wanyu/IteraTeR_v2 huggingface dataset:
Original:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com \emph{  \emph{  .
Revised:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com \emph{ challenge by \emph{ .
Edit intent: clarity
Original:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com \emph{  \emph{  .
Revised:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com \emph{ \emph{Intro to Programming with Karel .
Edit intent: fluency
Original:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com \emph{  \emph{  .
Revised:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com \emph{ \emph{ course by .
Edit intent: coherence
Original:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com \emph{  \emph{  .
Revised:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com \emph{ \emph{ CodeHS.com .
Edit intent: fluency
Original:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of the Code: Classic Maze challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com.
Revised:  We demonstrate the effectiveness of our algorithm through an extensive empirical evaluation and user study on reference tasks taken from the Hour of challenge by Code.org and the Intro to Programming with Karel course by CodeHS.com.
Edit intent: clarity
Original: Active mobility , traditionally referring to modes requiring physical activity to operate, offers an array of physical, emotional, and social well-being benefits.
Revised: Active mobility offers an array of physical, emotional, and social well-being benefits.
Edit intent: clarity
Original: Active mobility , traditionally referring to modes requiring physical activity to operate, offers an array of physical, emotional, and social well-being benefits.
Revised: Active mobility , traditionally referring to modes requiring physical activity to operate, offers an array of physical, emotional, and social wellbeing benefits.
Edit intent: fluency
Original:  The purpose of this research  is to investigate the adoption of three active travel modes , namely walking, cycling and bike-sharing , in a joint modeling framework.
Revised:  The purpose of this research study is to investigate the adoption of three active travel modes , namely walking, cycling and bike-sharing , in a joint modeling framework.
Edit intent: clarity
Original:  The purpose of this research  is to investigate the adoption of three active travel modes , namely walking, cycling and bike-sharing , in a joint modeling framework.
Revised:  The purpose of this research is to investigate the adoption of three active travel modes ; namely walking, cycling and bike-sharing , in a joint modeling framework.
Edit intent: fluency
Original:  The purpose of this research  is to investigate the adoption of three active travel modes , namely walking, cycling and bike-sharing , in a joint modeling framework.
Revised:  The purpose of this research is to investigate the adoption of three active travel modes , namely walking, cycling and bikesharing , in a joint modeling framework.
Edit intent: fluency
Original:  The joint model structures reveal different levels of interdependence among active mobility choices.
Revised:  The research suggests three important findings. 1) The joint model structure confirms interdependence among different active mobility choices.
Edit intent: clarity
Original:  The strongest positive association is found for walking and cycling adoption , whereas other joint model effects are less evident.
Revised:  The strongest complementarity is found for walking and cycling adoption , whereas other joint model effects are less evident.
Edit intent: clarity
Original: For constructing neuronal network models computational neuroscientists have access to a wealth of anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined.
Revised: For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined.
Edit intent: clarity
Original:  Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on  mammalian cerebral cortex.
Revised:  Estimating connectivity where the data are lacking based on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on mammalian cerebral cortex.
Edit intent: coherence
Original:  Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on  mammalian cerebral cortex.
Revised:  Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex.
Edit intent: fluency
Original: We discuss the efficient implementation of a high-performance second-order colocation-type finite-element scheme for solving the compressible Euler equations of gas dynamics on unstructured meshes.
Revised: We discuss the efficient implementation of a high-performance second-order collocation-type finite-element scheme for solving the compressible Euler equations of gas dynamics on unstructured meshes.
Edit intent: fluency
Original:  40, A3211--A3239 , 2018).
Revised:  40, A3211-A3239 , 2018).
Edit intent: fluency
Original:  We discuss the algorithmic design that allows a SIMD vectorization of the compute kernel, analyze the node-level performance  and report excellent weak and strong scaling of a hybrid thread/MPI parallelization.
Revised:  We discuss the algorithmic design that allows SIMD vectorization of the compute kernel, analyze the node-level performance and report excellent weak and strong scaling of a hybrid thread/MPI parallelization.
Edit intent: fluency
Original:  FathomNet is a novel baseline image training set, optimized to accelerate development of modern, intelligent, and automated analysis of underwater imagery.
