wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To demonstrate this, we show that for the distributive phosphorylation of a protein at two binding sites multistationarity is only possible if the total concentration of the substrate is larger than either the  concentration of the kinase or the  phosphatase.
Revised:  To demonstrate this, we show that for the distributive phosphorylation of a protein at two binding sites multistationarity is only possible if the total concentration of the substrate is larger than either the concentration of the kinase or the total concentration of the phosphatase.
Edit intent: fluency
Original:  To appropriately implement our method in GPUs taking into account memory coalescence problems, we devised a new graph representation based on a breadth-first search traversal that works harmoniously with our parallel front propagation approach.
Revised:  To appropriately implement our method in GPUs taking into account memory coalescence problems, we take advantage of a graph representation based on a breadth-first search traversal that works harmoniously with our parallel front propagation approach.
Edit intent: clarity
Original:  To appropriately implement our method in GPUs taking into account memory coalescence problems, we take advantage of a graph representation based on a breadth-first search traversal that works harmoniously with our parallel front propagation approach.
Revised:  To appropriately implement our method on GPUs taking into account memory coalescence problems, we take advantage of a graph representation based on a breadth-first search traversal that works harmoniously with our parallel front propagation approach.
Edit intent: fluency
Original:  In this work, we present a minimalistic parallel algorithm based on front propagation to compute approximate geodesic distances on meshes that is practical and simple to implement . Our method does not require any heavy pre-processing.
Revised:  In this work, we present a minimalistic parallel algorithm based on front propagation to compute approximate geodesic distances on meshes that is practical and simple to implement and does not require any heavy pre-processing.
Edit intent: clarity
Original:  We find that envy-free procedures, in particular Selfridge-Conway and the symmetric and asymmetric versions of cut-and-choose , are fairer and also are perceived as fairer than their proportional counterparts, despite the fact that agents very often manipulate them.
Revised:  We find that envy-free procedures, in particular Selfridge-Conway , are fairer and also are perceived as fairer than their proportional counterparts, despite the fact that agents very often manipulate them.
Edit intent: clarity
Original: The problem of inpainting consists of filling missing or damaged regions in images and videos in such a way that the filling pattern does not produce artifacts that deviate from the original data. In addition to restoring the missing data, the inpainting technique can also be used to remove undesired objects .
Revised: The sparse representation of signals defined on Euclidean domains has been successfully applied in signal processing. Bringing the power of sparse representations to non-regular domains is still a challenge, but promising approaches have started emerging recently .
Edit intent: meaning-changed
Original:  In this work, we address the problem of inpainting on surfaces through a new method based on dictionary learning and sparse coding. Our method learns the dictionary through the subdivision of the mesh into patches and rebuilds the mesh via a method of reconstruction inspired by the Non-local Means method on the computed sparse codes. One of the advantages of our method is that it is capable of filling the missing regions and simultaneously removes noise and enhances important features of the mesh.
Revised:  In this work, we address the problem of sparsely representing discrete surfaces and propose a new representation that is capable of filling the missing regions and simultaneously removes noise and enhances important features of the mesh.
Edit intent: clarity
Original:  One of the advantages of our method is that it is capable of filling the missing regions and simultaneously removes noise and enhances important features of the mesh. Moreover, the inpainting result is globally coherent as the representation based on the dictionaries captures all the geometric information in the transformed domain. We present two variations of the method: a direct one, in which the model is reconstructed and restored directly from the representation in the transformed domain and a second one, adaptive, in which the missing regions are recreated iteratively through the successive propagation of the sparse code computed in the hole boundaries, which guides the local reconstructions. The second method produces better results for large regions because the sparse codes of the patches are adapted according to the sparse codes of the boundary patches.
Revised:  One of the advantages of our method is that it is capable of providing tools for solving different geometry processing problems. The sparse discrete surface representation is obtained by combining innovative approaches into an integrated method. First, to deal with irregular mesh domains, we devised a new way to subdivide discrete meshes into a set of patches using a feature-aware seed sampling. Second, we achieve good surface approximation with over-fitting control by combining the power of a continuous global dictionary representation with a modified Orthogonal Marching Pursuit. The discrete surface approximation results produced were able to preserve the shape features while being robust to over-fitting. Our results show that the sparse codes of the boundary patches.
Edit intent: meaning-changed
Original:  Here it is shown how various delta-type hedging strategies perform and can be evaluated in such markets  . A precise characterization of the hedging cost, the replication cost caused by the volatility fluctuations, is presented in an asymptotic regime of rapid mean reversion for the volatility fluctuations.
