wanyu/IteraTeR_v2 huggingface dataset:
Original:  The notionof persistency leads to the problem of the Persistent Perfect Phylogeny (referred as P-PPH).
Revised:  Based on this notion, we define the problem of the Persistent Perfect Phylogeny (referred as P-PPH).
Edit intent: clarity
Original: In the given article the methods of parametric diagnostics of gas turbine based on fuzzy logic is proposed.
Revised: The creation of the systems models is very actual at present time, because it allow to simulate the work of some complex equipment without any additional spends. The given model of gas turbine based on fuzzy logic is proposed.
Edit intent: coherence
Original:  The interpretation we propose in this paper rests on the identity \ln\det B=\tr\ln B applied to the cofactors of the matrix I-tA, where A is the weighted adjacency matrix of a weighted multigraph and t is a sufficiently small positive parameter.
Revised:  The interpretation proposed in this paper rests on the identity \ln\det B=\tr\ln B applied to the cofactors of the matrix I-tA, where A is the weighted adjacency matrix of a weighted multigraph and t is a sufficiently small positive parameter.
Edit intent: clarity
Original:  It also cites the literature showing a correlation between species' morphological complexity and the length of their proteins.
Revised:  It also cites the literature showing a correlation between species' morphological complexity and the lengths of their proteins.
Edit intent: fluency
Original:  The running time of the algorithm is O(n^{1+\gamma(g)}\log^2n)  , where \gamma is an increasing function of the upper bound on  branch lengths in the phylogeny  and \gamma(g)<1 for all g.
Revised:  The running time of the algorithm is O(n^{1+\gamma(g)}\log^2n) , where \gamma is an increasing function of an upper bound on branch lengths in the phylogeny and \gamma(g)<1 for all g.
Edit intent: fluency
Original:  The running time of the algorithm is O(n^{1+\gamma(g)}\log^2n)  , where \gamma is an increasing function of the upper bound on  branch lengths in the phylogeny  and \gamma(g)<1 for all g.
Revised:  The running time of the algorithm is O(n^{1+\gamma(g)}\log^2n) , where \gamma is an increasing function of the upper bound on the branch lengths in the phylogeny and \gamma(g)<1 for all g.
Edit intent: fluency
Original:  Our results demonstrate that many large phylogenies can be reconstructed fast, without compromising the reconstruction accuracy.
Revised:  Our results demonstrate that many large phylogenies can be reconstructed fast, without compromising reconstruction accuracy.
Edit intent: clarity
Original: The realization of Daily Artificial Dispatcher as a quantum/relativistic computation consists of perturbative renormalization of the Electrical Power System (EPS), generating the flowcharts of computation , verification, validation, description and help.
Revised: The realization of Daily Artificial Dispatcher as a quantum/relativistic computation consists of perturbative renormalization of the EPS , verification, validation, description and help.
Edit intent: clarity
Original:  Perturbative renormalization of EPS energy and time has been carried out in this paper for a day ahead via virtual thermalization of the EPS for a day ahead .
Revised:  Perturbative renormalization of EPS energy and time has been carried out in this paper .
Edit intent: clarity
Original:  Furthermore, we investigate the effect of adaptation on the recruitment dynamics , as well as on network topology.
Revised:  Furthermore, we investigate the effect of adaptation on the recruitment level , as well as on network topology.
Edit intent: clarity
Original: In this paper, we first introduce the concepts of Laurent differential polynomials and Laurent differentially essential systems , and give a criterion for Laurent differentially essential systems in terms of their supports.
Revised: In this paper, we first introduce the concept of Laurent differentially essential systems , and give a criterion for Laurent differentially essential systems in terms of their supports.
Edit intent: coherence
Original: In this paper, we first introduce the concepts of Laurent differential polynomials and Laurent differentially essential systems , and give a criterion for Laurent differentially essential systems in terms of their supports.
Revised: In this paper, we first introduce the concepts of Laurent differential polynomials and Laurent differentially essential systems and give a criterion for Laurent differentially essential systems in terms of their supports.
Edit intent: fluency
Original:  Based on these bounds, an algorithm to compute the sparse differential resultant is proposed, which is single exponential in terms of the order, the number of variables , and the size of the Laurent differential system.
