wanyu/IteraTeR_v2 huggingface dataset:
Original:  This approach , Deep Filtering, was initially demonstrated using simulated LIGO noise.
Revised:  This method , Deep Filtering, was initially demonstrated using simulated LIGO noise.
Edit intent: clarity
Original:  In this article, we present the extension of Deep Filtering using real noise from the first observing run of LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers with continuous data streams from multiple LIGO detectors.
Revised:  In this article, we present the extension of Deep Filtering using real data from the first observing run of LIGO, for both detection and parameter estimation of gravitational waves from binary black hole mergers with continuous data streams from multiple LIGO detectors.
Edit intent: clarity
Original:  This framework is uniquely suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.
Revised:  This approach is uniquely suited to enable coincident detection campaigns of gravitational waves and their multimessenger counterparts in real-time.
Edit intent: clarity
Original: There are increasingly real-time live applications in virtual reality, where it plays an important role to capture and retarget 3D human pose.
Revised: There are increasing real-time live applications in virtual reality, where it plays an important role to capture and retarget 3D human pose.
Edit intent: fluency
Original: There are increasingly real-time live applications in virtual reality, where it plays an important role to capture and retarget 3D human pose.
Revised: There are increasingly real-time live applications in virtual reality, where it plays an important role in capturing and retargetting 3D human pose.
Edit intent: fluency
Original:  By incorporating hierarchical kinematics model of human pose into the learning procedure, we can directly estimate  3D joint angles instead of joint positions.
Revised:  By incorporating hierarchical kinematics model of human pose into the learning procedure, we can directly estimate accurate 3D joint angles instead of joint positions.
Edit intent: clarity
Original:  In our comparison against alternative pose estimation methods and commercial system such as Kinect 2017, we achieve  state-of-the-art accuracy.
Revised:  In our comparison against alternative pose estimation methods and commercial system such as Kinect 2017, we achieve the state-of-the-art accuracy.
Edit intent: fluency
Original:  In this work we generalize symbolic execution to support program analysis for relational properties, namely properties about two programs, or about two executions of a single program on different inputs.
Revised:  In this work we generalize symbolic execution to support program analysis for relational properties, that is properties about two programs, or about two executions of a single program on different inputs.
Edit intent: clarity
Original: Symbolic execution is a classical program analysis technique , widely used for program testing and bug finding .
Revised: Symbolic execution is a classical program analysis technique used to show that programs satisfy or violate given specifications .
Edit intent: clarity
Original:  In this work we generalize symbolic execution to support program analysis for relational  properties , that is properties about two programs , or about two executions of a single program on different inputs.
Revised:  In this work we generalize symbolic execution to support program analysis for relational properties about two programs , or about two executions of a single program on different inputs.
Edit intent: coherence
Original:  We design a relational symbolic execution engine, named RelSym , which supports bug finding as well as proving of relational properties for programs written in an imperative programming language with arrays and always terminating for-like loops.
Revised:  We design a relational symbolic execution engine, named RelSym , which supports bug finding as well as proving of relational properties for programs written in a language with arrays and always terminating for-like loops.
Edit intent: clarity
Original:  We design a relational symbolic execution engine, named RelSym , which supports bug finding as well as proving of relational properties for programs written in an imperative programming language with arrays and always terminating for-like loops.
Revised:  We design a relational symbolic execution engine, named RelSym , which supports bug finding as well as proving of relational properties for programs written in an imperative programming language with arrays and for-like loops.
Edit intent: clarity
Original:  Geometry acts collectively through the evolving space available to the population of cells, but it may also act on the individual behaviours of cells.
Revised:  Geometry modulates cell populations collectively through the evolving space available to the population of cells, but it may also act on the individual behaviours of cells.
Edit intent: clarity
Original:  Geometry acts collectively through the evolving space available to the population of cells, but it may also act on the individual behaviours of cells.
Revised:  Geometry acts collectively through the evolving space available to the cells, but it may also act on the individual behaviours of cells.
Edit intent: clarity
Original:  Geometry acts collectively through the evolving space available to the population of cells, but it may also act on the individual behaviours of cells.
Revised:  Geometry acts collectively through the evolving space available to the population of cells, but it may also modulate the individual behaviours of cells.
