wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We also show how our framework can jointly generate static and animated 3D scene efficiently. We modify CLEVR to generate a large, scalable dataset - Integrated static and animated 3D  scene (Iscene).
Revised:  We also show how our framework can jointly generate static and animated scene descriptions, respectively. We achieve 98.427\% accuracy on test data set in detecting the 3D scene (Iscene).
Edit intent: meaning-changed
Original:  We modify CLEVR to generate a large, scalable dataset - Integrated static and animated 3D  scene (Iscene).
Revised:  We modify CLEVR to generate a large, scalable dataset - Integrated static and animated 3D objects features successfully. Our work shows a proof of concept of one approach towards solving the problem, and we believe with enough training data, the same pipeline can be expanded to handle even broader set of 3D scene (Iscene).
Edit intent: meaning-changed
Original:  Our findings suggest live-streaming based education do help students and teachers achieve their education goal to a great extent, yet there are several key challenges unresolved by current modes. We further demonstrate the emergence of several novel learning experiences and modes of interactions, which contribute to variations in instructor-student, and student-student relationships. Our work not only reveals important design guidelines and insights to better support current remote learning experience during the pandemic, but also provides valuable implications towards constructing future collaborative education supporting systems and experience post pandemic.
Revised:  Our study not only reveals important design guidelines and insights to better support current remote learning experience during the pandemic, but also provides valuable implications towards constructing future collaborative education supporting systems and experience post pandemic.
Edit intent: clarity
Original: We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for offline utterance-wise and block-online continuous speaker separation in reverberant conditions , aiming at both speaker separation and dereverberation.
Revised: We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for speaker separation in reverberant conditions , aiming at both speaker separation and dereverberation.
Edit intent: clarity
Original: We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for offline utterance-wise and block-online continuous speaker separation in reverberant conditions , aiming at both speaker separation and dereverberation.
Revised: We propose multi-microphone complex spectral mapping, a simple way of applying deep learning for time-varying non-linear beamforming, for offline utterance-wise and block-online continuous speaker separation in reverberant conditions . We aim at both speaker separation and dereverberation.
Edit intent: fluency
Original:   Assuming a fixed array geometry between training and testing, we train deep neural networks (DNN) to predict the real and imaginary (RI) components of target speech at a reference microphone from the RI components of multiple microphones.
Revised:  Our study first investigates offline utterance-wise speaker separation and then extends to block-online continuous speech separation (CSS). Assuming a fixed array geometry between training and testing, we train deep neural networks (DNN) to predict the real and imaginary (RI) components of target speech at a reference microphone from the RI components of multiple microphones.
Edit intent: meaning-changed
Original:  We then integrate multi-microphone complex spectral mapping with  beamforming and post-filtering to further improve separation, and combine it with frame-level speaker counting for block-online continuous speaker separation (CSS) . Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry.
Revised:  We then integrate multi-microphone complex spectral mapping with minimum variance distortionless response (MVDR) beamforming and post-filtering to further improve separation, and combine it with frame-level speaker counting for block-online continuous speaker separation (CSS) . Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry.
Edit intent: meaning-changed
Original:  We then integrate multi-microphone complex spectral mapping with  beamforming and post-filtering to further improve separation, and combine it with frame-level speaker counting for block-online continuous speaker separation (CSS) . Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry.
Revised:  We then integrate multi-microphone complex spectral mapping with beamforming and post-filtering to further improve separation, and combine it with frame-level speaker counting for block-online CSS . Although our system is trained on simulated room impulse responses (RIR) based on a fixed number of microphones arranged in a given geometry, it generalizes well to a real array with the same geometry.
Edit intent: clarity
Original: The emergence of online political advertising has come with little regulation . As a result, social media platforms provide venues for political advertising with little accountabilityattached. Where accountability for political speech is lacking, we may expect the speech to be more divisive. This is particularly true for outside groups like Super PACs and dark money groups, most of whom disappear after elections. In this work, we analyze how campaign finance disclosure and microtargeting relate to the sentiment of political ads on Facebook.
