wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We showed that IM based social commerce: 1) enables more accessible , cost-reducing  and immersive user shopping experience, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity  and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships.
Revised:  We showed that IM based social commerce: 1) enables more reachable , cost-reducing and immersive user shopping experience, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships.
Edit intent: clarity
Original:  We showed that IM based social commerce: 1) enables more accessible , cost-reducing  and immersive user shopping experience, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity  and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships.
Revised:  We showed that IM based social commerce: 1) enables more accessible , cost-reducing , and immersive user shopping experience, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships.
Edit intent: fluency
Original:  We showed that IM based social commerce: 1) enables more accessible , cost-reducing  and immersive user shopping experience, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity  and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships.
Revised:  We showed that IM based social commerce: 1) enables more accessible , cost-reducing and immersive user shopping experience, 2) shapes user decision-making process in shopping through pre-existing social relationship, mutual trust, shared identity , and community norm, and 3) creates novel social interactions, which can contribute to new tie formation while maintaining existing social relationships.
Edit intent: fluency
Original:  Towards this goal, one class of methods is to translate the image style from the training environment to the current one . Conventional studies on image style translation mainly focus on two settings: paired data on images from two domains with exactly aligned content, and unpaired data, with independent content .
Revised:  Towards this goal, one class of methods is to translate the image style from another environment to the current one . Conventional studies on image style translation mainly focus on two settings: paired data on images from two domains with exactly aligned content, and unpaired data, with independent content .
Edit intent: clarity
Original:  We consider that this setting is more practical since robots withvarious sensors are able to align the data up to some error level, even with different styles. To solve this problem , we propose PRoGAN to learn a style translator by intentionally transforming the original domain images with a noisy pose, then matching the distribution of translated transformed images and the distribution of the target domain images.
Revised:  These images could be acquired by different sensors in different conditions that share an overlapping region, e.g. with LiDAR or stereo cameras, from sunny days or foggy nights. We consider this setting to be more practical with: (i) easier labeling than the paired data; (ii) better interpretability and detail retrieval than the unpaired data. To translate across such images , we propose PRoGAN to learn a style translator by intentionally transforming the original domain images with a noisy pose, then matching the distribution of translated transformed images and the distribution of the target domain images.
Edit intent: meaning-changed
Original:  To solve this problem , we propose PRoGAN to learn a style translator by intentionally transforming the original domain images with a noisy pose, then matching the distribution of translated transformed images and the distribution of the target domain images.
Revised:  To solve this problem , we propose PREGAN to train a style translator by intentionally transforming the original domain images with a noisy pose, then matching the distribution of translated transformed images and the distribution of the target domain images.
Edit intent: clarity
Original:  To solve this problem , we propose PRoGAN to learn a style translator by intentionally transforming the original domain images with a noisy pose, then matching the distribution of translated transformed images and the distribution of the target domain images.
Revised:  To solve this problem , we propose PRoGAN to learn a style translator by intentionally transforming the two images with a noisy pose, then matching the distribution of translated transformed images and the distribution of the target domain images.
Edit intent: meaning-changed
Original:  To solve this problem , we propose PRoGAN to learn a style translator by intentionally transforming the original domain images with a noisy pose, then matching the distribution of translated transformed images and the distribution of the target domain images. The adversarial training enforces the network to learn the style translation, avoiding being entangled with other variations.
Revised:  To solve this problem , we propose PRoGAN to learn a style translator by intentionally transforming the original domain images with a random pose, and to estimate the given random pose by differentiable non-trainable pose estimator given that the more aligned in style, the better the estimated result is. Such adversarial training enforces the network to learn the style translation, avoiding being entangled with other variations.
Edit intent: meaning-changed
Original: Driven by the increasingly larger deep learning models, neural language generation (NLG) has enjoyed unprecedentedly improvement and is now able to generate a diversity of human-like texts on demand, granting itself the capability of serving as a human writing assistant.
Revised: Text style transfer (TST) is an important task in natural language generation (NLG) has enjoyed unprecedentedly improvement and is now able to generate a diversity of human-like texts on demand, granting itself the capability of serving as a human writing assistant.
Edit intent: meaning-changed
Original: Driven by the increasingly larger deep learning models, neural language generation (NLG) has enjoyed unprecedentedly improvement and is now able to generate a diversity of human-like texts on demand, granting itself the capability of serving as a human writing assistant. Text attribute transfer is one of the most important NLG tasks, which aims to control certain attributes that people may expect the texts to possess , such as sentiment, tense, emotion, political position, etc .
