wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Therefore, this paper provides quantitative evidence which demonstrates how current 5G policy affects universal broadband, as well as drawing conclusions over how 4G and 5G decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies.
Revised:  Therefore, this paper undertakes quantitative assessment demonstrating how current 5G policy affects universal broadband, as well as drawing conclusions over how 4G and 5G decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies.
Edit intent: clarity
Original:  Therefore, this paper provides quantitative evidence which demonstrates how current 5G policy affects universal broadband, as well as drawing conclusions over how 4G and 5G decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies.
Revised:  Therefore, this paper provides quantitative evidence which demonstrates how current 5G policies affect universal broadband, as well as drawing conclusions over how 4G and 5G decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies.
Edit intent: fluency
Original:  Therefore, this paper provides quantitative evidence which demonstrates how current 5G policy affects universal broadband, as well as drawing conclusions over how 4G and 5G decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies.
Revised:  Therefore, this paper provides quantitative evidence which demonstrates how current 5G policy affects universal broadband, as well as drawing conclusions over how decisions made now affect future evolution to 6G. Using a method based on an open-source techno-economic codebase, combining remote sensing with least-cost network algorithms, performance analytics are provided for different 4G and 5G universal broadband strategies.
Edit intent: fluency
Original:  To map such lines in a real-time event stream, we introduce HoughCeption , a novel consecutive iterative event-based Hough transform framework capable of detecting, tracking, and triangulating close-by structures.
Revised:  To map such lines in a real-time event stream, we introduce Hough2Map , a novel consecutive iterative event-based Hough transform framework capable of detecting, tracking, and triangulating close-by structures.
Edit intent: meaning-changed
Original: The Barabasi-Albert model is a very popular model for creating random scale-free graphs . Despite its widespread use, there is a subtle ambiguity in the definition of the model and, consequently, the dependent models and applications.
Revised: The Barab\'asi-Albert model is a very popular model for creating random scale-free graphs . Despite its widespread use, there is a subtle ambiguity in the definition of the model and, consequently, the dependent models and applications.
Edit intent: fluency
Original: The Barabasi-Albert model is a very popular model for creating random scale-free graphs . Despite its widespread use, there is a subtle ambiguity in the definition of the model and, consequently, the dependent models and applications.
Revised: The Barabasi-Albert model is a popular scheme for creating scale-free graphs . Despite its widespread use, there is a subtle ambiguity in the definition of the model and, consequently, the dependent models and applications.
Edit intent: clarity
Original: The Barabasi-Albert model is a very popular model for creating random scale-free graphs . Despite its widespread use, there is a subtle ambiguity in the definition of the model and, consequently, the dependent models and applications.
Revised: The Barabasi-Albert model is a very popular model for creating random scale-free graphs but has been previously shown to have ambiguities in its definition. In this paper we discuss a new ambiguity in the definition of the model and, consequently, the dependent models and applications.
Edit intent: meaning-changed
Original: The Barabasi-Albert model is a very popular model for creating random scale-free graphs . Despite its widespread use, there is a subtle ambiguity in the definition of the model and, consequently, the dependent models and applications. This ambiguity is a result of the model's tight relation with the field of unequal probability random sampling , which dictates the exact process of edge creation after a newborn node has been added.
Revised: The Barabasi-Albert model is a very popular model for creating random scale-free graphs . Despite its widespread use, there is a subtle ambiguity in the definition of the BA model by identifying the tight relation between the preferential attachment process and unequal probability random sampling , which dictates the exact process of edge creation after a newborn node has been added.
Edit intent: meaning-changed
Original:  This ambiguity is a result of the model's tight relation with the field of unequal probability random sampling , which dictates the exact process of edge creation after a newborn node has been added. In this work, we identify the ambiguity, assess its impact and propose a more precise definition of the Barabasi-Albert model that is expressed as an application of unequal probability random sampling  .
Revised:  This ambiguity is a result of the model's tight relation with the field of unequal probability random sampling . While the probability that each individual vertex is selected is set to be proportional to their degree, the model does not specify the joint probabilities that any tuple of m vertices is selected together for m>1. We demonstrate the consequences using analytical, experimental, and empirical analyses and propose a concise definition of the Barabasi-Albert model that is expressed as an application of unequal probability random sampling .
Edit intent: meaning-changed
Original:  In this work, we identify the ambiguity, assess its impact and propose a more precise definition of the Barabasi-Albert model that is expressed as an application of unequal probability random sampling  .
