wanyu/IteraTeR_v2 huggingface dataset:
Original:  It is often convenient, in particular in the analysis oflarger systems, to summarize several subsequent reactions into asingle composite chemical reaction.
Revised:  It is often convenient, in particular in the analysis of larger systems, to summarize several subsequent reactions into asingle composite chemical reaction.
Edit intent: fluency
Original:  It is often convenient, in particular in the analysis oflarger systems, to summarize several subsequent reactions into asingle composite chemical reaction.
Revised:  It is often convenient, in particular in the analysis oflarger systems, to summarize several subsequent reactions into a single composite chemical reaction.
Edit intent: fluency
Original:  We use a generic approach forcomposing graph grammar rules to define a chemically useful rulecompositions .
Revised:  We use a generic approach for composing graph grammar rules to define a chemically useful rulecompositions .
Edit intent: fluency
Original:  We use a generic approach forcomposing graph grammar rules to define a chemically useful rulecompositions .
Revised:  We use a generic approach forcomposing graph grammar rules to define a chemically useful rule compositions .
Edit intent: fluency
Original:  We iteratively apply these rule compositions toelementary transformations in order to automatically infer complextransformation patterns.
Revised:  We iteratively apply these rule compositions to elementary transformations in order to automatically infer complextransformation patterns.
Edit intent: fluency
Original:  We iteratively apply these rule compositions toelementary transformations in order to automatically infer complextransformation patterns.
Revised:  We iteratively apply these rule compositions toelementary transformations in order to automatically infer complex transformation patterns.
Edit intent: fluency
Original:  This is useful for instance to understand thenet effect of complex catalytic cycles such as the Formosereaction .
Revised:  This is useful for instance to understand the net effect of complex catalytic cycles such as the Formosereaction .
Edit intent: fluency
Original:  This is useful for instance to understand thenet effect of complex catalytic cycles such as the Formosereaction .
Revised:  This is useful for instance to understand thenet effect of complex catalytic cycles such as the Formose reaction .
Edit intent: fluency
Original:  The automatically inferred graph grammar rule is a genericrepresentative that also covers the overall reaction pattern of theFormose cycle, namely two carbonyl groups that can react with a boundglycolaldehyde to a second glycolaldehyde.
Revised:  The automatically inferred graph grammar rule is a generic representative that also covers the overall reaction pattern of theFormose cycle, namely two carbonyl groups that can react with a boundglycolaldehyde to a second glycolaldehyde.
Edit intent: fluency
Original:  The automatically inferred graph grammar rule is a genericrepresentative that also covers the overall reaction pattern of theFormose cycle, namely two carbonyl groups that can react with a boundglycolaldehyde to a second glycolaldehyde.
Revised:  The automatically inferred graph grammar rule is a genericrepresentative that also covers the overall reaction pattern of the Formose cycle, namely two carbonyl groups that can react with a boundglycolaldehyde to a second glycolaldehyde.
Edit intent: fluency
Original:  The automatically inferred graph grammar rule is a genericrepresentative that also covers the overall reaction pattern of theFormose cycle, namely two carbonyl groups that can react with a boundglycolaldehyde to a second glycolaldehyde.
Revised:  The automatically inferred graph grammar rule is a genericrepresentative that also covers the overall reaction pattern of theFormose cycle, namely two carbonyl groups that can react with a bound glycolaldehyde to a second glycolaldehyde.
Edit intent: fluency
Original:  Rule composition also canbe used to study polymerization reactions as well as more complicatediterative reaction schemes.
Revised:  Rule composition also can be used to study polymerization reactions as well as more complicatediterative reaction schemes.
Edit intent: fluency
Original:  Rule composition also canbe used to study polymerization reactions as well as more complicatediterative reaction schemes.
Revised:  Rule composition also canbe used to study polymerization reactions as well as more complicated iterative reaction schemes.
Edit intent: fluency
Original:  Terpenes and the polyketides, forinstance , form two naturally occurring classes of compounds of utmostpharmaceutical interest that can be understood as " generalizedpolymers " consisting of five-carbon (isoprene) and two-carbon units, respectively.
Revised:  Terpenes and the polyketides, for instance , form two naturally occurring classes of compounds of utmostpharmaceutical interest that can be understood as " generalizedpolymers " consisting of five-carbon (isoprene) and two-carbon units, respectively.
Edit intent: fluency
Original:  Terpenes and the polyketides, forinstance , form two naturally occurring classes of compounds of utmostpharmaceutical interest that can be understood as " generalizedpolymers " consisting of five-carbon (isoprene) and two-carbon units, respectively.
