wanyu/IteraTeR_v2 huggingface dataset:
Original:  Path Graphs are closed under taking induced subgraphs and, answering a question posed by Renz P.L. Renz, Intersection Representations of Graphs by Arcs, Pacific J. Math., 34 (1970), 501-510%DIFDELCMD < ]%%% , L\'{ev\^{e}que, Maffray and Preissmann }%DIFDELCMD < [%%% B. L\'{ev\^{e}que, F. Maffray, and M. Preissmann, Characterizing Path Graphs by Forbidden Induced Subgraphs, J. Graph Theory, 62:
Revised:  Path Graphs are closed under taking induced subgraphs and, answering a question posed by Renz P.L. Renz, Intersection Representations of Graphs by Arcs, Pacific J. Math., 34 (1970), 501-510%DIFDELCMD < ]%%% v\^{e}que, Maffray and Preissmann }%DIFDELCMD < [%%% B. L\'{ev\^{e}que, F. Maffray, and M. Preissmann, Characterizing Path Graphs by Forbidden Induced Subgraphs, J. Graph Theory, 62:
Edit intent: fluency
Original:  Path Graphs are closed under taking induced subgraphs and, answering a question posed by Renz P.L. Renz, Intersection Representations of Graphs by Arcs, Pacific J. Math., 34 (1970), 501-510%DIFDELCMD < ]%%% , L\'{ev\^{e}que, Maffray and Preissmann }%DIFDELCMD < [%%% B. L\'{ev\^{e}que, F. Maffray, and M. Preissmann, Characterizing Path Graphs by Forbidden Induced Subgraphs, J. Graph Theory, 62:
Revised:  Path Graphs are closed under taking induced subgraphs and, answering a question posed by Renz P.L. Renz, Intersection Representations of Graphs by Arcs, Pacific J. Math., 34 (1970), 501-510%DIFDELCMD < ]%%% , L\'{ev\^{e}que, Maffray and Preissmann }%DIFDELCMD < [%%% v\^{e}que, F. Maffray, and M. Preissmann, Characterizing Path Graphs by Forbidden Induced Subgraphs, J. Graph Theory, 62:
Edit intent: coherence
Original:  In this paper, building on a result  due to Monma and Wei [C.L. Monma, and V.K.  Wei, Intersection Graphs of Paths in a Tree { {  , J. Combin.
Revised:  In this paper, building on a result due to Monma and Wei [C.L. ~Monma,~and~ V.K. Wei, Intersection Graphs of Paths in a Tree { { , J. Combin.
Edit intent: fluency
Original:  In this paper, building on a result  due to Monma and Wei [C.L. Monma, and V.K.  Wei, Intersection Graphs of Paths in a Tree { {  , J. Combin.
Revised:  In this paper, building on a result due to Monma and Wei [C.L. Monma, and V.K. Wei, Intersection Graphs of Paths in a Tree { aths in a{ , J. Combin.
Edit intent: fluency
Original:  In this paper, building on a result  due to Monma and Wei [C.L. Monma, and V.K.  Wei, Intersection Graphs of Paths in a Tree { {  , J. Combin.
Revised:  In this paper, building on a result due to Monma and Wei [C.L. Monma, and V.K. Wei, Intersection Graphs of Paths in a Tree { {T , J. Combin.
Edit intent: fluency
Original: 2 (1986) 141-181 ], we introduce the collection of the\textit{attachedness graphs of a graph and characterize Path Graphs in three ways:
Revised: 2 (1986) 141--181 ], we introduce the collection of the\textit{attachedness graphs of a graph and characterize Path Graphs in three ways:
Edit intent: fluency
Original: 2 (1986) 141-181 ], we introduce the collection of the\textit{attachedness graphs of a graph and characterize Path Graphs in three ways:
Revised: 2 (1986) 141-181 ], \textit{attachedness graphs of a graph and characterize Path Graphs in three ways:
Edit intent: clarity
Original:  a structural characterization of each of the \emph{attachedness graphs} , a characterization by a list of five families of minimal forbidden induced 2-edge colored subgraphs in each of the\textit{attachedness graphs and, finally, by a list of three families of minimal forbidden (not necessarily induced) 2-edge colored subgraphs in each of the \textit{attachedness graphs \emph{ .
Revised:  a structural characterization of each of the \emph{attachedness graphs} , a characterization by a list of five families of minimal forbidden induced 2-edge \textit{attachedness graphs and, finally, by a list of three families of minimal forbidden (not necessarily induced) 2-edge colored subgraphs in each of the \textit{attachedness graphs \emph{ .
Edit intent: fluency
Original:  a structural characterization of each of the \emph{attachedness graphs} , a characterization by a list of five families of minimal forbidden induced 2-edge colored subgraphs in each of the\textit{attachedness graphs and, finally, by a list of three families of minimal forbidden (not necessarily induced) 2-edge colored subgraphs in each of the \textit{attachedness graphs \emph{ .
