wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This result encompasses all the constructions and applications for topological optimization in the literature  , and comes with code that is easy to handle and  mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Revised:  This result encompasses all the constructions and applications of topological optimization in the literature , and comes with code that is easy to handle and mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Edit intent: fluency
Original:  This result encompasses all the constructions and applications for topological optimization in the literature  , and comes with code that is easy to handle and  mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Revised:  This result encompasses all the constructions and applications for topological optimization in the literature . Finally, we provide associated code , and comes with code that is easy to handle and mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Edit intent: meaning-changed
Original:  This result encompasses all the constructions and applications for topological optimization in the literature  , and comes with code that is easy to handle and  mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Revised:  This result encompasses all the constructions and applications for topological optimization in the literature , that is easy to handle and mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Edit intent: coherence
Original:  This result encompasses all the constructions and applications for topological optimization in the literature  , and comes with code that is easy to handle and  mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Revised:  This result encompasses all the constructions and applications for topological optimization in the literature , and comes with code that is easy to handle and to mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Edit intent: fluency
Original:  This result encompasses all the constructions and applications for topological optimization in the literature  , and comes with code that is easy to handle and  mix with other non-topological constraints, and that can be used to reproduce the experiments described in the literature .
Revised:  This result encompasses all the constructions and applications for topological optimization in the literature , and comes with code that is easy to handle and mix with other non-topological methods and constraints, as well as some experiments showcasing the versatility of our approach .
Edit intent: meaning-changed
Original:  While related literature has addressed the co-navigation problem focused on the scalability with the number of pedestrians in open spaces, typical indoor environments present the additional challenge of constrained spaces such as corridors , doorways and crosswalks that limit maneuverability and influence patterns of pedestrian interaction.
Revised:  While research on social navigation has focused mainly on the scalability with the number of pedestrians in open spaces, typical indoor environments present the additional challenge of constrained spaces such as corridors , doorways and crosswalks that limit maneuverability and influence patterns of pedestrian interaction.
Edit intent: clarity
Original:  While related literature has addressed the co-navigation problem focused on the scalability with the number of pedestrians in open spaces, typical indoor environments present the additional challenge of constrained spaces such as corridors , doorways and crosswalks that limit maneuverability and influence patterns of pedestrian interaction.
Revised:  While related literature has addressed the co-navigation problem focused on the scalability with the number of pedestrians in open spaces, typical indoor environments present the additional challenge of constrained spaces such as corridors and doorways that limit maneuverability and influence patterns of pedestrian interaction.
Edit intent: clarity
Original:  We explore a compositional principle for multi-layout training and find that policies trained in a small set of geometrically simple layouts successfully generalize to unseen and more complex layouts that exhibit composition of the simple structural elements available during training.
Revised:  We explore a compositional principle for multi-layout training and find that policies trained in a small set of geometrically simple layouts successfully generalize to more complex unseen layouts that exhibit composition of the simple structural elements available during training.
Edit intent: clarity
Original:  We explore a compositional principle for multi-layout training and find that policies trained in a small set of geometrically simple layouts successfully generalize to unseen and more complex layouts that exhibit composition of the simple structural elements available during training.
Revised:  We explore a compositional principle for multi-layout training and find that policies trained in a small set of geometrically simple layouts successfully generalize to unseen and more complex layouts that exhibit composition of the structural elements available during training.
Edit intent: clarity
Original: Linear programs (LPs) can be solved through a polynomial number of so-called deepest-descent circuit steps, each step following a circuit direction to a new feasible solution of most-improved objective function value.
Revised: Linear programs (LPs) can be solved by polynomially many moves along the circuit direction improving the objective the most, so-called deepest-descent circuit steps, each step following a circuit direction to a new feasible solution of most-improved objective function value.