Edit intent: clarity
Original: Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are the most salient.
Revised: Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient.
Edit intent: fluency
Original: Choreographies are widely used both for the specification and the programming of concurrent and distributed software architectures.
Revised: Choreographies are widely used for the specification and the programming of concurrent and distributed software architectures.
Edit intent: clarity
Original: Choreographies are widely used both for the specification and the programming of concurrent and distributed software architectures.
Revised: Choreographies are widely used both for the specification of concurrent and distributed software architectures.
Edit intent: clarity
Original:  Instead, we can just encode asynchronous communications in choreographies themselves, yielding a simpler approach .
Revised:  The practical consequence of our results is that we can reason about real-world systems within a choreography formalism that is simpler than those hitherto proposed .
Edit intent: clarity
Original:  In light of these reductions, our hardness results can be seen as a generalization of both a classical result by Kozen and a theorem by Beaudry, McKenzie and Th\'erien .
Revised:  In light of these reductions, our hardness results can be seen as a generalization of both a classical result by Kozen and a theorem by Beaudry, McKenzie and Therien .
Edit intent: fluency
Original: Critical periods are phases in the early development of humans and animals during which experience can affect the structure of neuronal networks irreversibly .
Revised: Critical periods are phases in the early development of humans and animals during which experience can irreversibly affect the architecture of neuronal networks irreversibly .
Edit intent: clarity
Original: Critical periods are phases in the early development of humans and animals during which experience can affect the structure of neuronal networks irreversibly .
Revised: Critical periods are phases in the early development of humans and animals during which experience can affect the structure of neuronal networks .
Edit intent: clarity
Original:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural network causes irreversible performance loss that closely mimics that reported in humans and animal models.
Revised:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural network causes a permanent performance loss that closely mimics that reported in humans and animal models.
Edit intent: clarity
Original:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural network causes irreversible performance loss that closely mimics that reported in humans and animal models.
Revised:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural network causes irreversible performance loss that closely mimics critical period behavior in humans and animal models.
Edit intent: clarity
Original:  Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANN' s performance and can be rapidly overcome with additional training, as observed in humans .
Revised:  Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANNs' performance and can be rapidly overcome with additional training, as observed in humans .
Edit intent: fluency
Original:  Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANN' s performance and can be rapidly overcome with additional training, as observed in humans .
Revised:  Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANN' s performance and can be rapidly overcome with further training .
Edit intent: coherence
Original:  In addition, deeper networks show a more prominent critical period.
Revised:  In addition, the deeper the ANN is, the more pronounced the critical period.
Edit intent: clarity
Original:  To better understand this phenomenon, we use techniques from information theory to study the strength of the network connections during training.
Revised:  To better understand this phenomenon, we use techniques from information theory to study the strength of the network 's connections during the training.
Edit intent: clarity
Original:  Our  analysis suggests that the first few epochs are critical for the allocation of resources across different layers, determined by the initial input data distribution.
Revised:  Our analysis suggests that the first few epochs are critical for the allocation of resources across different layers, optimal for processing the input data distribution.
Edit intent: clarity
Original:  Once such information organization is established, the network resources do not re-distribute through additional training.
Revised:  Once such strong connections are created, they do not appear to change during additional training.
Edit intent: clarity
Original:  These findings suggest that the initial rapid learning phase of training of ANNs , under-scrutinized compared to its asymptotic behavior, plays a key role in defining the final performance of networks  .
Revised:  These findings suggest that the initial rapid learning phase of ANN training , under-scrutinized compared to its asymptotic behavior, plays a key role in defining the final performance of networks .
Edit intent: clarity
Original: Critical periods are phases in the early development of humans and animals during which experience can irreversibly affect the architecture of neuronal networks.
Revised: Similar to humans and animals during which experience can irreversibly affect the architecture of neuronal networks.
Edit intent: clarity
Original:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural networkcauses a permanent performance loss that closely mimics critical period behavior in humans and animal models . Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANNs' performance and can be rapidly overcome with further training.
Revised:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural networkcauses a permanent performance loss that closely mimics critical period behavior in humans and animal models . Deficits that do not affect low-level statistics, such as vertical flipping of the images, have no lasting effect on the ANNs' performance and can be rapidly overcome with further training.