Revised:  Based on these bounds, an algorithm to compute the sparse differential resultant is proposed, which is single exponential in terms of the order, the number of variables , and the size of the system.
Edit intent: fluency
Original: We consider a generalization of the standard oracle model in which the oracle acts on the target with a permutation which is selected according to internal random coins.
Revised: We consider a generalization of the standard oracle model in which the oracle acts on the target with a permutation selected according to internal random coins.
Edit intent: clarity
Original:  Even stronger, we describe several problems which are impossible to solve classically but can be solved by a quantum algorithm using a single query;
Revised:  Even stronger, we describe several problems that are impossible to solve classically but can be solved by a quantum algorithm using a single query;
Edit intent: fluency
Original:  In the oracle model with internal randomness where the goal is to gain any nonzero advantage over guessing, we prove (roughly speaking) that (k ) quantum queries are equivalent in power to ( 2k ) classical queries, thus extending results of Meyer and Pommersheim , and Montanaro, Nishimura and Raymond .
Revised:  In the oracle model with internal randomness where the goal is to gain any nonzero advantage over guessing, we prove (roughly speaking) that k quantum queries are equivalent in power to ( 2k ) classical queries, thus extending results of Meyer and Pommersheim , and Montanaro, Nishimura and Raymond .
Edit intent: fluency
Original:  In the oracle model with internal randomness where the goal is to gain any nonzero advantage over guessing, we prove (roughly speaking) that (k ) quantum queries are equivalent in power to ( 2k ) classical queries, thus extending results of Meyer and Pommersheim , and Montanaro, Nishimura and Raymond .
Revised:  In the oracle model with internal randomness where the goal is to gain any nonzero advantage over guessing, we prove (roughly speaking) that (k ) quantum queries are equivalent in power to 2k ) classical queries, thus extending results of Meyer and Pommersheim , and Montanaro, Nishimura and Raymond .
Edit intent: fluency
Original:  In the oracle model with internal randomness where the goal is to gain any nonzero advantage over guessing, we prove (roughly speaking) that (k ) quantum queries are equivalent in power to ( 2k ) classical queries, thus extending results of Meyer and Pommersheim , and Montanaro, Nishimura and Raymond .
Revised:  In the oracle model with internal randomness where the goal is to gain any nonzero advantage over guessing, we prove (roughly speaking) that (k ) quantum queries are equivalent in power to ( 2k classical queries, thus extending results of Meyer and Pommersheim , and Montanaro, Nishimura and Raymond .
Edit intent: fluency
Original:  In the oracle model with internal randomness where the goal is to gain any nonzero advantage over guessing, we prove (roughly speaking) that (k ) quantum queries are equivalent in power to ( 2k ) classical queries, thus extending results of Meyer and Pommersheim , and Montanaro, Nishimura and Raymond .
Revised:  In the oracle model with internal randomness where the goal is to gain any nonzero advantage over guessing, we prove (roughly speaking) that (k ) quantum queries are equivalent in power to ( 2k ) classical queries, thus extending results of Meyer and Pommersheim .
Edit intent: coherence
Original: We  take inspiration from light-harvesting networks present in purple bacteria and simulate an incoherent dissipative energy transfer process on more general and abstract networks, considering both regular structures (Cayley trees and hyperbranched fractals) and randomly-generated ones.
Revised: We take inspiration from light harvesting networks present in purple bacteria and simulate an incoherent dissipative energy transfer process on more general and abstract networks, considering both regular structures (Cayley trees and hyperbranched fractals) and randomly-generated ones.
Edit intent: fluency
Original: We  take inspiration from light-harvesting networks present in purple bacteria and simulate an incoherent dissipative energy transfer process on more general and abstract networks, considering both regular structures (Cayley trees and hyperbranched fractals) and randomly-generated ones.
Revised: We take inspiration from light-harvesting networks present in purple bacteria and simulate an incoherent dissipative energy transport process on more general and abstract networks, considering both regular structures (Cayley trees and hyperbranched fractals) and randomly-generated ones.
Edit intent: clarity
Original:  We focus on the the two primary light harvesting complexes of purple bacteria, i.e.  the LH1 and LH2, and we use network-theoretical centrality measures in order to select different LH1 arrangements.
