wanyu/IteraTeR_full_sent huggingface dataset:
Original: Here we summarize abundant excuses that support the eye detection can be as a noninvasive approach to early diagnose some brain disorders, including Alzheimer disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ)  and major depressive disorder (MDD).
Revised: Here we summarize abundant excuses that support the eye detection can be as a noninvasive approach for the early diagnoses of various cognitive and psychiatric disorders, such as Alzheimer's disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ) and major depressive disorder (MDD).
Edit intent: clarity
Original: Here we summarize abundant excuses that support the eye detection can be as a noninvasive approach to early diagnose some brain disorders, including Alzheimer disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ)  and major depressive disorder (MDD).
Revised: Here we summarize abundant excuses that support the eye detection can be as a noninvasive approach to early diagnose some brain disorders, including Alzheimer disease (AD), autism spectrum disorder (ASD), schizophrenia (SZ) , and major depressive disorder (MDD).
Edit intent: fluency
Original:  Real-time ocular responses in everyone are tightly associated with emotional state and cognitive processing in the brain. At the eyeball areas, at least changes involved saccades, pupil response, blinking, microvascular network morphology and thickness of retinal nerve fiber layer (RNFL) are potentially to be candidates of biomarker to screen and evaluate brain disorders. These days medical big data will be more accessible especially with the help of advanced artificial intelligence (AI) to analyze precisely.
Revised:  Real-time ocular responses in everyone are tightly associated with emotional state and cognitive processing within the central nervous system. Patterns seen in saccades, pupillary responses, and blinking, as well as retinal microvasculature and morphology visualized via office-based ophthalmic imaging, are potential biomarkers for the screening and evaluation of cognitive and psychiatric disorders. Additionally, rapid advances in artificial intelligence (AI) to analyze precisely.
Edit intent: clarity
Original:  These days medical big data will be more accessible especially with the help of advanced artificial intelligence (AI) to analyze precisely. Computer vision combined with machine-learning based AIparticularly deep-learning neural networks sheds a new light on  cognitive neuroscience, and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders.
Revised:  These days medical big data will be more accessible especially with the help of advanced artificial intelligence (AI) present a growing opportunity to use machine-learning-based AI, especially deep-learning neural networks sheds a new light on cognitive neuroscience, and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders.
Edit intent: clarity
Original:  Computer vision combined with machine-learning based AIparticularly deep-learning neural networks sheds a new light on  cognitive neuroscience, and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders.
Revised:  Computer vision combined with machine-learning based AIparticularly deep-learning neural networks , to shed new light on cognitive neuroscience, and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders.
Edit intent: coherence
Original:  Computer vision combined with machine-learning based AIparticularly deep-learning neural networks sheds a new light on  cognitive neuroscience, and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders.
Revised:  Computer vision combined with machine-learning based AIparticularly deep-learning neural networks sheds a new light on the field of cognitive neuroscience, and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders.
Edit intent: clarity
Original:  Computer vision combined with machine-learning based AIparticularly deep-learning neural networks sheds a new light on  cognitive neuroscience, and many patients with psychotic disorders will primarily benefit from this interdisciplinary advance to induce self-evaluation and real-time intervention on brain disorders. Finally, we call for more AI applications going to approach brain disorder issues, meanwhile, AI technology used in brain healthcare should be working together with biological intelligence as we are running an AI manifestation for major depression .
Revised:  Computer vision combined with machine-learning based AIparticularly deep-learning neural networks sheds a new light on cognitive neuroscience, which may lead to novel evaluations and interventions via ocular approaches for cognitive and psychiatric disorders .
Edit intent: clarity
Original:  SA-CCR has major issues including: lack of self-consistency for linear trades; lack of appropriate risk sensitivity (zero positions can have material add-ons; moneyness is ignored); dependence on economically-equivalent confirmations. We show that SA-CCR is, by parameter identification and re-construction, based on a 3-factor Gaussian Market Model.
Revised:  SA-CCR to RSA-CCR: making SA-CCR is, by parameter identification and re-construction, based on a 3-factor Gaussian Market Model.
Edit intent: meaning-changed
Original:  We show that SA-CCR is, by parameter identification and re-construction, based on a 3-factor Gaussian Market Model. Hence we propose a Revised SA-CCR (RSA-CCR) based on cashflow decomposition and this 3-factor Gaussian Market Model . RSA-CCR is free of SA-CCR's issues, simple to use in practice, and can be extended easily given that it is model-based.
