wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Some software based on deep learning lacks accuracy (e.g., in F-1), and  many decision processes are not transparent  causing doubt about decision bias, such as perceived racial , age, and gender disparities.
Revised:  Some software based on deep learning lacks accuracy (e.g., in F-1), and importantly many decision processes are not transparent causing doubt about decision bias, such as perceived racial , age, and gender disparities.
Edit intent: clarity
Original:  Some software based on deep learning lacks accuracy (e.g., in F-1), and  many decision processes are not transparent  causing doubt about decision bias, such as perceived racial , age, and gender disparities.
Revised:  Some software based on deep learning lacks accuracy (e.g., in F-1), and many decision processes are not transparent , causing doubt about decision bias, such as perceived racial , age, and gender disparities.
Edit intent: fluency
Original:  Our analysis of the importance of each input feature shows the critical causal impact on decision-making, suggesting that criminal histories are statistically significant factors, while identifiers, such as race , gender, and age, are not.
Revised:  Our analysis of the importance of each input feature shows the critical causal impact on decision-making, suggesting that criminal histories are statistically significant factors, while identifiers, such as race and age, are not.
Edit intent: clarity
Original:  We show that the present code compares favorably with Mathematica's function, point to further improvements, and discuss the issue of singularities.
Revised:  We show that the present code compares favorably with Mathematica's HeunG and with an Octave/Matlab code of Motygin, in particular when the Heun function is to be evaluated at a large number of points if less accuracy is sufficient. We suggest further improvements concerning the accuracy and discuss the issue of singularities.
Edit intent: meaning-changed
Original:  In this work, we improve the original Pyramid Vision Transformer (PVTv1) by adding three improvement designs, which include (1) locally continuous features with convolutions , (2) position encodings with zero paddings , and (3) linear complexity attention layers with average pooling .
Revised:  In this work, we improve the original Pyramid Vision Transformer (PVTv1) by adding three improvement designs, which include (1) overlapping patch embedding , (2) position encodings with zero paddings , and (3) linear complexity attention layers with average pooling .
Edit intent: meaning-changed
Original:  In this work, we improve the original Pyramid Vision Transformer (PVTv1) by adding three improvement designs, which include (1) locally continuous features with convolutions , (2) position encodings with zero paddings , and (3) linear complexity attention layers with average pooling .
Revised:  In this work, we improve the original Pyramid Vision Transformer (PVTv1) by adding three improvement designs, which include (1) locally continuous features with convolutions , (2) convolutional feed-forward networks , and (3) linear complexity attention layers with average pooling .
Edit intent: meaning-changed
Original:  In this work, we improve the original Pyramid Vision Transformer (PVTv1) by adding three improvement designs, which include (1) locally continuous features with convolutions , (2) position encodings with zero paddings , and (3) linear complexity attention layers with average pooling .
Revised:  In this work, we improve the original Pyramid Vision Transformer (PVTv1) by adding three improvement designs, which include (1) locally continuous features with convolutions , (2) position encodings with zero paddings , and (3) linear complexity attention layers .
Edit intent: clarity
Original:  In this work, we improve the original Pyramid Vision Transformer (  PVTv1) by adding three improvement designs, which include (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers.
Revised:  In this work, we present new baselines by improving the original Pyramid Vision Transformer ( PVTv1) by adding three improvement designs, which include (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers.
Edit intent: meaning-changed
Original:  In this work, we improve the original Pyramid Vision Transformer (  PVTv1) by adding three improvement designs, which include (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers.
Revised:  In this work, we improve the original Pyramid Vision Transformer ( abbreviated as PVTv1) by adding three improvement designs, which include (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers.
Edit intent: clarity
Original:  In this work, we improve the original Pyramid Vision Transformer (  PVTv1) by adding three improvement designs, which include (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers.
Revised:  In this work, we improve the original Pyramid Vision Transformer ( PVTv1) by adding three designs, including (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers.
Edit intent: coherence
Original:  Specifically, they help achieve new state of the art performance on large-scale benchmark datasets - ActivityNet-1.3 ( 52.73 mAP@0.5) and THUMOS14 (57.18 mAP@0.5).
Revised:  Specifically, they help achieve new state of the art performance on large-scale benchmark datasets - ActivityNet-1.3 ( 54.34 mAP@0.5) and THUMOS14 (57.18 mAP@0.5).
Edit intent: meaning-changed
Original: We apply quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about 10,000 cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto , 2009) and the one with, by far, the largest market capitalization.
Revised: This discussion applies quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about 10,000 cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto , 2009) and the one with, by far, the largest market capitalization.
Edit intent: clarity
Original: We apply quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about 10,000 cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto , 2009) and the one with, by far, the largest market capitalization.
