wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Thanks to the deep results of Choulli et al (2016) on the ] martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) .
Revised:  Thanks to the deep results of Choulli et al (2016) on the 8] martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) .
Edit intent: meaning-changed
Original:  Thanks to the deep results of Choulli et al (2016) on the ] martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) .
Revised:  Thanks to the deep results of Choulli et al (2016) on the ], on martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) .
Edit intent: fluency
Original:  Thanks to the deep results of Choulli et al (2016) on the ] martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) .
Revised:  Thanks to the deep results of Choulli et al (2016) on the ] martingales classification and representation for progressive enlarged filtration, our aim is fully achieved for both cases of local martingale deflators and general supermartingale delators. The results are illustrated on several particular models for (\tau,S,\mathbb F) such as the discrete-time and the jump-diffusion settings for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) .
Edit intent: meaning-changed
Original:  Thanks to the deep results of Choulli et al (2016) on the ] martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) .
Revised:  Thanks to the deep results of Choulli et al (2016) on the ] martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) .
Edit intent: coherence
Original:  Thanks to the deep results of Choulli et al (2016) on the ] martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) . This constitute our first principal (and probably the principal) innovative contribution of this paper. Our second principal contribution lies in describing and measuring the impact of \tau on log-related optimal portfolios, namely the num\'eraire portfolio and the portfolio solution to the log-utility maximization problem .
Revised:  Thanks to the deep results of Choulli et al (2016) on the ] martingales classification and representation for progressive enlarged filtration, we completely and explicitly describe all the deflators for (S ^{\tau,\mathbb G) in terms of the deflators of the initial market model (S} ,\mathbb F) , and the case when \tau avoids \mathbb F-stopping times .
Edit intent: meaning-changed
Original: Several neuroimaging markers have been established for the early diagnosis of Alzheimer's disease, among them amyloid-beta deposition, glucose metabolism, and gray matter volume. Up to now, these imaging modalitieswere mostly analyzed separately from each other, and little is known about the regional interrelation and dependency of these markers.
Revised: Several neuroimaging markers have been established for the early diagnosis of Alzheimer's disease, which can be assessed in vivo using various brain imaging methods. Currently, there is no appropriate statistical model available that can easily integrate multiple imaging modalities, being able to utilize the additional information provided from the combined data. We applied
Edit intent: coherence
Original:  Gaussian graphical models (GGMs) are able to estimate the conditional dependency between many individual random variables. We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
Revised:  Gaussian graphical models (GGMs) are able to estimate the conditional dependency networks of multimodal neuroimaging data and assessed alterations of the network structure in mild cognitive impairment (MCI) and Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load , glucose metabolism , and gray matter volume was calculated for each brain region.
Edit intent: clarity
Original:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
Revised:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's dementia (AD) compared to cognitively healthy controls . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load , glucose metabolism , and gray matter volume was calculated for each brain region.
Edit intent: clarity
Original:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
Revised:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects were obtained from the ADNI . Mean amyloid load , glucose metabolism , and gray matter volume was calculated for each brain region.
Edit intent: coherence
Original:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
Revised:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative . Mean amyloid load , glucose metabolism , and gray matter volume was calculated for each brain region.
Edit intent: clarity
Original:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
Revised:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load (AV45-PET) , glucose metabolism , and gray matter volume was calculated for each brain region.
Edit intent: meaning-changed
Original:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
Revised:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load , glucose metabolism (FDG-PET) , and gray matter volume was calculated for each brain region.
Edit intent: meaning-changed
Original:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load  , glucose metabolism  , and gray matter volume  was calculated for each brain region.
Revised:  We applied GGMs for studying the inter-regional associations and dependencies between multimodal imaging markers in prodromal Alzheimer's disease . Data from N=667 subjects with mild cognitive impairment, dementia, and cognitively healthy controls were obtained from the ADNI . Mean amyloid load , glucose metabolism , and gray matter volume (MRI) was calculated for each brain region.
Edit intent: clarity
Original:  Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory  . GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities.
Revised:  Network measures clustering coefficient , path length and small-world coefficient were significantly altered across diagnostic groups, with a biphasic u-shape trajectory . GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities.
Edit intent: meaning-changed
Original:  Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory  . GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities.
Revised:  Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory , i.e. increased small-world coefficient in early MCI, intermediate values in late MCI, and decreased values in AD patients compared to controls. In contrast, no group differences were found for clustering coefficient and small-world coefficient when estimating conditional dependency networks on single imaging modalities . GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities.
Edit intent: meaning-changed
Original:  Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory  . GGMs showed almost no conditional dependencies between modalitieswhen at the same time considering various other regions within the same modalities. However, this approach could be used as a clustering method to derive graph statistics in future studies omitting the need to binarize the network as currently being done for connections based on Pearson correlation .
Revised:  Network measures clustering coefficient and path length were significantly altered across diagnostic groups, with a biphasic u-shape trajectory . GGMs provide a useful methodology to analyze the conditional dependency networks of multimodal neuroimaging data .
Edit intent: clarity
Original:  Community detection provides a way to computationally identify candidate modules as hypotheses, which then need to be experimentally validated . However, validation of detected communities requires expensive and time consuming experimental methods, such as mutagenesis in a wet biological laboratory.
