wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W)  . Approach:  We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Revised:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) were used . Approach: We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Edit intent: coherence
Original:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W)  . Approach:  We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Revised:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) . Approach: Specifically, we used labeled data from one session or one subject to classify trials in another session (within the same subject) or another subject. We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Edit intent: meaning-changed
Original:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W)  . Approach:  We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment with Hellinger distance as underlying metric to fNIRS data acquired during different n-back task levels.
Revised:  Aim: To address the domain shift in fNIRS data across sessions and subjects for task label alignment, we exploited two domain adaptation approaches - Gromov-Wasserstein (G-W) and Fused Gromov-Wasserstein (FG-W) . Approach: We applied G-W for session-by-session alignment and FG-W for subject-by-subject alignment to fNIRS data acquired during different n-back task levels.
Edit intent: meaning-changed
Original:  We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Revised:  We compared these approaches with three supervised methods: multi-class Support Vector Machine (SVM), Convolutional Neural Network (CNN) . Results: For session-by-session alignment, using G-W resulted in alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Edit intent: meaning-changed
Original:  We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Revised:  We also compared with a supervised method - Convolutional Neural Network (CNN) , and Recurrent Neural Network (RNN) . Results: For session-by-session alignment, using G-W resulted in alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Edit intent: meaning-changed
Original:  We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Revised:  We also compared with a supervised method - Convolutional Neural Network (CNN) . Results: In a sample of six subjects, G-W resulted in alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Edit intent: meaning-changed
Original:  We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Revised:  We also compared with a supervised method - Convolutional Neural Network (CNN) . Results: For session-by-session alignment, using G-W resulted in an alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Edit intent: fluency
Original:  We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Revised:  We also compared with a supervised method - Convolutional Neural Network (CNN) . Results: For session-by-session alignment, using G-W resulted in alignment accuracy of 68 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Edit intent: meaning-changed
Original:  We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects.
Revised:  We also compared with a supervised method - Convolutional Neural Network (CNN) . Results: For session-by-session alignment, using G-W resulted in alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) %DIFDELCMD < \pm %%% 5 \% across five subjects.
Edit intent: clarity
Original:  We also compared with a supervised method - Convolutional Neural Network (CNN)  . Results: For session-by-session alignment, using G-W resulted in  alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% 5 \% across five subjects. For subject-by-subject alignment, using FG-W resulted in  alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%.
Revised:  We also compared with a supervised method - Convolutional Neural Network (CNN) . Results: For session-by-session alignment, using G-W resulted in alignment accuracy of 70 \pm 4 \% (weighted mean \pm standard error) , whereas using CNN resulted in classification accuracy of 58%DIFDELCMD < \pm %%% for session-by-session alignment, FG-W resulted in alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%.
Edit intent: meaning-changed
Original:  For subject-by-subject alignment, using FG-W resulted in  alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%.
Revised:  For subject-by-subject alignment, using FG-W resulted in an alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%.
Edit intent: fluency
Original:  For subject-by-subject alignment, using FG-W resulted in  alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%.
Revised:  For subject-by-subject alignment, using FG-W resulted in alignment accuracy of 55 \pm %DIFDELCMD < \pm %%% 1 \%.
Edit intent: clarity
Original:  For subject-by-subject alignment, using FG-W resulted in  alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 1 \%. Where in both cases 25 \%  represents chance.
Revised:  For subject-by-subject alignment, using FG-W resulted in alignment accuracy of 55 \pm 3 \% , whereas using CNN resulted in classification accuracy of 45%DIFDELCMD < \pm %%% 2 \% for subject-by-subject alignment. In each of these cases, 25 \% represents chance.
Edit intent: meaning-changed
Original: 3CL-Pro (or M-Pro) is the SARS-CoV-2 main protease,  responsible for the cleavage of the large polyprotein 1ab transcript and the liberation of eleven proteins , responsible for viral growth and replication.
Revised: 3CL-Pro (or M-Pro) is the SARS-CoV-2 main protease, acting as a homodimer, is responsible for the cleavage of the large polyprotein 1ab transcript and the liberation of eleven proteins , responsible for viral growth and replication.
Edit intent: meaning-changed
Original: 3CL-Pro (or M-Pro) is the SARS-CoV-2 main protease,  responsible for the cleavage of the large polyprotein 1ab transcript and the liberation of eleven proteins , responsible for viral growth and replication.
Revised: 3CL-Pro (or M-Pro) is the SARS-CoV-2 main protease, responsible for the cleavage of the large polyprotein 1ab transcript in proteins acting on viral growth and replication.
Edit intent: clarity
Original:  It acts exclusively as a homodimer, while monomers are inactive. Due to its pivotal role, 3CL-Pro has been one of the most studied SARS-CoV-2 proteins and the subject of a number of therapeutic interventions, targeting its catalytic domain.
Revised:  3CL-Pro has been one of the most studied SARS-CoV-2 proteins and the subject of a number of therapeutic interventions, targeting its catalytic domain.