Revised:  We show that SA-CCR self-consistent and appropriately risk-sensitive by cashflow decomposition in a 3-Factor Gaussian Market Model . RSA-CCR is free of SA-CCR's issues, simple to use in practice, and can be extended easily given that it is model-based.
Edit intent: meaning-changed
Original:  Hence we propose a Revised SA-CCR (RSA-CCR) based on cashflow decomposition and this 3-factor Gaussian Market Model . RSA-CCR is free of SA-CCR's issues, simple to use in practice, and can be extended easily given that it is model-based. We recommend updating SA-CCR to RSA-CCR in order to resolve SA-CCR's issues of lack of self-consistency for linear trades, lack of appropriate risk sensitivity (zero positions can have material add-ons; moneyness is ignored), dependence on economically-equivalent confirmations, and ambiguity of application for cases not explicitly described.
Revised:  Hence we propose a Revised SA-CCR (RSA-CCR) based on cashflow decomposition and this 3-factor Gaussian Market Model 
Edit intent: coherence
Original:  In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different levels , which implies a wide variety of loads of local computation and communication.
Revised:  In fact, during SWA and AW different spatial extents of the cortical tissue are active in a given timespan and at different firing rates , which implies a wide variety of loads of local computation and communication.
Edit intent: clarity
Original:  Previous studies  tried to relate the inference equation of binary Markov random fields to the dynamic equation of spiking neural networks through belief propagation algorithm and reparameterization, but they are valid only for Markov random fields with limited network structure.
Revised:  Previous studies have tried to relate the inference equation of binary Markov random fields to the dynamic equation of spiking neural networks through belief propagation algorithm and reparameterization, but they are valid only for Markov random fields with limited network structure.
Edit intent: fluency
Original:  The VoroCrust algorithm is the first provably correct algorithm for conforming  Voronoi meshing for non-convex and possibly non-manifold domains with guarantees on the quality of both surface and volume elements.
Revised:  The VoroCrust algorithm is the first provably-correct algorithm for conforming Voronoi meshing for non-convex and possibly non-manifold domains with guarantees on the quality of both surface and volume elements.
Edit intent: fluency
Original:  The VoroCrust algorithm is the first provably correct algorithm for conforming  Voronoi meshing for non-convex and possibly non-manifold domains with guarantees on the quality of both surface and volume elements.
Revised:  The VoroCrust algorithm is the first provably correct algorithm for conforming polyhedral Voronoi meshing for non-convex and possibly non-manifold domains with guarantees on the quality of both surface and volume elements.
Edit intent: meaning-changed
Original:  The VoroCrust algorithm is the first provably correct algorithm for conforming  Voronoi meshing for non-convex and possibly non-manifold domains with guarantees on the quality of both surface and volume elements.
Revised:  The VoroCrust algorithm is the first provably correct algorithm for conforming Voronoi meshing for non-convex and non-manifold domains with guarantees on the quality of both surface and volume elements.
Edit intent: clarity
Original:  Starting with the celebrated Johnson-Lindenstrauss Lemma, such reductions have been studied in depth for the Euclidean (\ell_2) metric and, much less ,  for the Manhattan (\ell_1) metric.
Revised:  Starting with the celebrated Johnson-Lindenstrauss Lemma, such reductions have been studied in depth for the Euclidean (\ell_2) metric , for the Manhattan (\ell_1) metric.
Edit intent: coherence
Original:  Starting with the celebrated Johnson-Lindenstrauss Lemma, such reductions have been studied in depth for the Euclidean (\ell_2) metric and, much less ,  for the Manhattan (\ell_1) metric.
Revised:  Starting with the celebrated Johnson-Lindenstrauss Lemma, such reductions have been studied in depth for the Euclidean (\ell_2) metric and, much less , but much less for the Manhattan (\ell_1) metric.
Edit intent: meaning-changed
Original:  We study two cases of covering sets: c-approximate r-nets and randomly shifted grids, and we discuss the tradeoff between them in terms of preprocessing time and target dimension  .
Revised:  We study two cases of covering sets: c-approximate r-nets and randomly shifted grids, and we discuss the tradeoff between them in terms of preprocessing time and target dimension . We employ Cauchy variables: certain concentration bounds derived should be of independent interest .
Edit intent: meaning-changed
Original:  Yet the mechanisms underlying microbial diversity are not well understood . Simple resource-competition models do not allow for  a large number of URLanisms, permitting coexistence of only as many speciesas resources .
