wanyu/IteraTeR_v2 huggingface dataset:
Original:  A running theme throughout these contributions is an analysis of "cycle-lifted graphs , "  where we take a graph and "lift" it to a new graph whose adjacency matrix is the tensor product of the original adjacency matrix and a directed cycle (or variants of one).
Revised:  A running theme throughout these contributions is an analysis of "cycle-lifted graphs , " , where we take a graph and "lift" it to a new graph whose adjacency matrix is the tensor product of the original adjacency matrix and a directed cycle (or variants of one).
Edit intent: fluency
Original:  Specifically, we investigate three user groups from the Last.fm music platform that are categorized based on how much their listening preferences deviate from the most popular music among all Last.fm users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users.
Revised:  Specifically, we investigate three user groups from the LastFM music platform that are categorized based on how much their listening preferences deviate from the most popular music among all Last.fm users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users.
Edit intent: fluency
Original:  Specifically, we investigate three user groups from the Last.fm music platform that are categorized based on how much their listening preferences deviate from the most popular music among all Last.fm users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users.
Revised:  Specifically, we investigate three user groups from the Last.fm music platform that are categorized based on how much their listening preferences deviate from the most popular music among all LastFM users in the dataset: (i) low-mainstream users, (ii) medium-mainstream users, and (iii) high-mainstream users.
Edit intent: clarity
Original:  However, their proposed Group Average Popularity metric yields different results for Last.fm than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the Last.fm dataset we use.
Revised:  However, their proposed Group Average Popularity metric yields different results for LastFM than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the Last.fm dataset we use.
Edit intent: clarity
Original:  However, their proposed Group Average Popularity metric yields different results for Last.fm than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the Last.fm dataset we use.
Revised:  However, their proposed Group Average Popularity metric yields different results for Last.fm than for the movie domain, presumably due to the larger number of available items (i.e., music artists) in the LastFM dataset we use.
Edit intent: clarity
Original:  We propose a convolutional neural network transformer (CNN-Transfomer) for audio tagging and SED, and show that CNN-Transformer performs similarly to a convolutional recurrent neural network (CRNN)} Another challenge of SED is that only the presence probabilities of sound events are predicted and thresholds are required to predict the presence or absence of sound events.
Revised:  We propose a convolutional neural network transformer (CNN-Transfomer) for audio tagging and SED, and show that CNN-Transformer performs similarly to a convolutional recurrent neural network (CRNN)}. Another challenge of SED is that only the presence probabilities of sound events are predicted and thresholds are required to predict the presence or absence of sound events.
Edit intent: fluency
Original:  We propose a convolutional neural network transformer (CNN-Transfomer) for audio tagging and SED, and show that CNN-Transformer performs similarly to a convolutional recurrent neural network (CRNN)} Another challenge of SED is that only the presence probabilities of sound events are predicted and thresholds are required to predict the presence or absence of sound events.
Revised:  We propose a convolutional neural network transformer (CNN-Transfomer) for audio tagging and SED, and show that CNN-Transformer performs similarly to a convolutional recurrent neural network (CRNN)} Another challenge of SED is that thresholds are required to predict the presence or absence of sound events.
Edit intent: coherence
Original:  We propose a convolutional neural network transformer (CNN-Transfomer) for audio tagging and SED, and show that CNN-Transformer performs similarly to a convolutional recurrent neural network (CRNN)} Another challenge of SED is that only the presence probabilities of sound events are predicted and thresholds are required to predict the presence or absence of sound events.
Revised:  We propose a convolutional neural network transformer (CNN-Transfomer) for audio tagging and SED, and show that CNN-Transformer performs similarly to a convolutional recurrent neural network (CRNN)} Another challenge of SED is that only the presence probabilities of sound events are predicted and thresholds are required for detecting sound events.
Edit intent: clarity
Original:  Previous work set this threshold empiricallywhich is not an optimised solution .
Revised:  Previous works set thresholds empirically, and are not an optimal approaches .
Edit intent: clarity
Original:  The first stage is to optimize the system with respect to metrics that do not depend on the thresholds such as mean average precision (mAP).
Revised:  The first stage is to optimize the system with respect to metrics that do not depend on thresholds, such as mean average precision (mAP).
Edit intent: fluency
Original:  The second stage is to optimize the thresholds with respect to the metric that depends on those thresholds.
