wanyu/IteraTeR_v2 huggingface dataset:
Original:  Patient-specific models are then identified for ten patients from their first post-transplant RHC . Parameters representing ventricular diastolic filling , systemic resistance, pulmonary vein elastance, pulmonary resistance, pulmonary artery elastance, pulmonary valve resistance and aortic elastanceare identified for each patient using this approach.
Revised:  Patient-specific models are identified for ten patients from their first post-transplant RHC . Parameters representing ventricular diastolic filling , systemic resistance, pulmonary vein elastance, pulmonary resistance, pulmonary artery elastance, pulmonary valve resistance and aortic elastanceare identified for each patient using this approach.
Edit intent: coherence
Original:  Patient-specific models are then identified for ten patients from their first post-transplant RHC . Parameters representing ventricular diastolic filling , systemic resistance, pulmonary vein elastance, pulmonary resistance, pulmonary artery elastance, pulmonary valve resistance and aortic elastanceare identified for each patient using this approach.
Revised:  Patient-specific models are then identified for ten patients from their first post-transplant RHC . Parameters representing ventricular diastolic filling , systemic resistance, pulmonary venous elastance, pulmonary resistance, pulmonary artery elastance, pulmonary valve resistance and aortic elastanceare identified for each patient using this approach.
Edit intent: clarity
Original:  Patient-specific models are then identified for ten patients from their first post-transplant RHC . Parameters representing ventricular diastolic filling , systemic resistance, pulmonary vein elastance, pulmonary resistance, pulmonary artery elastance, pulmonary valve resistance and aortic elastanceare identified for each patient using this approach.
Revised:  Patient-specific models are then identified for ten patients from their first post-transplant RHC . Parameters representing ventricular diastolic filling , systemic resistance, pulmonary vein elastance, pulmonary resistance, pulmonary arterial elastance, pulmonary valve resistance and aortic elastanceare identified for each patient using this approach.
Edit intent: fluency
Original:  Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence that folds into a specified secondary structure .
Revised:  Since an RNA's function depends on its structural properties, the RNA Design problem is to find an RNA sequence which satisfies given structural constraints .
Edit intent: clarity
Original:  We implement a perception module as a set of mid-level visual representations and demonstrate that learning active taskswith mid-level features is significantly more sample-efficient than scratch and able to generalize in situations where the from-scratch approach fails  .
Revised:  We implement a perception module as a set of mid-level visual representations and demonstrate that learning active taskswith mid-level features is significantly more sample-efficient than scratch and able to generalize to situations where the from-scratch approach fails .
Edit intent: fluency
Original:  However, we show that realizing these gains requires careful selection of the particular mid-level features for each downstream task.
Revised:  However, we show that realizing these gains requires careful selection of the mid-level features for each downstream task.
Edit intent: clarity
Original:  We review epigenetic mechanisms which may yield early biomarkers . Can we devise novel early life modifying interventions to steer developmental trajectories to their optimum?
Revised:  We review the mediating mechanisms which may yield early biomarkers . Can we devise novel early life modifying interventions to steer developmental trajectories to their optimum?
Edit intent: clarity
Original:  We review epigenetic mechanisms which may yield early biomarkers . Can we devise novel early life modifying interventions to steer developmental trajectories to their optimum?
Revised:  We review epigenetic mechanisms which may yield early biomarkers . Can we devise novel early life-modifying interventions to steer developmental trajectories to their optimum?
Edit intent: fluency
Original: Cross-platform recommendation aims to improve the recommendation accuracy through aligning or fusing informationfrom different platformstogether . Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned.
Revised: Cross-platform recommendation aims to improve recommendation accuracy through aligning or fusing informationfrom different platformstogether . Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned.
Edit intent: clarity
Original: Cross-platform recommendation aims to improve the recommendation accuracy through aligning or fusing informationfrom different platformstogether . Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned.
Revised: Cross-platform recommendation aims to improve the recommendation accuracy through associating information from different platforms . Existing cross-platform recommendation approaches assume all cross-platform information to be consistent with each other and can be aligned.
Edit intent: clarity
Original:  However, there remain two challenges unsolved by previous works on cross-platform video recommendation : i) there exist inconsistencies in cross-platform association due to the platform-specific disparity, and ii) data from distinct platforms may share different semantic granularities.