Revised:  FathomNet is a novel baseline image training set, optimized to accelerate development of modern, intelligent, and automated analysis of underwater imagery.
Edit intent: fluency
Original:  FathomNet leverages this data set by providing imagery, localizations, and class labels of underwater concepts in order to enable machine learning algorithm development.
Revised:  FathomNet leverages this data set by providing imagery, localizations, and class labels of underwater concepts in order to enable machine learning algorithm development.
Edit intent: fluency
Original: Maximum entropy null models of networks come in different flavors that depend on the type of the constraints under which entropy is maximized.
Revised: Maximum entropy null models of networks come in different flavors that depend on the type of constraints under which entropy is maximized.
Edit intent: coherence
Original:  If the degree sequence is constrained only on average, the corresponding grandcanonical ensemble of random graphs with a given expected degree sequence is the soft configuration model.
Revised:  If the degree sequence is constrained only on average, the corresponding grand-canonical ensemble of random graphs with a given expected degree sequence is the soft configuration model.
Edit intent: fluency
Original:  As a by-product , we generalize the notions of sparse graphons and their entropy to weighted networks.
Revised:  As a byproduct , we generalize the notions of sparse graphons and their entropy to weighted networks.
Edit intent: fluency
Original:  We consider segmental models for whole-word ("acoustic-to-word") speech recognition, with the segment feature vectors defined using acoustic word embeddings .
Revised:  We consider segmental models for whole-word ("acoustic-to-word") speech recognition, with the feature vectors defined using acoustic word embeddings .
Edit intent: clarity
Original:  We consider segmental models for whole-word ("acoustic-to-word") speech recognition, with the segment feature vectors defined using acoustic word embeddings .
Revised:  We consider segmental models for whole-word ("acoustic-to-word") speech recognition, with the segment feature vectors defined using vector embeddings of segments .
Edit intent: clarity
Original:  Our final models improve over comparable A2W models.
Revised:  Our final models improve over prior A2W models.
Edit intent: clarity
Original: In this paper, we study a posteriori error estimators which aid multilevel iterative solvers for linear systems of graph Laplacians.
Revised: In this paper, we study a posteriori error estimators which aid multilevel iterative solvers for linear systems with graph Laplacians.
Edit intent: fluency
Original:  In earlier works such estimates were computed by solving a perturbed global optimization problem , which could be computationally expensive.
Revised:  In earlier works such estimates were computed by solving global optimization problems , which could be computationally expensive.
Edit intent: clarity
Original:  To compute the error estimator, we solve efficiently a linear system on the spanning tree and then a least-squares problem on the cycle space.
Revised:  To compute the error estimator, we solve efficiently the linear system on the spanning tree and then a least-squares problem on the cycle space.
Edit intent: fluency
Original:  To compute the error estimator, we solve efficiently a linear system on the spanning tree and then a least-squares problem on the cycle space.
Revised:  To compute the error estimator, we solve efficiently a linear system on the spanning tree , and then we solve approximately a least-squares problem on the cycle space.
Edit intent: clarity
Original:  As we show, such  estimator has a nearly-linear computational complexity for sparse graphs under certain assumptions.
Revised:  As we show, such an estimator has a nearly-linear computational complexity for sparse graphs under certain assumptions.
Edit intent: fluency
Original:  As a byproduct, we derive an algorithm to learn any mixture of two MNLs in linear time provided that a mixture of two MNLs over some finite universe is identifiable.
Revised:  We also devise an algorithm to learn any mixture of two MNLs in linear time provided that a mixture of two MNLs over some finite universe is identifiable.
Edit intent: clarity
Original: With the success of deep learning in speech signal processing, speaker-independent speech separation under the reverberant environment remains challenging.
Revised: With the success of deep learning in speech signal processing, speaker-independent speech separation under a reverberant environment remains challenging.
Edit intent: fluency
Original:  The deep attractor network (DAN) performs speech separation with speaker attractor, but it is conducted in the time-frequency domain , which is not optimal .
Revised:  The deep attractor network (DAN) performs speech separation with speaker attractors on the time-frequency domain , which is not optimal .
Edit intent: clarity
Original:  The deep attractor network (DAN) performs speech separation with speaker attractor, but it is conducted in the time-frequency domain , which is not optimal .
