wanyu/IteraTeR_full_sent huggingface dataset:
Original:  These motors rely on  ATP, supplied at the limits of diffusion from the sarcoplasm to the interior of the lattice of actin and myosin . Classic sliding filament theory suggests that lattice spacing is constant . If so, then the lattice changes volume during contraction and could provide fluid motion and hence assist in transport of molecules between the contractile lattice and  surrounding intracellular space.
Revised:  These motors rely on ATP, supplied at the limits of diffusion from the sarcoplasm to the interior of the lattice of actin and myosin filaments. The radial spacing of filaments in this lattice may change or remain constant during during contraction. If the lattice is isovolumetric, it must expand when the muscle shortens. If, however, the spacing is constant or has a different pattern of axial and radial motion, then the lattice changes volume during contraction and could provide fluid motion and hence assist in transport of molecules between the contractile lattice and surrounding intracellular space.
Edit intent: meaning-changed
Original:  Akin to ``breathing,'' convective-diffusive transport in sarcomeres is sufficient to promote metabolite exchange , and may play a role in the regulation of contraction itself.
Revised:  Volume change and advective solute transport varies with the phase of muscle stimulation during periodic contraction but remains significant at all conditions. Nonetheless, advective transport varies will occur in all cases where the sarcomere is not isovolumetric. Akin to "breathing," advective-diffusive transport in sarcomeres is sufficient to promote metabolite exchange , and may play a role in the regulation of contraction itself.
Edit intent: meaning-changed
Original:  The  results demonstrate the  value of the ResQu model as a descriptive model, considering some systematic deviations.
Revised:  The study implies that when humans interact with advanced intelligent systems, with capabilities that greatly exceed their own, their comparative causal responsibility will be small, even if formally the human is assigned major roles. Simply putting a human into the loop does not assure that the human will meaningfully contribute to the outcomes. The results demonstrate the value of the ResQu model as a descriptive model, considering some systematic deviations.
Edit intent: meaning-changed
Original:  The  results demonstrate the  value of the ResQu model as a descriptive model, considering some systematic deviations. It is possible to compute a ResQu model score to predict behavior or perceptions of responsibility , taking into account the characteristics of the human, the intelligent system and the environment .
Revised:  The results demonstrate the value of the ResQu model to predict behavior and perceptions of responsibility , taking into account the characteristics of the human, the intelligent system and the environment .
Edit intent: coherence
Original: With the improvement of  pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model , which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision.
Revised: With the improvement of the pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model , which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision.
Edit intent: fluency
Original: With the improvement of  pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model , which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision.
Revised: With the improvement of pattern recognition and feature extraction of Deep Neural Networks ( DPNNs), image-based design and optimization have been widely used in multidisciplinary researches . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model , which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision.
Edit intent: meaning-changed
Original: With the improvement of  pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model , which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision.
Revised: With the improvement of pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) has been proposed to obtain an image-based model from an analysis-based model , which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision.
Edit intent: clarity
Original: With the improvement of  pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model , which can help us to solve many high frequency problems with difficult sampling, e.g. sonic wave and collision. However,  due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
Revised: With the improvement of pattern recognition and feature extraction of Deep Neural Networks ( DNNs), more and more problems are attempted to solve from the view of images . Recently, a Reconstructive Neural Network (ReConNN) was proposed to obtain an image-based model from an analysis-based model 1, 2 due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
Edit intent: meaning-changed
Original:  However,  due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
Revised:  However, , and a steady-state heat transfer of a heat sink has been successfully reconstructed. Commonly, this method is suitable to handle stable-state problems. However, it has difficulties handling nonlinear transient impact problems, due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
Edit intent: meaning-changed
Original:  However,  due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
Revised:  However, due to the bottlenecks of the Deep Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
Edit intent: meaning-changed
Original:  However,  due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
Revised:  However, due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( DPNN). For example, nonlinear transient problems make it difficult for the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower.
Edit intent: meaning-changed
Original:  However,  due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) make the reconstruction process low-accuracy, poor-efficiency, expensive-computation and high-manpower. In this study, an improved ReConNN model is proposed to address the mentioned weaknesses.
Revised:  However, due to the slight difference between simulated images, the low-accuracy of the Convolutional Neural Network ( CNN)and poor-diversity of the Generative Adversarial Network (GAN) to generate various reasonable images. Therefore, in this study, an improved ReConNN model is proposed to address the mentioned weaknesses.
