wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability  .
Revised:  To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability . Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance .
Edit intent: meaning-changed
Original:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Revised:  We hypothesized that agonism on 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Edit intent: meaning-changed
Original:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Revised:  We hypothesized that agonism on \alpha alpha7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Edit intent: meaning-changed
Original:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Revised:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Edit intent: fluency
Original:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Revised:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha alpha7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Edit intent: meaning-changed
Original:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Revised:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of 7nAChR will achieve the opposite.
Edit intent: fluency
Original:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha 7nAChR will achieve the opposite.
Revised:  We hypothesized that agonism on \alpha 7 nicotinic acetylcholine receptor ( \alpha 7nAChR ) in fetal astrocytes will augment their neuroprotective transcriptome profile, while the antagonistic stimulation of \alpha alpha7nAChR will achieve the opposite.
Edit intent: meaning-changed
Original:  Using an in vivo - in vitro model of developmental programming of neuroinflammation induced by lipopolysaccharide (LPS), we validated this hypothesis in primary fetal sheep astrocytes cultures re-exposed to LPS in presence of a selective \alpha 7nAChR agonist or antagonist.
Revised:  Using an in vivo - in vitro model of developmental programming of neuroinflammation induced by lipopolysaccharide (LPS), we validated this hypothesis in primary fetal sheep astrocytes cultures re-exposed to LPS in presence of a selective 7nAChR agonist or antagonist.
Edit intent: clarity
Original:  Using an in vivo - in vitro model of developmental programming of neuroinflammation induced by lipopolysaccharide (LPS), we validated this hypothesis in primary fetal sheep astrocytes cultures re-exposed to LPS in presence of a selective \alpha 7nAChR agonist or antagonist.
Revised:  Using an in vivo - in vitro model of developmental programming of neuroinflammation induced by lipopolysaccharide (LPS), we validated this hypothesis in primary fetal sheep astrocytes cultures re-exposed to LPS in presence of a selective \alpha alpha7nAChR agonist or antagonist.
Edit intent: fluency
Original:  Our RNAseq findings show that a pro-inflammatory astrocyte transcriptome phenotype acquired in vitro by LPS stimulation is reversed with \alpha 7nAChR agonistic stimulation.
Revised:  Our RNAseq findings show that a pro-inflammatory astrocyte transcriptome phenotype acquired in vitro by LPS stimulation is reversed with 7nAChR agonistic stimulation.
Edit intent: clarity
Original:  Our RNAseq findings show that a pro-inflammatory astrocyte transcriptome phenotype acquired in vitro by LPS stimulation is reversed with \alpha 7nAChR agonistic stimulation.
Revised:  Our RNAseq findings show that a pro-inflammatory astrocyte transcriptome phenotype acquired in vitro by LPS stimulation is reversed with \alpha alpha7nAChR agonistic stimulation.
Edit intent: fluency
Original:  Furthermore, we conduct a secondary transcriptome analysis against the identical \alpha 7nAChR experiments in fetal sheep primary microglia cultures and against the Simons Simplex Collection for autism spectrum disorder and discuss the implications  .
Revised:  Furthermore, we conduct a secondary transcriptome analysis against the identical 7nAChR experiments in fetal sheep primary microglia cultures and against the Simons Simplex Collection for autism spectrum disorder and discuss the implications .
Edit intent: meaning-changed
Original:  Furthermore, we conduct a secondary transcriptome analysis against the identical \alpha 7nAChR experiments in fetal sheep primary microglia cultures and against the Simons Simplex Collection for autism spectrum disorder and discuss the implications  .
Revised:  Furthermore, we conduct a secondary transcriptome analysis against the identical \alpha alpha7nAChR experiments in fetal sheep primary microglia cultures and against the Simons Simplex Collection for autism spectrum disorder and discuss the implications .