Revised:  We focus on the the two primary light harvesting complexes of purple bacteria, i.e. , the LH1 and LH2, and we use network-theoretical centrality measures in order to select different LH1 arrangements.
Edit intent: fluency
Original:   Finally, we compare the networks considered with the real biological networks and find that while the latter have in general better performances,  the former with optimal arrangements can mimic the real networks' behaviour for a specific range of transport parameters.
Revised:  Finally, we compare the networks considered with the real biological networks and find that the latter have in general better performances, the former with optimal arrangements can mimic the real networks' behaviour for a specific range of transport parameters.
Edit intent: coherence
Original:  Oper. Res. 35 , (2010) pp. 233--256]   ,   some basic questions have remained unresolved.
Revised:  Oper. Res. 35 (2010) pp. 233--256] , some basic questions have remained unresolved.
Edit intent: fluency
Original:  Oper. Res. 35 , (2010) pp. 233--256]   ,   some basic questions have remained unresolved.
Revised:  Oper. Res. 35 , (2010) , 233--256] , some basic questions have remained unresolved.
Edit intent: coherence
Original:  Oper. Res. 35 , (2010) pp. 233--256]   ,   some basic questions have remained unresolved.
Revised:  Oper. Res. 35 , (2010) pp. 233--256] , , some basic questions have remained unresolved.
Edit intent: fluency
Original:  In this paper, we resolve this by showing that the triangle closure is indeed a polyhedron , and its number of facets can be bounded by a polynomial in the size of the input data  ]  .
Revised:  In this paper, we resolve this by showing that the triangle closure is indeed a polyhedron and its number of facets can be bounded by a polynomial in the size of the input data ] .
Edit intent: fluency
Original:  The special case of a geometric L\'evy model (GLM) with constant parameters can be regarded as a natural generalisation of the standard geometric Brownian motion model  used in the  Black-Scholes theory .
Revised:  The special case of a geometric L\'evy model (GLM) is a natural generalisation of the standard geometric Brownian motion model used in the Black-Scholes theory .
Edit intent: clarity
Original:  The pricing kernel is given by the product of a discount factor and the Esscher martingale associated with the risk aversion parameter.
Revised:  The pricing kernel is the product of a discount factor and the Esscher martingale associated with the risk aversion parameter.
Edit intent: clarity
Original:  In the GBMcase , the risk aversion factor is the so-called market price of risk.
Revised:  In the GBMcase , the risk aversion parameter is the market price of risk.
Edit intent: clarity
Original:  instead, the excess rate of return is given by a nonlinear function of the volatility and the risk aversion.
Revised:  instead, the excess rate of return is a nonlinear function of the volatility and the risk aversion.
Edit intent: clarity
Original:  It is shown that for positive values of the volatility and the risk aversion the excess rate of return above the interest rate is positive, and is monotonically increasing with respect to these variables.
Revised:  It is shown that for positive volatility and risk aversion the excess rate of return above the interest rate is positive, and is monotonically increasing with respect to these variables.
Edit intent: clarity
Original:  It is shown that for positive values of the volatility and the risk aversion the excess rate of return above the interest rate is positive, and is monotonically increasing with respect to these variables.
Revised:  It is shown that for positive values of the volatility and the risk aversion the excess rate of return above the interest rate is positive, and is increasing with respect to these variables.
Edit intent: clarity
Original:  This condition is shown to hold for any geometric L\'evy model for foreign exchange in which the volatility exceeds the risk aversion.
Revised:  This condition is shown to hold for any geometric L\'evy model for foreign exchange in which volatility exceeds risk aversion.
Edit intent: clarity
Original:  They consider hierarchical modulation , a practical implementation of superposition coding, but only for unequal error protection or backward compatibility purposes.
Revised:  They consider hierarchical modulation but only for unequal error protection or backward compatibility purposes.
Edit intent: clarity
Original:  Applications include tail and implied volatility asymptotics in some correlated stochastic volatility models ;
Revised:  Applications include tail and implied volatility asymptotics in some stochastic volatility models ;
Edit intent: clarity
Original:  Surprisingly, the space used by the data-structure is O (n/k) , that is, the space used is sublinear in the input size if k is sufficiently large.
