wanyu/IteraTeR_v2 huggingface dataset:
Original:  In two scene-viewing experiments , we manipulated observers' initial gaze positions and delayed their first saccades by a specific time interval after onset of the image.
Revised:  In two scene-viewing experiments , we manipulated observers' initial gaze positions and delayed their first saccade by a specific time interval after onset of the image.
Edit intent: fluency
Original:  In two scene-viewing experiments , we manipulated observers' initial gaze positions and delayed their first saccades by a specific time interval after onset of the image.
Revised:  In two scene-viewing experiments , we manipulated observers' initial gaze positions and delayed their first saccades by a specific time interval relative to the onset of an image.
Edit intent: clarity
Original:  We analyzed the distance to image center over time and demonstrate that the CFB of initial fixations was significantly reduced with this design.
Revised:  We analyzed the distance to image center over time and show that the central fixation bias of initial fixations was significantly reduced with this design.
Edit intent: clarity
Original:  Our findings of common growth pattern described by the interrelation between four characteristic growth yardsticks suggest a universal law of the communal data formation.
Revised:  Our findings of common growth pattern described by the interrelations between four characteristic growth yardsticks suggest a universal law of the communal data formation.
Edit intent: fluency
Original:  Our findings of common growth pattern described by the interrelation between four characteristic growth yardsticks suggest a universal law of the communal data formation.
Revised:  Our findings of common growth pattern described by the interrelation between four characteristic growth yardsticks suggest a universal law of communal data formation.
Edit intent: fluency
Original:  In this encyclopedic data set, we observe the interplay between the number of edits and the degree of inequality.
Revised:  In this encyclopaedic data set, we observe the interplay between the number of edits and the degree of inequality.
Edit intent: fluency
Original:  In particular, the rapid increasing of the Gini coefficient suggests that this entrenched inequality stems from the nature of such open-editing communal data sets, namely abiogenesis of supereditors' cartel .
Revised:  In particular, the rapid increase in the Gini coefficient suggests that this entrenched inequality stems from the nature of such open-editing communal data sets, namely abiogenesis of supereditors' cartel .
Edit intent: fluency
Original:  In particular, the rapid increasing of the Gini coefficient suggests that this entrenched inequality stems from the nature of such open-editing communal data sets, namely abiogenesis of supereditors' cartel .
Revised:  In particular, the rapid increasing of the Gini coefficient suggests that this entrenched inequality stems from the nature of such open-editing communal data sets, namely the abiogenesis of the supereditors' oligopoly .
Edit intent: clarity
Original:  We show that these groups are created at the early stage of these open-editing media and still alive .
Revised:  We show that these supereditor groups were created at the early stage of these open-editing media and still alive .
Edit intent: fluency
Original:  We show that these groups are created at the early stage of these open-editing media and still alive .
Revised:  We show that these groups are created at the early stages of these open-editing media and still alive .
Edit intent: fluency
Original:  We show that these groups are created at the early stage of these open-editing media and still alive .
Revised:  We show that these groups are created at the early stage of these open-editing media and are still active .
Edit intent: clarity
Original:  Furthermore, our model taking both short-term and long-term memories into account successfully elucidates the underlying mechanism to establish the oligarchy in Wikipedia.
Revised:  Furthermore, our model considers both short-term and long-term memories into account successfully elucidates the underlying mechanism to establish the oligarchy in Wikipedia.
Edit intent: clarity
Original:  Furthermore, our model taking both short-term and long-term memories into account successfully elucidates the underlying mechanism to establish the oligarchy in Wikipedia.
Revised:  Furthermore, our model taking both short-term and long-term memories to successfully elucidate the underlying mechanism to establish the oligarchy in Wikipedia.
Edit intent: clarity
Original:  Furthermore, our model taking both short-term and long-term memories into account successfully elucidates the underlying mechanism to establish the oligarchy in Wikipedia.
Revised:  Furthermore, our model taking both short-term and long-term memories into account successfully elucidates the underlying mechanism of the establishment of oligarchy in Wikipedia.
Edit intent: clarity
Original:  Eventually, our results forewarn a rather pessimistic prospect of such communal databases in the future: the inequality will endure extendedly .
Revised:  Our results anticipate a noticeable prospect of such communal databases in the future: the inequality will endure extendedly .
Edit intent: clarity
Original:  Eventually, our results forewarn a rather pessimistic prospect of such communal databases in the future: the inequality will endure extendedly .
Revised:  Eventually, our results forewarn a rather pessimistic prospect of such communal databases in the future: the disparity will not be resolved spontaneously .