Edit intent: meaning-changed
Original:  Furthermore, we conduct a secondary transcriptome analysis against the identical \alpha 7nAChR experiments in fetal sheep primary microglia cultures and against the Simons Simplex Collection for autism spectrum disorder and discuss the implications  .
Revised:  Furthermore, we conduct a secondary transcriptome analysis against the identical \alpha 7nAChR experiments in fetal sheep primary microglia cultures and discuss the implications .
Edit intent: coherence
Original:  Furthermore, we conduct a secondary transcriptome analysis against the identical \alpha 7nAChR experiments in fetal sheep primary microglia cultures and against the Simons Simplex Collection for autism spectrum disorder and discuss the implications  .
Revised:  Furthermore, we conduct a secondary transcriptome analysis against the identical \alpha 7nAChR experiments in fetal sheep primary microglia cultures and against the Simons Simplex Collection for autism spectrum disorder and discuss the implications for neurodevelopment .
Edit intent: meaning-changed
Original:  For the detection of sepsis, severe sepsis, and septic shock, our model achieves an AUC of 0.94, 0.91 and 0.89 , respectively.
Revised:  For the detection of sepsis, severe sepsis, and septic shock, our model achieves an AUC of 0.97, 0.96 and 0.91 , respectively.
Edit intent: meaning-changed
Original: Following the seminal works of Danzer (1956, 1986) and Stach\'{o} ( 1965, 1981) , and the recent result of Har-Peled et. al. (2018), we study the problem of stabbing disks by points.
Revised: In their seminal work, Danzer (1956, 1986) and Stach\'{o} ( 1965, 1981) , and the recent result of Har-Peled et. al. (2018), we study the problem of stabbing disks by points.
Edit intent: clarity
Original: Following the seminal works of Danzer (1956, 1986) and Stach\'{o} ( 1965, 1981) , and the recent result of Har-Peled et. al. (2018), we study the problem of stabbing disks by points.
Revised: Following the seminal works of Danzer (1956, 1986) and Stach\'{o} ( 1981) , and the recent result of Har-Peled et. al. (2018), we study the problem of stabbing disks by points.
Edit intent: coherence
Original: Following the seminal works of Danzer (1956, 1986) and Stach\'{o} ( 1965, 1981) , and the recent result of Har-Peled et. al. (2018), we study the problem of stabbing disks by points. We prove that any set of pairwise intersecting disks in the plane can be stabbed by four points.
Revised: Following the seminal works of Danzer (1956, 1986) and Stach\'{o} ( 1965, 1981) established that every set of pairwise intersecting disks in the plane can be stabbed by four points.
Edit intent: clarity
Original:  Our proof\etal  is constructive and yields a linear-time algorithm for finding the points .
Revised:  However, both these proofs are non-constructive, at least in the sense that they do not seem to imply an efficient algorithm for finding the stabbing points, given such a set of disks D. Recently, Har-Peled\etal is constructive and yields a linear-time algorithm for finding the points .
Edit intent: meaning-changed
Original:  Our proof\etal  is constructive and yields a linear-time algorithm for finding the points .
Revised:  Our proof\etal (2018) presented a relatively simple linear-time algorithm for finding five points that stab D. We present an alternative proof (and the first in English) to the assertion that four points are sufficient to stab D. Moreover, our proof is constructive and yields a linear-time algorithm for finding the points .
Edit intent: meaning-changed
Original: Cortical neurons exhibit rich dynamics, shaped by the presence of history-dependent biophysical mechanisms such as adaptation over different time scales. On the other hand, cortical circuits are highly recurrent which can result in chaotic network dynamics even if network nodes have simple dynamics .
Revised: While most models of randomly connected networks assume nodes with simple dynamics, nodes in realistic highly connected networks, such as neurons in the brain, exhibit intrinsic dynamics over multiple timescales .
Edit intent: clarity
Original:  A novel dynamical mean-field theory for  networks of multi-dimensional rate units shows that , despite the nonlinear interactions between units, the recurrent network dynamics inherit, in the chaotic phase , the qualitative features of the linear response properties of the single units.