Edit intent: meaning-changed
Original: Linear programs (LPs) can be solved through a polynomial number of so-called deepest-descent circuit steps, each step following a circuit direction to a new feasible solution of most-improved objective function value. A computation of deepest-descent steps has recently been shown to be NP-hard  De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Revised: Linear programs (LPs) can be solved through a polynomial number of so-called deepest-descent steps has recently been shown to be NP-hard De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Edit intent: clarity
Original:  A computation of deepest-descent steps has recently been shown to be NP-hard  De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Revised:  A computation of deepest-descent steps (dd-steps). Computing these steps is NP-hard De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Edit intent: meaning-changed
Original:  A computation of deepest-descent steps has recently been shown to be NP-hard  De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Revised:  A computation of deepest-descent steps has recently been shown to be NP-hard ( De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Edit intent: fluency
Original:  A computation of deepest-descent steps has recently been shown to be NP-hard  De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Revised:  A computation of deepest-descent steps has recently been shown to be NP-hard De Loera et al., arXiv, 2019 ), a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Edit intent: coherence
Original:  A computation of deepest-descent steps has recently been shown to be NP-hard  De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Revised:  A computation of deepest-descent steps has recently been shown to be NP-hard De Loera et al., arXiv, 2019 . This is a consequence of the hardness of deciding the existence of an optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Edit intent: clarity
Original:  A computation of deepest-descent steps has recently been shown to be NP-hard  De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Revised:  A computation of deepest-descent steps has recently been shown to be NP-hard De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor (OCNP) for LPs with non-unique optima.
Edit intent: clarity
Original:  A computation of deepest-descent steps has recently been shown to be NP-hard  De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) for LPs with non-unique optima.
Revised:  A computation of deepest-descent steps has recently been shown to be NP-hard De Loera et al., arXiv, 2019 . This is a consequence of the hardness of what we call the optimal circuit-neighbor problem (OCNP) on LPs with non-unique optima.
Edit intent: fluency
Original:  However, the non-uniqueness assumption is crucial to the hardness of OCNP, because we show that OCNP for LPs with a unique optimum is solvable in polynomial time. Moreover, in practical applications one is usually only interested in finding some optimum of an LP, in which case a simple perturbation of the objective yields an instance with a unique optimum. It is thus natural to ask whether deepest-descent steps are also easy to compute for LPs with unique optima, or whether this problem is hard despite OCNP being easy. We show that deepest-descent steps can be efficiently approximated within a factor of n, where n is the dimension of the polyhedron at hand, but not within a factor of O(n^{1- \epsilon }) for any \epsilon > 0. While we prove that OCNP can be solved efficiently for LPs with a unique optimum, our different hardness approach allows us to show strong negative results:
Revised:  We prove OCNP is easy under the promise of unique optima, but already O(n^{1- \epsilon }) for any \epsilon > 0. While we prove that OCNP can be solved efficiently for LPs with a unique optimum, our different hardness approach allows us to show strong negative results:
Edit intent: clarity
Original:  We show that deepest-descent steps can be efficiently approximated within a factor of n, where n is the dimension of the polyhedron at hand, but not within a factor of O(n^{1- \epsilon }) for any \epsilon > 0. While we prove that OCNP can be solved efficiently for LPs with a unique optimum, our different hardness approach allows us to show strong negative results:
Revised:  We show that deepest-descent steps can be efficiently approximated within a factor of n, where n is the dimension of the polyhedron at hand, but not within a factor of O(n^{1- \varepsilon }) for any \epsilon > 0. While we prove that OCNP can be solved efficiently for LPs with a unique optimum, our different hardness approach allows us to show strong negative results:
Edit intent: fluency
Original:  We show that deepest-descent steps can be efficiently approximated within a factor of n, where n is the dimension of the polyhedron at hand, but not within a factor of O(n^{1- \epsilon }) for any \epsilon > 0. While we prove that OCNP can be solved efficiently for LPs with a unique optimum, our different hardness approach allows us to show strong negative results: computing deepest-descent steps is NP-hard and inapproximable even for 0/ 1 linear programs with a unique optimum which are defined by a totally unimodular constraint matrix .