Revised: We apply quantitative finance methods and economic arguments to cryptocurrencies in general and bitcoin in particular -- as there are about 10,000 cryptocurrencies, we focus (unless otherwise specified) on the most discussed crypto of those that claim to hew to the original protocol (Nakamoto 2009) and the one with, by far, the largest market capitalization.
Edit intent: fluency
Original:  In its current version, in spite of the hype, bitcoin failed to satisfy the notion of "currency without government" (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than 0), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, nor a tail protection vehicle for catastrophic episodes.
Revised:  In its current version, in spite of the hype, bitcoin failed to satisfy the notion of "currency without government" (it proved to not even be a currency at all), can be neither a short nor long term store of value (its expected value is no higher than 0), cannot operate as a reliable inflation hedge, and, worst of all, does not constitute, not even remotely, a safe haven for one's investments, a shield against government tyranny, or a tail protection vehicle for catastrophic episodes.
Edit intent: fluency
Original:  Furthermore, there appears to be an underlying conflation between the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, and the speculative variations in the price of a zero-sum  asset with massive negative externalities.
Revised:  Furthermore, bitcoin promoters appear to conflate the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, and the speculative variations in the price of a zero-sum asset with massive negative externalities.
Edit intent: meaning-changed
Original:  Furthermore, there appears to be an underlying conflation between the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, and the speculative variations in the price of a zero-sum  asset with massive negative externalities.
Revised:  Furthermore, there appears to be an underlying conflation between the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, with the speculative variations in the price of a zero-sum asset with massive negative externalities.
Edit intent: fluency
Original:  Furthermore, there appears to be an underlying conflation between the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, and the speculative variations in the price of a zero-sum  asset with massive negative externalities.
Revised:  Furthermore, there appears to be an underlying conflation between the success of a payment mechanism (as a decentralized mode of exchange), which so far has failed, and the speculative variations in the price of a zero-sum maximally fragile asset with massive negative externalities.
Edit intent: meaning-changed
Original:  Going through monetary history, we also show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value.
Revised:  Going through monetary history, we show how a true numeraire must be one of minimum variance with respect to an arbitrary basket of goods and services, how gold and silver lost their inflation hedge status during the Hunt brothers squeeze in the late 1970s and what would be required from a true inflation hedged store of value.
Edit intent: clarity
Original:  In this work , complex network analysis was performed on the basis of brain connectivity obtained from Electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects.
Revised:  This work is structured around the exploratory analysis of the brain processes involved in low-level auditory processing. A complex network analysis was performed on the basis of brain connectivity obtained from Electroencephalography (EEG) data, while different auditory stimuli were presented to the subjects.
Edit intent: meaning-changed
Original:  The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint enhancement and denoising branch works in a full-resolution space and  performs joint enhancement and denoising in a progressive manner .
Revised:  The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint enhancement and denoising branch works in a full-resolution space and progressively performs joint enhancement and denoising in a progressive manner .
Edit intent: coherence
Original:  The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint enhancement and denoising branch works in a full-resolution space and  performs joint enhancement and denoising in a progressive manner .
Revised:  The coefficient estimation branch works in a low-resolution space and predicts the coefficients for enhancement via bilateral learning, whereas the joint enhancement and denoising branch works in a full-resolution space and performs joint enhancement and denoising .
Edit intent: clarity
Original:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to  an equivalent of the Mohring Effect ( that is present in public transport), and  due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Revised:  The routing of the on-demand fleet follows some of the rules of public transport systems; namely, it is not-for-profit, some users can be required to walk, and all requests must be served . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to an equivalent of the Mohring Effect ( that is present in public transport), and due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Edit intent: meaning-changed
Original:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to  an equivalent of the Mohring Effect ( that is present in public transport), and  due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Revised:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs we identify two sources of scale economies: when demand grows, the average cost is reduced due to an equivalent of the Mohring Effect ( that is present in public transport), and due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Edit intent: fluency
Original:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to  an equivalent of the Mohring Effect ( that is present in public transport), and  due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Revised:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to a) an equivalent of the Mohring Effect ( that is present in public transport), and due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Edit intent: fluency
Original:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to  an equivalent of the Mohring Effect ( that is present in public transport), and  due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Revised:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to an equivalent of the Mohring Effect ( also present in public transport), and due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Edit intent: clarity
Original:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to  an equivalent of the Mohring Effect ( that is present in public transport), and  due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Revised:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to an equivalent of the Mohring Effect ( that is present in public transport), and b) due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Edit intent: coherence
Original:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to  an equivalent of the Mohring Effect ( that is present in public transport), and  due to matching the users in more compatible groups when they are assigned to the vehicles, which we call Better-matching Effect.
