wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our approach, based on the oracle principle with an ideal ratio mask, is a good proxy to estimate the separation performances of state-of-the-art deep learning approaches based on time-frequency masking such as TasNet or Open-Unmix.
Revised:  Based on the oracle principle with an ideal ratio mask, is a good proxy to estimate the separation performances of state-of-the-art deep learning approaches based on time-frequency masking such as TasNet or Open-Unmix.
Edit intent: clarity
Original:  Our approach, based on the oracle principle with an ideal ratio mask, is a good proxy to estimate the separation performances of state-of-the-art deep learning approaches based on time-frequency masking such as TasNet or Open-Unmix.
Revised:  Our approach, based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches based on time-frequency masking such as TasNet or Open-Unmix.
Edit intent: clarity
Original:  Our approach, based on the oracle principle with an ideal ratio mask, is a good proxy to estimate the separation performances of state-of-the-art deep learning approaches based on time-frequency masking such as TasNet or Open-Unmix.
Revised:  Our approach, based on the oracle principle with an ideal ratio mask, is a good proxy to estimate the separation performances of state-of-the-art deep learning approaches based on Time-Frequency (TF) masking such as TasNet or Open-Unmix.
Edit intent: clarity
Original:  Recent breakthroughs on this challenge have gravitated around a single dataset, MUSDB,  limited to four instrument classes only . Larger datasets and more instruments are costly and time-consuming in collecting data and training deep neural networks (DNNs).
Revised:  Recent breakthroughs on this challenge have gravitated around a single dataset, MUSDB, only limited to four instrument classes only . Larger datasets and more instruments are costly and time-consuming in collecting data and training deep neural networks (DNNs).
Edit intent: coherence
Original:  Recent breakthroughs on this challenge have gravitated around a single dataset, MUSDB,  limited to four instrument classes only . Larger datasets and more instruments are costly and time-consuming in collecting data and training deep neural networks (DNNs).
Revised:  Recent breakthroughs on this challenge have gravitated around a single dataset, MUSDB, limited to four instrument classes . Larger datasets and more instruments are costly and time-consuming in collecting data and training deep neural networks (DNNs).
Edit intent: coherence
Original:  Based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches based on Time-Frequency (TF) masking such as TasNet or Open-Unmix.
Revised:  Based on the oracle principle with an ideal ratio mask, our approach is an excellent proxy to estimate the separation performances of state-of-the-art deep learning approaches such as TasNet or Open-Unmix.
Edit intent: coherence
Original:  a crucial aspect that this work reports is best practices to application developers who use OMP offloading compilers.
Revised:  a crucial aspect of our work is reporting best practices to application developers who use OMP offloading compilers.
Edit intent: clarity
Original:  a crucial aspect that this work reports is best practices to application developers who use OMP offloading compilers.
Revised:  a crucial aspect that this work reports is best practices to application developers who use OpenMP offloading compilers.
Edit intent: fluency
Original:  Complex Step Derivative (CSD)  allows easy and accurate differentiation up to machine precision of real  functions by evaluating them a small imaginary step next to the real  line.
Revised:  Complex Step Derivative (CSD) method allows easy and accurate differentiation up to machine precision of real functions by evaluating them a small imaginary step next to the real line.
Edit intent: clarity
Original:  Additionally, the extra degree of freedom means second derivatives of real functions are straightforwardly computed by taking a small step in both a quaternionic and complex direction, making the technique a powerful extension of CSD.
Edit intent: coherence
Original:  To demonstrate the ease of implementation  numerical experiments were performed using complex quaternions, the geometric algebra of space, and a 2 \times 2 matrix representation thereof.
Revised:  To demonstrate the ease of implementation , numerical experiments were performed using complex quaternions, the geometric algebra of space, and a 2 \times 2 matrix representation thereof.
Edit intent: fluency
Original:  Our results show there are significant predictive gains just by including centrality multilayer network information to the model, and this gains are increased by more complex information such as the multilayer PageRank variables.
Revised:  Our results show there are significant predictive gains just by including centrality multilayer network information in the model, and this gains are increased by more complex information such as the multilayer PageRank variables.
Edit intent: clarity
Original:  Our results show there are significant predictive gains just by including centrality multilayer network information to the model, and this gains are increased by more complex information such as the multilayer PageRank variables.
Revised:  Our results show there are significant predictive gains just by including centrality multilayer network information to the model, and these gains are increased by more complex information such as the multilayer PageRank variables.
Edit intent: fluency
Original:  Here we introduce the ``Newtonian blurring'' in human braingraph (or connectome) augmentation: Started from a dataset of 1053 subjects, we first repeat a probabilistic weighted braingraph construction algorithm 10 times for describing the connections of distinct cerebral areas, then take 7 repetitions in every possible way, delete the lower and upper extremes, and average the remaining 7-2=5 edge-weights for the data of each subject.