Revised:  Yet the mechanisms underlying microbial diversity are not well understood ; simple resource-competition models do not allow for a large number of URLanisms, permitting coexistence of only as many speciesas resources .
Edit intent: fluency
Original:  The large variation seen in this simple model suggests that real ecosystems may not obey a single universal relationship between nutrient supply and diversity  .
Revised:  The large variation seen in this simple model suggests that real ecosystems may not obey a single universal relationship between nutrient supply and diversity . To connect to real microbial communities, we validate our model framework against previously published Escherichia coli batch and chemostat experiments and outline potential future experiments to test the model's multispecies predictions .
Edit intent: meaning-changed
Original:  The form of this dependence varies with the precision of trade-offs and the presence of cross-feeding, immigration, or evolution .
Revised:  These changes in diversity are driven by an "early-bird" effect in which species specialized for nutrients that are initially more abundant gain a population advantage over other species. We explore the interplay of this effect with different environmental factors and diversity-supporting mechanisms, such as immigration and cross-feeding, immigration, or evolution .
Edit intent: meaning-changed
Original:  To connect to real microbial communities, we validate our model framework against previously published Escherichia coli batch and chemostat experiments and outline potential future experiments to test the model's multispecies predictions.
Revised:  To connect to real microbial communities, we validate our growth model against previously published Escherichia coli batch and chemostat experiments and outline potential future experiments to test the model's multispecies predictions.
Edit intent: clarity
Original:  However, they are also often inherently parallel, making them prime targets for modern massively parallel central processing unit (CPU) architectures. While the use of multi-core and distributed computing is widely applied in the Bayesian community,  very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs . Rather, most fine-grain tuning in the literature has centred around general purpose graphics processing units (GPGPUs).
Revised:  However, they are also often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied in the Bayesian community, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs . Rather, most fine-grain tuning in the literature has centred around general purpose graphics processing units (GPGPUs).
Edit intent: clarity
Original:  While the use of multi-core and distributed computing is widely applied in the Bayesian community,  very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs . Rather, most fine-grain tuning in the literature has centred around general purpose graphics processing units (GPGPUs).
Revised:  While the use of multi-core and distributed computing is widely applied in the Bayesian community, however, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs . Rather, most fine-grain tuning in the literature has centred around general purpose graphics processing units (GPGPUs).
Edit intent: coherence
Original:  While the use of multi-core and distributed computing is widely applied in the Bayesian community,  very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs . Rather, most fine-grain tuning in the literature has centred around general purpose graphics processing units (GPGPUs). Since the effective utilisation of GPGPUs typically requires specialised programming languages, such technologies are not ideal for the wider Bayesian community .
Revised:  While the use of multi-core and distributed computing is widely applied in the Bayesian community, very little attention has been given to fine-grain parallelisation using single instruction multiple data (SIMD) operations that are available on most modern commodity CPUs and is the basis of GPGPU computing .
Edit intent: clarity
Original:  In particular, we consider sampling of the prior predictive distribution for approximate Bayesian computation (ABC), the computation of Bayesian p-values for testing prior weak informativeness, and inference on a computationally challenging econometrics model. Through minor code alterations, we show that SIMD operations can improve the floating point arithmetic performance resulting in up to 6\times improvement in the overall serial algorithm performance.
Revised:  We show that SIMD operations can improve the floating point arithmetic performance resulting in up to 6\times improvement in the overall serial algorithm performance.
Edit intent: coherence
Original:  We performed forward simulations of diploid populations under increasing rates of clonality (c), analysed their relationships with genotypic and genetic indices, and tested predictions of c from population genetics data through supervised machine learning.
Revised:  We performed forward simulations of diploid populations at increasing rates of clonality (c), analysed their relationships with genotypic and genetic indices, and tested predictions of c from population genetics data through supervised machine learning.
Edit intent: clarity
Original:  While the impact of c on genotypic descriptors (R and \beta ) was easily described by simple mathematical equations, it was noticeable only at the highest levels (c>0.95) on genetic indices (F_{IS . Consequently, genotypic richness allowed reliable estimates of c, while genetic descriptors led to poorer performances when c<0.95.
Revised:  While the impact of c on genotypic descriptors (R and \b{eta ) was easily described by simple mathematical equations, it was noticeable only at the highest levels (c>0.95) on genetic indices (F_{IS . Consequently, genotypic richness allowed reliable estimates of c, while genetic descriptors led to poorer performances when c<0.95.