Revised:  a structural characterization of each of the \emph{attachedness graphs} , a characterization by a list of five families of minimal forbidden induced 2-edge colored subgraphs in each of the\textit{ and, finally, by a list of three families of minimal forbidden (not necessarily induced) 2-edge colored subgraphs in each of the \textit{attachedness graphs \emph{ .
Edit intent: fluency
Original:  a structural characterization of each of the \emph{attachedness graphs} , a characterization by a list of five families of minimal forbidden induced 2-edge colored subgraphs in each of the\textit{attachedness graphs and, finally, by a list of three families of minimal forbidden (not necessarily induced) 2-edge colored subgraphs in each of the \textit{attachedness graphs \emph{ .
Revised:  a structural characterization of each of the \emph{attachedness graphs} , a characterization by a list of five families of minimal forbidden induced 2-edge colored subgraphs in each of the\textit{attachedness graphs and, finally, by a list of three families of minimal forbidden (not necessarily induced) 2-edge colored subgraphs in each of the \textit{ \emph{ .
Edit intent: fluency
Original:  a structural characterization of each of the \emph{attachedness graphs} , a characterization by a list of five families of minimal forbidden induced 2-edge colored subgraphs in each of the\textit{attachedness graphs and, finally, by a list of three families of minimal forbidden (not necessarily induced) 2-edge colored subgraphs in each of the \textit{attachedness graphs \emph{ .
Revised:  a structural characterization of each of the \emph{attachedness graphs} , a characterization by a list of five families of minimal forbidden induced 2-edge colored subgraphs in each of the\textit{attachedness graphs and, finally, by a list of three families of minimal forbidden (not necessarily induced) 2-edge colored subgraphs in each of the \textit{attachedness graphs \emph{attachedness graphs .
Edit intent: fluency
Original: Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than  shape.
Revised: Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify images by texture rather than by shape.
Edit intent: fluency
Original:  Our results suggest general strategies to reduce texture bias in neural networks .
Revised:  Our results indicate that apparent differences in the way humans and ImageNet-trained CNNs process images may arise not from differences in their internal workings, but from differences in the data that they see .
Edit intent: clarity
Original:  Our results indicate that apparent differences in the way humans and ImageNet-trained CNNs process images may arise not  from differences in their internal workings, but from differences in the data that they see.
Revised:  Our results indicate that apparent differences in the way humans and ImageNet-trained CNNs process images may arise not primarily from differences in their internal workings, but from differences in the data that they see.
Edit intent: clarity
Original:  The aim of Petri net synthesis is to find an unlabeled Petri net in some target class with a reachability graph isomorphic to a given finite labeled transition system (lts).
Revised:  The aim of Petri net synthesis is to find an unlabelled Petri net in some target class with a reachability graph isomorphic to a given finite labeled transition system (lts).
Edit intent: fluency
Original:  The aim of Petri net synthesis is to find an unlabeled Petri net in some target class with a reachability graph isomorphic to a given finite labeled transition system (lts).
Revised:  The aim of Petri net synthesis is to find an unlabeled Petri net in some target class with a reachability graph isomorphic to a given finite labelled transition system (lts).
Edit intent: fluency
Original:  Recently, control barrier functions (CBFs) have been proposed for synthesizing decentralized controllers that guarantee collision avoidance (safety) and goal stabilization (performance) for multiple robots.
Revised:  Recently, control barrier functions (CBFs) have been proposed for synthesizing controllers guarantee collision avoidance (safety) and goal stabilization (performance) for multiple robots.
Edit intent: clarity
Original:  Recently, control barrier functions (CBFs) have been proposed for synthesizing decentralized controllers that guarantee collision avoidance (safety) and goal stabilization (performance) for multiple robots.
Revised:  Recently, control barrier functions (CBFs) have been proposed for synthesizing decentralized controllers that guarantee collision avoidance and goal stabilization (performance) for multiple robots.
Edit intent: clarity
Original:  Recently, control barrier functions (CBFs) have been proposed for synthesizing decentralized controllers that guarantee collision avoidance (safety) and goal stabilization (performance) for multiple robots.
Revised:  Recently, control barrier functions (CBFs) have been proposed for synthesizing decentralized controllers that guarantee collision avoidance (safety) and goal stabilization for multiple robots.
Edit intent: clarity
Original:  However, it has been noted in several works that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that causes the robots to come to a standstill before they reach their goals.
Revised:  However, it has been noted reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that causes the robots to come to a standstill before they reach their goals.
Edit intent: clarity
Original:  However, it has been noted in several works that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that causes the robots to come to a standstill before they reach their goals.
Revised:  However, it has been noted in several works that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics causes robots to come to a standstill before they reach their goals.
