wanyu/IteraTeR_v2 huggingface dataset:
Original:  We present TopoLines, a method for smoothing line charts by leveraging techniques from Topological Data Analysis.
Revised:  We present TopoLines, a method for smoothing line charts using techniques from Topological Data Analysis.
Edit intent: clarity
Original:  The design goal of TopoLines is to smooth line charts while maintaining prominent peaks , as defined by persistent homology, in the data .
Revised:  The design goal of TopoLines is to maintain prominent peaks in the data .
Edit intent: clarity
Original:  We compare TopoLines to 5 popular line smoothing methods using data from 4 application domains .
Revised:  We compare TopoLines to 5 popular line smoothing methods with data from 4 application domains .
Edit intent: fluency
Original:  This paper focuses on quantifying the unwanted or unplanned changes that come along with the application of an intervention to produce a desired effect , which we define as the \emph{side effects of the intervention.
Revised:  This paper focuses on quantifying the unwanted or unplanned changes that come along with the application of an intervention , which we define as the \emph{side effects of the intervention.
Edit intent: clarity
Original:  These formulas rely on the stratification of the inputs of the target function where the number of changed transitions depends on the layer of canalization that includes the input to be deleted.
Revised:  These formulas rely on the canalizing structure of the target function where the number of changed transitions depends on the layer of canalization that includes the input to be deleted.
Edit intent: clarity
Original:  These formulas rely on the stratification of the inputs of the target function where the number of changed transitions depends on the layer of canalization that includes the input to be deleted.
Revised:  These formulas rely on the stratification of the inputs of the target function where the number of changed transitions depends on the canalizing layer that includes the input to be deleted.
Edit intent: clarity
Original:  Applications from using these formulas to estimate the number of changes in the state space and comparisons with the actual number of changes are also presented .
Revised:  We applied our methods to two published models to compare the accuracy of the upper bounds and shown that the bounds are sharp .
Edit intent: coherence
Original:  The identification of potential interventions can be achieved through mathematical modeling of the state of a cell by finding appropriate input manipulations in the model that represent external interventions.
Edit intent: coherence
Original:  This paper focuses on quantifying the unwanted or unplanned changes that come along with the application of an intervention  , which we define as the total effect of the intervention.
Revised:  This paper focuses on quantifying the global changes that result from the application of an intervention , which we define as the total effect of the intervention.
Edit intent: clarity
Original:  This paper focuses on quantifying the unwanted or unplanned changes that come along with the application of an intervention  , which we define as the total effect of the intervention.
Revised:  This paper focuses on quantifying the unwanted or unplanned changes that come along with the application of an intervention , which we define as the total effect of the intervention.
Edit intent: fluency
Original:  The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system.
Revised:  The potential interventions can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system.
Edit intent: clarity
Original:  Regulatory rules that appear in published models tend to have special features such as the property of being nested canalizing, a concept inspired by the concept of canalization in evolutionary biology.
Revised:  Regulatory rules that appear in published models and were inspired by the concept of canalization in evolutionary biology.
Edit intent: clarity
Original:  We provide a polynomial normal form based on the canalizing properties of regulatory functions.
Revised:  In this paper, we provide a polynomial normal form based on the canalizing properties of regulatory functions.
Edit intent: clarity
Original:  This polynomial normal form provides a partition of the inputs of the function into canalizing and non-canalizing variables and, within the canalizing ones, we categorize the input variables into layers of canalization.
Edit intent: coherence
Original:  Using this polynomial normal form, we provide a set of formulas for counting the maximum number of transitions that will change in the state space upon an edge deletion in the wiring diagram.
Revised:  Using this polynomial normal form, we give a set of formulas for counting the maximum number of transitions that will change in the state space upon an edge deletion in the wiring diagram.
Edit intent: clarity
Original:  These formulas rely on the canalizing structure of the target function where the number of changed transitions depends on the canalizing layer that includes the input to be deleted.
Revised:  These formulas rely on the canalizing structure of the target function since the number of changed transitions depends on the canalizing layer that includes the input to be deleted.
Edit intent: coherence
Original:  This paper focuses on quantifying the global changes that result from the application of an intervention to produce a desired effect, which we define as the total effect of the intervention.
Revised:  This paper focuses on quantifying the global changes that result from the application of an intervention to produce a desired effect, which we define as the total effect of the intervention.
