wanyu/IteraTeR_full_sent huggingface dataset:
Original: For use of cameras on an intelligent vehicle, driving over a major bump could challenge the calibration. It is then of interest to do dynamic calibration. What structures can be used for calibration? How about using traffic signs that you recognize? In this paper an approach is presented for dynamic camera calibration based on recognition of stop signs.
Revised: Calibration of sensors is fundamental to robust performance for intelligent vehicles. In natural environments, disturbances can easily challenge calibration. One possibility is to use natural objects of known shape to recalibrate sensors. An approach based on recognition of stop signs.
Edit intent: clarity
Original:  In this paper an approach is presented for dynamic camera calibration based on recognition of stop signs. The detection is performed based on convolutional neural networks (CNNs). A recognized sign is modeled as a polygon and matched to a model. Parameters are tracked over time. Experimental results show clear convergence and improved performance for the calibration .
Revised:  In this paper an approach is presented for dynamic camera calibration based on recognition of traffic signs, such as stop signs, and use of them for recalibration of cameras is presented. The approach is based on detection, geometry estimation, calibration, and recursive updating. Results from natural environments are presented that clearly show convergence and improved performance for the calibration .
Edit intent: meaning-changed
Original:  Classical methods based on the MirrorProx algorithm require two operator evaluations per iteration, which is the dominant factor in the running time in many settings. Additionally, the algorithms typically require careful settings of the step sizes based on the parameters of the problems such as smoothness. In this work, we develop new algorithms addressing both of these shortcomings simultaneously. Our algorithms use a single operator evaluation per iteration and automatically adapt to  problem parameters such as smoothness .
Revised:  Our algorithms automatically adapt to problem parameters such as smoothness .
Edit intent: coherence
Original: A semantic parsing model is crucial to natural language processing applications such as goal-oriented dialogue systems .
Revised: Currently used semantic parsing systems deployed in voice assistants can require weeks to train. Datasets for these models often receive small and frequent updates, data patches. Each patch requires training a new model. To reduce training time, one can fine-tune the previously trained model on each patch, but naive fine-tuning exhibits catastrophic URLetting - degradation of the model performance on the data not represented in the data patch .
Edit intent: meaning-changed
Original:  In this work, we show how to efficiently (in terms of computational budget) improve model performance given a new portion of labeled data for a specific low-resource class or a set of classes. We demonstrate that a simple approach with a specific fine-tuning procedure for the old model can reduce the computational costs by ~90\% compared to the training of a new model. The resulting performance is on-par with a model trained from scratch on a full dataset. We showcase the efficacy of our approach on two popular semantic parsing datasets, Facebook TOP , and SNIPS .
Revised:  In this work, we propose a simple method that alleviates catastrophic URLetting and show that it is possible to match the performance of a model trained from scratch in less than 10\% of a time via fine-tuning. The key to achieving this is supersampling and EWC regularization. We demonstrate the effectiveness of our method on multiple splits of the Facebook TOP and SNIPS datasets .
Edit intent: clarity
Original: In this paper we consider joint perception and control of a pick-place system. It is important to consider perception and control jointly as some actions are more likely to succeed than others given non-uniform, perceptual uncertainty.Our approach is to combine 3D object instance segmentation and shape completion with classical regrasp planning.
Revised: We consider robotic pick-and-place of partially visible, novel objects, where goal placements are non-trivial, e.g., tightly packed into a bin. One approach is (a) use object instance segmentation and shape completion with classical regrasp planning.
Edit intent: meaning-changed
Original: Our approach is to combine 3D object instance segmentation and shape completion with classical regrasp planning. We use the perceptual modules to estimate their own uncertainty and then incorporate this uncertainty as a regrasp planning cost .
Revised: Our approach is to combine 3D object instance segmentation and shape completion to model the objects and (b) use a regrasp planner to decide grasps and places displacing the models to their goals. However, it is critical for the planner to account for uncertainty in the perceived models, as object geometries in unobserved areas are just guesses. We account for perceptual uncertainty by incorporating it into the regrasp planner's cost function .
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Original:  We compare 7 different regrasp planning cost functions, 4 of which explicitly model probability of plan execution success. Results show uncertainty-aware costs improve performance for complex tasks, e. g., for a bin packing task, object placement success is 6.2\% higher in simulation and 4.1\% higher in the real world with an uncertainty-aware cost versus the commonly used minimum-number-of-grasps cost.