Revised:  Community detection provides a way to computationally identify candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a wet biological laboratory.
Edit intent: clarity
Original:  Here we develop CRank, an automatic method for prioritizing network communities and identifying the most promising ones for further experimentation . CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features to obtain the community prioritization.
Revised:  Here we develop CRank, a mathematically principled approach for prioritizing network communities and identifying the most promising ones for further experimentation . CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features to obtain the community prioritization.
Edit intent: clarity
Original:  Here we develop CRank, an automatic method for prioritizing network communities and identifying the most promising ones for further experimentation . CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features to obtain the community prioritization.
Revised:  Here we develop CRank, an automatic method for prioritizing network communities . CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features to obtain the community prioritization.
Edit intent: coherence
Original:  Here we develop CRank, an automatic method for prioritizing network communities and identifying the most promising ones for further experimentation . CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features to obtain the community prioritization.
Revised:  Here we develop CRank, an automatic method for prioritizing network communities and identifying the most promising ones for further experimentation . CRank efficiently evaluates robustness and magnitude of structural features of each community and then combines these features into the community prioritization.
Edit intent: clarity
Original:  Experiments on many diverse and important biological networks demonstrate that the proposed approach effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization over a baseline ordering of detected communities. Taken together, CRank represents a network-based approach to identify high-quality communities, even for domains at the frontier of science where supervised meta information is not available .
Revised:  Experiments on many diverse and important biological networks demonstrate that the proposed approach effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization .
Edit intent: clarity
Original: In his seminal, three paper series, Linsker provided a mechanism for how random activity in the visual pathway could give rise to many of the features observed experimentally in the early stages of visual processing  .
Revised: In his seminal, three paper series, Linsker provided a mechanism for how random activity in the visual pathway may give rise through neural plasticity to many of the features observed experimentally in the early stages of visual processing .
Edit intent: meaning-changed
Original: In his seminal, three paper series, Linsker provided a mechanism for how random activity in the visual pathway could give rise to many of the features observed experimentally in the early stages of visual processing  .
Revised: In his seminal, three paper series, Linsker provided a mechanism for how random activity in the visual pathway could give rise to many of the features observed experimentally in the early stages of visual processing was provided by Linkser in a seminal, three-paper series .
Edit intent: meaning-changed
Original:  Owing to the complexity of multilayer models, an implicit assumption in Linsker's and subsequent papers has been that propagation delay is homogeneous and , consequently, plays little functional role in neural behaviour.
Revised:  Owing to the complexity of multi-layer models, an implicit assumption in Linsker's and subsequent papers has been that propagation delay is homogeneous and , consequently, plays little functional role in neural behaviour.
Edit intent: fluency
Original:  The performances of our implementation have been measured on three different processors,  Intel core-i7,  Intel Xeon Phi and  AMD EPYC, on which we have been able to compute 3 billion tetrahedra in 53 seconds.
Revised:  The performances of our implementation have been measured on three different processors, an Intel core-i7, Intel Xeon Phi and AMD EPYC, on which we have been able to compute 3 billion tetrahedra in 53 seconds.
Edit intent: clarity
Original:  The performances of our implementation have been measured on three different processors,  Intel core-i7,  Intel Xeon Phi and  AMD EPYC, on which we have been able to compute 3 billion tetrahedra in 53 seconds.
Revised:  The performances of our implementation have been measured on three different processors, Intel core-i7, an Intel Xeon Phi and AMD EPYC, on which we have been able to compute 3 billion tetrahedra in 53 seconds.
Edit intent: clarity
Original:  The performances of our implementation have been measured on three different processors,  Intel core-i7,  Intel Xeon Phi and  AMD EPYC, on which we have been able to compute 3 billion tetrahedra in 53 seconds.
Revised:  The performances of our implementation have been measured on three different processors, Intel core-i7, Intel Xeon Phi and an AMD EPYC, on which we have been able to compute 3 billion tetrahedra in 53 seconds.
Edit intent: clarity
Original:  This corresponds to a generation rate of over 55 million tetrahedra per second which is, to our best knowledge, three times the rate reached by the current fastest implementation. It is finally shown how this very efficient parallel Delaunay triangulation can be integrated in a Delaunay refinement mesh generator taking as input the  boundary of the domain to mesh.
Revised:  This corresponds to a generation rate of over 55 million tetrahedra per second . We finally show how this very efficient parallel Delaunay triangulation can be integrated in a Delaunay refinement mesh generator taking as input the boundary of the domain to mesh.
Edit intent: clarity
Original: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Revised: Protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Edit intent: fluency
Original: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Revised: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments largely relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Edit intent: clarity
Original: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Revised: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Edit intent: coherence
Original: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Revised: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion (MC) methods, such as ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Edit intent: fluency
Original: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Revised: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied to image processing, which inspired us to apply them in protein structure reconstruction.
Edit intent: fluency
Original: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspired us to apply them in protein structure reconstruction.
Revised: The protein structure reconstruction from Nuclear Magnetic Resonance (NMR) experiments heavily relies on computational algorithms , such as matrix completion (MC) . Recently, some effective low-rank matrix completion methods, like ASD and ScaledASD, have been successfully applied in image processing, which inspires us to apply them in protein structure reconstruction.
Edit intent: fluency