Revised:  The deep attractor network (DAN) performs speech separation with speaker attractor, but it is conducted in the time-frequency domain .
Edit intent: clarity
Original:  In this study, we propose a time-domain deep attractor network (TD-DAN) with two-stream convolutional networks , which efficiently performs both dereverberation and separation tasks under the condition of variable numbers of speakers.
Revised:  In this study, we propose a time-domain deep attractor network (TD-DAN) with two-stream convolutional networks that efficiently performs both dereverberation and separation tasks under the condition of variable numbers of speakers.
Edit intent: coherence
Original:  The speaker encoding stream (SES) of  TD-DAN models speaker information, and is explored with various waveform encoders.
Revised:  The speaker encoding stream (SES) of the TD-DAN models speaker information, and is explored with various waveform encoders.
Edit intent: fluency
Original:  Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40/9.78 dB and 9.15/7.92 dB on the reverberant 2- and 3-speaker development/evaluation set, exceeding Conv-TasNet  1.55/1.33 dB and 0.94/1.21 dB, respectively.
Revised:  Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40/9.78 dB and 9.15/7.92 dB on the reverberant two- and three-speaker development/evaluation set, exceeding Conv-TasNet 1.55/1.33 dB and 0.94/1.21 dB, respectively.
Edit intent: clarity
Original:  Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40/9.78 dB and 9.15/7.92 dB on the reverberant 2- and 3-speaker development/evaluation set, exceeding Conv-TasNet  1.55/1.33 dB and 0.94/1.21 dB, respectively.
Revised:  Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40/9.78 dB and 9.15/7.92 dB on the reverberant 2- and 3-speaker development/evaluation set, exceeding Conv-TasNet by 1.55/1.33 dB and 0.94/1.21 dB, respectively.
Edit intent: clarity
Original:  The recently proposed convolutional time-domain audio separation network (Conv-TasNet) surpasses ideal masks in anechoic mixture signals, while its architecture renders the problem of separating signals with variable numbers of speakers.
Revised:  The recently proposed convolutional time-domain audio separation network (Conv-TasNet) surpasses ideal masks in anechoic mixture signals, while its architecture renders the problem of separating signals with arbitrary numbers of speakers.
Edit intent: clarity
Original:  In this study , we propose a time-domain deep attractor network (TD-DAN) with two-stream convolutional networksthat efficiently performs both dereverberation and separation tasks under the condition of variable numbers of speakers.
Revised:  In this study , we propose a time-domain DAN (TD-DAN) with two-stream convolutional networksthat efficiently performs both dereverberation and separation tasks under the condition of variable numbers of speakers.
Edit intent: clarity
Original:  In this study , we propose a time-domain deep attractor network (TD-DAN) with two-stream convolutional networksthat efficiently performs both dereverberation and separation tasks under the condition of variable numbers of speakers.
Revised:  In this study , we propose a time-domain deep attractor network (TD-DAN) with two-streams of convolutional networks, which efficiently perform both dereverberation and separation tasks under the condition of variable numbers of speakers.
Edit intent: clarity
Original:  In this study , we propose a time-domain deep attractor network (TD-DAN) with two-stream convolutional networksthat efficiently performs both dereverberation and separation tasks under the condition of variable numbers of speakers.
Revised:  In this study , we propose a time-domain deep attractor network (TD-DAN) with two-stream convolutional networksthat efficiently performs both dereverberation and separation tasks under the condition of a variable number of speakers.
Edit intent: fluency
Original:  The speech decoding steam (SDS) accepts speaker attractors from SES, and learns to predict early reflections . Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40 / 9.78 dB and 9.15/7.92 dB on the reverberant two- and three-speaker development /  evaluation set, exceeding  Conv-TasNet by 1.55 / 1.33 dB and 0.94 / 1.21 dB, respectively.
Revised:  The speech decoding stream (SDS) accepts speaker attractors from SES, and learns to predict early reflections . Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40 / 9.78 dB and 9.15/7.92 dB on the reverberant two- and three-speaker development / evaluation set, exceeding Conv-TasNet by 1.55 / 1.33 dB and 0.94 / 1.21 dB, respectively.