Revised:  The second stage is to optimize the thresholds with respect to metrics that depends on those thresholds.
Edit intent: fluency
Original:  This proposed automatic threshold optimization system achieved state-of-the-art audio tagging and SED F1 score of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Revised:  Our proposed automatic threshold optimization system achieved state-of-the-art audio tagging and SED F1 score of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Edit intent: fluency
Original:  This proposed automatic threshold optimization system achieved state-of-the-art audio tagging and SED F1 score of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Revised:  This proposed automatic threshold optimization system achieves a state-of-the-art audio tagging and SED F1 score of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Edit intent: fluency
Original:  This proposed automatic threshold optimization system achieved state-of-the-art audio tagging and SED F1 score of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Revised:  This proposed automatic threshold optimization system achieved state-of-the-art audio tagging F1 score of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Edit intent: clarity
Original:  This proposed automatic threshold optimization system achieved state-of-the-art audio tagging and SED F1 score of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Revised:  This proposed automatic threshold optimization system achieved state-of-the-art audio tagging and SED F1 of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Edit intent: clarity
Original:  This proposed automatic threshold optimization system achieved state-of-the-art audio tagging and SED F1 score of 0.646, 0.584, outperforming the performance with best manually selected thresholds of 0.629 and 0.564, respectively .
Revised:  This proposed automatic threshold optimization system achieved state-of-the-art audio tagging and SED F1 score of 0.646, outperforming that without threshold optimization of 0.629 and 0.564, respectively .
Edit intent: clarity
Original: Infinite-duration games with disturbances extend the classical framework of infinite-duration games, which captures the reactive synthesis problem, with a discrete measure of resilience against non-antagonistic disturbances, i. e., unmodeled situations in which the actual controller action differs from the intended one  .
Revised: Infinite-duration games with disturbances extend the classical framework of infinite-duration games, which captures the reactive synthesis problem, with a discrete measure of resilience against non-antagonistic external influence. This concerns events where the observed system behavior differs from the intended one .
Edit intent: clarity
Original: Infinite-duration games with disturbances extend the classical framework of infinite-duration games, which captures the reactive synthesis problem, with a discrete measure of resilience against non-antagonistic disturbances, i. e., unmodeled situations in which the actual controller action differs from the intended one  .
Revised: Infinite-duration games with disturbances extend the classical framework of infinite-duration games, which captures the reactive synthesis problem, with a discrete measure of resilience against non-antagonistic disturbances, i. e., unmodeled situations in which the actual controller action differs from the intended one prescribed by the controller .
Edit intent: clarity
Original: We undertake a fundamental study of network equilibria modeled as solutions of fixed point of monotone linear functions with saturation nonlinearities.
Revised: We undertake a fundamental study of network equilibria modeled as solutions of fixed point equations for monotone linear functions with saturation nonlinearities.
Edit intent: fluency
Original:   We first derive explicit expressions for network equilibria and prove necessary and sufficient conditions for their uniqueness encompassing and generalizing several results in the literature.
Revised:  We first derive explicit expressions for network equilibria and prove necessary and sufficient conditions for their uniqueness encompassing and generalizing results available in the literature.
Edit intent: clarity
Original:  Here we use a neural network-based architecture for symbolic regression that we call the Sequential Equation Learner ( SEQL ) network and integrate it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation.
Revised:  Here we use a neural network-based architecture for symbolic regression called the Equation Learner ( SEQL ) network and integrate it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation.
Edit intent: clarity
Original:  We derive a scaling limit as the sampling probability goes to 0, corresponding to the underlying population growing to infinity  .
Revised:  We derive a scaling limit as \psi approaches 0, corresponding to the underlying population growing to infinity .
Edit intent: clarity
Original:  In particular, we redesign  generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images.
Revised:  In particular, we redesign the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images.
Edit intent: fluency
Original:  In particular, we redesign  generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent vectors to images.
Revised:  In particular, we redesign generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images.
Edit intent: clarity
Original:  This makes it possible to reliably detect if an image is generated by a particular network.
Revised:  This makes it possible to reliably attribute a generated image to a particular network.
Edit intent: clarity
Original: Extracting relevant information from clinical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness.
Revised: Extracting relevant information from medical conversations and providing it to doctors and patients might help in addressing doctor burnout and patient forgetfulness.