Revised:  Terpenes and the polyketides, forinstance , form two naturally occurring classes of compounds of utmost pharmaceutical interest that can be understood as " generalizedpolymers " consisting of five-carbon (isoprene) and two-carbon units, respectively.
Edit intent: fluency
Original:  Terpenes and the polyketides, forinstance , form two naturally occurring classes of compounds of utmostpharmaceutical interest that can be understood as " generalizedpolymers " consisting of five-carbon (isoprene) and two-carbon units, respectively.
Revised:  Terpenes and the polyketides, forinstance , form two naturally occurring classes of compounds of utmostpharmaceutical interest that can be understood as " generalized polymers " consisting of five-carbon (isoprene) and two-carbon units, respectively.
Edit intent: fluency
Original:  Toward this goal, we explore the properties of genealogies that emerge from models of continual adaptation  .
Revised:  Toward this goal, we explore the properties of genealogies in a model of continual adaptation .
Edit intent: clarity
Original:  Since multiple merger coalescents emerge in various evolutionary scenarios characterized by sustained selection pressures , we argue that they should be considered as null-models for adapting populations.
Revised:  Since multiple merger coalescents emerge in various evolutionary scenarios characterized by sustained selection pressures , we argue that they should be considered as a null-model for adapting populations.
Edit intent: fluency
Original:  On the other hand, contribution-based ordering results in a denser collaboration network and a greater number of publications than is achieved using alphabetical author ordering .
Revised:  On the other hand, contribution-based ordering leads to a denser collaboration network and a greater number of publications than is achieved using alphabetical author ordering .
Edit intent: fluency
Original: Motivation: Detection and quantification of active transcripts using RNA-Seq is a central task to transcriptomics research.
Revised: Identification and quantification of active transcripts using RNA-Seq is a central task to transcriptomics research.
Edit intent: clarity
Original: Motivation: Detection and quantification of active transcripts using RNA-Seq is a central task to transcriptomics research.
Revised: Motivation: Detection and quantification of active transcripts using RNA-Seq is vital in transcriptomics research.
Edit intent: clarity
Original:  Initial efforts on mathematical or statistical modeling of read counts or per-base exonic expression signal have been successful but may face an increasing risk of model misspecification and overfitting .
Revised:  While initial efforts using mathematical or statistical modeling of read counts or per-base exonic expression signal have been successful but may face an increasing risk of model misspecification and overfitting .
Edit intent: clarity
Original:  Initial efforts on mathematical or statistical modeling of read counts or per-base exonic expression signal have been successful but may face an increasing risk of model misspecification and overfitting .
Revised:  Initial efforts on mathematical or statistical modeling of read counts or per-base exonic signal have been successful but may face an increasing risk of model misspecification and overfitting .
Edit intent: clarity
Original:  This is because the number of reference transcripts in the database is much larger than that of the active transcripts expressed under a single biological condition, and the difference is getting larger with the accelerated augmentation of transcripts database.
Revised:  This is because the number of reference transcripts in a database are expressed under a single biological condition, and the difference is getting larger with the accelerated augmentation of transcripts database.
Edit intent: clarity
Original:  The standard shrinkage approaches , such as Lasso, shrink all the transcript abundances to zero in a blind way, thus, it does not necessarily lead to the set of active transcripts.
Revised:  The standard shrinkage approaches , such as Lasso, shrink all the transcript abundances to zero in a non-discriminative manner. Thus it does not necessarily lead to the set of active transcripts.
Edit intent: clarity
Original:  The standard shrinkage approaches , such as Lasso, shrink all the transcript abundances to zero in a blind way, thus, it does not necessarily lead to the set of active transcripts.
Revised:  The standard shrinkage approaches , such as Lasso, shrink all the transcript abundances to zero in a blind way, thus, it does not necessarily yield the set of active transcripts.
Edit intent: clarity
Original:  Informed shrinkage approaches, motivated by the observed exonic expression signal, are thus desirable.
Revised:  Informed shrinkage approaches, using the observed exonic expression signal, are thus desirable.
Edit intent: clarity
Original:  Informed shrinkage approaches, motivated by the observed exonic expression signal, are thus desirable.
Revised:  Informed shrinkage approaches, motivated by the observed exonic coverage signal, are thus desirable.
Edit intent: clarity
Original:  Results: We propose a new  mathematical model of the observed exonic expression signal and the underlying transcript structure, we introduce a tuning parameter to penalize the selected regions in the selected transcripts that were not supported by the uncovered exonic regions, and we develop a constrained least square algorithm to adaptively adjust the shrinkage level based on the exonic expression signal.