Revised: Driven by the increasingly larger deep learning models, neural language generation (NLG) , which aims to control certain attributes that people may expect the texts to possess , such as sentiment, tense, emotion, political position, etc .
Edit intent: coherence
Original:  Overall, we have covered the task formulation, existing datasets and metrics for model development and evaluation , and all methods developed over the last several years. We reveal that existing methods are indeed based on a combination of several loss functions with each of which serving a certain goal. Such a unique perspective we provide could shed light on the design of new methods.
Revised:  Overall, we have covered the task formulation, existing datasets and subtasks, evaluation metrics, and methods on parallel and non-parallel data. We also provide discussions a variety of important topics regarding TST, which can shed light on the design of new methods.
Edit intent: meaning-changed
Original:  In this paper, we present a systematic survey of the research done on neural text style transfer . We have collected, summarized, and discussed nearly 70 representative articles since the first neural text style transfer work in 2017.
Revised:  In this paper, we present a systematic survey of the research done on neural text style transfer , spanning over 100 representative articles since the first neural text style transfer work in 2017.
Edit intent: meaning-changed
Original:  The proposed algorithm is called Factor Graphical Lasso (FGL) . We study a high-dimensional portfolio allocation problem when the asset returns admit the approximate factor model. In high dimensions, when the number of assets is large relative to the sample size, the sample covariance matrix of the excess returns is subject to the large estimation uncertainty, which leads to unstable solutions for portfolio weights. To resolve this issue, we consider the decomposition of low-rank and sparse components.
Revised:  The proposed algorithm is called Factor Graphical Lasso (FGL) , which integrates graphical models with the factor structure in the context of portfolio allocation by decomposing a precision matrix into low-rank and sparse components.
Edit intent: clarity
Original:  To resolve this issue, we consider the decomposition of low-rank and sparse components. This strategy allows us to consistently estimate the optimal portfolio in high dimensions, even when the covariance matrix is ill-behaved. We establish consistency of the portfolio weights in a high-dimensional setting without assuming sparsity on the covariance or precision matrix of stock returns .
Revised:  To resolve this issue, we consider the decomposition of low-rank and sparse components .
Edit intent: coherence
Original:  The empirical application uses daily and monthly data for the constituents of the S&P500 to demonstrate superior performance of FGL compared to the equal-weighted portfolio, index and some prominent precision and covariance-based estimators .
Revised:  FGL-based portfolios are shown to exhibit superior performance over several prominent competitors including equal-weighted and Index portfolios in the empirical application for the S&P500 to demonstrate superior performance of FGL compared to the equal-weighted portfolio, index and some prominent precision and covariance-based estimators .
Edit intent: meaning-changed
Original:  However, because of the complexity of the models, the bound gait built by the conventional control method is often unnatural and slow to compute. In the present work, we introduce a method to achieve the bound gait based on model-free pre-fit deep reinforcement learning (PF-DRL) . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .
Revised:  However, due to a large number of robot and environmental constraints, conventional planning and control has limited ability to adapt bounding gaits on various terrains in real-time. We proposed an efficient approach to learn robust bounding gaits by first pretraining the deep neural network (DNN) using data from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .
Edit intent: clarity
Original:  . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .
Revised:  . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot that used conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .
Edit intent: clarity
Original:  . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .
Revised:  . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controllers . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .
Edit intent: fluency
Original:  . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points .
Revised:  . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the pretrained DNN weights are optimized further via deep reinforcement learning (DRL). Also, we designed a reward function based on contact points .
Edit intent: clarity
Original:  . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function based on contact points . We also used feature engineering to improve the input features of the DRL model and improve performanceon flat ground.
Revised:  . We first constructed a net with the same structure as an actor net in the PPO2 and pre-fit it using the data collected from a robot using conventional model-based controller . Next, the trained weights are transferred into the PPO2 and be optimized further . Moreover, target on the symmetrical and periodic characteristic during bounding, we designed a reward function considering contact points to enforce the gait symmetry and periodicity, and used feature engineering to improve the input features of the DRL model and improve performanceon flat ground.
Edit intent: clarity
Original:  We also used feature engineering to improve the input features of the DRL model and improve performanceon flat ground. Finally, we trained the bound controller in simulation and successfully deployed it on the Jueying Mini robot . It performs better than the conventional method with higher computational efficiency and more stable center-of-mass height in our experiments.
