wanyu/IteraTeR_full_sent huggingface dataset:
Original:  However, due to the low efficiency of the 2P absorption process, the imaging speed of this technique is typically limited (~ 1 Hz) by the signal to noise ratio achievable without sample photodamage. Here we describe a custom-made 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) . ...
Revised:  However, due to the low efficiency of the 2P absorption process, the imaging speed of this technique is typically limited by the signal-to-noise-ratio. Here, we describe a custom-made 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) . ...
Edit intent: clarity
Original:  Here we describe a custom-made 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) . ...
Revised:  Here we describe a 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) . ...
Edit intent: clarity
Original:  Here we describe a custom-made 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) . ...
Revised:  Here we describe a custom-made 2P LSFM setup designed for non-invasive imaging that enables quintuplicating state-of-the-art volumetric acquisition rate of the larval zebrafish brain (5 Hz) . ...
Edit intent: clarity
Original:  Here we describe a custom-made 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) . ...
Revised:  Here we describe a custom-made 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) ...
Edit intent: fluency
Original:  Here we describe a custom-made 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) . ...
Revised:  Here we describe a custom-made 2P LSF microscope that overcomes this limit, being specifically designed to perform whole-brain cellular-resolution calcium imaging in zebrafish larvae quintuplicating previous volumetric acquisition frequency (5 Hz) . 
Edit intent: fluency
Original: %DIFDELCMD < ] %%% We employed this system to investigate, in real-time on a brain-wide scale, the onset and propagation of acute seizures, as induced by the convulsant drug pentylenetetrazol (PTZ), avoiding detrimental visual stimulation on a highly susceptible system such as an epileptic brain. ...
Revised: %DIFDELCMD < ] %%% while keeping low the laser intensity on the specimen. We applied our system to the study of pharmacologically-induced acute seizures, as induced by the convulsant drug pentylenetetrazol (PTZ), avoiding detrimental visual stimulation on a highly susceptible system such as an epileptic brain. ...
Edit intent: meaning-changed
Original: %DIFDELCMD < ] %%% We employed this system to investigate, in real-time on a brain-wide scale, the onset and propagation of acute seizures, as induced by the convulsant drug pentylenetetrazol (PTZ), avoiding detrimental visual stimulation on a highly susceptible system such as an epileptic brain. ...
Revised: %DIFDELCMD < ] %%% We employed this system to investigate, in real-time on a brain-wide scale, the onset and propagation of acute seizures, ...
Edit intent: coherence
Original: %DIFDELCMD < ] %%% We employed this system to investigate, in real-time on a brain-wide scale, the onset and propagation of acute seizures, as induced by the convulsant drug pentylenetetrazol (PTZ), avoiding detrimental visual stimulation on a highly susceptible system such as an epileptic brain. ...
Revised: %DIFDELCMD < ] %%% We employed this system to investigate, in real-time on a brain-wide scale, the onset and propagation of acute seizures, as induced by the convulsant drug pentylenetetrazol (PTZ), avoiding detrimental visual stimulation on a highly susceptible system such as an epileptic brain. 
Edit intent: fluency
Original: %DIFDELCMD < ] %%% In conclusion, the presented 2P LSF microscope design affords high spatio-temporal resolution while avoiding visual stimuli and allows unprecedented access to whole zebrafish brain epileptic dynamics .
Revised: %DIFDELCMD < ] %%% characterizing the spatial-temporal dynamics of pathological activity and describing for the first time the appearance of caudo-rostral ictal waves (CRIWs) .
Edit intent: meaning-changed
Original:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at  corpus callosum, and cumulative strain damage (15\%) on  18 BIC  .
Revised:  To evaluate the BIC's risk estimation accuracy across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at corpus callosum, and cumulative strain damage (15\%) on 18 BIC .
Edit intent: clarity
Original:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at  corpus callosum, and cumulative strain damage (15\%) on  18 BIC  .
Revised:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact types , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at corpus callosum, and cumulative strain damage (15\%) on 18 BIC .
Edit intent: clarity
Original:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at  corpus callosum, and cumulative strain damage (15\%) on  18 BIC  .
Revised:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at the corpus callosum, and cumulative strain damage (15\%) on 18 BIC .
Edit intent: fluency
Original:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at  corpus callosum, and cumulative strain damage (15\%) on  18 BIC  .
Revised:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at corpus callosum, and cumulative strain damage (15\%) on each of 18 BIC .
Edit intent: clarity
Original:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at  corpus callosum, and cumulative strain damage (15\%) on  18 BIC  .
Revised:  To evaluate the BIC's ability to predict brain strain across five datasets comprising different head impact subtypes , linear regression was used to model 95\% maximum principal strain, 95\% maximum principal strain at corpus callosum, and cumulative strain damage (15\%) on 18 BIC respectively .
Edit intent: clarity
Original:  The accuracy of  regression is generally decreasing if the BIC regression models are fit on a dataset with a different head impact subtype rather than on the dataset with the same subtype . Given this finding, this study raises concerns for applying BIC to predict the brain strain for head impacts different from the head impacts on which the BIC was developed.