Edit intent: coherence
Original:  However, it has been noted in several works that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that causes the robots to come to a standstill before they reach their goals.
Revised:  However, it has been noted in several works that reactive control synthesis methods (such as CBFs) are prone to deadlock, an equilibrium of system dynamics that causes the robots to come to a standstill before reaching their goals.
Edit intent: fluency
Original:  In this paper, we analyze the incidence of deadlocks in a multirobot system that uses CBFsfor goal stabilization and collision avoidance.
Revised:  In this paper, we formally derive characteristics of deadlock in a multirobot system that uses CBFsfor goal stabilization and collision avoidance.
Edit intent: clarity
Original:  Our analysis is formal, in that we demonstrate that system deadlock is indeed the result of a force-equilibrium on robots .
Revised:  Our analysis is formal, in that we demonstrate that system deadlock is characterized by a force-equilibrium on robots .
Edit intent: clarity
Original:  We show how to interpret deadlock as a subset of the state space and prove that this set is non-empty, bounded , of measure zero and located on the boundary of the safe set.
Revised:  We show how to interpret deadlock as a subset of the state space , and we prove this set is non-empty, bounded , of measure zero and located on the boundary of the safe set.
Edit intent: coherence
Original:  We show how to interpret deadlock as a subset of the state space and prove that this set is non-empty, bounded , of measure zero and located on the boundary of the safe set.
Revised:  We show how to interpret deadlock as a subset of the state space and prove that this set is non-empty, bounded and located on the boundary of the safe set.
Edit intent: clarity
Original: Function inversion is  that given a random function f: [M] \to [N], we want to compute some auxiliary information of size S that we can find pre-image of any image with a few queries to the function given as a black box in time T.
Revised: Function inversion is the problem that given a random function f: [M] \to [N], we want to compute some auxiliary information of size S that we can find pre-image of any image with a few queries to the function given as a black box in time T.
Edit intent: clarity
Original: Function inversion is  that given a random function f: [M] \to [N], we want to compute some auxiliary information of size S that we can find pre-image of any image with a few queries to the function given as a black box in time T.
Revised: Function inversion is that given a random function f: [M] \to [N], we want to find pre-image of any image with a few queries to the function given as a black box in time T.
Edit intent: coherence
Original:   It is a well-studied problem in the classical settings, however, it is not clear how a quantum adversary can do better at this task besides invoking Grover's algorithm  . Nayebi et al. proved a lower bound for adversaries inverting permutationsleveraging only quantum queries to the black box.
Revised:  It is a well-studied problem in the classical settings, however, it is not clear how quantum algorithms can solve this task any better besides invoking Grover's algorithm . Nayebi et al. proved a lower bound for adversaries inverting permutationsleveraging only quantum queries to the black box.
Edit intent: clarity
Original:  We give a matching lower bound for functions and permutations\ge \tilde where M = O(N) , and allowing adversaries to be fully quantum, and thus resolving the open question positively raised by Nayebi et al. of whether such lower bound is achievable for inverters with quantum advice.
Revised:  We give a matching lower bound for functions and permutations\ge \tilde where M = O(N) .
Edit intent: coherence
Original:  In order to prove these bounds, we also proved a lower bound for a generalized version of quantum random access code ( originally introduced by Ambainis et al. ), i.e. under the setting where the encoding length is variable and each element can be arbitrarily correlated , which may be of independent interest.
Revised:  In order to prove these bounds, we generalize the notion of quantum random access code ( originally introduced by Ambainis et al. ), i.e. under the setting where the encoding length is variable and each element can be arbitrarily correlated , which may be of independent interest.
Edit intent: clarity
Original:  In order to prove these bounds, we also proved a lower bound for a generalized version of quantum random access code ( originally introduced by Ambainis et al. ), i.e. under the setting where the encoding length is variable and each element can be arbitrarily correlated , which may be of independent interest.
Revised:  In order to prove these bounds, we also proved a lower bound for a generalized version of quantum random access code , originally introduced by Ambainis et al. ), i.e. under the setting where the encoding length is variable and each element can be arbitrarily correlated , which may be of independent interest.
Edit intent: fluency
Original:  In order to prove these bounds, we also proved a lower bound for a generalized version of quantum random access code ( originally introduced by Ambainis et al. ), i.e. under the setting where the encoding length is variable and each element can be arbitrarily correlated , which may be of independent interest.
Revised:  In order to prove these bounds, we also proved a lower bound for a generalized version of quantum random access code ( originally introduced by Ambainis et al. , to the setting where the encoding length is variable and each element can be arbitrarily correlated , which may be of independent interest.
Edit intent: coherence
Original: Since the inception of Bitcoin, the distributed and database community has shown interest in the design of efficient blockchain systems.
