wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800  and 1000 vphpln are 61\%, 23 \%, and 41 \%, respectively, for the follower non-CAV if the intent of the follower vehicle is considered by a CAV in making a left turn at an intersection.
Revised:  The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800 , and 1000 vphpln are 61\%, 23 \%, and 41 \%, respectively, for the follower non-CAV if the intent of the follower vehicle is considered by a CAV in making a left turn at an intersection.
Edit intent: fluency
Original:  The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800  and 1000 vphpln are 61\%, 23 \%, and 41 \%, respectively, for the follower non-CAV if the intent of the follower vehicle is considered by a CAV in making a left turn at an intersection.
Revised:  The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800 and 1000 vehicle/hour/lane are 58\%, 52 \%, and 41 \%, respectively, for the follower non-CAV if the intent of the follower vehicle is considered by a CAV in making a left turn at an intersection.
Edit intent: meaning-changed
Original:  The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800  and 1000 vphpln are 61\%, 23 \%, and 41 \%, respectively, for the follower non-CAV if the intent of the follower vehicle is considered by a CAV in making a left turn at an intersection.
Revised:  The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800 and 1000 vphpln are 61\%, 23 \%, and 62 \%, respectively, for the follower non-CAV if the intent of the follower vehicle is considered by a CAV in making a left turn at an intersection.
Edit intent: meaning-changed
Original:  The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800  and 1000 vphpln are 61\%, 23 \%, and 41 \%, respectively, for the follower non-CAV if the intent of the follower vehicle is considered by a CAV in making a left turn at an intersection.
Revised:  The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800 and 1000 vphpln are 61\%, 23 \%, and 41 \%, respectively, for the aggressive human driver following the CAV if the following vehicles intent is considered by a CAV in making a left turn at an intersection.
Edit intent: meaning-changed
Original: By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused  axons of squid  we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a  model which is simpler , more quantitative, and based more fundamentally on electrodiffusion principles.
Revised: By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused giant axons of squid we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a model which is simpler , more quantitative, and based more fundamentally on electrodiffusion principles.
Edit intent: meaning-changed
Original: By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused  axons of squid  we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a  model which is simpler , more quantitative, and based more fundamentally on electrodiffusion principles.
Revised: By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused axons of squid , we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a model which is simpler , more quantitative, and based more fundamentally on electrodiffusion principles.
Edit intent: fluency
Original: By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused  axons of squid  we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a  model which is simpler , more quantitative, and based more fundamentally on electrodiffusion principles.
Revised: By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused axons of squid we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a new model which is simpler , more quantitative, and based more fundamentally on electrodiffusion principles.
Edit intent: clarity
Original: By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused  axons of squid  we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a  model which is simpler , more quantitative, and based more fundamentally on electrodiffusion principles.
Revised: By extending the crude Goldman-Hodgkin-Katz electrodiffusion model for resting-state membrane potentials in perfused axons of squid we reformulate the Hodgkin-Huxley (HH) phenomenological quantitative model to create a model which is simpler and based more fundamentally on electrodiffusion principles.
Edit intent: coherence
Original:  Unlike its HH counterpart it does not predict spike trains during prolonged constant-current stimulation, in agreement with recent experiments .
Revised:  After the external concentration of calcium ions is reduced, the generation of repetitive rebound action potentials is predicted by our model, in agreement with experiment, when the membrane is stimulated by a brief (0.1 ms) depolarizing current. Unlike the HH model, our model predicts, in agreement with experiment, that prolonged constant-current stimulation, in agreement with recent experiments .
Edit intent: meaning-changed
Original:  Spiking promoted by brief (0.1 ms) hyperpolarization is also predicted  at room temperature  by our electrodiffusion model  but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model.
Revised:  Spiking promoted by brief hyperpolarization is also predicted at room temperature by our electrodiffusion model but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model.
Edit intent: clarity
Original:  Spiking promoted by brief (0.1 ms) hyperpolarization is also predicted  at room temperature  by our electrodiffusion model  but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model.
Revised:  Spiking promoted by brief (0.1 ms) hyperpolarization is also predicted , at room temperature by our electrodiffusion model but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model.
Edit intent: fluency
Original:  Spiking promoted by brief (0.1 ms) hyperpolarization is also predicted  at room temperature  by our electrodiffusion model  but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model.
Revised:  Spiking promoted by brief (0.1 ms) hyperpolarization is also predicted at room temperature , by our electrodiffusion model but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model.
Edit intent: fluency
Original:  Spiking promoted by brief (0.1 ms) hyperpolarization is also predicted  at room temperature  by our electrodiffusion model  but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model.
Revised:  Spiking promoted by brief (0.1 ms) hyperpolarization is also predicted at room temperature by our electrodiffusion model , but only at much lower temperatures (ca. 6 degrees Celsius) by the HH model.
Edit intent: fluency
Original:  This has made the profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the desired latency, throughput,  and energy requirements while maximizing resource utilization.