Revised:  Surprisingly, the space used by the data-structure is (n/k) , that is, the space used is sublinear in the input size if k is sufficiently large.
Edit intent: coherence
Original:  Surprisingly, the space used by the data-structure is O (n/k) , that is, the space used is sublinear in the input size if k is sufficiently large.
Revised:  Surprisingly, the space used by the data-structure is O (n/k) ; that is, the space used is sublinear in the input size if k is sufficiently large.
Edit intent: fluency
Original: For a set of n points in \Re^d, and parameters k and \epsilon \eps , we present a data structure that answers (1+ \epsilon \eps ) -approximate knearest neighbor\ANN queries in logarithmic time.
Revised: For a set of n points in \Re^d, and parameters k and \eps , we present a data structure that answers (1+ \epsilon \eps ) -approximate knearest neighbor\ANN queries in logarithmic time.
Edit intent: fluency
Original: For a set of n points in \Re^d, and parameters k and \epsilon \eps , we present a data structure that answers (1+ \epsilon \eps ) -approximate knearest neighbor\ANN queries in logarithmic time.
Revised: For a set of n points in \Re^d, and parameters k and \epsilon \eps , we present a data structure that answers (1+ \eps ) -approximate knearest neighbor\ANN queries in logarithmic time.
Edit intent: fluency
Original:  Using this  we provide a sublinear space data-structure that can estimate the density of a point set under various measures, including: (i) inparaenum[(i)] \item sum of distances of k closest points to the query point, and (ii) \item sum of squared distances of k closest points to the query point.
Revised:  Using this , we provide a sublinear space data-structure that can estimate the density of a point set under various measures, including: (i) inparaenum[(i)] \item sum of distances of k closest points to the query point, and (ii) \item sum of squared distances of k closest points to the query point.
Edit intent: fluency
Original:  Using this  we provide a sublinear space data-structure that can estimate the density of a point set under various measures, including: (i) inparaenum[(i)] \item sum of distances of k closest points to the query point, and (ii) \item sum of squared distances of k closest points to the query point.
Revised:  Using this we provide a sublinear space data-structure that can estimate the density of a point set under various measures, including: inparaenum[(i)] \item sum of distances of k closest points to the query point, and (ii) \item sum of squared distances of k closest points to the query point.
Edit intent: fluency
Original:  Using this  we provide a sublinear space data-structure that can estimate the density of a point set under various measures, including: (i) inparaenum[(i)] \item sum of distances of k closest points to the query point, and (ii) \item sum of squared distances of k closest points to the query point.
Revised:  Using this we provide a sublinear space data-structure that can estimate the density of a point set under various measures, including: (i) inparaenum[(i)] \item sum of distances of k closest points to the query point, and \item sum of squared distances of k closest points to the query point.
Edit intent: fluency
Original:  Generalizing the standard Eden growth model, we introduce a simple one-parameter family of reproduction time distributions indexed by the variation coefficient \delta \in [0, 1], which includes deterministic ( \delta = 0) and memory-less exponential distribution ( \delta = 1) as extreme cases.
Revised:  Generalizing the standard Eden growth model, we introduce a simple one-parameter family of reproduction time distributions indexed by the variation coefficient \delta \in [0, 1], which includes deterministic ( \delta = 0) and memory-less exponential distribution ( \delta = 1) as extreme cases.
Edit intent: fluency
Original:  Generalizing the standard Eden growth model, we introduce a simple one-parameter family of reproduction time distributions indexed by the variation coefficient \delta \in [0, 1], which includes deterministic ( \delta = 0) and memory-less exponential distribution ( \delta = 1) as extreme cases.
Revised:  Generalizing the standard Eden growth model, we introduce a simple one-parameter family of reproduction time distributions indexed by the variation coefficient \delta \in [0, 1], which includes deterministic ( \delta = 0) and memoryless exponential distribution ( \delta = 1) as extreme cases.
Edit intent: fluency
Original:  At the example of S. cerevisiae, we show that our approach using the variation coefficient also works for more realistic reproduction time distributions.