Revised: Since the inception of Bitcoin, the distributed systems community has shown interest in the design of efficient blockchain systems.
Edit intent: clarity
Original:  As a result, existing works blame this low throughput on the associated BFT protocol and expend resources in developing optimizes protocols.
Revised:  As a result, existing works blame this low throughput on the associated BFT protocol and expend resources in developing optimized protocols.
Edit intent: fluency
Original:  We show that designing such a well-crafted system is possible and illustrate cases where a three-phase protocol can outperform a single-phase protocol.
Revised:  We show that designing such a well-crafted system is possible and illustrate that even if such a system employs a three-phase protocol can outperform a single-phase protocol.
Edit intent: clarity
Original:  We show that designing such a well-crafted system is possible and illustrate cases where a three-phase protocol can outperform a single-phase protocol.
Revised:  We show that designing such a well-crafted system is possible and illustrate cases where a three-phase protocol , it can outperform another system utilizing a single-phase protocol.
Edit intent: clarity
Original:  Further, we dissect a permissioned blockchain system and state several factors that affect its performance.
Revised:  This endeavor requires us to dissect a permissioned blockchain system and state several factors that affect its performance.
Edit intent: clarity
Original:  Further, we dissect a permissioned blockchain system and state several factors that affect its performance.
Revised:  Further, we dissect a permissioned blockchain system and highlight different factors that affect its performance.
Edit intent: clarity
Original: Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed from random noises.
Revised: Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed of random noises.
Edit intent: fluency
Original:  The qualitative and quantitative results suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the categorical objects, and the later layers finally render the scene attributes as well as color scheme.
Revised:  The qualitative and quantitative results suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the objects, and the later layers finally render the scene attributes as well as color scheme.
Edit intent: fluency
Original: Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what networks have learned inside the deep generative representations and how photo-realistic images are able to be composed of random noises .
Revised: Despite the success of Generative Adversarial Networks (GANs) in image synthesis, there lacks enough understanding on what generative models have learned inside the deep generative representations and how photo-realistic images are able to be composed of random noises .
Edit intent: clarity
Original:  In this work, we show that highly-structured semantic hierarchy emerges as variation factors for synthesizing scenes from the generative representations in state-of-the-art GAN models, like StyleGAN and BigGAN.
Revised:  In this work, we show that highly-structured semantic hierarchy emerges as variation factors from synthesizing scenes from the generative representations in state-of-the-art GAN models, like StyleGAN and BigGAN.
Edit intent: fluency
Original:  The qualitative and quantitative results  suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the  objects, and the later layers finally render the scene attributes as well as color scheme.
Revised:  The qualitative and quantitative results further suggest that the generative representations learned by the GANs with layer-wise latent codes are specialized to synthesize different hierarchical semantics: the early layers tend to determine the spatial layout and configuration, the middle layers control the objects, and the later layers finally render the scene attributes as well as color scheme.
Edit intent: clarity
Original:  To efficiently determine which modelsbetter explain human responses, we synthesize controversial stimuli: images for which different models produce distinct responses.
Revised:  To efficiently compare models' ability to predict human responses, we synthesize controversial stimuli: images for which different models produce distinct responses.
Edit intent: clarity
Original:  The best performing model was the generative Analysis-by-Synthesis model (based on variational autoencoders) .
Revised:  The best performing models were a generative Analysis-by-Synthesis model (based on variational autoencoders) .
Edit intent: fluency
Original:  The concept of shape that we tackle is termed temporally "neutral " to consider that it may possibly exist outside of any temporal specification, as it is the case for a geometric form.
Revised:  The concept ofshape that we tackle is termed temporally "neutral " to consider that it may possibly exist outside of any temporal specification, as it is the case for a geometric form.
Edit intent: fluency
Original:  The concept of shape that we tackle is termed temporally "neutral " to consider that it may possibly exist outside of any temporal specification, as it is the case for a geometric form.
Revised:  The concept of shape that we tackle is termed temporally neutral to consider that it may possibly exist outside of any temporal specification, as it is the case for a geometric form.
Edit intent: fluency
Original:  We propose to exploit and adapt a probabilistic temporal alignment algorithm, initially designed to estimate the centroid of a set of time series, to build some heuristic elements of solution to this separation problem.
Revised:  We propose to exploit and adapt a probabilistic temporal alignment algorithm, initially designed to estimate the centroid of a set of time series, to build some heuristicelements of solution to this separation problem.
Edit intent: fluency
Original:  On the three evaluated tasks, our approach based on the separation of signature shape and associated temporal patterns is positioned slightly above the current state of the art demonstrating the application benefit of this separating problem.
Revised:  On the three evaluated tasks, our approach based on the separation of signature shape and associated temporal patterns is positioned slightly above the current state of the art demonstrating the applicative benefit of this separating problem.
Edit intent: clarity