Revised:  Here it is shown how various delta-type hedging strategies perform and can be evaluated in such markets in the case of European options . A precise characterization of the hedging cost, the replication cost caused by the volatility fluctuations, is presented in an asymptotic regime of rapid mean reversion for the volatility fluctuations.
Edit intent: meaning-changed
Original:  It is moreover shown that the performances of the delta-type hedging schemes are essentially independent of the regularity of the volatility paths in the considered regime and that the hedging costs are related to a vega risk martingale whose magnitude is proportional to a new market risk parameter  .
Revised:  It is moreover shown that the performances of the delta-type hedging schemes are essentially independent of the regularity of the volatility paths in the considered regime and that the hedging costs are related to a vega risk martingale whose magnitude is proportional to a new market risk parameter . It is also shown via numerical simulations that the proposed hedging schemes which derive from option price approximations in the regime of rapid mean reversion, are robust: the `practitioners' delta hedging scheme that is identified as being optimal by our asymptotic analysis when the mean reversion time is small seems to be optimal with arbitrary mean reversion times .
Edit intent: meaning-changed
Original:  For this model, under the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect (MTE) from one's own treatment and that from his/her partner's can be separately point-identified using a latent index framework . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the MTE parameters using series approximation.
Revised:  For this model, using a latent index framework and the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect (MTE) from one's own treatment and that from his/her partner's can be separately point-identified using a latent index framework . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the MTE parameters using series approximation.
Edit intent: clarity
Original:  For this model, under the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect (MTE) from one's own treatment and that from his/her partner's can be separately point-identified using a latent index framework . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the MTE parameters using series approximation.
Revised:  For this model, under the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect from one's own treatment and that from his/her partner's can be separately point-identified using a latent index framework . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the MTE parameters using series approximation.
Edit intent: clarity
Original:  For this model, under the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect (MTE) from one's own treatment and that from his/her partner's can be separately point-identified using a latent index framework . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the MTE parameters using series approximation.
Revised:  For this model, under the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect (MTE) from one's own treatment and that from his/her partner's can be separately point-identified with potential over-identifiability . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the MTE parameters using series approximation.
Edit intent: clarity
Original:  For this model, under the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect (MTE) from one's own treatment and that from his/her partner's can be separately point-identified using a latent index framework . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the MTE parameters using series approximation.
Revised:  For this model, under the assumption of a stochastic equilibrium selection rule, we prove that the marginal treatment effect (MTE) from one's own treatment and that from his/her partner's can be separately point-identified using a latent index framework . Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the marginal treatment effects using series approximation.
Edit intent: clarity
Original:  For this model, using a latent index framework and the assumption of a stochastic equilibrium selection rule , we prove that the marginal treatment effect from one's own treatment and that from his/her partner 's can be separately point-identified with potential over-identifiability .
Revised:  Using a latent index framework and the assumption of a stochastic equilibrium selection rule , we prove that the marginal treatment effect from one's own treatment and that from his/her partner 's can be separately point-identified with potential over-identifiability .
Edit intent: clarity
Original:  For this model, using a latent index framework and the assumption of a stochastic equilibrium selection rule , we prove that the marginal treatment effect from one's own treatment and that from his/her partner 's can be separately point-identified with potential over-identifiability .
Revised:  For this model, using a latent index framework and assuming a stochastic equilibrium selection rule , we prove that the marginal treatment effect from one's own treatment and that from his/her partner 's can be separately point-identified with potential over-identifiability .
Edit intent: fluency
Original:  For this model, using a latent index framework and the assumption of a stochastic equilibrium selection rule , we prove that the marginal treatment effect from one's own treatment and that from his/her partner 's can be separately point-identified with potential over-identifiability .
Revised:  For this model, using a latent index framework and the assumption of a stochastic equilibrium selection , we prove that the marginal treatment effect from one's own treatment and that from his/her partner 's can be separately point-identified with potential over-identifiability .
Edit intent: clarity
Original:  For this model, using a latent index framework and the assumption of a stochastic equilibrium selection rule , we prove that the marginal treatment effect from one's own treatment and that from his/her partner 's can be separately point-identified with potential over-identifiability .
Revised:  For this model, using a latent index framework and the assumption of a stochastic equilibrium selection rule , we prove that the marginal treatment effect from one's own treatment and that from the partner can be over-identified separately .
Edit intent: clarity
Original:  In this paper we consider a localized version of the classical notion of clique number for the aforementioned ER-perturbed random geometric graphs : Specifically, we study the edge clique number for each edge in a graph, defined as the size of the largest clique(s) in the graph containing that edge.