Edit intent: fluency
Original: TV series such as HBO's most popular show Game of Thrones have seen a high number of dedicated followers, who watch and thoroughly analyze every minute of the show.
Revised: TV series such as HBO's Game of Thrones have seen a high number of dedicated followers, who watch and thoroughly analyze every minute of the show.
Edit intent: clarity
Original:  Largely discussed aspect of the show between viewers seems to be the dramatic murders of the most important characters , the thing that the series is most known for .
Revised:  Largely discussed aspect of the show between viewers seems to be the dramatic murders of the most important characters .
Edit intent: clarity
Original:  In our work, we try to predict characters' kills ( killer and victim pairs ) using data about previous kills by the characters and additional metadata.
Revised:  In our work, we try to predict killer and victim pairs ) using data about previous kills by the characters and additional metadata.
Edit intent: clarity
Original:  In our work, we try to predict characters' kills ( killer and victim pairs ) using data about previous kills by the characters and additional metadata.
Revised:  In our work, we try to predict characters' kills ( killer and victim pairs using data about previous kills by the characters and additional metadata.
Edit intent: fluency
Original:  In our work, we try to predict characters' kills ( killer and victim pairs ) using data about previous kills by the characters and additional metadata.
Revised:  In our work, we try to predict characters' kills ( killer and victim pairs ) using data about previous kills and additional metadata.
Edit intent: clarity
Original:  We construct a network with characters as nodes, where two nodes are linked if one killed the other .
Revised:  We construct a network where two character nodes are linked if one killed the other .
Edit intent: clarity
Original:  Then we use a link prediction framework and evaluate different techniques to predict the next possible kills . Lastly, we construct features from various network properties on a social network of characters , which we use in conjunction with classic data mining techniques.
Revised:  Then we use a link prediction framework to evaluate different techniques to predict the next possible kills . Lastly, we construct features from various network properties on a social network of characters , which we use in conjunction with classic data mining techniques.
Edit intent: coherence
Original:  Then we use a link prediction framework and evaluate different techniques to predict the next possible kills . Lastly, we construct features from various network properties on a social network of characters , which we use in conjunction with classic data mining techniques.
Revised:  Then we use a link prediction framework and evaluate different techniques for kill predictions . Lastly, we construct features from various network properties on a social network of characters , which we use in conjunction with classic data mining techniques.
Edit intent: clarity
Original:  Then we use a link prediction framework and evaluate different techniques to predict the next possible kills . Lastly, we construct features from various network properties on a social network of characters , which we use in conjunction with classic data mining techniques.
Revised:  Then we use a link prediction framework and evaluate different techniques to predict the next possible kills . Lastly, we compute various network properties on a social network of characters , which we use in conjunction with classic data mining techniques.
Edit intent: clarity
Original:  We see that due to the small size of the kills dataset and the somewhat random distribution of kills , we cannot predict much with standard indices .
Revised:  Due to the small size of the kills dataset and the somewhat random distribution of kills , we cannot predict much with standard indices .
Edit intent: coherence
Original:  We see that due to the small size of the kills dataset and the somewhat random distribution of kills , we cannot predict much with standard indices .
Revised:  We see that due to the small size of the dataset and the somewhat random distribution of kills , we cannot predict much with standard indices .
Edit intent: fluency
Original:  We see that due to the small size of the kills dataset and the somewhat random distribution of kills , we cannot predict much with standard indices .
Revised:  We see that due to the small size of the kills dataset and the somewhat random kill distribution , we cannot predict much with standard indices .
Edit intent: clarity
Original:  We also see that the features we compute on the social network of characters help with standard machine learning approaches as well but do not yield as accurate predictionsas we would hope .
Revised:  We also see that the features we compute on the social network help the classic machine learning approaches as well but do not yield as accurate predictionsas we would hope .
Edit intent: clarity
Original:  We also see that the features we compute on the social network of characters help with standard machine learning approaches as well but do not yield as accurate predictionsas we would hope .
Revised:  We also see that the features we compute on the social network of characters help with standard machine learning approaches , but do not yield as accurate predictionsas we would hope .
Edit intent: clarity
Original:  We also see that the features we compute on the social network of characters help with standard machine learning approaches as well but do not yield as accurate predictionsas we would hope .