Revised:  This has made profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the desired latency, throughput, and energy requirements while maximizing resource utilization.
Edit intent: fluency
Original:  This has made the profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the desired latency, throughput,  and energy requirements while maximizing resource utilization.
Revised:  This has made the profiling and characterization of ML model performance an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the desired latency, throughput, and energy requirements while maximizing resource utilization.
Edit intent: meaning-changed
Original:  This has made the profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the desired latency, throughput,  and energy requirements while maximizing resource utilization.
Revised:  This has made the profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible system to serve ML models with the desired latency, throughput, and energy requirements while maximizing resource utilization.
Edit intent: clarity
Original:  This has made the profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the desired latency, throughput,  and energy requirements while maximizing resource utilization.
Revised:  This has made the profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the target latency, throughput, and energy requirements while maximizing resource utilization.
Edit intent: clarity
Original:  This has made the profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the desired latency, throughput,  and energy requirements while maximizing resource utilization.
Revised:  This has made the profiling and characterization of ML models an increasingly pressing task for both hardware designers and system providers, as they would like to offer the best possible computing system to serve ML models with the desired latency, throughput, cost, and energy requirements while maximizing resource utilization.
Edit intent: meaning-changed
Original:  This paper proposes a leveled profiling design that leverages existing profiling tools to perform across-stack profiling. The design does so in spite of the profiling overheads incurred from the profiling providers.
Revised:  This paper proposes a leveled profiling design that gives a holistic and hierarchical view of ML model execution. XSP leverages distributed tracing to aggregate and correlate profile data from different sources. XSP introduces a leveled and iterative measurement approach that accurately captures the latencies at all levels of the HW/SW stack in spite of the profiling overheads incurred from the profiling providers.
Edit intent: meaning-changed
Original:  The design does so in spite of the profiling overheads incurred from the profiling providers. We coupled the profiling capability with an automatic analysis pipeline to systematically characterize 65 state-of-the-art ML models.
Revised:  The design does so in spite of the profiling overhead. We couple the profiling design with an automated analysis pipeline to systematically characterize 65 state-of-the-art ML models.
Edit intent: clarity
Original:  We outline an experimental protocol which enables expert iMD-VR users to generate reversible pathways for guiding ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5 - 10 minutes.
Revised:  We outline an experimental protocol which enables expert iMD-VR users to guide ligands into and out of the binding pockets of trypsin, neuraminidase, and HIV-1 protease, and recreate their respective crystallographic protein-ligand binding poses within 5 - 10 minutes.
Edit intent: clarity
Original:  Detailed test carried out to evaluate the use of iMD-VR by novices showed that (following a brief training phase ) they were similarly able to generate unbinding and rebinding pathways which recovered binding poses on similar timescales as the experts .
Revised:  Detailed test carried out to evaluate the use of iMD-VR by novices showed that (following a brief training phase , our studies shown that iMD-VR novices were able to generate unbinding and rebinding pathways which recovered binding poses on similar timescales as the experts .
Edit intent: meaning-changed
Original:  Detailed test carried out to evaluate the use of iMD-VR by novices showed that (following a brief training phase ) they were similarly able to generate unbinding and rebinding pathways which recovered binding poses on similar timescales as the experts .
Revised:  Detailed test carried out to evaluate the use of iMD-VR by novices showed that (following a brief training phase ) they were similarly able to generate unbinding and rebinding pathways on similar timescales as the experts .
Edit intent: clarity
Original:  Detailed test carried out to evaluate the use of iMD-VR by novices showed that (following a brief training phase ) they were similarly able to generate unbinding and rebinding pathways which recovered binding poses on similar timescales as the experts .
Revised:  Detailed test carried out to evaluate the use of iMD-VR by novices showed that (following a brief training phase ) they were similarly able to generate unbinding and rebinding pathways which recovered binding poses on similar timescales as iMD-VR experts, with the majority able to recover binding poses within 2.15 Angstrom RMSD of the crystallographic binding pose .
Edit intent: meaning-changed
Original:  These results indicate that iMD-VR affords sufficient control for users to generate reversible binding pathways that recover crystallographic poses, offering a new approach for simulating drug docking and generating binding hypothesis .
Revised:  These results indicate that iMD-VR affords sufficient control for users to carry out the detailed atomic manipulations required to dock flexible ligands into dynamic enzyme active sites and recover crystallographic poses, offering a new approach for simulating drug docking and generating binding hypothesis .
Edit intent: meaning-changed
Original:  These results indicate that iMD-VR affords sufficient control for users to generate reversible binding pathways that recover crystallographic poses, offering a new approach for simulating drug docking and generating binding hypothesis .
Revised:  These results indicate that iMD-VR affords sufficient control for users to generate reversible binding pathways that recover crystallographic poses, offering an interesting new approach for simulating drug docking and generating binding hypothesis .
Edit intent: clarity
Original:  These results indicate that iMD-VR affords sufficient control for users to generate reversible binding pathways that recover crystallographic poses, offering a new approach for simulating drug docking and generating binding hypothesis .