Edit intent: clarity
Original:  The first approach yields lower Net Coding Gains ( NCG ), but can usually work at higher code rates and have lower complexity decoders.
Revised:  The first approach yields lower Net Coding Gains ( NCGs ), but can usually work at higher code rates and have lower complexity decoders.
Edit intent: fluency
Original:  It can achieve an NCG of  9.96  dB at a BER of 10^{-18} without encountering an error floor , an error-correction performance that sits between that of current hard-decision and soft-decision FECs.
Revised:  It can achieve an NCG of 9.96 dB at a BER of 10^{-18} , an error-correction performance that sits between that of current hard-decision and soft-decision FECs.
Edit intent: clarity
Original:  We also study the switching times properties of the model by exploiting approximate mappings onto the model of [Phys.
Revised:  We also study the switching times properties of the model by exploiting an approximate mapping onto the model of [Phys.
Edit intent: fluency
Original:  We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected to other nodes by directed, labelled edges;
Revised:  We first discuss two popular graph data models: edge-labelled graphs, where nodes are connected by directed, labelled edges;
Edit intent: clarity
Original:  Next we discuss the two most basic graph querying functionalities: graph patterns and navigational expressions.
Revised:  Next we discuss the two most fundamental graph querying functionalities: graph patterns and navigational expressions.
Edit intent: clarity
Original:  we give an overview of what kinds of expressions have been proposed, and how such expressions can be combined with graph patterns.
Revised:  we give an overview of what kinds of expressions have been proposed, and how they can be combined with graph patterns.
Edit intent: clarity
Original:  We also discuss a variety of semantics under which queries using the previous features can be evaluated, what effects the introduction of additional features and the selection of semantics has on complexity, as well as offering examples of said features in three modern languages that can be used to query graphs: SPARQL, Cypher and Gremlin.
Revised:  We also discuss several semantics under which queries using the previous features can be evaluated, what effects the introduction of additional features and the selection of semantics has on complexity, as well as offering examples of said features in three modern languages that can be used to query graphs: SPARQL, Cypher and Gremlin.
Edit intent: clarity
Original:  We also discuss a variety of semantics under which queries using the previous features can be evaluated, what effects the introduction of additional features and the selection of semantics has on complexity, as well as offering examples of said features in three modern languages that can be used to query graphs: SPARQL, Cypher and Gremlin.
Revised:  We also discuss a variety of semantics under which queries using the previous features can be evaluated, what effects the selection of features and semantics has on complexity, as well as offering examples of said features in three modern languages that can be used to query graphs: SPARQL, Cypher and Gremlin.
Edit intent: clarity
Original:  We also discuss a variety of semantics under which queries using the previous features can be evaluated, what effects the introduction of additional features and the selection of semantics has on complexity, as well as offering examples of said features in three modern languages that can be used to query graphs: SPARQL, Cypher and Gremlin.
Revised:  We also discuss a variety of semantics under which queries using the previous features can be evaluated, what effects the introduction of additional features and the selection of semantics has on complexity, and offer examples of such features in three modern languages that can be used to query graphs: SPARQL, Cypher and Gremlin.
Edit intent: clarity
Original:  We also discuss a variety of semantics under which queries using the previous features can be evaluated, what effects the introduction of additional features and the selection of semantics has on complexity, as well as offering examples of said features in three modern languages that can be used to query graphs: SPARQL, Cypher and Gremlin.
Revised:  We also discuss a variety of semantics under which queries using the previous features can be evaluated, what effects the introduction of additional features and the selection of semantics has on complexity, as well as offering examples of said features in three modern languages that are used to query graphs: SPARQL, Cypher and Gremlin.
Edit intent: clarity
Original:  We conclude with discussion of the importance of formalisation for graph query languages , as well as possible future directions in which such languages can be extended .
Revised:  We conclude by discussing the importance of formalisation for graph query languages , as well as possible future directions in which such languages can be extended .
Edit intent: clarity
Original:  We conclude with discussion of the importance of formalisation for graph query languages , as well as possible future directions in which such languages can be extended .
Revised:  We conclude with discussion of the importance of formalisation for graph query languages , as well as possible future directions for the area .
Edit intent: clarity
Original:  We present an algorithm to over-approximate the reach set of executions that take epsilon-equivalent action sequences.
Revised:  Our reachability algorithm generalizes individual executions to cover sets of executions that take epsilon-equivalent action sequences.
Edit intent: clarity
Original: Objective: Research into network modelling of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights.
Revised: Objective: Research into network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights.
Edit intent: clarity
Original:  We present a novel, non-arbitrary threshold for functional connectivitybrain networks named the Cluster-Span Threshold (CST) which ensures a trade-off of sparsity and information richness in the network .