Revised:  We also see that the features we compute on the social network of characters help with standard machine learning approaches as well but do not yield very accurate predictions .
Edit intent: clarity
Original:  Second, we demonstrate that standard fully-connected architectures of a fixed width independent of smoothness can adapt to smoothness and achieve almost optimal rates.
Revised:  Second, we demonstrate that standard fully-connected architectures with a fixed width independent of smoothness can adapt to smoothness and achieve almost optimal rates.
Edit intent: coherence
Original:  We propose a new input model in which one can query single entries of the sample covariance matrix.
Revised:  We propose a new input model in which one can query single entries of the covariance matrix.
Edit intent: clarity
Original:  Our algorithms work in a regime of tree-like graphs and, more generally, for graphs of small treewidth.
Revised:  Our algorithms work in a regime when this support is represented by tree-like graphs and, more generally, for graphs of small treewidth.
Edit intent: clarity
Original:  Our results demonstrate that for large classes of graphs, the structure of the corresponding Gaussian graphical models can be determined much faster than even computing the empirical covariance matrix.
Revised:  Our results demonstrate that for large classes of graphs, the structure of the corresponding partial correlation graphs can be determined much faster than even computing the empirical covariance matrix.
Edit intent: clarity
Original:  Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal behavior for different dynamically changing motivation vectors.
Revised:  Here, we propose how reinforcement learning neural networks can learn optimal behavior for different dynamically changing motivation vectors.
Edit intent: clarity
Original:  Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal behavior for different dynamically changing motivation vectors.
Revised:  Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal behavior for dynamically changing motivational salience vectors.
Edit intent: clarity
Original:  First, we show that Q-learning neural networks can learn to navigate towards variable goals whose relative salience is determined by a multidimensional motivational vector .
Revised:  First, we show that Q-learning neural networks with motivation can navigate in environment with dynamic rewards .
Edit intent: clarity
Original:  Second, we show that a Q-learning network with motivation can learn complex behaviors  towards several goals distributed in an environment.
Revised:  Second, we show that such networks can learn complex behaviors towards several goals distributed in an environment.
Edit intent: clarity
Original:  Networks with motivationmay also be parts of a larger hierarchical reinforcement learning system in the brain.
Revised:  Networks with motivationmay also be parts of a larger hierarchical reinforcement learning is implemented in the brain.
Edit intent: clarity
Original:  In this paper, we employ this likelihood-free importance weighting framework to correct for the bias in state-of-the-art deep generative models.
Revised:  We employ this likelihood-free importance weighting framework to correct for the bias in state-of-the-art deep generative models.
Edit intent: coherence
Original:  In this paper, we employ this likelihood-free importance weighting framework to correct for the bias in state-of-the-art deep generative models.
Revised:  In this paper, we employ this likelihood-free importance weighting method to correct for the bias in state-of-the-art deep generative models.
Edit intent: clarity
Original:  In this paper, we employ this likelihood-free importance weighting framework to correct for the bias in state-of-the-art deep generative models.
Revised:  In this paper, we employ this likelihood-free importance weighting framework to correct for the bias in generative models.
Edit intent: clarity
Original:  The objective is to find the trajectory of the UAVs so that the sum-rate of the users served by each UAV is maximized.
Revised:  The objective is to find the trajectory of the ABSs so that the sum-rate of the users served by each UAV is maximized.
Edit intent: fluency
Original:  The objective is to find the trajectory of the UAVs so that the sum-rate of the users served by each UAV is maximized.
Revised:  The objective is to find the trajectory of the UAVs so that the sum-rate of the users served by each ABS is maximized.
Edit intent: style
Original:  Then, based on the Q-learning method, we develop a distributed algorithm which solves these sub-problems efficiently, and does not need significant amount of information exchange between the UAVs and the core network.
Revised:  Then, based on the Q-learning method, we develop a distributed algorithm which solves these sub-problems efficiently, and does not need significant amount of information exchange between the ABSs and the core network.
Edit intent: clarity
Original:  Simulation results show that although Q-learning is a model-free reinforcement learning technique, it has a remarkable capability to train the UAVs to optimize their trajectories based on the received reward signals, which carry decent information from the topology of the network.
Revised:  Simulation results show that although Q-learning is a model-free reinforcement learning technique, it has a remarkable capability to train the ABSs to optimize their trajectories based on the received reward signals, which carry decent information from the topology of the network.