Edit intent: fluency
Original:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural networkcauses a permanent performance loss that closely mimics critical period behavior in humans and animal models . Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANNs' performance and can be rapidly overcome with further training.
Revised:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural networkcauses a permanent performance loss that closely mimics critical period behavior in humans and animal models . Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on performance and can be rapidly overcome with further training.
Edit intent: clarity
Original:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural networkcauses a permanent performance loss that closely mimics critical period behavior in humans and animal models . Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANNs' performance and can be rapidly overcome with further training.
Revised:  Introducing well-characterized visual deficits, such as cataract-like blurring, in the early training phase of a standard deep neural networkcauses a permanent performance loss that closely mimics critical period behavior in humans and animal models . Deficits that do not affect low-level image statistics, such as vertical flipping of the images, have no lasting effect on the ANNs' performance and can be overcome with further training.
Edit intent: clarity
Original:  In addition, the deeper the ANN is, the more pronounced the critical period.
Edit intent: coherence
Original:  To better understand this phenomenon, we use Fisher Information as a measure of the strength of the network's connections during the training.
Revised:  To better understand this phenomenon, we use the Fisher Information of the training.
Edit intent: clarity
Original:  Our information-theoretic analysis suggests that the first few epochs are critical for the creation of strong connections across different layers, optimal for processing the input data distribution.
Revised:  Our information-theoretic analysis suggests that the first few epochs are critical for the creation of strong connections that are optimal relative to the input data distribution.
Edit intent: clarity
Original:  These findings suggest that the initial rapid learning phase of ANN training , under-scrutinized compared to its asymptotic behavior, plays a key role in defining the final performance of networks.
Revised:  These findings suggest that the initial learning transient , under-scrutinized compared to its asymptotic behavior, plays a key role in defining the final performance of networks.
Edit intent: clarity
Original:  These findings suggest that the initial rapid learning phase of ANN training , under-scrutinized compared to its asymptotic behavior, plays a key role in defining the final performance of networks.
Revised:  These findings suggest that the initial rapid learning phase of ANN training , under-scrutinized compared to asymptotic behavior, plays a key role in defining the final performance of networks.
Edit intent: clarity
Original:  In state-of-the-art deep HDR imaging such as 13%DIFDELCMD < ]%%% , the problem is formulated as an image composition problem, by first aligning input images using optical flows  which are still error-prone due to occlusion and large motions.
Revised:  In state-of-the-art deep HDR imaging 13%DIFDELCMD < ]%%% , the problem is formulated as an image composition problem, by first aligning input images using optical flows which are still error-prone due to occlusion and large motions.
Edit intent: clarity
Original:  In state-of-the-art deep HDR imaging such as 13%DIFDELCMD < ]%%% , the problem is formulated as an image composition problem, by first aligning input images using optical flows  which are still error-prone due to occlusion and large motions.
Revised:  In state-of-the-art deep HDR imaging such as %DIFDELCMD < ]%%% , the problem is formulated as an image composition problem, by first aligning input images using optical flows which are still error-prone due to occlusion and large motions.
Edit intent: clarity
Original:  In state-of-the-art deep HDR imaging such as 13%DIFDELCMD < ]%%% , the problem is formulated as an image composition problem, by first aligning input images using optical flows  which are still error-prone due to occlusion and large motions.
Revised:  In state-of-the-art deep HDR imaging such as 13%DIFDELCMD < ]%%% , input images are first aligned using optical flows which are still error-prone due to occlusion and large motions.
Edit intent: coherence
Original:  In our end-to-end approach, HDR imaging is formulated as an image translation problem and no optical flowsare used .
Revised:  In stark contrast to flow-based methods, we formulate HDR imaging as an image translation problem and no optical flowsare used .
Edit intent: clarity
Original:  In our end-to-end approach, HDR imaging is formulated as an image translation problem and no optical flowsare used .
Revised:  In our end-to-end approach, HDR imaging is formulated as an image translation problem without optical flows .
Edit intent: clarity
Original:  We perform extensive qualitative and quantitative comparisons to show that our end-to-end HDR approach produces excellent results where color artifacts and geometry distortion are significantly reduced compared with existing state-ofthe-art methods .