Revised:  At the example of Saccharomyces cerevisiae, we show that our approach using the variation coefficient also works for more realistic reproduction time distributions.
Edit intent: clarity
Original:  We show the utility of this approach a.) to identify an extensively overlapping modular structure ;
Revised:  We show the utility of this approach a.) extensively overlapping network modules ;
Edit intent: clarity
Original:  b.) to define a modular core and hierarchy allowing an easier functional annotation ;
Revised:  b.) several hierarchical layers of modules, where meta-nodes of the higher hierarchical layer represent modules of the lower layer ;
Edit intent: clarity
Original:   The plug-in and its user guide can be downloaded  from: URL
Revised:  The plug-in and its user guide can be downloaded freely from: URL
Edit intent: clarity
Original: Summary: The ModuLand plug-in provides Cytoscape users an algorithm determining a.) extensively overlapping network modules ;
Revised: Summary: The ModuLand plug-in provides Cytoscape users an algorithm for determining extensively overlapping network modules ;
Edit intent: fluency
Original:  and d.) key nodes bridging two or multiple modules in complex networks .
Revised:  and d.) key nodes bridging two or multiple modules .
Edit intent: clarity
Original:  The plug-in was written in C++, has a detailed JAVA-based graphical interface with various colouring options , can be installed as a single file and can run on Windows, Linux, or Mac OS.
Revised:  The plug-in has a detailed JAVA-based graphical interface with various colouring options , can be installed as a single file and can run on Windows, Linux, or Mac OS.
Edit intent: coherence
Original:  In this paper, we propose novel bounds based on Doob's maximal inequality for N-demisupermartingales to compute end-to-end probabilistic bounds which improves the existing bounds from network calculus.
Revised:  In this paper, we propose novel bounds based on Doob's maximal inequality for N-demisupermartingales to compute end-to-end probabilistic bounds which improve the existing bounds from network calculus.
Edit intent: fluency
Original:  In particular, we show that reasonably accurate bounds are achieved using the new novel bounds in network calculus, by comparing new bounds with the exact results from queueing theory for a network of M/M/1 queues.
Revised:  In particular, we show that reasonably accurate bounds are achieved by comparing the new bounds with the exact results from queueing theory for a network of M/M/1 queues.
Edit intent: clarity
Original:  The accuracy of probabilistic end-to-end delay or backlog bounds computed using the elegant theory of network calculus has been always a concern.
Revised:  The accuracy of probabilistic end-to-end delay or backlog bounds computed using network calculus has been always a concern.
Edit intent: clarity
Original:  The accuracy of probabilistic end-to-end delay or backlog bounds computed using the elegant theory of network calculus has been always a concern.
Revised:  The accuracy of probabilistic end-to-end delay or backlog bounds computed using the elegant theory of network calculus has always been a concern.
Edit intent: fluency
Original:  In this paper, we propose novel bounds based on Doob's maximal inequality for N-demisupermartingales to compute end-to-end probabilistic bounds  which improve the existing bounds from network calculus .
Revised:  In this paper, we propose novel end-to-end probabilistic bounds which improve the existing bounds from network calculus .
Edit intent: clarity
Original:  In particular, we show that reasonably accurate bounds are achieved by comparing the new bounds with the exact results from queueing theory for a network of M/M/1 queues.
Revised:  In particular, we show that reasonably accurate bounds are achieved by comparing the new bounds with the existing results for a network of M/M/1 queues.
Edit intent: clarity
Original: A problem of both theoretical and practical interest is to determine estimates or bounds for the diameter of Cayley graphs of groups, and to know for which cases the bounds are exact or how far away the bounds can be from the true value in the worst case .
Revised: This work concerns the problem of determining or estimating the diameter of Cayley graphs of groups, and to know for which cases the bounds are exact or how far away the bounds can be from the true value in the worst case .
Edit intent: clarity
Original: A problem of both theoretical and practical interest is to determine estimates or bounds for the diameter of Cayley graphs of groups, and to know for which cases the bounds are exact or how far away the bounds can be from the true value in the worst case .
Revised: A problem of both theoretical and practical interest is to determine estimates or bounds for the diameter of some families of Cayley networks .
Edit intent: clarity