Edit intent: clarity
Original:  Objective and subjective experimental results show that our proposed HiNet vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder and a 16-bit WaveNet vocoder using open source implementation , and obtains similar performance with a 16-bit WaveRNN vocoder , no matter using natural or predicted acoustic features as input .
Revised:  Objective and subjective experimental results show that our proposed HiNet vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder , a 16-bit WaveNet vocoder using open source implementation , and obtains similar performance with a 16-bit WaveRNN vocoder , no matter using natural or predicted acoustic features as input .
Edit intent: coherence
Original:  Objective and subjective experimental results show that our proposed HiNet vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder and a 16-bit WaveNet vocoder using open source implementation , and obtains similar performance with a 16-bit WaveRNN vocoder , no matter using natural or predicted acoustic features as input .
Revised:  Objective and subjective experimental results show that our proposed HiNet vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder and a 16-bit WaveNet vocoder using open source implementation , and obtains similar performance with a 16-bit WaveRNN vocoder .
Edit intent: coherence
Original: Formation of a hierarchy within an organization is a natural way of optimizing the duties, responsibilities and flow of information.
Revised: Formation of a hierarchy within an organization is a natural way of assigning the duties, responsibilities and flow of information.
Edit intent: clarity
Original: Formation of a hierarchy within an organization is a natural way of optimizing the duties, responsibilities and flow of information.
Revised: Formation of a hierarchy within an organization is a natural way of optimizing the duties, delegating responsibilities and optimizing the flow of information.
Edit intent: clarity
Original:  Only for the smallest organizations the lack of the hierarchy is possible, yet, if they grow, its appearance is inevitable.
Revised:  Only for the smallest companies the lack of the hierarchy is possible, yet, if they grow, its appearance is inevitable.
Edit intent: clarity
Original:  Only for the smallest organizations the lack of the hierarchy is possible, yet, if they grow, its appearance is inevitable.
Revised:  Only for the smallest organizations the lack of the hierarchy is possible, yet, if they grow, the introduction of a hierarchy is inevitable.
Edit intent: clarity
Original:  Most often, its existence results in a different nature of the tasks and duties of its members located at different organizational levels .
Revised:  Most often, its existence results in different nature of the tasks and duties of its members located at different organizational levels .
Edit intent: fluency
Original:  At first, such a social network may seem distinct from the organizational one.
Revised:  At first, such a social network seems distinct from the organizational one.
Edit intent: clarity
Original:  This is due to the fact that that people holding a similar position in the hierarchy can possibly share also a similar way of behaving and communicating attributed to their role.
Revised:  This is due to the fact that that people holding a similar position in the hierarchy possibly share also a similar way of behaving and communicating attributed to their role.
Edit intent: clarity
Original:  As a technique for answering the research question, machine learning apparatus was employed.
Revised:  As a technique for answering this research question, machine learning apparatus was employed.
Edit intent: fluency
Original:  Here, for the classification task, Decision Tree and Random Forestalgorithms where used, }  as well as a simple collective classification algorithm, which is also proposed in this paper.
Revised:  Here, for the classification task, Decision Trees, Random Forest, } as well as a simple collective classification algorithm, which is also proposed in this paper.
Edit intent: coherence
Original:  Here, for the classification task, Decision Tree and Random Forestalgorithms where used, }  as well as a simple collective classification algorithm, which is also proposed in this paper.
Revised:  Here, for the classification task, Decision Tree and Random Forestalgorithms where used, } as well as a collective classification algorithm, which is also proposed in this paper.
Edit intent: clarity
Original:  The used approach allowed to compare how traditional methods of machine learning classification, while supported by social network analysis, performed in comparison to a typical graph algorithm .
Revised:  The results demonstrate that the social network built using the metadata on communication highly exposes the organizational structure .
Edit intent: coherence
Original:       In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Revised: A graph In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Edit intent: clarity
Original:       In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Revised: G In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Edit intent: clarity
Original:       In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Revised:  V In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Edit intent: fluency
Original:       In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Revised:  , In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Edit intent: fluency
Original:       In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Revised:  E In 1 we used a cycle basis of the cycle space to represent a simple connected graph G. If G is Hamiltonian, then there exists a set of cycles to be a solution of Grinberg's Equation.
Edit intent: fluency