Revised:  In this paper we consider a localized version of the classical notion of clique number for the aforementioned ER-perturbed random geometric graphs : Specifically, we study the edge clique number for each edge in a graph, defined as the size of the largest clique(s) in the graph containing that edge.
Edit intent: fluency
Original:   We assessed the presence of constraints on activity and functional connectivity using an inter-subject correlation approach. Constraints on activity and connectivity were greater in magnitude than expected in a non-parametric permutation-based null model, particularly in primary sensory and motor systems, as well as in regions associated with the learning of value.
Revised:  We assessed the presence of URLanization in activity and connectivity were greater in magnitude than expected in a non-parametric permutation-based null model, particularly in primary sensory and motor systems, as well as in regions associated with the learning of value.
Edit intent: clarity
Original:  Constraints on activity and connectivity were greater in magnitude than expected in a non-parametric permutation-based null model, particularly in primary sensory and motor systems, as well as in regions associated with the learning of value. Notably, inter-subject connectivity in activity and connectivity displayed marked temporal variations, with inter-subject correlations in activity exceeding those in connectivity during early learning and visa versa in later learning .
Revised:  Constraints on activity and connectivity both during value learning and during the resting-state, allowing us to differentiate common elicited processes from intrinsic processes. We found a complex and dynamic URLanization of brain connectivity and visa versa in later learning .
Edit intent: meaning-changed
Original:  Notably, inter-subject connectivity in activity and connectivity displayed marked temporal variations, with inter-subject correlations in activity exceeding those in connectivity during early learning and visa versa in later learning . Finally, individual differences in performance accuracy tracked the degree to which a subject's connectivity, but not activity, tracked subject-general patterns. Taken together, our results support the notion that brain activity and connectivity are constrained across subjects in early learning, with constraints on activity, but not connectivity, decreasing in later learning .
Revised:  Notably, inter-subject connectivity in activity and connectivity displayed marked temporal variations, with inter-subject correlations in activity exceeding those in connectivity during early learning and visa versa activity--as a function of time, space, and performance--that occurs while subjects learn. Spatially localized brain activity URLanizes across the brain to a common functional architecture early in learning, and this URLanization tracks early learning performance. In contrast, spatially distributed connectivity URLanizes across the brain to a common functional architecture as training progresses, and this URLanization tracks later learning performance. Particularly good performance is associated with a sticky connectivity, that persists into the resting state. Broadly, our work uncovers distinct principles of URLanization in activity and connectivity are constrained across subjects in early learning, with constraints on activity, but not connectivity, decreasing in later learning .
Edit intent: meaning-changed
Original:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, the point sets P \in D that are closest in terms of bottleneck distance are returned .
Revised:  In this work, we consider the problem of building a data structure \mathbb{D of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, the point sets P \in D that are closest in terms of bottleneck distance are returned .
Edit intent: meaning-changed
Original:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, the point sets P \in D that are closest in terms of bottleneck distance are returned .
Revised:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) and supports nearest bottleneck distance queries: given a query point set Q of size n, the point sets P \in D that are closest in terms of bottleneck distance are returned .
Edit intent: clarity
Original:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, the point sets P \in D that are closest in terms of bottleneck distance are returned .
Revised:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, we would like to find the point set(s) P \in \mathbb{D that are closest in terms of bottleneck distance are returned .
Edit intent: clarity
Original:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, the point sets P \in D that are closest in terms of bottleneck distance are returned .
Revised:  In this work, we consider the problem of indexing a set of m planar point sets (of varying sizes) to create a database D that supports nearest bottleneck distance queries: given a query point set Q of size n, the point sets P \in D that are closest in terms of bottleneck distance .
Edit intent: clarity
Original:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Revised:  The main contribution is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Edit intent: clarity
Original:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Revised:  The main contribution of this work is a {\em trie}-based data structure finds a 6-approximate nearest neighbor in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Edit intent: meaning-changed
Original:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Revised:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( \mathbb{D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Edit intent: meaning-changed
Original:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Revised:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Edit intent: meaning-changed
Original:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, 10%DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Revised:  The main contribution of this work is a {\em trie}-based data structure that is space efficient and supports 8-approximate nearest bottleneck queries in O(-\lg(d_B( D ,Q)) \min(m, %DIFDELCMD < }%%% ^n) n\lg^3 n ) time, where d_B( D ,Q) is the minimum bottleneck distance from Q to any point set in D. A direct consequence, of independent interest, is a simple O(-\lg(d_B(P,Q)) n \lg^3 n) time algorithm to 2 \sqrt{2 .
Edit intent: clarity