Edit intent: clarity
Original:  We find that the LPPLS confidence indicator based on the daily data of Bitcoin price fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles.
Revised:  We find that the LPPLS confidence indicator based on the daily Bitcoin price data fails to provide effective warnings for detecting the bubbles when the Bitcoin price suffers from a large fluctuation in a short time, especially for positive bubbles.
Edit intent: clarity
Original:  In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model  .
Revised:  In order to diagnose the existence of bubbles and accurately predict the bubble crashes in the cryptocurrency market, this study proposes an adaptive multilevel time series detection methodology based on the LPPLS model and finer (than daily) timescale for the Bitcoin price data .
Edit intent: meaning-changed
Original:  The results show that the LPPLS confidence indicator based on the adaptive multilevel time series detection methodology have not only an outstanding performance to effectively detect the bubbles and accurately forecast the bubble crashes, but can also monitor the development and the crash of bubbles even if a bubble exists in a short time.
Revised:  The results show that the LPPLS confidence indicator based on this new method is an outstanding instrument to effectively detect the bubbles and accurately forecast the bubble crashes, but can also monitor the development and the crash of bubbles even if a bubble exists in a short time.
Edit intent: clarity
Original:  The results show that the LPPLS confidence indicator based on the adaptive multilevel time series detection methodology have not only an outstanding performance to effectively detect the bubbles and accurately forecast the bubble crashes, but can also monitor the development and the crash of bubbles even if a bubble exists in a short time.
Revised:  The results show that the LPPLS confidence indicator based on the adaptive multilevel time series detection methodology have not only an outstanding performance to effectively detect the bubbles and accurately forecast the bubble crashes, even if a bubble exists in a short time.
Edit intent: coherence
Original:  In addition, we discover that the short-term LPPLS confidence indicator greatly affected by the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale  , and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale  .
Revised:  In addition, we discover that the short-term LPPLS confidence indicator highly sensitive to the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale , and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale .
Edit intent: clarity
Original:  In addition, we discover that the short-term LPPLS confidence indicator greatly affected by the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale  , and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale  .
Revised:  In addition, we discover that the short-term LPPLS confidence indicator greatly affected by the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale - on a day to week scale , and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale .
Edit intent: meaning-changed
Original:  In addition, we discover that the short-term LPPLS confidence indicator greatly affected by the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale  , and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale  .
Revised:  In addition, we discover that the short-term LPPLS confidence indicator greatly affected by the extreme fluctuations of Bitcoin price can provide some useful insights into the bubble status on a shorter time scale , and the long-term LPPLS confidence indicator has a stable performance in terms of effectively monitoring the bubble status on a longer time scale - on a week to month scale .
Edit intent: meaning-changed
Original:  However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone , is limiteddue to multicollinearity of features and multiple testing problem, and limited performance of conventional machine learning classifiers. Deep learning architectures, such as convolutional neural networks (CNNs) , have been shown to outperform traditional techniques in computer vision tasks , such as object detection .
Revised:  However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited. Convolutional neural networks (CNNs) , have been shown to outperform traditional techniques in computer vision tasks , such as object detection .
Edit intent: coherence
Original:  In this work, we developed  a CNN-based transfer learning approach for prognostication in PDAC patients for overall survival . The results showed that transfer learning approach outperformed the traditional radiomics model on PDAC data.
Revised:  In this work, we developed a CNN-based transfer learning approach for prognostication in PDAC patients for overall survival using two independent resectable PDAC cohorts. The proposed deep transfer learning model for prognostication of PDAC achieved the area under the receiver operating characteristic curve of 0.74, which was significantly higher than that of the traditional radiomics model on PDAC data.
Edit intent: meaning-changed
Original:  The results showed that transfer learning approach outperformed the traditional radiomics model on PDAC data. A transfer learning approach may fill the gap between radiomics and deep learning analytics for cancer prognosis and improve performance beyond what CNNs can achieve using small datasets  .
Revised:  The results showed that transfer learning approach outperformed the traditional radiomics model (0.56) as well as a CNN model trained from scratch (0.50). These results suggest that deep transfer learning may significantly improve prognosis performance using small datasets .
Edit intent: meaning-changed
Original: Nowadays a huge number of user-generated videos are uploaded to social media every second, capturing glimpses of events all over the world .
Revised: Nowadays a huge number of user-generated videos are uploaded to social media sites and the Internet from around the world. This material creates a visual record of the experiences of a significant percentage of humanity and can help illuminate how we live in the present moment. When properly analyzed, this video can also help analysts to reconstruct events of interest, including war crimes, human rights violations, and terrorist acts .