Revised:  We show that deepest-descent steps can be efficiently approximated within a factor of n, where n is the dimension of the polyhedron at hand, but not within a factor of O(n^{1- \epsilon }) -approximating dd-steps remains hard even for totally unimodular n-dimensional 0/ 1 linear programs with a unique optimum which are defined by a totally unimodular constraint matrix .
Edit intent: clarity
Original:  computing deepest-descent steps is NP-hard and inapproximable even for 0/ 1 linear programs with a unique optimum which are defined by a totally unimodular constraint matrix .
Revised:  computing deepest-descent steps is NP-hard and inapproximable even for 0/ 1-LPs with a unique optimum which are defined by a totally unimodular constraint matrix .
Edit intent: clarity
Original:  computing deepest-descent steps is NP-hard and inapproximable even for 0/ 1 linear programs with a unique optimum which are defined by a totally unimodular constraint matrix .
Revised:  computing deepest-descent steps is NP-hard and inapproximable even for 0/ 1 linear programs with a unique optimum . We provide a matching n-approximation .
Edit intent: meaning-changed
Original: Let G = (V, E) be a undirected graph and let W \subseteq V be a set of terminals. ASteiner subgraph of (G, W) is a subgraph of G that contains all vertices of W and there is a path between every pair of vertices of W in the subgraph.
Revised: Steiner subgraph of (G, W) is a subgraph of G that contains all vertices of W and there is a path between every pair of vertices of W in the subgraph.
Edit intent: clarity
Original:  ASteiner subgraph of (G, W) is a subgraph of G that contains all vertices of W and there is a path between every pair of vertices of W in the subgraph. We say that a Steiner subgraph is minimal if it has no proper Steiner subgraph. It is easy to observe that every minimal Steiner subgraph forms a tree, which is called a minimal Steiner tree. We propose a linear delay and polynomial space ] ]  algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Revised:  ASteiner subgraph Kimelfeld and Sagiv ] ] algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Edit intent: meaning-changed
Original:  We propose a linear delay and polynomial space ] ]  algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Revised:  We propose a linear delay and polynomial space Kimelfeld and Sagiv, PODS 2006] ] algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Edit intent: meaning-changed
Original:  We propose a linear delay and polynomial space ] ]  algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Revised:  We propose a linear delay and polynomial space ], ] algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Edit intent: fluency
Original:  We propose a linear delay and polynomial space ] ]  algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Revised:  We propose a linear delay and polynomial space ] Kimelfeld and Sagiv, Inf. Syst. 2008] algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Edit intent: meaning-changed
Original:  We propose a linear delay and polynomial space ] ]  algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Revised:  We propose a linear delay and polynomial space ] ] pointed out the problem of enumerating K-fragments is of great importance in a keyword search on data graphs. In a graph-theoretic term, the problem corresponds to enumerating minimal Steiner trees in (directed) graphs. In this paper, we propose a linear-delay and polynomial-space algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Edit intent: meaning-changed
Original:  We propose a linear delay and polynomial space ] ]  algorithm for enumerating all minimal Steiner trees of (G, W), which improves a previously known polynomial delay enumeration algorithm in [Kimelfeld and Sagiv, Inf.
Revised:  We propose a linear delay and polynomial space ] ] algorithm for enumerating all minimal Steiner trees , improving on a previous result in [Kimelfeld and Sagiv, Inf.
Edit intent: clarity
Original:  We propose a polynomial delay and exponential space enumeration algorithm of minimal induced Steiner subgraphs for claw-free graphs , whereas the problem on general graphs is shown to be at least as hard as the problem of enumerating minimal transversals in hypergraphs . Contrary to these tractable results, we show that the problem of enumerating minimal group Steiner trees is at least as hard as the minimal transversal enumeration problem on hypergraphs.
Revised:  We propose a polynomial-delay and exponential-space enumeration algorithm of minimal induced Steiner subgraphs for claw-free graphs , whereas the problem on general graphs is shown to be at least as hard as the problem of enumerating minimal transversals in hypergraphs . Contrary to these tractable results, we show that the problem of enumerating minimal group Steiner trees is at least as hard as the minimal transversal enumeration problem on hypergraphs.