Edit intent: meaning-changed
Original:  While the impact of c on genotypic descriptors (R and \beta ) was easily described by simple mathematical equations, it was noticeable only at the highest levels (c>0.95) on genetic indices (F_{IS . Consequently, genotypic richness allowed reliable estimates of c, while genetic descriptors led to poorer performances when c<0.95.
Revised:  While the impact of c on genotypic descriptors (R and \beta ) was easily described by simple mathematical equations, its effects on genetic indices (FIS and linkage disequilibrium) were noticeable only at the highest levels (c>0.95) on genetic indices (F_{IS . Consequently, genotypic richness allowed reliable estimates of c, while genetic descriptors led to poorer performances when c<0.95.
Edit intent: meaning-changed
Original:  While the impact of c on genotypic descriptors (R and \beta ) was easily described by simple mathematical equations, it was noticeable only at the highest levels (c>0.95) on genetic indices (F_{IS . Consequently, genotypic richness allowed reliable estimates of c, while genetic descriptors led to poorer performances when c<0.95.
Revised:  While the impact of c on genotypic descriptors (R and \beta ) was easily described by simple mathematical equations, it was noticeable only at the highest levels (c>0.95) . Consequently, genotypic richness allowed reliable estimates of c, while genetic descriptors led to poorer performances when c<0.95.
Edit intent: clarity
Original:  Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, calling for a reappraisal of many interpretations hitherto proposed in the literature and mostly based on genotypic richness.
Revised:  Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, calling for a reappraisal of many interpretations hitherto proposed in the literature , mostly based on genotypic richness.
Edit intent: fluency
Original:  We performed forward simulations of diploid populations at increasing rates of clonality (c), analysed their relationships with genotypic and genetic indices, and tested predictions of c from population genetics data through supervised machine learning.
Revised:  We performed forward simulations of diploid populations at increasing rates of clonality (c), analysed their relationships with genotypic (clonal richness, R, and distribution of clonal sizes, Pareto \beta) and genetic (FIS and linkage disequilibrium) indices, and tested predictions of c from population genetics data through supervised machine learning.
Edit intent: meaning-changed
Original:  While the impact of c on genotypic descriptors (R and \b{eta was easily described by simple mathematical equations, its effects on genetic indices (FIS and linkage disequilibrium) were noticeable only at the highest levels (c>0.95).
Revised:  While the impact of c on R and Pareto \beta was easily described by simple mathematical equations, its effects on genetic indices (FIS and linkage disequilibrium) were noticeable only at the highest levels (c>0.95).
Edit intent: meaning-changed
Original:  While the impact of c on genotypic descriptors (R and \b{eta was easily described by simple mathematical equations, its effects on genetic indices (FIS and linkage disequilibrium) were noticeable only at the highest levels (c>0.95).
Revised:  While the impact of c on genotypic descriptors (R and \b{eta was easily described by simple mathematical equations, its effects on genetic indices were noticeable only at the highest levels (c>0.95).
Edit intent: clarity
Original:  Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, calling for a reappraisal of many interpretations hitherto proposed in the literature, mostly based on genotypic richness  .
Revised:  Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, suggesting that many interpretations hitherto proposed in the literature, mostly based on genotypic richness .
Edit intent: clarity
Original:  Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, calling for a reappraisal of many interpretations hitherto proposed in the literature, mostly based on genotypic richness  .
Revised:  Worryingly, however, the use of realistic sample sizes to acquire empirical data systematically led to gross underestimates (often of one to two orders of magnitude) of c, calling for a reappraisal of many interpretations hitherto proposed in the literature, mostly based on genotypic richness , should be reappraised .
Edit intent: meaning-changed
Original:  There phases are identified: an inactive phase in which mean time to extinction T  scales logarithmically with the initial population size , a temporal Griffiths phase where T \sim N^q , where N is the carrying capacity, and an exponential phase with T \sim exp(\alpha N).
Revised:  Three phases are identified: an inactive phase in which mean time to extinction T scales logarithmically with the initial population size , a temporal Griffiths phase where T \sim N^q , where N is the carrying capacity, and an exponential phase with T \sim exp(\alpha N).
Edit intent: fluency
Original:  There phases are identified: an inactive phase in which mean time to extinction T  scales logarithmically with the initial population size , a temporal Griffiths phase where T \sim N^q , where N is the carrying capacity, and an exponential phase with T \sim exp(\alpha N).
Revised:  There phases are identified: in the inactive phase the mean time to extinction T scales logarithmically with the initial population size , a temporal Griffiths phase where T \sim N^q , where N is the carrying capacity, and an exponential phase with T \sim exp(\alpha N).
Edit intent: clarity