Revised:  A novel dynamical mean-field theory for strongly connected networks of multi-dimensional rate units shows that , despite the nonlinear interactions between units, the recurrent network dynamics inherit, in the chaotic phase , the qualitative features of the linear response properties of the single units.
Edit intent: meaning-changed
Original:  A novel dynamical mean-field theory for  networks of multi-dimensional rate units shows that , despite the nonlinear interactions between units, the recurrent network dynamics inherit, in the chaotic phase , the qualitative features of the linear response properties of the single units.
Revised:  A novel dynamical mean-field theory for networks of multi-dimensional rate units shows that the power spectrum of the network activity in the chaotic phase , the qualitative features of the linear response properties of the single units.
Edit intent: meaning-changed
Original:  A novel dynamical mean-field theory for  networks of multi-dimensional rate units shows that , despite the nonlinear interactions between units, the recurrent network dynamics inherit, in the chaotic phase , the qualitative features of the linear response properties of the single units.
Revised:  A novel dynamical mean-field theory for networks of multi-dimensional rate units shows that , despite the nonlinear interactions between units, the recurrent network dynamics inherit, in the chaotic phase emerges from a nonlinear sharpening of the frequency response function of single units.
Edit intent: meaning-changed
Original:  For the case of two-dimensional rate units with strong adaptation, we find that the network exhibits a state of "resonant chaos", whose resonance frequency can be predicted from the properties of isolated units .
Revised:  For the case of two-dimensional rate units with strong adaptation, we find that the network exhibits a state of "resonant chaos", characterized by robust, narrow-band stochastic oscillations. The coherence of stochastic oscillations is maximal at the onset of chaos and their correlation time scales with the adaptation timescale of single units. Surprisingly, the resonance frequency can be predicted from the properties of isolated units .
Edit intent: meaning-changed
Original:  For the case of two-dimensional rate units with strong adaptation, we find that the network exhibits a state of "resonant chaos", whose resonance frequency can be predicted from the properties of isolated units . The correlation time of the chaotic network scales with the adaptation timescale of a single unit. Our theory for network analysis can be applied to numerous other mechanisms at the level of single nodes, such as synaptic filtering, refractoriness or spike synchronization.
Revised:  For the case of two-dimensional rate units with strong adaptation, we find that the network exhibits a state of "resonant chaos", whose resonance frequency can be predicted from the properties of isolated units , even in the presence of heterogeneity in the adaptation parameters. In the presence of these internally-generated chaotic fluctuations, the transmission of weak, low-frequency signals is strongly enhanced by adaptation, whereas signal transmission is not influenced by adaptation in the non-chaotic regime. Our theoretical framework can be applied to numerous other mechanisms at the level of single nodes, such as synaptic filtering, refractoriness or spike synchronization.
Edit intent: meaning-changed
Original:  These results advance our understanding of the interaction between single-neuron mechanisms and recurrent connectivity,  a fundamental step toward the description of biologically realistic network models in the brain, or, more generally, networks of other physical or man-made complex dynamical units.
Revised:  These results advance our understanding of the interaction between the dynamics of single units and recurrent connectivity, a fundamental step toward the description of biologically realistic network models in the brain, or, more generally, networks of other physical or man-made complex dynamical units.
Edit intent: clarity
Original:  These results advance our understanding of the interaction between single-neuron mechanisms and recurrent connectivity,  a fundamental step toward the description of biologically realistic network models in the brain, or, more generally, networks of other physical or man-made complex dynamical units.
Revised:  These results advance our understanding of the interaction between single-neuron mechanisms and recurrent connectivity, which is a fundamental step toward the description of biologically realistic network models in the brain, or, more generally, networks of other physical or man-made complex dynamical units.
Edit intent: coherence
Original: Deformable registration of clinical scans is a fundamental task for many applications, such as population studies or the monitoring of long-term disease progression in individual patients.