Revised:  We perform extensive qualitative and quantitative comparisons to show that our end-to-end HDR approach produces excellent results where color artifacts and geometric distortions are significantly reduced compared with existing state-ofthe-art methods .
Edit intent: fluency
Original:  Two significant contributions are made in the audio branch, as follows: i) pre-trained CNN followed by fully-connected layers is investigated for representing music audio. ii) We further suggest an end-to-end architecture that simultaneously train convolutional layers and fully-connected layers to better learn temporal structures of music audio.
Revised:  Two significant contributions are made in the audio branch, as follows: i) pre-trained CNN followed by fully-connected layers is investigated for representing music audio. ii) We further suggest an end-to-end architecture that simultaneously trains convolutional layers and fully-connected layers to better learn temporal structures of music audio.
Edit intent: fluency
Original:  The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network.
Revised:  The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network.
Edit intent: fluency
Original:  Our algorithm employs posterior sampling to actively trade off exploitation (selecting news that directly maximize the objective value at a given epoch) and exploration (selecting news that maximize the value of information towards learning about users' flagging accuracy).
Revised:  Our algorithm employs posterior sampling to actively trade off exploitation (selecting news that maximize the objective value at a given epoch) and exploration (selecting news that maximize the value of information towards learning about users' flagging accuracy).
Edit intent: clarity
Original:  We demonstrate the effectiveness of our approach via extensive experiments and show the power of leveraging community signals  .
Revised:  We demonstrate the effectiveness of our approach via extensive experiments and show the power of leveraging community signals for fake news detection .
Edit intent: clarity
Original:  During a particular phase of the retina development (stage II ) these waves are triggered by a transient network of neurons  called Starburst Amacrine Cells (SACs)  showing a bursting activity which disappears upon further maturation.
Revised:  During the stage II these waves are triggered by a transient network of neurons called Starburst Amacrine Cells (SACs) showing a bursting activity which disappears upon further maturation.
Edit intent: clarity
Original:  During a particular phase of the retina development (stage II ) these waves are triggered by a transient network of neurons  called Starburst Amacrine Cells (SACs)  showing a bursting activity which disappears upon further maturation.
Revised:  During a particular phase of the retina development (stage II ) these waves are triggered by a transient network of neurons , called Starburst Amacrine Cells (SACs) showing a bursting activity which disappears upon further maturation.
Edit intent: fluency
Original:  During a particular phase of the retina development (stage II ) these waves are triggered by a transient network of neurons  called Starburst Amacrine Cells (SACs)  showing a bursting activity which disappears upon further maturation.
Revised:  During a particular phase of the retina development (stage II ) these waves are triggered by a transient network of neurons called Starburst Amacrine Cells (SACs) , showing a bursting activity which disappears upon further maturation.
Edit intent: fluency
Original:  The underlying mechanisms of the spontaneous bursting and the transient excitability of immature SACs are not completely clear yet.
Edit intent: coherence
Original:  While several models have tried to reproduce retinal waves, none of them is able to mimic the rhythmic autonomous bursting of individual SACs and understand how these cells change their intrinsic properties during development.
Revised:  While several models have attempted to reproduce retinal waves, none of them is able to mimic the rhythmic autonomous bursting of individual SACs and understand how these cells change their intrinsic properties during development.
Edit intent: fluency
Original:  While several models have tried to reproduce retinal waves, none of them is able to mimic the rhythmic autonomous bursting of individual SACs and understand how these cells change their intrinsic properties during development.
Revised:  While several models have tried to reproduce retinal waves, none of them is able to mimic the rhythmic autonomous bursting of individual SACs and reveal how these cells change their intrinsic properties during development.
Edit intent: clarity
Original:   Based on a bifurcation analysis we exhibit a few biophysical parameters, especially regulating calcium and potassium activity, controlling bursting .
Revised:  Based on a bifurcation analysis we show how biophysical parameters, especially regulating calcium and potassium activity, controlling bursting .
Edit intent: clarity
Original:   Based on a bifurcation analysis we exhibit a few biophysical parameters, especially regulating calcium and potassium activity, controlling bursting .
Revised:  Based on a bifurcation analysis we exhibit a few biophysical parameters, regulating calcium and potassium activity, controlling bursting .
Edit intent: coherence