Revised:  Here we introduce the "Newtonian blurring" in human braingraph (or connectome) augmentation: Started from a dataset of 1053 subjects, we first repeat a probabilistic weighted braingraph construction algorithm 10 times for describing the connections of distinct cerebral areas, then take 7 repetitions in every possible way, delete the lower and upper extremes, and average the remaining 7-2=5 edge-weights for the data of each subject.
Edit intent: fluency
Original:  This could potentially be expanded to evaluate the efficacy different medical simulations to help medical staff and to measure cognitive and mental load under real laparoscopic surgeries.
Revised:  This could potentially be expanded to evaluate efficacy of different medical simulations to help medical staff and to measure cognitive and mental load under real laparoscopic surgeries.
Edit intent: fluency
Original:  This could potentially be expanded to evaluate the efficacy different medical simulations to help medical staff and to measure cognitive and mental load under real laparoscopic surgeries.
Revised:  This could potentially be expanded to evaluate the efficacy different medical simulations to provide more efficient training to medical staff and to measure cognitive and mental load under real laparoscopic surgeries.
Edit intent: clarity
Original:  This could potentially be expanded to evaluate the efficacy different medical simulations to help medical staff and to measure cognitive and mental load under real laparoscopic surgeries.
Revised:  This could potentially be expanded to evaluate the efficacy different medical simulations to help medical staff and to measure cognitive and mental load in real laparoscopic surgeries.
Edit intent: clarity
Original:  The result show that our systems rated between soft-attention-based methods (Transformer-TTS, Tacotron2) and explicit duration modeling-based methods (Fastspeech).
Revised:  The results showed that our systems rated between soft-attention-based methods (Transformer-TTS, Tacotron2) and explicit duration modeling-based methods (Fastspeech).
Edit intent: fluency
Original:  For the degree bifiltrations , only weakerresults are possible .
Revised:  Our results for the degree bifiltrations , only weakerresults are possible .
Edit intent: clarity
Original:  The proposed solution is  independent of the adversary position, works under  conservative assumption of channel fading ({\sigma} = 8dB), and is capable of generating a secret key of 128 bits with only 620 transmissions.
Revised:  The proposed solution is independent of the adversary position, works under the conservative assumption of channel fading ({\sigma} = 8dB), and is capable of generating a secret key of 128 bits with only 620 transmissions.
Edit intent: fluency
Original:  This usually works, but creates problems in situations where densities with respect to different base measures are accidentally combined or compared.
Revised:  This mostly works, but creates problems in situations where densities with respect to different base measures are accidentally combined or compared.
Edit intent: coherence
Original:  This usually works, but creates problems in situations where densities with respect to different base measures are accidentally combined or compared.
Revised:  This usually works, but creates problems when densities with respect to different base measures are accidentally combined or compared.
Edit intent: clarity
Original:  We also propose to solve the problem by standardizing on Hausdorff measure as a base, and by deriving a formula and software architecture for updating densities with respect to Hausdorff measure under diffeomorphic transformations  .
Revised:  We solve the problem by standardizing on Hausdorff measure as a base, and by deriving a formula and software architecture for updating densities with respect to Hausdorff measure under diffeomorphic transformations .
Edit intent: clarity
Original:  In a similar fashion to  James-Stein estimator, this has the effect of drawing the noisy inverse operator closer to the ground truth, and hence reduces error.
Revised:  In a similar fashion to the James-Stein estimator, this has the effect of drawing the noisy inverse operator closer to the ground truth, and hence reduces error.
Edit intent: fluency
Original:  Numerical simulations of feedforward and recurrent networks verify our analytical results.
Edit intent: coherence
Original:  Our theoretical framework could  be applied to neural activity data to make  anatomical predictions that follow generally from the model architecture .
Revised:  Our theoretical framework could thus be applied to neural activity data to make anatomical predictions that follow generally from the model architecture .
Edit intent: coherence
Original:  A careful treatment of the degeneracy at the axis of rotation for the one dimensional partial differential equation for a parameterization of the generating curve allows us to prove error bounds with respect to discrete L^2 -- and H^1 --norms for a fully discrete approximation.
Revised:  A careful treatment of the degeneracy at the axis of rotation for the one dimensional partial differential equation for a parameterization of the generating curve allows us to prove error bounds with respect to discrete L^2 - and H^1 --norms for a fully discrete approximation.
Edit intent: fluency
Original:  A careful treatment of the degeneracy at the axis of rotation for the one dimensional partial differential equation for a parameterization of the generating curve allows us to prove error bounds with respect to discrete L^2 -- and H^1 --norms for a fully discrete approximation.