Revised:  We present a novel, non-arbitrary threshold for functional connectivitybrain networks named the Cluster-Span Threshold (CST) .
Edit intent: coherence
Original:  Methods: We compare CST networks with weighted networks, minimum spanning trees, union of shortest path graphs and proportional arbitrary thresholds.
Revised:  Methods: We compare CST networks with weighted networks, minimum spanning trees, union of shortest path graphs and arbitrary proportional thresholds.
Edit intent: coherence
Original:  We test these techniques on weighted complex hierarchy models by contrasting models with small parametric differences via t-tests of network metrics.
Revised:  We test these techniques on weighted complex hierarchy models by contrasting model realisations with small parametric differences via t-tests of network metrics.
Edit intent: clarity
Original:  Using this comparison approach we also test the robustness of the techniques to alterations in the network in the form of random and targeted attacks.
Revised:  Using this comparison approach we also test the robustness of these techniques to random and targeted attacks.
Edit intent: clarity
Original:  Results: We find that the CST consistently outperforms other state-of-the-art binarisation methods for topological accuracy and robustness .
Revised:  Results: The CST consistently outperforms other state-of-the-art binarisation methods for topological accuracy and robustness .
Edit intent: coherence
Original:  We also provide insights into the effects of network size and density on the topological accuracy of a binarised functional connectivity network .
Revised:  We also provide insights into the effects of network size and density on the topological accuracy of binarised networks .
Edit intent: clarity
Original: Objective: Research into network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights.
Revised: Research into binary network analysis of brain function faces a methodological challenge in selecting an appropriate threshold to binarise edge weights.
Edit intent: clarity
Original:  We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques including the recently proposed Cluster-Span Threshold (CST) . Methods: We compare CST networks with weighted networks , minimum spanning trees,  union of shortest path graphs and arbitrary proportional thresholds  .
Revised:  We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques , the recently proposed Cluster-Span Threshold (CST) . Methods: We compare CST networks with weighted networks , minimum spanning trees, union of shortest path graphs and arbitrary proportional thresholds .
Edit intent: coherence
Original:  We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques including the recently proposed Cluster-Span Threshold (CST) . Methods: We compare CST networks with weighted networks , minimum spanning trees,  union of shortest path graphs and arbitrary proportional thresholds  .
Revised:  We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques including the recently proposed Cluster-Span Threshold (CST) , minimum spanning trees, union of shortest path graphs and arbitrary proportional thresholds .
Edit intent: coherence
Original:  We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques including the recently proposed Cluster-Span Threshold (CST) . Methods: We compare CST networks with weighted networks , minimum spanning trees,  union of shortest path graphs and arbitrary proportional thresholds  .
Revised:  We explore the density range suitable for such structure and provide a comparison of state-of-the-art binarisation techniques including the recently proposed Cluster-Span Threshold (CST) . Methods: We compare CST networks with weighted networks , minimum spanning trees, union of shortest path graphs , with arbitrary proportional thresholds .
Edit intent: fluency
Original:  We also show that for some of these groups the conjugacy search problem reduces to the discrete logarithm problem .
Revised:  We also show that for a different subfamily the conjugacy search problem reduces to the discrete logarithm problem .
Edit intent: clarity
Original:  To address this, we propose several modifications: A log-domain stabilized formulation, the well-known epsilon-scaling heuristic, an adaptive truncation of the kernel and a coarse-to-fine scheme.
Revised:  To address this, we combine several modifications: A log-domain stabilized formulation, the well-known epsilon-scaling heuristic, an adaptive truncation of the kernel and a coarse-to-fine scheme.
Edit intent: clarity
Original:  This allows to solve larger problems with smaller regularization and negligible truncation error.
Revised:  This permits the solution of larger problems with smaller regularization and negligible truncation error.
Edit intent: clarity
Original:  We introduce and study the connected-domishold graphs , defined as graphs that admit non-negative real weights associated to their vertices such that a set of vertices is a connected dominating set if and only if the sum of the corresponding weights exceeds a certain threshold.
Revised:  We introduce and study the connected-domishold graphs , defined as graphs that admit non-negative real weights associated to its vertices such that a set of vertices is a connected dominating set if and only if the sum of the corresponding weights exceeds a certain threshold.
Edit intent: fluency
Original:  More specifically, we show that connected-domishold graphs form a non-hereditary class of graphs properly containing two well known classes of chordal graphs : the block graphs and the trivially perfect graphs.
Revised:  We characterize the graphs in this non-hereditary class of graphs properly containing two well known classes of chordal graphs : the block graphs and the trivially perfect graphs.
