societies, this paper extended this model by assuming existence of private signal structure bias : each social member in each time step uses an imperfect signal knowledge to form its Bayesian part belief and then incorporates its neighbors' beliefs into this Bayesian part belief to form a new belief report.
Edit intent: coherence
Original:  According to these conditions, agents' signal structures were further divided into three different types, "conservative " ," radical " , and "negative ".
Revised:  According to these conditions, agents' signal structures were further divided into three different types, "conservative ," radical " , and "negative ".
Edit intent: fluency
Original:  According to these conditions, agents' signal structures were further divided into three different types, "conservative " ," radical " , and "negative ".
Revised:  According to these conditions, agents' signal structures were further divided into three different types, "conservative " ," " , and "negative ".
Edit intent: coherence
Original: Then we switched the context from isolated agents to a connected network , our propositions and simulations show the "conservative " agents is the dominant force for the social network to learn the real state, while the other two types might prevent the network from successful learning.
Revised: Then we switched the context from isolated agents to a connected network ; our propositions and simulations show the "conservative " agents is the dominant force for the social network to learn the real state, while the other two types might prevent the network from successful learning.
Edit intent: fluency
Original: Then we switched the context from isolated agents to a connected network , our propositions and simulations show the "conservative " agents is the dominant force for the social network to learn the real state, while the other two types might prevent the network from successful learning.
Revised: Then we switched the context from isolated agents to a connected network , our propositions and simulations show that the conservative agents are the dominant force for the social network to learn the real state, while the other two types might prevent the network from successful learning.
Edit intent: fluency
Original:  Here, we develop an information-theoretic framework that enables us to examine how network structure, noise, input coding, and interference between consecutive symbols jointly determine transmission performance in networks with linear dynamics at single nodes and arbitrary topologies.
Revised:  Here, we develop a framework that enables us to examine how network structure, noise, input coding, and interference between consecutive symbols jointly determine transmission performance in networks with linear dynamics at single nodes and arbitrary topologies.
Edit intent: clarity
Original:  Here, we develop an information-theoretic framework that enables us to examine how network structure, noise, input coding, and interference between consecutive symbols jointly determine transmission performance in networks with linear dynamics at single nodes and arbitrary topologies.
Revised:  Here, we develop an information-theoretic framework that enables us to examine how network structure, noise, and interference between consecutive symbols jointly determine transmission performance in networks with linear dynamics at single nodes and arbitrary topologies.
Edit intent: clarity
Original:  Here, we develop an information-theoretic framework that enables us to examine how network structure, noise, input coding, and interference between consecutive symbols jointly determine transmission performance in networks with linear dynamics at single nodes and arbitrary topologies.
Revised:  Here, we develop an information-theoretic framework that enables us to examine how network structure, noise, input coding, and interference between consecutive packets jointly determine transmission performance in networks with linear dynamics at single nodes and arbitrary topologies.
Edit intent: clarity
Original:  In this work we use a NTM to learn and generalise two arithmetical tasks: binary addition and multiplication.
Revised:  In this work we use NTMs to learn and generalise two arithmetical tasks: binary addition and multiplication.
Edit intent: fluency
Original:  These tasks are two fundamental algorithmic examples in computer science, and are a lot more challenging than the previously explored ones, with which we aim to shed some light on the  capabilities on this neural model.
Revised:  These tasks are two fundamental algorithmic examples in computer science, and are a lot more challenging than the previously explored ones, with which we aim to shed some light on the real capabilities on this neural model.
Edit intent: clarity
Original:  We show that all attractors are fixed points for the system, independently of the neighbour relation  .
Revised:  In this framework, one can show that all attractors are fixed points for the system, independently of the neighbour relation .
Edit intent: clarity
Original:  The reachability of fixed points is studied, and a description of  the trap spaces is used to investigate the robustness of patterns to perturbations.
Revised:  The reachability of fixed points is studied, and a description of the trap spaces to investigate the robustness of patterns to perturbations.
Edit intent: coherence
Original: Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a  range of natural language processing tasks , in part by pretraining on a large and topically-diverse corpus .
Revised: Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks , in part by pretraining on a large and topically-diverse corpus .
Edit intent: clarity
Original: Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a  range of natural language processing tasks , in part by pretraining on a large and topically-diverse corpus .
Revised: Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a range of natural language processing tasks by pretraining on a large and topically-diverse corpus .