Revised:  We compare seven different costs. One of these, which uses neural networks to estimate probability of grasp and place stability, consistently outperforms uncertainty-unaware costs and evaluates faster than Monte Carlo sampling. On a real robot, the proposed cost results in successfully packing objects tightly into a bin 7.8\% more often versus the commonly used minimum-number-of-grasps cost.
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Original:  We employ an uncertainty-aware neural network ensemble model to learn the dynamics, and we infer the unknown constraint function through indicator constraint violation signals. We use model predictive control (MPC) as the basic control framework and propose the robust cross-entropy method (RCE) to optimize the control sequence considering the model uncertainty and constraints.
Revised:  We employ an uncertainty-aware neural network ensemble model to estimate the prediction uncertainty and use model predictive control (MPC) as the basic control framework and propose the robust cross-entropy method (RCE) to optimize the control sequence considering the model uncertainty and constraints.
Edit intent: clarity
Original:  Experimental results show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLMR-large model which has 4.6 x the parameters of AMBER. 
Revised:  Experimental results show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLMR-large model which has 3.2 x the parameters of AMBER. 
Edit intent: meaning-changed
Original:  Experimental results show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLMR-large model which has 4.6 x the parameters of AMBER. 
Revised:  Experimental results show that AMBER obtains gains of up to 1.1 average F1 score on sequence tagging and up to 27.3 average accuracy on retrieval over the XLMR-large model which has 4.6 x the parameters of AMBER. Our code and models are available at URL
Edit intent: meaning-changed
Original:  To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes a novel form of intrinsic motivation that can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards.
Revised:  To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards.
Edit intent: meaning-changed
Original: We show that under plausible levels of background risk, no theory of choice under risk---such as expected utility theory, prospect theory, or rank dependent utility---can simultaneously satisfy the following three economic postulates: (i) Decision makers are risk-averse over small gambles, (ii) they respect stochastic dominance, and (iii) they account for background risk.
Revised: We show that under plausible levels of background risk, no theory of choice under risk -- such as expected utility theory, prospect theory, or rank dependent utility---can simultaneously satisfy the following three economic postulates: (i) Decision makers are risk-averse over small gambles, (ii) they respect stochastic dominance, and (iii) they account for background risk.
Edit intent: fluency
Original: We show that under plausible levels of background risk, no theory of choice under risk---such as expected utility theory, prospect theory, or rank dependent utility---can simultaneously satisfy the following three economic postulates: (i) Decision makers are risk-averse over small gambles, (ii) they respect stochastic dominance, and (iii) they account for background risk.
Revised: We show that under plausible levels of background risk, no theory of choice under risk---such as expected utility theory, prospect theory, or rank dependent utility -- can simultaneously satisfy the following three economic postulates: (i) Decision makers are risk-averse over small gambles, (ii) they respect stochastic dominance, and (iii) they account for background risk.
Edit intent: fluency
Original:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG  and makes two important improvements on prioritized experience replay to stabilize training.
Revised:  Each learned policy from our approach is robot-agnostic, which can be applied to different robotic arms. These two features can greatly reduce complexity and cost to perform robotic assembly in the real world, especially in unstructured settings such as in architectural construction. To achieve it, we have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG and makes two important improvements on prioritized experience replay to stabilize training.
Edit intent: meaning-changed
Original:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG  and makes two important improvements on prioritized experience replay to stabilize training.
Revised:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which extends Ape-X DDPG and makes two important improvements on prioritized experience replay to stabilize training.
Edit intent: clarity
Original:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG  and makes two important improvements on prioritized experience replay to stabilize training.
Revised:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG with recurrency and makes two important improvements on prioritized experience replay to stabilize training.
Edit intent: meaning-changed
Original:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG  and makes two important improvements on prioritized experience replay to stabilize training.
Revised:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG and makes two structural improvements on prioritized experience replay to stabilize training.
Edit intent: clarity
Original:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG  and makes two important improvements on prioritized experience replay to stabilize training. We demonstrate the effectiveness of RD2 on a variety of joint assembly tasks and a partially observable version of the pendulum task from OpenAI Gym.
Revised:  We have developed a new distributed RL agent, named Recurrent Distributed DDPG (RD2), which adds a recurrent neural network layer to Ape-X DDPG and makes two important improvements on prioritized experience replay .
Edit intent: coherence
Original:  Our results show that RD2 is able to achieve better performance than Ape-X DDPG and PPO with LSTM on partially observable tasks with varying complexity.