Revised: The emergence of online political advertising has come with little regulation , allowing political advertisers on social media to avoid accountability. We analyze how transparency deficits caused by dark money and group impermanence relate to the sentiment of political ads on Facebook.
Edit intent: clarity
Original:  We compare ads run by candidates, parties, and two types of outside groups: those that disclose their donors and those that are funded by dark money . Dark money outside groups ran ads with the most negative sentiment on average, whereas candidates ran ads with the most positive sentiment .
Revised:  We compare ads run by candidates, parties, and outside groups, which we classify by (i) their donor transparency (dark money or disclosed) and (ii) the group's permanence (disappearing after 2018 or re-registering). Ads run by dark money . Dark money outside groups ran ads with the most negative sentiment on average, whereas candidates ran ads with the most positive sentiment .
Edit intent: meaning-changed
Original:  We compare ads run by candidates, parties, and two types of outside groups: those that disclose their donors and those that are funded by dark money . Dark money outside groups ran ads with the most negative sentiment on average, whereas candidates ran ads with the most positive sentiment .
Revised:  We compare ads run by candidates, parties, and two types of outside groups: those that disclose their donors and those that are funded by dark money and disappearing outside groups were more negative than transparent and re-registering groups, respectively .
Edit intent: meaning-changed
Original:  Outside groups ran 62\% of Facebook ads, with dark money ads outnumbering ads with more transparent campaign financing (3:2). A majority of ads (63\%) were microtargeted to less than 1000 Facebook users. Outside groups used microtargeting to run ads that have more negative sentiment, and candidates and parties used it to run ads with more positive sentiment .
Revised:  Outside groups as a whole also ran more negative ads than candidates and parties used it to run ads with more positive sentiment .
Edit intent: clarity
Original:  We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information  , the higher will be its capacity of predicting the correct label .
Revised:  We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence , the higher will be its capacity of predicting the correct label .
Edit intent: meaning-changed
Original:  We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information  , the higher will be its capacity of predicting the correct label .
Revised:  We also find that BERT's capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information , the higher will be its capacity of predicting the expected label assigned to that sentence .
Edit intent: clarity
Original: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers \bar z_{\ell+1}=\bar z_\ell + \text{Re k=1^K\bar b_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell}+ \text{Re k=1^K\bar c_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x}.
Revised: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers \bar z_{\ell+1}=\bar z_\ell + \mathrm{Re k=1^K\bar b_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell}+ \text{Re k=1^K\bar c_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x}.
Edit intent: clarity
Original: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers \bar z_{\ell+1}=\bar z_\ell + \text{Re k=1^K\bar b_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell}+ \text{Re k=1^K\bar c_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x}.
Revised: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers \bar z_{\ell+1}=\bar z_\ell + \text{Re k=1^K\bar b_{\ell k}e^{ %DIFDELCMD < {\rm %%% \mathrm{i \ell k\bar z_\ell}+ \text{Re k=1^K\bar c_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x}.
Edit intent: meaning-changed
Original: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers \bar z_{\ell+1}=\bar z_\ell + \text{Re k=1^K\bar b_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell}+ \text{Re k=1^K\bar c_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x}.
Revised: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers \bar z_{\ell+1}=\bar z_\ell + \text{Re k=1^K\bar b_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell}+ \mathrm{Re k=1^K\bar c_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x}.
Edit intent: coherence
Original: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers \bar z_{\ell+1}=\bar z_\ell + \text{Re k=1^K\bar b_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell}+ \text{Re k=1^K\bar c_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x}.
Revised: Estimates of the generalization error are proved for a residual neural network with L random Fourier features layers \bar z_{\ell+1}=\bar z_\ell + \text{Re k=1^K\bar b_{\ell k}e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell}+ \text{Re k=1^K\bar c_{\ell k}e^{ %DIFDELCMD < {\rm %%% \mathrm{i \omega'_{\ell k}\cdot x}.