Revised:  We also used feature engineering to improve the input features of the DRL model and the bounding performance. The DNN-based feedback controller was learned in simulation first and deployed directly on the real Jueying-Mini robot successfully, which was computationally more efficient and performed much better than the conventional method with higher computational efficiency and more stable center-of-mass height in our experiments.
Edit intent: meaning-changed
Original: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but  at the cost of efficiency.
Revised: Achieving the right balance between planning quality, safety and efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but at the cost of efficiency.
Edit intent: clarity
Original: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but  at the cost of efficiency.
Revised: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving . Optimisation-based planners are typically capable of producing high-quality, safe plans, but at the cost of efficiency.
Edit intent: clarity
Original: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but  at the cost of efficiency.
Revised: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based motion planners are capable of producing high-quality, safe plans, but at the cost of efficiency.
Edit intent: meaning-changed
Original: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but  at the cost of efficiency.
Revised: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing safe, smooth and comfortable plans, but at the cost of efficiency.
Edit intent: clarity
Original: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but  at the cost of efficiency.
Revised: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but often at the cost of efficiency.
Edit intent: coherence
Original: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but  at the cost of efficiency. We present PILOT , a two-stage planning framework comprising an imitation neural network and an efficient optimisation component that guarantees the satisfaction of requirements of safety and comfort .
Revised: Achieving the right balance between planning quality, safety and runtime efficiency is a major challenge for autonomous driving research . Optimisation-based planners are typically capable of producing high-quality, safe plans, but at the cost of runtime efficiency. On the other hand, naively deploying trajectories produced by efficient-to-run deep imitation learning approaches might risk compromising safety. In this paper, we present PILOT -- a planning framework that comprises an imitation neural network and an efficient optimisation component that guarantees the satisfaction of requirements of safety and comfort .
Edit intent: meaning-changed
Original:  We present PILOT , a two-stage planning framework comprising an imitation neural network and an efficient optimisation component that guarantees the satisfaction of requirements of safety and comfort . The neural network is trained to imitate an expensive-to-run optimisation-based planning system with the same objective as the efficient optimisation component of PILOT.
Revised:  We present PILOT , a two-stage planning framework comprising an imitation neural network and an efficient optimisation component that guarantees the satisfaction of requirements of safety and comfort requirements. The objective of the efficient optimiser is the same as the objective of an expensive-to-run optimisation-based planning system with the same objective as the efficient optimisation component of PILOT.
Edit intent: meaning-changed
Original:  The neural network is trained to imitate an expensive-to-run optimisation-based planning system with the same objective as the efficient optimisation component of PILOT. We demonstrate in simulated autonomous driving experiments that the proposed framework achieves a significant reduction in runtime when compared to the optimisation-based expert it imitates , without sacrificing the planning quality.
Revised:  The neural network is trained to imitate an expensive-to-run optimisation-based planning system that the neural network is trained offline to imitate. This efficient optimiser provides a key layer of online protection from learning failures or deficiency on out-of-distribution situations that might compromise safety or comfort. Using a state-of-the-art, runtime-intensive optimisation-based method as the expert, we demonstrate in simulated autonomous driving experiments that the proposed framework achieves a significant reduction in runtime when compared to the optimisation-based expert it imitates , without sacrificing the planning quality.
Edit intent: meaning-changed
Original:  Moreover, we show that all such binary trees are refinements of the unique binary-resolvable tree (BRT), which in general is a substantial refinement of the also unique least resolved tree of a BMG  .
Revised:  Moreover, we show that all such binary trees are refinements of the unique binary-resolvable tree (BRT), which in general is a substantial refinement of the also unique least resolved tree of a BMG . Finally, we show that the problem of editing an arbitrary vertex-colored graph to a binary-explainable BMG is NP-complete and provide an integer linear program formulation for this task .
Edit intent: meaning-changed
Original:  We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction ).
Revised:  We consider a robot navigating in an environment modeled as a Markov decision process (MDP), and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction ).
Edit intent: clarity
Original:  We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal robotic plan (i.e., an optimal MDP policy) based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction ).
Revised:  We consider robotic planning using Markov decision processes (MDPs) and develop an algorithm to automatically generate a personalized explanation of an optimal MDP policy, based on the user preference regarding four elements (i.e., objective, locality, specificity, and abstraction ).
Edit intent: fluency