Edit intent: fluency
Original:  We propose a polynomial delay and exponential space enumeration algorithm of minimal induced Steiner subgraphs for claw-free graphs , whereas the problem on general graphs is shown to be at least as hard as the problem of enumerating minimal transversals in hypergraphs . Contrary to these tractable results, we show that the problem of enumerating minimal group Steiner trees is at least as hard as the minimal transversal enumeration problem on hypergraphs.
Revised:  We propose a polynomial delay and exponential space enumeration algorithm of minimal induced Steiner subgraphs on claw-free graphs , whereas the problem on general graphs is shown to be at least as hard as the problem of enumerating minimal transversals in hypergraphs . Contrary to these tractable results, we show that the problem of enumerating minimal group Steiner trees is at least as hard as the minimal transversal enumeration problem on hypergraphs.
Edit intent: fluency
Original:  We propose a polynomial delay and exponential space enumeration algorithm of minimal induced Steiner subgraphs for claw-free graphs , whereas the problem on general graphs is shown to be at least as hard as the problem of enumerating minimal transversals in hypergraphs . Contrary to these tractable results, we show that the problem of enumerating minimal group Steiner trees is at least as hard as the minimal transversal enumeration problem on hypergraphs.
Revised:  We propose a polynomial delay and exponential space enumeration algorithm of minimal induced Steiner subgraphs for claw-free graphs . Contrary to these tractable results, we show that the problem of enumerating minimal group Steiner trees is at least as hard as the minimal transversal enumeration problem on hypergraphs.
Edit intent: coherence
Original:  We initialize an encoder-decoder setup with large transformer-based language models pre-trained on a generic corpus and enhance its re-writing capability to multiple styles by employing multiple  language models as discriminators.
Revised:  We initialize an encoder-decoder setup with large transformer-based language models pre-trained on a generic corpus and enhance its re-writing capability to multiple styles by employing multiple style-aware language models as discriminators.
Edit intent: meaning-changed
Original:  Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style-dimensions while preserving content of the input text and compare it against baselines which involve cascaded state-of-the-art uni-dimensional style transfer models.
Revised:  Through quantitative and qualitative evaluation, we show the ability of our model to control styles across multiple style-dimensions while preserving content of the input text and compare it against baselines which involve cascaded state-of-the-art uni-dimensional style transfer models.
Edit intent: clarity
Original:  Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style-dimensions while preserving content of the input text and compare it against baselines which involve cascaded state-of-the-art uni-dimensional style transfer models.
Revised:  Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style dimensions while preserving content of the input text and compare it against baselines which involve cascaded state-of-the-art uni-dimensional style transfer models.
Edit intent: clarity
Original:  Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style-dimensions while preserving content of the input text and compare it against baselines which involve cascaded state-of-the-art uni-dimensional style transfer models.
Revised:  Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style-dimensions while preserving content of the input text . We compare it against baselines which involve cascaded state-of-the-art uni-dimensional style transfer models.
Edit intent: coherence
Original:  Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style-dimensions while preserving content of the input text and compare it against baselines which involve cascaded state-of-the-art uni-dimensional style transfer models.
Revised:  Through quantitative and qualitative evaluation, we show the ability of our model to control for styles across multiple style-dimensions while preserving content of the input text and compare it against baselines involving cascaded state-of-the-art uni-dimensional style transfer models.
Edit intent: fluency
Original:  We analyze two such datasets ( Natural Questions and TyDi QA ) to identify remaining headrooms : paragraph selection and answerability classification , i.e. determining whether the paired evidence document contains the answer to the query or not.
Revised:  We analyze why answering information-seeking queries is more challenging and where their prevalent unanswerabilities arise, on Natural Questions and TyDi QA ) to identify remaining headrooms : paragraph selection and answerability classification , i.e. determining whether the paired evidence document contains the answer to the query or not.
Edit intent: meaning-changed