Revised:  The accuracy of brain strain regression is generally decreasing if the BIC regression models are fit on a dataset with a different head impact subtype rather than on the dataset with the same subtype . Given this finding, this study raises concerns for applying BIC to predict the brain strain for head impacts different from the head impacts on which the BIC was developed.
Edit intent: clarity
Original:  The accuracy of  regression is generally decreasing if the BIC regression models are fit on a dataset with a different head impact subtype rather than on the dataset with the same subtype . Given this finding, this study raises concerns for applying BIC to predict the brain strain for head impacts different from the head impacts on which the BIC was developed.
Revised:  The accuracy of regression is generally decreasing if the BIC regression models are fit on a dataset with a different type of head impact rather than on the dataset with the same subtype . Given this finding, this study raises concerns for applying BIC to predict the brain strain for head impacts different from the head impacts on which the BIC was developed.
Edit intent: clarity
Original:  The accuracy of  regression is generally decreasing if the BIC regression models are fit on a dataset with a different head impact subtype rather than on the dataset with the same subtype . Given this finding, this study raises concerns for applying BIC to predict the brain strain for head impacts different from the head impacts on which the BIC was developed.
Revised:  The accuracy of regression is generally decreasing if the BIC regression models are fit on a dataset with a different head impact subtype rather than on the dataset with the same type . Given this finding, this study raises concerns for applying BIC to predict the brain strain for head impacts different from the head impacts on which the BIC was developed.
Edit intent: clarity
Original:  The accuracy of  regression is generally decreasing if the BIC regression models are fit on a dataset with a different head impact subtype rather than on the dataset with the same subtype . Given this finding, this study raises concerns for applying BIC to predict the brain strain for head impacts different from the head impacts on which the BIC was developed.
Revised:  The accuracy of regression is generally decreasing if the BIC regression models are fit on a dataset with a different head impact subtype rather than on the dataset with the same subtype . Given this finding, this study raises concerns for applying BIC to estimate the brain injury risks for head impacts different from the head impacts on which the BIC was developed.
Edit intent: clarity
Original: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise \mbox{%DIFAUXCMD kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Revised: We study {\em online} active learning of homogeneous halfspaces in R^d with adversarial noise \mbox{%DIFAUXCMD kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Edit intent: clarity
Original: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise \mbox{%DIFAUXCMD kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Revised: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Edit intent: clarity
Original: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise \mbox{%DIFAUXCMD kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu and the marginal distribution over unlabeled data is unchanged .
Revised: We study {\em online} active learning of homogeneous s-sparse halfspaces in R^d with adversarial noise \mbox{%DIFAUXCMD kearns1992toward where the overall probability of a noisy label is constrained to be at most \nu .
Edit intent: clarity
Original:  Our main contribution is a state-of-the-art online active learning algorithm that achieves near-optimal attribute efficiency, label and sample complexity under mild distributional assumptions. In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  Our main contribution is a state-of-the-art online active learning algorithm that runs in polynomial time, and under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: fluency
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( d \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: fluency
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: clarity
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( {\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Edit intent: fluency
Original:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} {\epsilon} } \big).
Revised:  In particular, under the conditions that the marginal distribution is isotropic log-concave and \nu = \Omega(\epsilon), where \epsilon \in (0, 1) is the target error rate, we show that our algorithm PAC learns the underlying halfspace in polynomial time with near-optimal label complexity bound of O\big( s \cdot polylog( d, \frac{1}{\epsilon})\big) and sample complexity bound of \tilde{O}\big( \frac{s{\epsilon} \cdot polylog(d)} \frac{d{\epsilon} } \big).
Edit intent: fluency
Original:  Prior to this work, existing online algorithms designed for tolerating the adversarial noise are either subject to label complexity polynomial in d or \frac{1}{\epsilon}, or work under the restrictive uniform marginal distribution.
Revised:  Prior to this work, existing online algorithms designed for tolerating the adversarial noise are subject to either label complexity polynomial in d or \frac{1}{\epsilon}, or work under the restrictive uniform marginal distribution.
Edit intent: fluency
Original:  Prior to this work, existing online algorithms designed for tolerating the adversarial noise are either subject to label complexity polynomial in d or \frac{1}{\epsilon}, or work under the restrictive uniform marginal distribution.
Revised:  Prior to this work, existing online algorithms designed for tolerating the adversarial noise are either subject to label complexity polynomial in \frac{1}{\epsilon}, or work under the restrictive uniform marginal distribution.
Edit intent: clarity
Original:  Prior to this work, existing online algorithms designed for tolerating the adversarial noise are either subject to label complexity polynomial in d or \frac{1}{\epsilon}, or work under the restrictive uniform marginal distribution.
Revised:  Prior to this work, existing online algorithms designed for tolerating the adversarial noise are either subject to label complexity polynomial in d or \frac{1}{\epsilon}, or suboptimal noise tolerance, or restrictive marginal distributions. With the additional prior knowledge that the underlying halfspace is s-sparse, we obtain attribute-efficient label complexity of
Edit intent: meaning-changed