Edit intent: fluency
Original:  The speech decoding steam (SDS) accepts speaker attractors from SES, and learns to predict early reflections . Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40 / 9.78 dB and 9.15/7.92 dB on the reverberant two- and three-speaker development /  evaluation set, exceeding  Conv-TasNet by 1.55 / 1.33 dB and 0.94 / 1.21 dB, respectively.
Revised:  The speech decoding steam (SDS) accepts speaker attractors from the SES and learns to predict early reflections . Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40 / 9.78 dB and 9.15/7.92 dB on the reverberant two- and three-speaker development / evaluation set, exceeding Conv-TasNet by 1.55 / 1.33 dB and 0.94 / 1.21 dB, respectively.
Edit intent: fluency
Original:  The speech decoding steam (SDS) accepts speaker attractors from SES, and learns to predict early reflections . Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40 / 9.78 dB and 9.15/7.92 dB on the reverberant two- and three-speaker development /  evaluation set, exceeding  Conv-TasNet by 1.55 / 1.33 dB and 0.94 / 1.21 dB, respectively.
Revised:  The speech decoding steam (SDS) accepts speaker attractors from SES, and learns to predict early reflections . Experiment results demonstrated that the TD-DAN achieved scale-invariant source-to-distortion ratio (SI-SDR) gains of 10.40 / 9.78 dB and 9.15/7.92 dB on the reverberant 2 / evaluation set, exceeding Conv-TasNet by 1.55 / 1.33 dB and 0.94 / 1.21 dB, respectively.
Edit intent: clarity
Original:  Such  highly developed source model can effectively solve the permutation problem of the frequency-domain BSS ,
Revised:  Such a highly developed source model can effectively solve the permutation problem of the frequency-domain BSS ,
Edit intent: fluency
Original:  Such  highly developed source model can effectively solve the permutation problem of the frequency-domain BSS ,
Revised:  Such highly developed source model can solve the permutation problem of the frequency-domain BSS ,
Edit intent: clarity
Original:  which should be the reason of the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrogram called consistency, and hence we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other.
Revised:  which should be the reason of the excellence of ILRMA. In this paper, we further improve the separation performance of ILRMA by additionally considering the general structure of spectrograms, which is called consistency, and hence we call the proposed method Consistent ILRMA. Since a spectrogram is calculated by an overlapping window (and a window function induces spectral smearing called main- and side-lobes), the time-frequency bins depend on each other.
Edit intent: coherence
Original:  In other words, the time-frequency components are related  each other via the uncertainty principle.
Revised:  In other words, the time-frequency components are related to each other via the uncertainty principle.
Edit intent: fluency
Original:  Such co-occurrence among the spectral components can be an assistant for solving the permutation problem, which has been demonstrated by a recent study.
Revised:  Such co-occurrence among the spectral components can function as an assistant for solving the permutation problem, which has been demonstrated by a recent study.
Edit intent: clarity
Original:  Based on these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm.
Revised:  On the basis of these facts, we propose an algorithm for realizing Consistent ILRMA by slightly modifying the original algorithm.
Edit intent: fluency
Original:  Its performance was extensively studied through the experiments performed with various window lengths and shift lengths.
Revised:  Its performance was extensively evaluated through experiments performed with various window lengths and shift lengths.
Edit intent: clarity
Original:  The results indicated several tendencies of the original and proposed ILRMA which include some topics have not discussed well in the literature.
Revised:  The results indicated several tendencies of the original and proposed ILRMA that include some topics have not discussed well in the literature.
Edit intent: fluency
Original:  The results indicated several tendencies of the original and proposed ILRMA which include some topics have not discussed well in the literature.
Revised:  The results indicated several tendencies of the original and proposed ILRMA which include some topics not fully discussed in the literature.
Edit intent: clarity
Original: The planted p-spin interaction model is a paradigm of random-graph systems possessing both a ferromagnetic ground state and an intermediate spin glass phase . Conventional simulated annealing and message-passing algorithms could not reach the planted ground state but are trapped by an exponential number of spin glass states.
Revised: The planted p-spin interaction model is a paradigm of random-graph systems possessing both a ferromagnetic ground state and an intermediate spin glass phase . Conventional simulated annealing dynamics is easily blocked by these spin glass states.
Edit intent: clarity