Edit intent: clarity
Original:  In this paper , we describe the DAISY system, enabled by established machine learning techniques and physics models, that can localize the shooter location only based on a couple of user-generated videos that capture the gun shot sound .
Revised:  In this technical report , we describe the DAISY system, enabled by established machine learning techniques and physics models, that can localize the shooter location only based on a couple of user-generated videos that capture the gun shot sound .
Edit intent: meaning-changed
Original:  In this paper , we describe the DAISY system, enabled by established machine learning techniques and physics models, that can localize the shooter location only based on a couple of user-generated videos that capture the gun shot sound . The DAISY system utilizes machine learning techniques like video synchronization and gunshot temporal localization URLanize the unstructured social media videos and quickly localize gunshot in the videos.
Revised:  In this paper , we describe a newly developed tool, the Video Event Reconstruction and Analysis (VERA) system, that enables the localization of a shooter from just a few videos that include the sound of gunshots using established machine learning techniques like video synchronization and gunshot temporal localization URLanize the unstructured social media videos and quickly localize gunshot in the videos.
Edit intent: meaning-changed
Original:  The DAISY system utilizes machine learning techniques like video synchronization and gunshot temporal localization URLanize the unstructured social media videos and quickly localize gunshot in the videos.
Revised:  The DAISY system utilizes machine learning techniques and straightforward physics models. This tool relies on other tools we have already developed including video synchronization and gunshot temporal localization URLanize the unstructured social media videos and quickly localize gunshot in the videos.
Edit intent: meaning-changed
Original:  The DAISY system utilizes machine learning techniques like video synchronization and gunshot temporal localization URLanize the unstructured social media videos and quickly localize gunshot in the videos. It provides a web interface for human-in-the-loop verification to ensure accurate estimations.
Revised:  The DAISY system utilizes machine learning techniques like video synchronization and geolocation to order unstructured videos lacking metadata over time and space, and sound detection algorithms. Both this gunshot localization tool and the previous systems it incorporates are run through a web interface for human-in-the-loop verification to ensure accurate estimations.
Edit intent: meaning-changed
Original:  We present the results of estimating the shooter's location of the Las Vegas Shooting in 2017 and show that DAISY is able to get accurate location using only the first few shots .
Revised:  To demonstrate the efficacy of this suite of tools, we present the results of estimating the shooter's location of the Las Vegas Shooting in 2017 and show that DAISY is able to get accurate location using only the first few shots .
Edit intent: meaning-changed
Original:  We publish all relevant source code  including the web interface and machine learning models in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Revised:  All relevant source code including the web interface and machine learning models in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Edit intent: coherence
Original:  We publish all relevant source code  including the web interface and machine learning models in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Revised:  We publish all relevant source code , including the web interface and machine learning models in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Edit intent: fluency
Original:  We publish all relevant source code  including the web interface and machine learning models in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Revised:  We publish all relevant source code including the web interface and machine learning models , are freely available in Github. We do so in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Edit intent: meaning-changed
Original:  We publish all relevant source code  including the web interface and machine learning models in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Revised:  We publish all relevant source code including the web interface and machine learning models in the hope that this tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Edit intent: clarity
Original:  We publish all relevant source code  including the web interface and machine learning models in the hope that such tool can be of use to help preserve life and get contributions from the research and software engineering community to make the tool better .
Revised:  We publish all relevant source code including the web interface and machine learning models in the hope that such tool can be used by anyone who needs it to protect and promote human rights and public safety. We also hope that researchers and software developers will be inspired to improve and expand this system moving forward to better meet the needs of public safety and human rights .
Edit intent: clarity
Original:  Recently, algorithms based on Direct Coupling Analysis (DCA) pairwise maximum-entropy models have allowed to identify interaction partners among the paralogs of ubiquitous prokaryotic proteins families, starting from sequence data alone. Since DCA allows to infer the three-dimensional structure of protein complexes, its success in predicting protein-protein interactions could be mainly based on contacting residues coevolving to remain physicochemically complementary.
Revised:  Recently, algorithms based on Direct Coupling Analysis (DCA) pairwise maximum-entropy models have allowed to identify interaction partners among the paralogs of ubiquitous prokaryotic proteins families, starting from sequence data . This success of DCA at predicting protein-protein interactions could be mainly based on contacting residues coevolving to remain physicochemically complementary.
Edit intent: coherence