Edit intent: coherence
Original:  Due to its pivotal role, 3CL-Pro has been one of the most studied SARS-CoV-2 proteins and the subject of a number of therapeutic interventions, targeting its catalytic domain.
Revised:  Due to its pivotal role, 3CL-Pro has been one of the most studied SARS-CoV-2 proteins and the subject of therapeutic interventions, targeting its catalytic domain.
Edit intent: clarity
Original:  We report that fortunellin (acacetin 7-O-neohesperidoside), a natural flavonoid O-glycoside, isolated from the fruits of Citrus japonica var. margarita (kumquat), is a potent inhibitor of 3CL-Pro dimerization.
Revised:  We report that fortunellin (acacetin 7-O-neohesperidoside), a natural flavonoid O-glycoside, is a potent inhibitor of 3CL-Pro dimerization.
Edit intent: coherence
Original:  We also investigate the relative advantages of deep learning (DL), radiomics, and DL of radiomic-embedded feature maps in predicting these outcomes. Methods: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Revised:  This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Edit intent: clarity
Original:  Methods: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Revised:  Methods: This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Edit intent: meaning-changed
Original:  Methods: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Revised:  Methods: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 patients treated at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Edit intent: clarity
Original:  Methods: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Revised:  Methods: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Edit intent: clarity
Original:  Methods: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020.
Revised:  Methods: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center between March and August 2020.
Edit intent: meaning-changed
Original:  A DL segmentation pipeline was developed to generate masks for both lung fields and artifacts for each CXR. Machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated on 353 baseline CXRstaken from COVID-19 positive patients .
Revised:  DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated on 353 baseline CXRstaken from COVID-19 positive patients .
Edit intent: clarity
Original:  Results: Classification models for mechanical ventilation requirement (test N=154) and mortality (test N=190) had AUCs of up to 0.904 and 0.936 , respectively.
Revised:  All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05 for mechanical ventilation requirement (test N=154) and mortality (test N=190) had AUCs of up to 0.904 and 0.936 , respectively.
Edit intent: meaning-changed
Original:  We also found that the inclusion of radiomic-embedded maps improved DL model predictions of clinical outcomes. Conclusions: We demonstrate the potential for computerized analysis of baseline CXR in predicting disease outcomes in COVID-19 patients .
Revised:  Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone .
Edit intent: meaning-changed
Original: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Revised: We study the computation complexity of deep ReLU (Rectified Linear Unit) neural networks for the approximation of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Edit intent: clarity
Original: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Revised: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Edit intent: coherence
Original: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Revised: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( %DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Edit intent: fluency
Original: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Revised: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Edit intent: fluency
Original: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Revised: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Edit intent: fluency
Original: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Revised: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I %DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Edit intent: coherence
Original: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Revised: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Edit intent: meaning-changed
Original: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 ^d, by linear algorithms of sparse-grid sampling recovery and by deep ReLU (Rectified Linear Unit) neural networks when the dimension d may be very large.
Revised: We investigate approximations of functions from the H\"older-Zygmund space of mixed smoothness H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d) defined on the d-dimensional unit cube \mathbb I ^d:=%DIFDELCMD < [%%% 0,1 when the dimension d may be very large.
Edit intent: meaning-changed
Original:  The approximation error is measured in the norm of the isotropic Sobolev space W^{1,p \mathbb I%DIFDELCMD < }%%% ^d). The optimality of this sampling recovery is studied in terms of sampling n-widths. Optimal linear sampling algorithms are constructed on sparse grids using the piece-wise linear B-spline interpolation representation. We prove some tight dimension-dependent bounds of the sampling n-widths explicit in d and n. Based on the results on sampling recovery, we investigate the expressive power of deep ReLU neural networks to approximate functions in H\"older-Zygmund space . Namely,
Revised:  The approximation error is measured in the norm of the isotropic Sobolev space W^{1,p \mathbb I%DIFDELCMD < }%%% . For every function f from the H\"older-Zygmund space . Namely,
Edit intent: meaning-changed
Original:  we investigate the expressive power of deep ReLU neural networks to approximate functions in H\"older-Zygmund space . Namely, for any function f\in H^\alpha_\infty( \mathbb I%DIFDELCMD < }%%% ^d), we explicitly construct a deep ReLU neural network having an output that approximates f in the W_0^{1,p \mathbb I%DIFDELCMD < }%%% ^d)-norm with a prescribed accuracy \varepsilon, and prove tight dimension-dependent  bounds of the computation complexity of this approximation, characterized as the number of weights and the depth of this deep ReLU neural network, explicitly in d and \varepsilon.
Revised:  we investigate the expressive power of deep ReLU neural networks to approximate functions in H\"older-Zygmund space \mathbb I%DIFDELCMD < }%%% ^d), we explicitly construct a deep ReLU neural network having an output that approximates f in the W_0^{1,p \mathbb I%DIFDELCMD < }%%% ^d)-norm with a prescribed accuracy \varepsilon, and prove tight dimension-dependent bounds of the computation complexity of this approximation, characterized as the number of weights and the depth of this deep ReLU neural network, explicitly in d and \varepsilon.
Edit intent: coherence