Revised:  However, there remain two unsolved challenges : i) there exist inconsistencies in cross-platform association due to the platform-specific disparity, and ii) data from distinct platforms may share different semantic granularities.
Edit intent: clarity
Original:  However, there remain two challenges unsolved by previous works on cross-platform video recommendation : i) there exist inconsistencies in cross-platform association due to the platform-specific disparity, and ii) data from distinct platforms may share different semantic granularities.
Revised:  However, there remain two challenges unsolved by previous works on cross-platform video recommendation : i) there exist inconsistencies in cross-platform association due to platform-specific disparity, and ii) data from distinct platforms may share different semantic granularities.
Edit intent: fluency
Original:  However, there remain two challenges unsolved by previous works on cross-platform video recommendation : i) there exist inconsistencies in cross-platform association due to the platform-specific disparity, and ii) data from distinct platforms may share different semantic granularities.
Revised:  However, there remain two challenges unsolved by previous works on cross-platform video recommendation : i) there exist inconsistencies in cross-platform association due to the platform-specific disparity, and ii) data from distinct platforms may have different semantic granularities.
Edit intent: clarity
Original:  The proposed DCA model employs a partially-connected multi-modal autoencoder  capable of explicitly capturing and preserving platform-specific information in deep latent representation , as well as utilizes nonlinear mapping functions to handle granularity differences from various platforms .
Revised:  The proposed DCA model employs a partially-connected multi-modal autoencoder , which is capable of explicitly capturing and preserving platform-specific information in deep latent representation , as well as utilizes nonlinear mapping functions to handle granularity differences from various platforms .
Edit intent: coherence
Original:  The proposed DCA model employs a partially-connected multi-modal autoencoder  capable of explicitly capturing and preserving platform-specific information in deep latent representation , as well as utilizes nonlinear mapping functions to handle granularity differences from various platforms .
Revised:  The proposed DCA model employs a partially-connected multi-modal autoencoder capable of explicitly capturing platform-specific information in deep latent representation , as well as utilizes nonlinear mapping functions to handle granularity differences from various platforms .
Edit intent: clarity
Original:  The proposed DCA model employs a partially-connected multi-modal autoencoder  capable of explicitly capturing and preserving platform-specific information in deep latent representation , as well as utilizes nonlinear mapping functions to handle granularity differences from various platforms .
Revised:  The proposed DCA model employs a partially-connected multi-modal autoencoder capable of explicitly capturing and preserving platform-specific information , as well as utilizes nonlinear mapping functions to handle granularity differences from various platforms .
Edit intent: clarity
Original:  The proposed DCA model employs a partially-connected multi-modal autoencoder  capable of explicitly capturing and preserving platform-specific information in deep latent representation , as well as utilizes nonlinear mapping functions to handle granularity differences from various platforms .
Revised:  The proposed DCA model employs a partially-connected multi-modal autoencoder capable of explicitly capturing and preserving platform-specific information in deep latent representation , as well as utilizing nonlinear mapping functions to handle granularity differences from various platforms .
Edit intent: fluency
Original:  The proposed DCA model employs a partially-connected multi-modal autoencoder  capable of explicitly capturing and preserving platform-specific information in deep latent representation , as well as utilizes nonlinear mapping functions to handle granularity differences from various platforms .
Revised:  The proposed DCA model employs a partially-connected multi-modal autoencoder capable of explicitly capturing and preserving platform-specific information in deep latent representation , as well as utilizes nonlinear mapping functions to handle granularity differences .