Revised:  Results: We propose a new mathematical model of the observed exonic expression signal and the underlying transcripts structure. We introduce a tuning parameter to penalize the selected regions in the selected transcripts that were not supported by the uncovered exonic regions, and we develop a constrained least square algorithm to adaptively adjust the shrinkage level based on the exonic expression signal.
Edit intent: fluency
Original:  Results: We propose a new  mathematical model of the observed exonic expression signal and the underlying transcript structure, we introduce a tuning parameter to penalize the selected regions in the selected transcripts that were not supported by the uncovered exonic regions, and we develop a constrained least square algorithm to adaptively adjust the shrinkage level based on the exonic expression signal.
Revised:  Results: We propose a new mathematical model of the observed exonic expression signal and the underlying transcript structure, we introduce a tuning parameter to penalize the specific regions of the selected transcripts that were not supported by the uncovered exonic regions, and we develop a constrained least square algorithm to adaptively adjust the shrinkage level based on the exonic expression signal.
Edit intent: clarity
Original: For a bounded degree graph G on n vertices, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by: Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda \geq 0 \geq0 with additive error \epsilon n.
Revised: For a graph G on n vertices, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by: Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda \geq 0 \geq0 with additive error \epsilon n.
Edit intent: clarity
Original: For a bounded degree graph G on n vertices, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by: Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda \geq 0 \geq0 with additive error \epsilon n.
Revised: For a bounded degree graph G on n vertices, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by: Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda \geq0 with additive error \epsilon n.
Edit intent: clarity
Original: For a graph G on n vertices , let Z(G,\lambda) be the partition function of the monomer-dimer system defined by :
Revised: For a graph G , let Z(G,\lambda) be the partition function of the monomer-dimer system defined by :
Edit intent: clarity
Original: For a graph G on n vertices , let Z(G,\lambda) be the partition function of the monomer-dimer system defined by :
Revised: For a graph G on n vertices , let Z(G,\lambda) be the partition function of the monomer-dimer system defined by 
Edit intent: fluency
Original:  Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda%DIFDELCMD < \geq0 %%% with additive error \epsilon n . In the bounded degree model, the query complexity of our algorithm  is polynomial in 1/\epsilon, and we  provide a lower bound quadratic in 1/\epsilon for this problem.
Revised:  Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary %DIFDELCMD < \geq0 %%% with additive error \epsilon n . In the bounded degree model, the query complexity of our algorithm is polynomial in 1/\epsilon, and we provide a lower bound quadratic in 1/\epsilon for this problem.
Edit intent: clarity
Original:  Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda%DIFDELCMD < \geq0 %%% with additive error \epsilon n . In the bounded degree model, the query complexity of our algorithm  is polynomial in 1/\epsilon, and we  provide a lower bound quadratic in 1/\epsilon for this problem.
Revised:  Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda%DIFDELCMD < \geq0 %%% with additive error \epsilon n with high probability. The query complexity of our algorithm is polynomial in 1/\epsilon, and we provide a lower bound quadratic in 1/\epsilon for this problem.
Edit intent: clarity
Original:  Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda%DIFDELCMD < \geq0 %%% with additive error \epsilon n . In the bounded degree model, the query complexity of our algorithm  is polynomial in 1/\epsilon, and we  provide a lower bound quadratic in 1/\epsilon for this problem.
Revised:  Z(G,\lambda)=\sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We develop a constant-time algorithm for approximating \log Z(G,\lambda) at an arbitrary point \lambda%DIFDELCMD < \geq0 %%% with additive error \epsilon n . In the bounded degree model, the query complexity of our algorithm is polynomial in 1/\epsilon, and we also provide a lower bound quadratic in 1/\epsilon for this problem.
Edit intent: clarity
Original:  We show that our algorithm approximates the probability for a vertex to be covered by a matching  sampled according to this Gibbs distribution  in a near-optimal sublinear-time .
Revised:  We show that our algorithm approximates the probability for a vertex to be covered by a matching , sampled according to this Gibbs distribution in a near-optimal sublinear-time .
Edit intent: fluency
Original:  We show that our algorithm approximates the probability for a vertex to be covered by a matching  sampled according to this Gibbs distribution  in a near-optimal sublinear-time .
Revised:  We show that our algorithm approximates the probability for a vertex to be covered by a matching sampled according to this Gibbs distribution , in a near-optimal sublinear-time .
Edit intent: fluency
Original:  We show that our algorithm approximates the probability for a vertex to be covered by a matching  sampled according to this Gibbs distribution  in a near-optimal sublinear-time .