Edit intent: meaning-changed
Original:  An optimal distribution for the frequencies (\ell k,\omega'_{\ell k}) of the random Fourier features e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell} and e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x} is derived.
Revised:  An optimal distribution for the frequencies (\ell k,\omega'_{\ell k}) of the random Fourier features e^{ %DIFDELCMD < {\rm %%% \mathrm{i \ell k\bar z_\ell} and e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x} is derived.
Edit intent: meaning-changed
Original:  An optimal distribution for the frequencies (\ell k,\omega'_{\ell k}) of the random Fourier features e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell} and e^{ %DIFDELCMD < {\rm %%% i \omega'_{\ell k}\cdot x} is derived.
Revised:  An optimal distribution for the frequencies (\ell k,\omega'_{\ell k}) of the random Fourier features e^{ %DIFDELCMD < {\rm %%% i \ell k\bar z_\ell} and e^{ %DIFDELCMD < {\rm %%% \mathrm{i \omega'_{\ell k}\cdot x} is derived.
Edit intent: meaning-changed
Original:  The generalization error turns out to be smaller than the estimate {\|\hat f\|^2_{L^1(\mathbb{R}^d)}}/{( LK )} of the generalization error for random Fourier features with one hidden layer and the same total number of nodes LK , in the case the L^\infty-norm of f is much less than the L^1-norm of its Fourier transform \hat f.
Revised:  The generalization error turns out to be smaller than the estimate {\|\hat f\|^2_{L^1(\mathbb{R}^d)}}/{( KL )} of the generalization error for random Fourier features with one hidden layer and the same total number of nodes LK , in the case the L^\infty-norm of f is much less than the L^1-norm of its Fourier transform \hat f.
Edit intent: clarity
Original:  The generalization error turns out to be smaller than the estimate {\|\hat f\|^2_{L^1(\mathbb{R}^d)}}/{( LK )} of the generalization error for random Fourier features with one hidden layer and the same total number of nodes LK , in the case the L^\infty-norm of f is much less than the L^1-norm of its Fourier transform \hat f.
Revised:  The generalization error turns out to be smaller than the estimate {\|\hat f\|^2_{L^1(\mathbb{R}^d)}}/{( LK )} of the generalization error for random Fourier features with one hidden layer and the same total number of nodes KL , in the case the L^\infty-norm of f is much less than the L^1-norm of its Fourier transform \hat f.
Edit intent: clarity
Original: During the Covid-19 pandemics, we also experience another dangerous pandemics based on misinformation. Narratives disconnected from fact-checking on the origin and cure of the disease intertwined with pre-existing political fights. We collect a database on Twitter posts and analyse the topology of the networks of retweeters (usersbroadcasting again the same elementary piece of information, or tweet) and validate its structure with methods of statistical physics of networks. Furthermore, by using commonly available fact checking software , we assess the reputation of the pieces of news exchanged.
Revised: The COVID-19 pandemic has impacted on every human activity and, because of the urgency of finding the proper responses to such an unprecedented emergency, it generated a diffused societal debate. The online version of this discussion was not exempted by the presence of d/misinformation campaigns, but differently from what already witnessed in other debates, the COVID-19 -- intentional or not -- flow of false information put at severe risk the public health, reducing the effectiveness of governments' countermeasures. In the present manuscript, we study the effective impact of misinformation in the Italian societal debate on Twitter during the pandemic, focusing on the various discursive communities. In order to extract the discursive communities, we focus on verified users, i.e. accounts whose identity is officially certified by Twitter. We thus infer the various discursive communities based on how verified users are perceived by standard ones: if two verified accounts are considered as similar by non unverified ones, we link them in the network of certified accounts. We first observe that, beside being a mostly scientific subject, the COVID-19 discussion show a clear division in what results to be different political groups. At this point, by using a commonly available fact-checking software (NewsGuard) , we assess the reputation of the pieces of news exchanged.