Edit intent: clarity
Original:  We then present a cross-platform video recommendation approach based on the proposed DCA model , which automatically concentrates on valuable cross-platform information from a comprehensive semantic level . Extensive experiments for our cross-platform recommendation framework on Twitter and YouTube datasets demonstrate that the proposed DCA model can significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
Revised:  We then present a cross-platform video recommendation approach based on the proposed DCA model . Extensive experiments for our cross-platform recommendation framework on Twitter and YouTube datasets demonstrate that the proposed DCA model can significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
Edit intent: coherence
Original:  We then present a cross-platform video recommendation approach based on the proposed DCA model , which automatically concentrates on valuable cross-platform information from a comprehensive semantic level . Extensive experiments for our cross-platform recommendation framework on Twitter and YouTube datasets demonstrate that the proposed DCA model can significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
Revised:  We then present a cross-platform video recommendation approach based on the proposed DCA model , which automatically concentrates on valuable cross-platform information from a comprehensive semantic level . Extensive experiments for our cross-platform recommendation framework on real-world dataset demonstrate that the proposed DCA model can significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
Edit intent: clarity
Original:  We then present a cross-platform video recommendation approach based on the proposed DCA model , which automatically concentrates on valuable cross-platform information from a comprehensive semantic level . Extensive experiments for our cross-platform recommendation framework on Twitter and YouTube datasets demonstrate that the proposed DCA model can significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
Revised:  We then present a cross-platform video recommendation approach based on the proposed DCA model , which automatically concentrates on valuable cross-platform information from a comprehensive semantic level . Extensive experiments for our cross-platform recommendation framework on Twitter and YouTube datasets demonstrate that the proposed DCA model significantly outperform existing cross-platform recommendation methods in terms of various evaluation metrics.
Edit intent: clarity
Original:  We propose an empirical Bayes approach called spinlets, which first constructs a partitioning tree to guide the reduction with mixed spatial granularities, and then refines the representation of predictors according to the relevance to the response.
Revised:  We propose an empirical Bayes approach called spinlets, which first constructs a partitioning tree to guide the reduction over multiple spatial granularities, and then refines the representation of predictors according to the relevance to the response.
Edit intent: clarity
Original:  We consider an inverse Poisson regression model , regularized by a multiscale extension of the generalized double Pareto prior,  induced via a novel tree-structured parameter expansion scheme.
Revised:  We consider an inverse Poisson regression model , regularized by a multiscale extension of the generalized double Pareto prior, which is induced via a novel tree-structured parameter expansion scheme.
Edit intent: coherence
Original:  We consider an inverse Poisson regression model , regularized by a multiscale extension of the generalized double Pareto prior,  induced via a novel tree-structured parameter expansion scheme.
Revised:  We consider an inverse Poisson regression model , regularized by a multiscale extension of the generalized double Pareto prior, induced via a tree-structured parameter expansion scheme.
Edit intent: clarity
Original:  Our approach is motivated by an application in soccer analytics, in which we obtain spinlets visualizations of soccer passing networks under the supervision of team performance .
Revised:  Our approach is motivated by an application in soccer analytics, in which we obtain compact vectorial representations and readily interpretable visualizations of the complex network objects, supervised by the response of interest .
Edit intent: clarity
Original:  In our contribution, the manifestation of nonlinearity of whether healthy or diseased brain electrical activities is highlighted, and the highest time irreversibility of epileptic EEG during seizures is demonstrated.
Revised:  In our contribution, 
Edit intent: coherence
Original: In this paper, we theoretically prove that we can eliminate all suboptimal local minima by adding one neuron per output unit to any deep neural network, for multi-class classification, binary classification, and regression with an arbitrary loss function  .
Revised: In this paper, we theoretically prove that we can eliminate all suboptimal local minima by adding one neuron per output unit eliminates all suboptimal local minima of any deep neural network, for multi-class classification, binary classification, and regression with an arbitrary loss function .
Edit intent: clarity
Original:  At every local minimum of any deep neural network with  added neurons, the set of parameters of the original neural network (without added neurons) is guaranteed to be a global minimum of the original neural network.
Revised:  At every local minimum of any deep neural network with these added neurons, the set of parameters of the original neural network (without added neurons) is guaranteed to be a global minimum of the original neural network.
Edit intent: clarity
Original:  Unlike many related results in the literature, our theoretical results are directly applicable to common deep learning tasks because the results only rely on the assumptions that automatically hold in the common tasks.
Edit intent: coherence
Original:  Moreover, we discuss several limitations in eliminating the suboptimal local minima in this manner by providing additional theoretical results and several examples  .
Revised:  Moreover, we discuss several limitations in eliminating the suboptimal local minima via an additional theorem and several examples .
Edit intent: clarity
Original:  Application of the proposed framework to adolescents' tobacco smoking and friendship decisions suggests that: (a.)
Revised:  Application of the proposed framework to adolescents' smoking suggests that: (a.)