Revised:  In this work, we identify the ambiguity, assess its impact and propose a more precise definition of the Barabasi-Albert model that is expressed as an application of unequal probability random sampling , we also highlight a confusion about the process via which nodes are selected on each time step, for which -- despite being implicitly indicated in the original paper -- current literature appears fragmented .
Edit intent: meaning-changed
Original:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Revised:  We implemented four different machine learning algorithms typically used in time-series classification problems: ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Edit intent: coherence
Original:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Revised:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Edit intent: fluency
Original:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Revised:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) , ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Edit intent: coherence
Original:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Revised:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Edit intent: fluency
Original:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Revised:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) , andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Edit intent: clarity
Original:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Revised:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network Attention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Edit intent: fluency
Original:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andAttention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Revised:  We implemented four different machine learning algorithms typically used in time-series classification problems: Multi Layers Perceptron (MLP) ,Convolutional Neural Network (CNN) ,Long Short Term Memory (LSTM) neural network andMulti Layers Perceptron (MLP), Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) neural network and Attention Long Short Term Memory (ALSTM) . We devised the experiments using the advanced bootstrap technique to consider the variance problem on test samples, which allowed us to evaluate a more reliable estimate of the model's performance.
Edit intent: meaning-changed
Original: We analyze steady interface shapes in zero gravity in rotating right circular cylindrical containers under rigid body rotation  . Predictions are made near criticality, in which the interface, or part thereof, becomes straight and parallel to the axis of rotation.
Revised: We analyze steady interface shapes in rotating right-circular cylindrical containers under rigid body rotation . Predictions are made near criticality, in which the interface, or part thereof, becomes straight and parallel to the axis of rotation.
Edit intent: clarity
Original: We analyze steady interface shapes in zero gravity in rotating right circular cylindrical containers under rigid body rotation  . Predictions are made near criticality, in which the interface, or part thereof, becomes straight and parallel to the axis of rotation.
Revised: We analyze steady interface shapes in zero gravity in rotating right circular cylindrical containers under rigid body rotation in zero gravity . Predictions are made near criticality, in which the interface, or part thereof, becomes straight and parallel to the axis of rotation.
Edit intent: clarity
Original:  We  propose the "Fairness Compass", a tool which formalises the selection process and makes identifying the most appropriate fairness metric for a given system a simple, straightforward procedure.
Revised:  We first explain the different options by example. Furthermore, we propose the "Fairness Compass", a tool which formalises the selection process and makes identifying the most appropriate fairness metric for a given system a simple, straightforward procedure.
Edit intent: meaning-changed
Original:  Some of them are in conflict with each other and there is no uniformly accepted notion of fairness . The most appropriate fairness definition for an artificial intelligence system is often a matter of application and the right choice depends on ethical standards and legal requirements.
Revised:  Some of them are in conflict with each other and there is no uniformly accepted notion of fairness , choosing the right kind for a specific situation has always been a central issue in human history. When it comes to implementing sustainable fairness in artificial intelligence systems, this old question plays a key role once again: How to identify the most appropriate fairness definition for an artificial intelligence system is often a matter of application and the right choice depends on ethical standards and legal requirements.
Edit intent: meaning-changed
Original:  Our contributions are to formalize the problem, find the theoretically optimal revenue attribution scheme for anyreasonable conversion value scheme and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
Revised:  Our contributions are to formalize the problem, find the theoretically optimal revenue attribution reasonable conversion value scheme and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
Edit intent: clarity
Original:  Our contributions are to formalize the problem, find the theoretically optimal revenue attribution scheme for anyreasonable conversion value scheme and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
Revised:  Our contributions are to formalize the problem, find the theoretically optimal revenue attribution scheme for any conversion value scheme and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
Edit intent: fluency
Original:  Our contributions are to formalize the problem, find the theoretically optimal revenue attribution scheme for anyreasonable conversion value scheme and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
Revised:  Our contributions are to formalize the problem, find the theoretically optimal revenue attribution scheme for anyreasonable function for any conversion value schema, and show empirical results on past data of a free-to-play mobile game using different conversion value schemas.
Edit intent: clarity
Original:  Finally we demonstrate a 6-18 \times speedup versus CPU in an X-ray diffraction 3D iterative reconstruction task at 10^{-12} accuracy, observing excellent multi-GPU weak scaling up to one rank per GPU.