Edit intent: clarity
Original:  In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a clinical conversation.
Revised:  In this paper, we focus on extracting the Medication Regimen (dosage and frequency for medications) discussed in a medical conversation.
Edit intent: clarity
Original:  We frame the problem as a Question Answering (QA) task and perform comparative analysis over: a QA approach, a new combined QA and Information Extraction approach  and other baselines.
Revised:  We frame the problem as a Question Answering (QA) task and perform comparative analysis over: a QA approach, a new combined QA and Information Extraction approach , and other baselines.
Edit intent: fluency
Original:  Clinical conversation corpora are costly to create, difficult to handle (because of data privacy concerns), and thus `scarce' .
Revised:  Clinical conversation corpora are costly to create, difficult to handle (because of data privacy concerns), and thus scarce .
Edit intent: fluency
Original:  We address this data scarcity challenge through data augmentation methods, using publicly available embeddings and pretrain part of the network on a related task of summarization to improve the model's performance.
Revised:  We address this data scarcity challenge through data augmentation methods, using publicly available embeddings and pretrain part of the network on a related task (summarization) to improve the model's performance.
Edit intent: coherence
Original:  Using our best-performing model, we present the first fully automated system that can extract Medication Regimen (MR) tags from spontaneous doctor-patient conversations with about ~71\% accuracy.
Revised:  Using our best-performing model, we present the first fully automated system that can extract Medication Regimen tags from spontaneous doctor-patient conversations with about ~71\% accuracy.
Edit intent: clarity
Original:  Using our best-performing model, we present the first fully automated system that can extract Medication Regimen tags from spontaneous doctor-patient conversations with about ~71 \approx71 \% accuracy.
Revised:  Using our best-performing model, we present the first fully automated system that can extract Medication Regimen tags from spontaneous doctor-patient conversations with about \approx71 \% accuracy.
Edit intent: clarity
Original: In this paper, we develop a sharp interface tumor growth model to study of the effect of the tumor microenvironment using a complex far-field geometry that mimics a heterogeneous distribution of vasculature.
Revised: In this paper, we develop a sharp interface tumor growth model to study the effect of the tumor microenvironment using a complex far-field geometry that mimics a heterogeneous distribution of vasculature.
Edit intent: fluency
Original:  We investigate the fully nonlinear dynamics using a spectral accurate boundary integral method.
Revised:  We investigate the fully nonlinear dynamics using a spectrally accurate boundary integral method.
Edit intent: fluency
Original: Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used for studying the (presumably) awake and alert human brain  .
Revised: Resting-state functional magnetic resonance imaging (rsfMRI) have been widely used for studying the (presumably) awake and alert human brain .
Edit intent: fluency
Original:  EEG is the modality extensively used for estimating vigilance  during eyes-closed resting state.
Revised:  EEG is the modality extensively used for estimating vigilance , especially during eyes-closed resting state.
Edit intent: coherence
Original:  On the other hand , pupil size measured using an eye-tracker device could provide an indirect index of vigilance.
Revised:  However , pupil size measured using an eye-tracker device could provide an indirect index of vigilance.
Edit intent: coherence
Original:  Other than the retrieval of visually similar items, the retrieval of visually compatible or complimentary items is also an important aspect of it.
Revised:  Other than the retrieval of visually similar items, the retrieval of visually compatible or complementary items is also an important aspect of it.
Edit intent: fluency
Original:  Normal Siamese Networks tend to work well on complimentary items retrieval.
Revised:  Normal Siamese Networks tend to work well on complementary items retrieval.
Edit intent: fluency
Original:  The experimental results indicate that the proposed method outperforms traditional siamese networks in retrieval tasks for complimentary items.
Revised:  The experimental results indicate that the proposed method outperforms traditional siamese networks in retrieval tasks for complementary items.
Edit intent: fluency
Original:  Our descriptors are designed to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape  and projection angle.
Revised:  Our descriptors are designed to locally match image pairs where the object of interest belongs to the same semantic category, yet still differ drastically in shape , form, and projection angle.
Edit intent: clarity
Original:  We have specifically crafted a data set of synthetic sketches using a non-photorealistic rendering algorithm over a large collection of part-based registered 3D models.
Revised:  We have specifically crafted a data set of synthetic sketches using non-photorealistic rendering algorithm over a large collection of part-based registered 3D models.