Revised:  A careful treatment of the degeneracy at the axis of rotation for the one dimensional partial differential equation for a parameterization of the generating curve allows us to prove error bounds with respect to discrete L^2 -- and H^1 -norms for a fully discrete approximation.
Edit intent: fluency
Original:  We show that subspace iteration with a rational filter is stable even when an eigenvalue is located near a pole of the filter .
Revised:  We show that subspace iteration with a rational filter is robust even when an eigenvalue is located near a pole of the filter .
Edit intent: clarity
Original:  We show that subspace iteration with a rational filter is stable even when an eigenvalue is located near a pole of the filter .
Revised:  We show that subspace iteration with a rational filter is stable even when an eigenvalue is near a filter's pole .
Edit intent: clarity
Original:  The complexity of each of our methods is calculated, and we include experimental verification on examples in dimensions two and three.
Revised:  The complexity of each of our methods is calculated, and we include experimental verifications on examples in dimensions two and three.
Edit intent: fluency
Original: As polarised discussions is defined by spread of ideas within echo chambers , characterising what drives in-group spreading is essential for understanding the problem.
Revised: Characterising the spreading of ideas within echo chambers , characterising what drives in-group spreading is essential for understanding the problem.
Edit intent: coherence
Original: As polarised discussions is defined by spread of ideas within echo chambers , characterising what drives in-group spreading is essential for understanding the problem.
Revised: As polarised discussions is defined by spread of ideas within echo chambers is essential for understanding the problem.
Edit intent: clarity
Original:  We explore the characteristics of popular and viral content in a Twitter retweet network of climate change discussion around the 2019 announcement of the Nobel Peace Prize .
Revised:  We explore the characteristics of popular and viral content in climate change discussions on Twitter around the 2019 announcement of the Nobel Peace Prize .
Edit intent: clarity
Original:  Most importantly, our results reveal that the features most strongly related to virality are also ones that enhance intragroup ties while repulsing outgroup engagement.
Revised:  Most importantly, we find that the most viral themes in the two groups reflect different types of bonds that tie the community together, yet both function to enhance ingroup connections while repulsing outgroup engagement.
Edit intent: clarity
Original:  With this, our study sheds light, from an information spreading perspective, on the formation and upkeep of echo chambers of climate discussions.
Revised:  With this, our study sheds light, from an information spreading perspective, on the formation and upkeep of echo chambers in climate discussions.
Edit intent: clarity
Original: We present a novel, Multi-window Data Augmentation (MWA-SER) , approach for speech emotion recognition.
Revised: We present a novel, Multi-Window Data Augmentation (MWA-SER) , approach for speech emotion recognition.
Edit intent: fluency
Original: We present a novel, Multi-window Data Augmentation (MWA-SER) , approach for speech emotion recognition.
Revised: We present a novel, Multi-window Data Augmentation (MWA-SER) approach for speech emotion recognition.
Edit intent: fluency
Original:  We propose a novel multi-window augmentation method to extract more audio features from the speech signal by employing multiple window sizes into the audio feature extraction process.
Revised:  The multi-window augmentation method to extract more audio features from the speech signal by employing multiple window sizes into the audio feature extraction process.
Edit intent: clarity
Original:  We propose a novel multi-window augmentation method to extract more audio features from the speech signal by employing multiple window sizes into the audio feature extraction process.
Revised:  We propose a novel multi-window augmentation method extracts more audio features from the speech signal by employing multiple window sizes into the audio feature extraction process.
Edit intent: fluency
Original:  We propose a novel multi-window augmentation method to extract more audio features from the speech signal by employing multiple window sizes into the audio feature extraction process.
Revised:  We propose a novel multi-window augmentation method to extract more audio features from the speech signal by employing multiple window sizes in the audio feature extraction process.
Edit intent: fluency
Original:  We show that our proposed augmentation method with minimally extracted features combined with a deep learning model improves the performance of speech emotion recognition .
Revised:  We show that our proposed augmentation method , combined with a deep learning model improves the performance of speech emotion recognition .
Edit intent: clarity
Original:  We show that our proposed augmentation method with minimally extracted features combined with a deep learning model improves the performance of speech emotion recognition .
Revised:  We show that our proposed augmentation method with minimally extracted features combined with a deep learning model , improves the speech emotion recognition .
Edit intent: clarity
Original: We present a novel, Multi-Window Data Augmentation (MWA-SER) approach for speech emotion recognition.
Revised: We present a Multi-Window Data Augmentation (MWA-SER) approach for speech emotion recognition.
Edit intent: clarity
Original:  designing the speech augmentation method to generate additional data samples and building the deep learning models to recognize the underlying emotion of an audio signal.
Revised:  designing the speech augmentation method and building the deep learning models to recognize the underlying emotion of an audio signal.
Edit intent: clarity
Original:  designing the speech augmentation method to generate additional data samples and building the deep learning models to recognize the underlying emotion of an audio signal.