Revised:  Finally we demonstrate a 5-12 \times speedup versus CPU in an X-ray diffraction 3D iterative reconstruction task at 10^{-12} accuracy, observing excellent multi-GPU weak scaling up to one rank per GPU.
Edit intent: meaning-changed
Original:  Graph Representation Learning (GRL) has been advancing at an unprecedented rate. However, many results rely on careful design and tuning of architectures, objectives, and training schemes. We propose efficient GRL methods that optimize convexified objectives with known closed form solutions. Guaranteed convergence to a global optimum releases practitioners from hyper-parameter and architecture tuning. Nevertheless, our proposed method achieves competitive or state-of-the-art performance on popular GRL tasks while providing orders of magnitude speedup.
Revised:  Graph Representation Learning (GRL) methods: message passing for node classification and network embedding for link prediction. For each, we pick a popular model that we: (i) linearize and (ii) and switch its training objective to Frobenius norm error minimization. These simplifications can cast the training into finding the optimal parameters in closed-form. We program in TensorFlow a functional form of Truncated Singular Value Decomposition (SVD), such that, we could decompose a dense matrix \mathbf{M performance on popular GRL tasks while providing orders of magnitude speedup.
Edit intent: meaning-changed
Original:  To help users better characterize and reason about a model's uncertainty, we visualize raw and aggregate information about a given input's nearest neighbors in the training dataset . Using an interactive editor, users can manipulate this input in semantically-meaningful ways, determine the effect on the output, and compare against their prior expectations.
Revised:  To help users better characterize and reason about a model's uncertainty, we visualize raw and aggregate information about a given input's nearest neighbors . Using an interactive editor, users can manipulate this input in semantically-meaningful ways, determine the effect on the output, and compare against their prior expectations.
Edit intent: clarity
Original:  It offers novel and intuitive insights on Q-learning mainly based on control theoretic frameworks. For instance, the proposed analysis explains the overestimation phenomenon in Q-learning due to the maximization bias . Based on the control system theoretic argument and some nice structures of Q-learning , a new finite-time analysis of the Q-learning is given with a novel error bound .
Revised:  It offers novel and intuitive insights on Q-learning estimation error is over- and underestimated by trajectories of two dynamical systems . Based on the control system theoretic argument and some nice structures of Q-learning , a new finite-time analysis of the Q-learning is given with a novel error bound .
Edit intent: meaning-changed
Original:  For instance, the proposed analysis explains the overestimation phenomenon in Q-learning due to the maximization bias . Based on the control system theoretic argument and some nice structures of Q-learning , a new finite-time analysis of the Q-learning is given with a novel error bound .
Revised:  For instance, the proposed analysis explains the overestimation phenomenon in Q-learning due to the maximization bias . Based on the schemes , a new finite-time analysis of the Q-learning is given with a novel error bound .
Edit intent: clarity
Original:  For instance, the proposed analysis explains the overestimation phenomenon in Q-learning due to the maximization bias . Based on the control system theoretic argument and some nice structures of Q-learning , a new finite-time analysis of the Q-learning is given with a novel error bound .
Revised:  For instance, the proposed analysis explains the overestimation phenomenon in Q-learning due to the maximization bias . Based on the control system theoretic argument and some nice structures of Q-learning , a new finite-time analysis of the Q-learning is given with a finite-time error bound. It offers novel intuitive insights on analysis of Q-learning mainly based on control theoretic frameworks. By filling the gap between both domains in a synergistic way, this approach can potentially facilitate further progress in each field .
Edit intent: meaning-changed
Original: This paper develops a novel  framework to analyze the  convergence of Q-learning algorithm by using its connections to dynamical systems. We prove that asynchronous Q-learning with a constant step-size can be naturally formulated as  discrete-time stochastic switched linear systems .
Revised: This paper develops a novel framework to analyze the convergence of Q-learning . We show that the dynamics of asynchronous Q-learning with a constant step-size can be naturally formulated as discrete-time stochastic switched linear systems .
Edit intent: clarity
Original:  Based on the schemes, a new finite-time analysis of the Q-learning is given with a finite-time error bound. It offers novel intuitive insights on analysis of Q-learning mainly based on control theoretic frameworks.
Revised:  Based on the schemes, a new finite-time analysis of the Q-learning when a constant stepsize is used. Our analysis also sheds light on the overestimation phenomenon of Q-learning mainly based on control theoretic frameworks.
Edit intent: meaning-changed