Edit intent: clarity
Original: Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a  range of natural language processing tasks , in part by pretraining on a large and topically-diverse corpus .
Revised: Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a range of natural language processing tasks , in part by pretraining on large corpora of unlabeled text .
Edit intent: clarity
Original:  However, the applicability of this approach is unknown when the target domain varies substantially from the text used during pretraining . Specifically, we are interested the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled corpus and the pretraining data .
Revised:  However, the applicability of this approach is unknown when the target domain varies substantially from the pretraining corpus. We are specifically interested in the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled corpus and the pretraining data .
Edit intent: clarity
Original:  However, the applicability of this approach is unknown when the target domain varies substantially from the text used during pretraining . Specifically, we are interested the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled corpus and the pretraining data .
Revised:  However, the applicability of this approach is unknown when the target domain varies substantially from the text used during pretraining . Specifically, we are interested the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled and pretraining texts .
Edit intent: clarity
Original:  We test this approach on the challenging domain of Early Modern English , which differs substantially from existing pretraining corpora . Domain-adaptive fine-tuning yields an improvement of 4%DIFDELCMD < \\%%% %DIF < in part-of-speech tagging accuracy over a BERT baseline, substantially improving on prior work on this task .
Revised:  We test this approach on the challenging domain of Early Modern English , which differs substantially from existing pretraining corpora , and domain-adaptive fine-tuning yields an improvement of 4%DIFDELCMD < \\%%% %DIF < in part-of-speech tagging accuracy over a BERT baseline, substantially improving on prior work on this task .
Edit intent: coherence
Original:  We test this approach on the challenging domain of Early Modern English , which differs substantially from existing pretraining corpora . Domain-adaptive fine-tuning yields an improvement of 4%DIFDELCMD < \\%%% %DIF < in part-of-speech tagging accuracy over a BERT baseline, substantially improving on prior work on this task .
Revised:  We test this approach on the challenging domain of Early Modern English , which differs substantially from existing pretraining corpora . Domain-adaptive fine-tuning yields %DIFDELCMD < \\%%% %DIF < in part-of-speech tagging accuracy over a BERT baseline, substantially improving on prior work on this task .
Edit intent: clarity
Original: We propose a signed network formation game, in which myopic agents dynamically and strategically change the signs of the edges in a complete network.
Revised: We propose a signed network formation game, in which pairs of individuals strategically change the signs of the edges in a complete network.
Edit intent: clarity
Original:  Our agents are members of a social network who strategically reduce cognitive dissonances by changing their interpersonal appraisals.
Revised:  These individuals are members of a social network who strategically reduce cognitive dissonances by changing their interpersonal appraisals.
Edit intent: clarity
Original:  We characterize the best-response policy for this game and prove that its implementation dynamically drives the network to a sociologically meaningful sign configuration called clustering balance.
Revised:  We characterize the best-response dynamics for this game and prove that its implementation dynamically drives the network to a sociologically meaningful sign configuration called clustering balance.
Edit intent: clarity
Original:  We characterize the best-response policy for this game and prove that its implementation dynamically drives the network to a sociologically meaningful sign configuration called clustering balance.
Revised:  We characterize the best-response policy for this game and prove that its implementation dynamically drive the network to a sociologically meaningful sign configuration called clustering balance.
Edit intent: fluency
Original: We show how to set up a forward rate model in the presence of volatility uncertainty by using the theory of G-Brownian motion.
Revised: We study a forward rate model in the presence of volatility uncertainty by using the theory of G-Brownian motion.
Edit intent: clarity
Original:  In contrast to the traditional model, the drift condition consists of two equations and two market prices of risk and  uncertainty, respectively.
Revised:  In contrast to the traditional model, the drift condition consists of two equations and two market prices of risk and market prices of uncertainty, respectively.
Edit intent: fluency
Original:  Similar to the traditional model, the risk-neutral dynamics of the forward rate are completely determined by the diffusion coefficient .
Revised:  Similar to the traditional model, the risk-neutral dynamics of the forward rate are completely determined by its diffusion term .
Edit intent: clarity
Original:  Furthermore, we obtain some classical term structures under volatility uncertainty as examples of our model .
Revised:  Furthermore, we obtain robust versions of classical term structure models as examples in this framework .
Edit intent: clarity
Original:  The uncertaintyabout the volatility is represented by a G-Brownian motion , being the driver of the forward rate dynamics .