Revised: We introduce SparseVM, a method to register clinical 3D scans faster and more accurately than previously possible. Deformable alignment, or registration, of clinical scans is a fundamental task for many applications, such as population studies or the monitoring of long-term disease progression in individual patients.
Edit intent: meaning-changed
Original: Deformable registration of clinical scans is a fundamental task for many applications, such as population studies or the monitoring of long-term disease progression in individual patients. This task is challenging because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85\% of the slices in comparison.
Revised: Deformable registration of clinical scans is a fundamental task for many medical image applications such as longitudinal population studies. Most registration algorithms are designed for high-resolution research-quality scans and under-perform when applied to clinical data. Clinical scans present unique challenges because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85\% of the slices in comparison.
Edit intent: clarity
Original:  This task is challenging because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85\% of the slices in comparison. Furthermore, the anatomy in the acquired slices is not consistent across scansbecause of variations in patient orientation with respect to the scanner. In this work, we introduce Sparse VoxelMorph (SparseVM), which adapts a state-of-the-art learning-based registration method to improve the registration of sparse clinical images.
Revised:  This task is challenging because, in contrast to high-resolution research-quality scans, clinical images are often sparse, missing up to 85\% of the slices available in research scans. We build on a state-of-the-art learning-based registration method to improve the registration of sparse clinical images.
Edit intent: clarity
Original:  In this work, we introduce Sparse VoxelMorph (SparseVM), which adapts a state-of-the-art learning-based registration method to improve the registration of sparse clinical images.
Revised:  In this work, we introduce Sparse VoxelMorph (SparseVM), which adapts a state-of-the-art learning-based registration method to improve the accuracy of sparse clinical images.
Edit intent: clarity
Original:  In this work, we introduce Sparse VoxelMorph (SparseVM), which adapts a state-of-the-art learning-based registration method to improve the registration of sparse clinical images. SparseVM is a fast, unsupervised method that weights voxel contributions to registration in proportion to confidence in the voxels. This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.
Revised:  In this work, we introduce Sparse VoxelMorph (SparseVM), which adapts a state-of-the-art learning-based registration method to improve the registration of sparse clinical image registration and demonstrate our method on a clinically-acquired MRI dataset of stroke patients . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.
Edit intent: coherence
Original:  This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.
Revised:  This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on a GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.
Edit intent: fluency
Original:  This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.
Revised:  This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is over 1000x faster than the best performing clinical registration methods, while still achieving comparable accuracy.
Edit intent: meaning-changed
Original:  This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.
Revised:  This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the most accurate clinical registration methods, while still achieving comparable accuracy.
Edit intent: clarity
Original:  This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, while still achieving comparable accuracy.
Revised:  This leads to improved registration performance on volumes with voxels of varying reliability, such as interpolated clinical scans . SparseVM registers 3D scans in under a second on the GPU, which is orders of magnitudes faster than the best performing clinical registration methods, without compromising accuracy.
Edit intent: clarity
Original:  Deformable alignment, or registration, of clinical scans is a fundamental task for many medical image applications such as longitudinal population studies. Most registration algorithms are designed for high-resolution research-quality scans and under-perform when applied to clinical data.
Revised:  Deformable alignment, or registration, of clinical scans is a fundamental task for many clinical neuroscience studies. However, most registration algorithms are designed for high-resolution research-quality scans and under-perform when applied to clinical data.
Edit intent: meaning-changed
Original:  Most registration algorithms are designed for high-resolution research-quality scans and under-perform when applied to clinical data. Clinical scans present unique challenges because, in contrast to research-quality scans, clinical scans are often sparse, missing up to 85 \% of the slices available in research scans.
Revised:  Most registration algorithms are designed for high-resolution research-quality scans . In contrast to research-quality scans, clinical scans are often sparse, missing up to 85 \% of the slices available in research scans.
Edit intent: clarity