Revised:  We show that our algorithm approximates the probability for a vertex to be covered by a matching sampled according to this Gibbs distribution in a near-optimal sublinear time .
Edit intent: fluency
Original:  We extend our results to approximate the average size and the entropy of such a matching with an additive error in constant time , where again the query complexity is polynomial in 1/\epsilon and the lower bound is quadratic in 1/\epsilon.
Revised:  We extend our results to approximate the average size and the entropy of such a matching within an additive error in constant time , where again the query complexity is polynomial in 1/\epsilon and the lower bound is quadratic in 1/\epsilon.
Edit intent: clarity
Original:  Our results extend to many other problems where the correlation decay is known to hold as for independent sets or the Ising model up to the critical activity.
Revised:  Our results extend to other systems where the correlation decay is known to hold as for independent sets or the Ising model up to the critical activity.
Edit intent: clarity
Original:  Our results extend to other systems where the correlation decay is known to hold as for independent sets or the Ising model up to the critical activity.
Revised:  Our results extend to other systems where the correlation decay is known to hold as for the independent set problem up to the critical activity.
Edit intent: clarity
Original: For a graph G, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by Z(G,\lambda)= \sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We consider graphs of bounded degree and develop a sublinear-time algorithm for approximating \log Z(G,\lambda) at an arbitrary value \lambda>0 within additive error \epsilon n with high probability.
Revised: For a graph G, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by \sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We consider graphs of bounded degree and develop a sublinear-time algorithm for approximating \log Z(G,\lambda) at an arbitrary value \lambda>0 within additive error \epsilon n with high probability.
Edit intent: coherence
Original: For a graph G, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by Z(G,\lambda)= \sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We consider graphs of bounded degree and develop a sublinear-time algorithm for approximating \log Z(G,\lambda) at an arbitrary value \lambda>0 within additive error \epsilon n with high probability.
Revised: For a graph G, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by Z(G,\lambda)= \sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of size k in G. We consider graphs of bounded degree and develop a sublinear-time algorithm for approximating \log Z(G,\lambda) at an arbitrary value \lambda>0 within additive error \epsilon n with high probability.
Edit intent: clarity
Original: For a graph G, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by Z(G,\lambda)= \sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We consider graphs of bounded degree and develop a sublinear-time algorithm for approximating \log Z(G,\lambda) at an arbitrary value \lambda>0 within additive error \epsilon n with high probability.
Revised: For a graph G, let Z(G,\lambda) be the partition function of the monomer-dimer system defined by Z(G,\lambda)= \sum_k m_k(G)\lambda^k, where m_k(G) is the number of matchings of cardinality k in G. We consider graphs of bounded degree and develop a sublinear-time algorithm for estimating \log Z(G,\lambda) at an arbitrary value \lambda>0 within additive error \epsilon n with high probability.
Edit intent: fluency
Original:  For doing so we introduce a new family of simple games: the influence games whose de?nition is based on the linear threshold model for influence propagation.
Revised:  For doing so we introduce a new family of simple games: the influence games whose definition is based on the linear threshold model for influence propagation.
Edit intent: fluency
Original:  We show ?rst that any simple game can be described by an influence game and concentrate in the cases in which such construction can be done in polynomial time.
Revised:  We show first that any simple game can be described by an influence game and concentrate in the cases in which such construction can be done in polynomial time.
Edit intent: fluency
Original:  For doing so we introduce a new family of simple games: the influence games whose definition is based on the linear threshold model for influence propagation .
Revised:  For doing so we introduce a new family of simple games: the influence games whose definition is based on the linear threshold model as the influence model .
Edit intent: clarity
Original:  We characterize the computational complexity of various problems for general influence graphs . Finally, we analyze those problems for some particular extremal cases, with respect to the propagation of influence, showing tighter complexity characterizations.
Revised:  Then we characterize the computational complexity of various problems for general influence graphs . Finally, we analyze those problems for some particular extremal cases, with respect to the propagation of influence, showing tighter complexity characterizations.
Edit intent: coherence
Original: Involvements of major financial institutions in the recent financial crisis have generated renewed interests in fragility of global financial networks among economists and regulatory authorities.
Revised: The recent financial crisis have generated renewed interests in fragility of global financial networks among economists and regulatory authorities.
Edit intent: clarity
Original: Involvements of major financial institutions in the recent financial crisis have generated renewed interests in fragility of global financial networks among economists and regulatory authorities.
Revised: Involvements of major financial institutions in the recent financial crisis have generated renewed interests in fragilities of global financial networks among economists and regulatory authorities.
Edit intent: fluency