Edit intent: fluency
Original:  We have specifically crafted a data set of synthetic sketches using a non-photorealistic rendering algorithm over a large collection of part-based registered 3D models.
Revised:  We have specifically crafted a data set of synthetic sketches using a non-photorealistic rendering over a large collection of part-based registered 3D models.
Edit intent: clarity
Original:  Once trained, our network can generate descriptors for every pixel in an input image .
Revised:  After training, a neural network generates descriptors for every pixel in an input image .
Edit intent: clarity
Original:  We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with  descriptors that have been proven effective in sketches.
Revised:  We evaluate our method against a baseline of correspondences data collected from expert designers, in addition to comparisons with other descriptors that have been proven effective in sketches.
Edit intent: clarity
Original:  This is partially evidenced in recent developments such as the MLIR proposed by Google for deep learning .
Revised:  This is partially evidenced in recent developments such as the MLIR proposed by Google .
Edit intent: clarity
Original:  Hence , using this mode with macro-cell deployments requires interference mitigation techniques to reduce the strong DL to UL interference.
Revised:  Therefore , using this mode with macro-cell deployments requires interference mitigation techniques to reduce the strong DL to UL interference.
Edit intent: fluency
Original:  Therefore, combining D-TDD and 3D beamforming can make D-TDD feasible for macro-cells.
Revised:  Combining D-TDD and 3D beamforming can make D-TDD feasible for macro-cells.
Edit intent: coherence
Original:  This revenue bound obtains for any arbitrary number of segments and prices that the seller would use in case he would engage in third-degree price discrimination.
Revised:  This revenue bound holds for any arbitrary number of segments and prices that the seller would use in case he would engage in third-degree price discrimination.
Edit intent: clarity
Original:  This revenue bound obtains for any arbitrary number of segments and prices that the seller would use in case he would engage in third-degree price discrimination.
Revised:  This revenue bound obtains for any arbitrary number of segments and prices that the seller would use with third-degree price discrimination.
Edit intent: clarity
Original:  We further establish that these conditions are tight , and that a weakening of common support or concavity leads to arbitrarily poor revenue comparisons.
Revised:  We further establish that these conditions are tight and that weakening either common support or concavity leads to arbitrarily poor revenue comparisons.
Edit intent: clarity
Original: We compare the revenue of the optimal third-degree price discrimination policy against a uniform pricing policy.
Revised: We compare the profit of the optimal third-degree price discrimination policy against a uniform pricing policy.
Edit intent: clarity
Original:  This revenue bound holds for any arbitrary number of segments and prices that the seller would use with third-degree price discrimination.
Revised:  This profit bound holds for any arbitrary number of segments and prices that the seller would use with third-degree price discrimination.
Edit intent: clarity
Original:  This revenue bound holds for any arbitrary number of segments and prices that the seller would use with third-degree price discrimination.
Revised:  This revenue bound holds for any number of segments and prices that the seller would use with third-degree price discrimination.
Edit intent: clarity
Original:  This revenue bound holds for any arbitrary number of segments and prices that the seller would use with third-degree price discrimination.
Revised:  This revenue bound holds for any arbitrary number of segments and prices that the seller might use under third-degree price discrimination.
Edit intent: clarity
Original:  We further establish that these conditions are tight and that weakening either common support or concavity leads to arbitrarily poor revenue comparisons .
Revised:  We establish that these conditions are tight and that weakening either common support or concavity leads to arbitrarily poor revenue comparisons .
Edit intent: clarity
Original:  We further establish that these conditions are tight and that weakening either common support or concavity leads to arbitrarily poor revenue comparisons .
Revised:  We further establish that these conditions are tight and that weakening either common support or concavity can lead to arbitrarily poor revenue comparisons .
Edit intent: clarity
Original: In websites that collect user-generated recipes, recipes are often posted that have a major component, such as the cooking instructions , that is very similar to those in other recipes.
Revised: In user-generated recipes, recipes are often posted that have a major component, such as the cooking instructions , that is very similar to those in other recipes.
Edit intent: clarity
Original: In websites that collect user-generated recipes, recipes are often posted that have a major component, such as the cooking instructions , that is very similar to those in other recipes.
Revised: In websites that collect user-generated recipes, recipes are often posted that have a major component, such as the cooking instructions to other recipes.
Edit intent: clarity