Edit intent: clarity
Original:  More specifically, we show that connected-domishold graphs form a non-hereditary class of graphs properly containing two well known classes of chordal graphs : the block graphs and the trivially perfect graphs.
Revised:  More specifically, we show that connected-domishold graphs form a non-hereditary class of graphs properly containing two well known classes of chordal graphs , the block graphs and the trivially perfect graphs.
Edit intent: fluency
Original:  As a side result, our approach leads to new polynomially solvable cases of the minimum-weight connected domination problem.
Revised:  As a side result, our approach leads to new polynomially solvable cases of the weighted connected dominating set problem.
Edit intent: clarity
Original:  however, many theoretical studies of spiking statistics donot include this feature .
Revised:  however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship .
Edit intent: clarity
Original:  Here, we study the population distribution of first- and second-order spiking statistics of recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses.
Revised:  Here, we study the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses.
Edit intent: clarity
Original:  This structure was related to a positive relationship  between pairwise correlations and firing rates , which emerged in this firing regime .
Revised:  This structure was related to a positive relationship between pairwise correlations and firing rates .
Edit intent: clarity
Original:  We then used this decomposition to explain why covariation of correlations with firing rate --- a relationship previously explained in common input-driven feedforward networks --- emerges in some recurrent networks but not in others  .
Revised:  We then used this decomposition to explain why covariation of correlations with firing rate -- a relationship previously explained in common input-driven feedforward networks --- emerges in some recurrent networks but not in others .
Edit intent: fluency
Original:  We then used this decomposition to explain why covariation of correlations with firing rate --- a relationship previously explained in common input-driven feedforward networks --- emerges in some recurrent networks but not in others  .
Revised:  We then used this decomposition to explain why covariation of correlations with firing rate --- a relationship previously explained in feedforward networks driven by correlated input -- emerges in some recurrent networks but not in others .
Edit intent: clarity
Original:  In this paper, such explicit error bounds are given in the remaining cases.
Revised:  In this paper, such explicit error bounds are given in other cases.
Edit intent: clarity
Original:  Even for other functions, the Sinc approximation still works accurately when combined with a proper variable transformation.
Revised:  Even for other functions, the Sinc approximation works accurately when combined with a proper variable transformation.
Edit intent: clarity
Original:  Recently, for verified numerical computation, more explicit, "computable" error bound has been given in the case of a finite interval.
Revised:  Recently, for verified numerical computations, a more explicit, "computable" error bound has been given in the case of a finite interval.
Edit intent: fluency
Original:  In this paper, such explicit error bounds are given in other cases.
Revised:  In this paper, such explicit error bounds are derived for other cases.
Edit intent: clarity
Original:  Internal consistency ( .981) and correspondence with the original version ( .976) were high.
Revised:  Internal consistency ( .98) and concordance with the original version ( .976) were high.
Edit intent: clarity
Original:  Using a novel numerical approach , we show that soft local maxima are organized into a reliable sequence across the different spike count levels , which altogether has the geometry of a "ridge . " These ridges closely corresponded with the clusters formulated by the hidden Markov model, and moreover with neuron "communities," in the sense defined in computer science.
Revised:  Next , we show that soft local maxima are organized into a reliable sequence across the different spike count levels , which altogether has the geometry of a "ridge . " These ridges closely corresponded with the clusters formulated by the hidden Markov model, and moreover with neuron "communities," in the sense defined in computer science.
Edit intent: coherence
Original:  Using a novel numerical approach , we show that soft local maxima are organized into a reliable sequence across the different spike count levels , which altogether has the geometry of a "ridge . " These ridges closely corresponded with the clusters formulated by the hidden Markov model, and moreover with neuron "communities," in the sense defined in computer science.
Revised:  Using a novel numerical approach , we show that soft local maxima are robustly present, and can moreover be linked across different spike count levels , which altogether has the geometry of a "ridge . " These ridges closely corresponded with the clusters formulated by the hidden Markov model, and moreover with neuron "communities," in the sense defined in computer science.
Edit intent: clarity
Original:  Using a novel numerical approach , we show that soft local maxima are organized into a reliable sequence across the different spike count levels , which altogether has the geometry of a "ridge . " These ridges closely corresponded with the clusters formulated by the hidden Markov model, and moreover with neuron "communities," in the sense defined in computer science.
Revised:  Using a novel numerical approach , we show that soft local maxima are organized into a reliable sequence across the different spike count levels , which altogether has the geometry of a "ridge " These ridges closely corresponded with the clusters formulated by the hidden Markov model, and moreover with neuron "communities," in the sense defined in computer science.
Edit intent: fluency