Revised:  The fleet of low-capacity vehicles is endogenously adapted to the demand . Considering both users' and operators' costs , we identify two sources of scale economies: when demand grows, the average cost is reduced due to an equivalent of the Mohring Effect ( that is present in public transport), and due to matching users with more similar routes when they are assigned to the vehicles, which we call Better-matching Effect.
Edit intent: clarity
Original:  We compare the ODRP results against public transport, for a feeder line and a circular line with homogeneous demand. We show that ODRP is more competitive when users share a common destination (the feeder line) and when the demand is low, although scale effects suggest that ODRP can also play a role when the demand is high. Relaxing door-to-door vehicle requirements to allow short walks , is shown to be crucial for ODRP to become a viable alternative for both human-driven and automated vehicles , if the ODRP must serve all requests .
Revised:  Our simulations show that relaxing door-to-door vehicle requirements to allow short walks , is shown to be crucial for ODRP to become a viable alternative for both human-driven and automated vehicles , if the ODRP must serve all requests .
Edit intent: clarity
Original:  Relaxing door-to-door vehicle requirements to allow short walks , is shown to be crucial for ODRP to become a viable alternative for both human-driven and automated vehicles , if the ODRP must serve all requests .
Revised:  Relaxing door-to-door vehicle requirements to allow short walks is crucial for the performance of ODRP. In fact, we observe that an ODRP system with human-driven and automated vehicles , if the ODRP must serve all requests .
Edit intent: clarity
Original:  Relaxing door-to-door vehicle requirements to allow short walks , is shown to be crucial for ODRP to become a viable alternative for both human-driven and automated vehicles , if the ODRP must serve all requests .
Revised:  Relaxing door-to-door vehicle requirements to allow short walks , is shown to be crucial for ODRP to become a viable alternative for both human-driven vehicles and walks allowed has a total cost at a similar level to that of a door-to-door ODRP system with driverless vehicles .
Edit intent: meaning-changed
Original:  Our experiments show that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping us to train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at 128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep  .
Revised:  Our experiments show that conditioning augmentation prevents compounding error during sampling in a cascaded model, helping us to train cascading pipelines achieving FID scores of 1.48 at 64x64, 3.52 at 128x128 and 4.88 at 256x256 resolutions, outperforming BigGAN-deep , and classification accuracy scores of 63.02\% (top-1) and 84.06\% (top-5) at 256x256, outperforming VQ-VAE-2 .
Edit intent: meaning-changed
Original: Women bear a disproportionate burden of unpaid household and care work across the world and especially in South Asia. However, due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been made in the context of India.
Revised: Due to the unavailability of nationally representative data on time use, a systematic analysis of the gender gap in unpaid household and care work has not been made in the context of India.
Edit intent: coherence
Original:  The present paper, using the recent Time Use Survey (2019) data addresses two concerns. First, it examines the socioeconomic and demographic factors associated with variation in the time spent on unpaid household and care work among men and women.
Revised:  The present paper, using the recent Time Use Survey (2019) data , examines the socioeconomic and demographic factors associated with variation in the time spent on unpaid household and care work among men and women.
Edit intent: coherence
Original:  Second, it analyses how much of the gender gap in the time allocated to unpaid work can be explained by the difference in socioeconomic and demographic factorsof men and women by employing the Oaxaca-Blinder technique .
Revised:  It analyses how much of the gender gap in the time allocated to unpaid work can be explained by the difference in socioeconomic and demographic factorsof men and women by employing the Oaxaca-Blinder technique .
Edit intent: coherence
Original:  In addition, the design and testing of complex systems, e.g., robots, which often employ deep learning-based perception models, may face severe difficulties as even state-of-the-art methods trained on real and large-scale datasets cannot always perform adequately as they have not adapted to the visual differences between the virtual and the real world data.
Revised:  In addition, the design and testing of complex systems, e.g., robots, which often employ deep learning-based perception models, may face severe difficulties as even state-of-the-art methods trained on real and large-scale datasets cannot always perform adequately due to not having been adapted to the visual differences between the virtual and the real world data.
Edit intent: clarity
Original:  In contrast to the fast marching method and the fast sweeping method for the Eikonal equation, our improved method does not need to compute the solution with any special ordering in either the remedy list or the active list so that it can be implemented in parallel.
Revised:  In contrast to the fast marching method and the fast sweeping method for the Eikonal equation, our improved method does not need to compute the solution with any special ordering in neither the remedy list or the active list so that it can be implemented in parallel.
Edit intent: fluency
Original:  In contrast to the fast marching method and the fast sweeping method for the Eikonal equation, our improved method does not need to compute the solution with any special ordering in either the remedy list or the active list so that it can be implemented in parallel.
Revised:  In contrast to the fast marching method and the fast sweeping method for the Eikonal equation, our improved method does not need to compute the solution with any special ordering in either the remedy list nor the active list so that it can be implemented in parallel.
Edit intent: fluency