Revised:  The uncertaintyabout the volatility is represented by a G-Brownian motion . The G-Brownian motion represents the uncertainty about the volatility .
Edit intent: clarity
Original:  Furthermore , we obtain robust versions of classical term structure models as examples in this framework .
Revised:  Furthermore , we obtain robust versions of classical term structure models .
Edit intent: coherence
Original: In the Topological Minor Containment problem (TMC) problem two undirected graphs, Gand H are given and the objective is to check{\cal   whether G contains H{\cal  as a topological minor.
Revised: In the Topological Minor Containment problem (TMC) problem two undirected graphs, Gand H are given and the objective is to check{\cal F whether G contains H{\cal as a topological minor.
Edit intent: fluency
Original:  Grohe, Kawarabayashi, Marx, and Wollan~ STOC 2011%DIFDELCMD < ] %%% resolved the parameterized complexity of this problem by designing an algorithm with running time f( k)n^{3 | V(H)| and n=| V(G)| . Among several other tools, this algorithm was based on a variant of the linkage theorem of Robertson-Seymour, tailor-made for TMC, by adapting elements of the new proof of the unique linkage theorem of Kawarabayashi and Wollan~.
Revised:  STOC 2011%DIFDELCMD < ] %%% resolved the parameterized complexity of this problem by designing an algorithm with running time f( k)n^{3 | V(H)| and n=| V(G)| . Among several other tools, this algorithm was based on a variant of the linkage theorem of Robertson-Seymour, tailor-made for TMC, by adapting elements of the new proof of the unique linkage theorem of Kawarabayashi and Wollan~.
Edit intent: coherence
Original:  Grohe, Kawarabayashi, Marx, and Wollan~ STOC 2011%DIFDELCMD < ] %%% resolved the parameterized complexity of this problem by designing an algorithm with running time f( k)n^{3 | V(H)| and n=| V(G)| . Among several other tools, this algorithm was based on a variant of the linkage theorem of Robertson-Seymour, tailor-made for TMC, by adapting elements of the new proof of the unique linkage theorem of Kawarabayashi and Wollan~.
Revised:  Grohe, Kawarabayashi, Marx, and Wollan~ %DIFDELCMD < ] %%% resolved the parameterized complexity of this problem by designing an algorithm with running time f( k)n^{3 | V(H)| and n=| V(G)| . Among several other tools, this algorithm was based on a variant of the linkage theorem of Robertson-Seymour, tailor-made for TMC, by adapting elements of the new proof of the unique linkage theorem of Kawarabayashi and Wollan~.
Edit intent: clarity
Original:  Grohe, Kawarabayashi, Marx, and Wollan~ STOC 2011%DIFDELCMD < ] %%% resolved the parameterized complexity of this problem by designing an algorithm with running time f( k)n^{3 | V(H)| and n=| V(G)| . Among several other tools, this algorithm was based on a variant of the linkage theorem of Robertson-Seymour, tailor-made for TMC, by adapting elements of the new proof of the unique linkage theorem of Kawarabayashi and Wollan~.
Revised:  Grohe, Kawarabayashi, Marx, and Wollan~ STOC 2011%DIFDELCMD < ] %%% We give an algorithm for TM-Deletionwith running time f( k)n^{3 | V(H)| and n=| V(G)| . Among several other tools, this algorithm was based on a variant of the linkage theorem of Robertson-Seymour, tailor-made for TMC, by adapting elements of the new proof of the unique linkage theorem of Kawarabayashi and Wollan~.
Edit intent: clarity
Original:  Grohe, Kawarabayashi, Marx, and Wollan~ STOC 2011%DIFDELCMD < ] %%% resolved the parameterized complexity of this problem by designing an algorithm with running time f( k)n^{3 | V(H)| and n=| V(G)| . Among several other tools, this algorithm was based on a variant of the linkage theorem of Robertson-Seymour, tailor-made for TMC, by adapting elements of the new proof of the unique linkage theorem of Kawarabayashi and Wollan~.
Revised:  Grohe, Kawarabayashi, Marx, and Wollan~ STOC 2011%DIFDELCMD < ] %%% resolved the parameterized complexity of this problem by designing an algorithm with running time f( k)n^{3 | V(G)| . Among several other tools, this algorithm was based on a variant of the linkage theorem of Robertson-Seymour, tailor-made for TMC, by adapting elements of the new proof of the unique linkage theorem of Kawarabayashi and Wollan~.
Edit intent: coherence