Edit intent: meaning-changed
Original:  By using a combination of theoretical and practical weapons, we are able to track down the flow of misinformation in a snapshot of the Twitter ecosystem.Thanks to the presence of verified users, we can also assign a polarization to the network nodes (users) and see the impact of low-quality information producers and spreaders in the Twitter ecosystem .
Revised:  We filter the network of retweets (i.e. users re-broadcasting the same elementary piece of information, or tweet) from random noise and check the presence of messages displaying an url. The impact of misinformation posts reaches the 22.1\% in the right and center-right wing community and its contribution is even stronger in absolute numbers, due to the activity of this group: 96\% of all non reputable urls shared by political groups come from this community .
Edit intent: meaning-changed
Original:   The surfaces generated approximate the smooth component of a cloud of data points within user specified tolerances.
Revised:  In regions where many data points belong to the same smooth area LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The surfaces generated approximate the smooth component of a cloud of data points within user specified tolerances.
Edit intent: meaning-changed
Original:  The LMC significantly outperforms a diverse set of baselines at a fraction of the pre-training cost and learns clinically coherent representations  .
Revised:  The LMC significantly outperforms a diverse set of baselines at a fraction of the pre-training cost and learns clinically coherent representations . We demonstrate that not only is metadata itself very helpful for the task, but that the LMC inference algorithm provides an additional large benefit .
Edit intent: meaning-changed
Original: In attempts to produce machine learning models less reliant on spurious patterns in training data , researchers have recently proposed  a human-in-the-loop process for generating counterfactually augmented datasets.
Revised: In attempts to produce machine learning models less reliant on spurious patterns in training data , researchers have recently proposed curating counterfactually augmented data (CAD) via a human-in-the-loop process for generating counterfactually augmented datasets.
Edit intent: meaning-changed
Original: In attempts to produce machine learning models less reliant on spurious patterns in training data , researchers have recently proposed  a human-in-the-loop process for generating counterfactually augmented datasets. As applied in NLP, given some documents and their (initial) labels, humans are tasked with revising the text to make a (given) counterfactual label applicable.
Revised: In attempts to produce machine learning models less reliant on spurious patterns in training data , researchers have recently proposed a human-in-the-loop process in which given some documents and their (initial) labels, humans are tasked with revising the text to make a (given) counterfactual label applicable.
Edit intent: clarity
Original:  While this work draws  on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed improvements in out-of-domain evaluation .
Revised:  While this work draws loosely on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed improvements in out-of-domain evaluation .
Edit intent: meaning-changed
Original:  While this work draws  on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed improvements in out-of-domain evaluation .
Revised:  While this work draws on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model (even at an abstract level) and the principles underlying the observed improvements in out-of-domain evaluation .
Edit intent: meaning-changed
Original:  While this work draws  on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed improvements in out-of-domain evaluation .
Revised:  While this work draws on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed out-of-domain evaluation .
Edit intent: clarity
Original:  While this work draws  on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed improvements in out-of-domain evaluation .
Revised:  While this work draws on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed improvements in out-of-domain improvements remain unclear .
Edit intent: clarity
Original:  In this paper, we explore a toy analog , using linear Gaussian models . Our analysis reveals interesting relationships between causal models, measurement noise, out-of-domain generalization, and reliance on spurious signals.
Revised:  In this paper, we explore a toy analog , using linear Gaussian models , observing interesting relationships between causal models, measurement noise, out-of-domain generalization, and reliance on spurious signals.
Edit intent: clarity
Original:  Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain .
Revised:  Our analysis provides some insights that help to explain the efficacy of CAD. Moreover, we develop the hypothesis that while adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain .
Edit intent: meaning-changed
Original:  Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain .
Revised:  Interestingly our analysis suggests that data corrupted by adding noise to causal features should degrade both in-domain and out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain .
Edit intent: meaning-changed
Original:  Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain .
Revised:  Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, adding noise to non-causal features may make models more robust out-of-domain .
Edit intent: coherence
Original:  Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain .
Revised:  Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features should lead to relative improvements in out-of-domain .
Edit intent: clarity