Edit intent: clarity
Original:  the response of the friendship network to changes in tobacco price amplifies the intended effect  on smoking, (b.) racially desegregating high-schools decreases the overall smoking prevalence, (c.) the response of the social network is quantitatively important in analyzing the aggregate spillovers, (d.) the estimation biases when the network externalities are mis-specified and when peer effects are omitted are of the same sign .
Revised:  the response of the friendship network to changes in tobacco price amplifies the intended effect on smoking, (b.) racial desegregation of high-schools decreases the overall smoking prevalence, (c.) the response of the social network is quantitatively important in analyzing the aggregate spillovers, (d.) the estimation biases when the network externalities are mis-specified and when peer effects are omitted are of the same sign .
Edit intent: clarity
Original:  the response of the friendship network to changes in tobacco price amplifies the intended effect  on smoking, (b.) racially desegregating high-schools decreases the overall smoking prevalence, (c.) the response of the social network is quantitatively important in analyzing the aggregate spillovers, (d.) the estimation biases when the network externalities are mis-specified and when peer effects are omitted are of the same sign .
Revised:  the response of the friendship network to changes in tobacco price amplifies the intended effect on smoking, (b.) racially desegregating high-schools decreases the overall smoking prevalence, (c.) peer effect complementarities are substantially stronger between smokers compared to between non-smokers. (d.) the estimation biases when the network externalities are mis-specified and when peer effects are omitted are of the same sign .
Edit intent: clarity
Original:  We have employed our new method to explore persistent homology of several unweighted model and real-world networks.
Revised:  We have employed our filtration scheme to explore the persistent homology of several unweighted model and real-world networks.
Edit intent: clarity
Original:  We have employed our new method to explore persistent homology of several unweighted model and real-world networks.
Revised:  We have employed our new method to explore persistent homology of several model and real-world networks.
Edit intent: clarity
Original:  We show that the persistence diagrams from our method can distinguish between the topology of different types of model and real networks.
Revised:  In particular, we show that our method can detect differences in the higher-order structure of networks, and the corresponding persistence diagrams can be used to distinguish between different model networks.
Edit intent: clarity
Original:  In summary, our method based on discrete Morse theory further increases the applicability of persistent homology to investigate  global topology of complex networks.
Revised:  In summary, our method based on discrete Morse theory further increases the applicability of persistent homology to investigate the global topology of complex networks.
Edit intent: fluency
Original: While virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks  , existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions.
Revised: Virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks , existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions.
Edit intent: coherence
Original: While virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks  , existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions.
Revised: While virtual assistants, also known as intelligent conversational systems such as Google's Virtual Assistant and Apple's Siri, interact with human-like responses to users' queries and finish specific tasks . Meanwhile , existing recommendation technologies model users' evolving, diverse and multi-aspect preferences to generate recommendations in various domains/applications, aiming to improve the citizens' daily life by making suggestions.
Edit intent: coherence
Original:  In particular, we try to answer the most fundamental research question, which is the right mechanism for implementing a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions ?
Revised:  In particular, we try to answer the most fundamental research question, which is the right mechanism for implementing a personalized intelligent conversational agent for producing accurate recommendations while taking into account how users behave under different conditions .
Edit intent: fluency
Original:  The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in their daily lives .
Revised:  The goal is, instead of adapting humans to machines, to actually provide users with better recommendation services so that machines will be adapted to humans in daily life .
Edit intent: clarity
Original:  The capacity is an upper bound on the number of bits that can be "communicated" from the training data to the architecture over the learningchannel .
Revised:  The capacity provides an upper bound on the number of bits that can be "communicated" from the training data to the architecture over the learningchannel .
Edit intent: clarity
Original:  The capacity is an upper bound on the number of bits that can be "communicated" from the training data to the architecture over the learningchannel .
Revised:  The capacity is an upper bound on the number of bits that can be extracted from the training data to the architecture over the learningchannel .
Edit intent: clarity
Original:  The capacity is an upper bound on the number of bits that can be "communicated" from the training data to the architecture over the learningchannel .
Revised:  The capacity is an upper bound on the number of bits that can be "communicated" from the training data and stored in the architecture during learning .
Edit intent: clarity