Revised:  These results indicate that iMD-VR affords sufficient control for users to generate reversible binding pathways that recover crystallographic poses, offering a new approach for simulating drug docking and generating binding hypotheses .
Edit intent: fluency
Original:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University , and 24 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images.
Revised:  We utilized 310 Hematoxylin and Eosin stained (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University , and 24 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images.
Edit intent: meaning-changed
Original:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University , and 24 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images.
Revised:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of prostate cancer cases from TCGA-PRAD and Stanford University , and 24 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images.
Edit intent: clarity
Original:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University , and 24 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images.
Revised:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University and 398 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images.
Edit intent: meaning-changed
Original:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University , and 24 H&E whole-slide images with hepatocellular carcinoma from TCGA-LIHC diagnostic histology images. Base models were DenseNet, Inception V3, and MobileNet and compared with PlexusNet. The dice coefficient (DSC) was evaluated for each model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from normal tissues.
Revised:  We utilized 62 Hematoxylin and eosin stain (H&E) annotated histological images of radical prostatectomy cases from TCGA-PRAD and Stanford University , and 24 H&E whole slides images from the Camelyon 2016 challenge. PlexusNet-architecture -derived models were compared to models derived from several existing "state of the art" architectures. We measured discrimination accuracy, calibration, and clinical utility. An ablation study was conducted to study the effect of each component of PlexusNet on model performance. A well-fitted PlexusNet-based model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from normal tissues.
Edit intent: meaning-changed
Original:  The dice coefficient (DSC) was evaluated for each model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from normal tissues.
Revised:  The dice coefficient (DSC) was evaluated for each model delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from normal tissues.
Edit intent: clarity
Original:  The dice coefficient (DSC) was evaluated for each model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from normal tissues.
Revised:  The dice coefficient (DSC) was evaluated for each model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H AUC: 0.963) in distinguishing prostate cancer from normal tissues.
Edit intent: meaning-changed
Original:  The dice coefficient (DSC) was evaluated for each model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from normal tissues. The parameter capacity of PlexusNet is 9 times smallerthan MobileNet or 58 times smaller than Inception V3, respectively. Similar findings were observed in distinguishing hepatocellular carcinoma from non-cancerous liver histologies (DSC at patch level: 0.85). As conclusion, PlexusNet represents a novel model architecture for histological image analysis that achieves classification performance comparable to the base models while providing orders-of-magnitude memory savings .
Revised:  The dice coefficient (DSC) was evaluated for each model . PlexusNet delivered comparable classification performance ( DSC at patch level: 0.89) for H E whole-slice images in distinguishing prostate cancer from healthy tissues, although it was at least 23 times smaller, had a better model calibration and clinical utility than the comparison models. A separate smaller PlexusNet model accurately detected slides with breast cancer metastases (AUC: 0.978); it helped reduce the slide number to examine by 43.8\% without consequences, although its parameter capacity was 200 times smaller than ResNet18. We found that the partitioning of the development set influences the model calibration for all models. However, with PlexusNet architecture, we could achieve comparable well-calibrated models trained on different partitions. In conclusion, PlexusNet represents a novel model architecture for histological image analysis that achieves classification performance comparable to the base models while providing orders-of-magnitude memory savings .
Edit intent: meaning-changed
Original: Income inequality is one of the most significant socio-economic challenges confronting India, with potentially long-lasting implications for the future of its democracy and society. In this work, we explore income inequality in India, without assumptions of equilibrium, and illustrate the nature and direction of re-distribution within the income distributionin a dynamic sense. Given that both mean income and income inequality show a rising trend post the Industrial Revolution, we argue that such a process is appropriately modeled using Geometric Brownian Motion (GBM).
Revised: Income inequality is one of the bottom of the distribution. We argue that income evolution is appropriately modelled using Geometric Brownian Motion (GBM).
Edit intent: clarity
Original:  We find that since the mid-1990s, reallocation is negative, meaning that incomes are exponentially diverging , indicating that there is a perverse re-distribution of resources from the poor to the rich.
Revised:  We find that since the early 2000s reallocation is negative, meaning that incomes are exponentially diverging , indicating that there is a perverse re-distribution of resources from the poor to the rich.
Edit intent: meaning-changed
Original:  We find that since the mid-1990s, reallocation is negative, meaning that incomes are exponentially diverging , indicating that there is a perverse re-distribution of resources from the poor to the rich.
Revised:  We find that since the mid-1990s, reallocation is negative, meaning that incomes are exponentially diverging and that there is a perverse re-distribution of resources from the poor to the rich.
Edit intent: clarity
Original:  We find that since the mid-1990s, reallocation is negative, meaning that incomes are exponentially diverging , indicating that there is a perverse re-distribution of resources from the poor to the rich.
Revised:  We find that since the mid-1990s, reallocation is negative, meaning that incomes are exponentially diverging , indicating that there is a perverse redistribution of resources from the poor to the rich.
Edit intent: clarity