Revised:  designing the speech augmentation method to generate additional data samples and building the deep learning model to recognize the underlying emotion of an audio signal.
Edit intent: fluency
Original:  The multi-window augmentation method extracts more audio features from the speech signal by employing multiple window sizes in the audio feature extraction process.
Revised:  Our proposed multi-window augmentation method extracts more audio features from the speech signal by employing multiple window sizes in the audio feature extraction process.
Edit intent: clarity
Original:  The multi-window augmentation method extracts more audio features from the speech signal by employing multiple window sizes in the audio feature extraction process.
Revised:  The multi-window augmentation approach generates additional data samples from the speech signal by employing multiple window sizes in the audio feature extraction process.
Edit intent: clarity
Original:  We show that our proposed augmentation method, combined with a deep learning model, improves the speech emotion recognition performance.
Revised:  We show that our augmentation method, combined with a deep learning model, improves the speech emotion recognition performance.
Edit intent: clarity
Original:  We show that our proposed augmentation method, combined with a deep learning model, improves the speech emotion recognition performance.
Revised:  We show that our proposed augmentation method, combined with a deep learning model, improves speech emotion recognition performance.
Edit intent: coherence
Original:  However, experimental settings  such as social networks, where users are interacting and influencing one another,  violate conventional assumptions of no interference needed for credible causal inference.
Revised:  However, experimental settings , such as social networks, where users are interacting and influencing one another, violate conventional assumptions of no interference needed for credible causal inference.
Edit intent: fluency
Original:  However, experimental settings  such as social networks, where users are interacting and influencing one another,  violate conventional assumptions of no interference needed for credible causal inference.
Revised:  However, experimental settings such as social networks, where users are interacting and influencing one another, may violate conventional assumptions of no interference needed for credible causal inference.
Edit intent: coherence
Original:  However, experimental settings  such as social networks, where users are interacting and influencing one another,  violate conventional assumptions of no interference needed for credible causal inference.
Revised:  However, experimental settings such as social networks, where users are interacting and influencing one another, violate conventional assumptions of no interference for credible causal inference.
Edit intent: clarity
Original:  Capturing local network structures is important because it can account for theories, such as structural diversity and echo chambers.
Edit intent: coherence
Original:  We first introduce and employ "causal network motifs , " i.e. network motifs that characterize the assignment conditions in local ego networks;
Revised:  We first introduce and employ "causal network motifs " i.e. network motifs that characterize the assignment conditions in local ego networks;
Edit intent: fluency
Original:  We first introduce and employ "causal network motifs , " i.e. network motifs that characterize the assignment conditions in local ego networks;
Revised:  We first introduce and employ "causal network motifs , " , which are network motifs that characterize the assignment conditions in local ego networks;
Edit intent: coherence
Original:   We test our method on a  real-world experiment on a large-scale network and a synthetic network setting , which highlight how accounting for local structures can better account for different interference patterns in networks.
Revised:  We test our method on a real-world experiment on a large-scale network , which highlight how accounting for local structures can better account for different interference patterns in networks.
Edit intent: clarity
Original:  Smooth ReLU (SmeLU), designed to give such better tradeoffs, while also keeping the mathematical expression simple, and thus training speed fast and implementation cheap.
Revised:  Smooth ReLU (SmeLU), designed to give such better tradeoffs, while also keeping the mathematical expression simple, and thus implementation cheap.
Edit intent: clarity
Original:  With the augmented methods in Eland, the performance of anomaly detection at an earlier stage is comparable with or better than non-augmented methods on a greater amount of observation .
Revised:  With Eland, anomaly detection performance at an earlier stage is comparable with or better than non-augmented methods on a greater amount of observation .
Edit intent: clarity
Original:  With the augmented methods in Eland, the performance of anomaly detection at an earlier stage is comparable with or better than non-augmented methods on a greater amount of observation .
Revised:  With the augmented methods in Eland, the performance of anomaly detection at an earlier stage is better than non-augmented methods on a greater amount of observation .
Edit intent: clarity
Original:  In an extensive evaluation  we compare our method against 5 other methods with over 3895 hyperparameter configurations .
Revised:  In an extensive evaluation , we compare our method against 5 other methods with over 3895 hyperparameter configurations .
Edit intent: fluency
Original:  Our reduction is mathematically justified within a singular perturbation setting using a recent result by Cardin and Teixeira .
Revised:  Our reduction is mathematically justified within a singular perturbation setting .
Edit intent: clarity
Original:  The existence of invariant manifolds is subject to hyperbolicity conditions, which we test algorithmically using Hurwitz criteria.
Revised:  The existence of invariant manifolds is subject to hyperbolicity conditions, for which we propose an algorithmic test based on Hurwitz criteria.
Edit intent: clarity