Revised:  Our results show that RD2 is able to solve two fundamental high-precision assembly tasks, lap-joint and peg-in-hole, and outperforms two state-of-the-art algorithms, Ape-X DDPG and PPO with LSTM on partially observable tasks with varying complexity.
Edit intent: meaning-changed
Original:  Our results show that RD2 is able to achieve better performance than Ape-X DDPG and PPO with LSTM on partially observable tasks with varying complexity. We also show that the trained models adapt well to different initial states and different types of noise injected in the simulated environment .
Revised:  Our results show that RD2 is able to achieve better performance than Ape-X DDPG and PPO with LSTM . We have successfully evaluated our robot-agnostic policies on three robotic arms, Kuka KR60, Franka Panda, and UR10, in simulation .
Edit intent: meaning-changed
Original: Exploiting the SIQR model for COVID-19, I show that the wavy infection curve in Japan is the result of fluctuation of policy on isolation measure imposed by the government and obeyed by citizens , Assuming the infection coefficient be a 2-valued function of the number of infected individuals , I show that when the removal rate of infected individuals is between these two values, the wavy infection curve is URLanized.
Revised: Exploiting the SIQR model for COVID-19, I show that the wavy infection curve in Japan is the result of fluctuation of policy on isolation measure imposed by the government and obeyed by citizens . Assuming the infection coefficient be a 2-valued function of the number of infected individuals , I show that when the removal rate of infected individuals is between these two values, the wavy infection curve is URLanized.
Edit intent: fluency
Original: Exploiting the SIQR model for COVID-19, I show that the wavy infection curve in Japan is the result of fluctuation of policy on isolation measure imposed by the government and obeyed by citizens , Assuming the infection coefficient be a 2-valued function of the number of infected individuals , I show that when the removal rate of infected individuals is between these two values, the wavy infection curve is URLanized.
Revised: Exploiting the SIQR model for COVID-19, I show that the wavy infection curve in Japan is the result of fluctuation of policy on isolation measure imposed by the government and obeyed by citizens , Assuming the infection coefficient be a two-valued function of the number of infected individuals , I show that when the removal rate of infected individuals is between these two values, the wavy infection curve is URLanized.
Edit intent: fluency
Original: Exploiting the SIQR model for COVID-19, I show that the wavy infection curve in Japan is the result of fluctuation of policy on isolation measure imposed by the government and obeyed by citizens , Assuming the infection coefficient be a 2-valued function of the number of infected individuals , I show that when the removal rate of infected individuals is between these two values, the wavy infection curve is URLanized.
Revised: Exploiting the SIQR model for COVID-19, I show that the wavy infection curve in Japan is the result of fluctuation of policy on isolation measure imposed by the government and obeyed by citizens , Assuming the infection coefficient be a 2-valued function of the number of daily confirmed new cases , I show that when the removal rate of infected individuals is between these two values, the wavy infection curve is URLanized.
Edit intent: meaning-changed
Original:  On the basis of the infection curve, I classify the outbreak of COVID-19 in each country into five types and show that these differences can be related to the relative magnitude of the transmission coefficient and the quarantine rate of infected individuals.
Revised:  On the basis of the infection curve, I classify the outbreak of COVID-19 into five types and show that these differences can be related to the relative magnitude of the transmission coefficient and the quarantine rate of infected individuals.
Edit intent: clarity
Original: Reduced order modeling (ROM) techniques are numerical methods that approximate the solution to parametric partial differential equation (PDE) is approximated by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem.
Revised: Reduced order modeling (ROM) techniques are numerical methods that approximate the solution of parametric partial differential equation (PDE) is approximated by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem.
Edit intent: fluency
Original: Reduced order modeling (ROM) techniques are numerical methods that approximate the solution to parametric partial differential equation (PDE) is approximated by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem.
Revised: Reduced order modeling (ROM) techniques are numerical methods that approximate the solution to parametric partial differential equation (PDE) by properly combining the high-fidelity solutions of the problem obtained for several configurations, i.e. for several properly chosen values of the physical/geometrical parameters characterizing the problem.
Edit intent: clarity
Original: We propose the Active Visual Analytics technique (ActiveVA), an augmentation of interactive visualizations with active search to aid data foragingand information extraction.
Revised: Data foraging is a process commonly arising in interactive data analysis where a user sifts through a large amount of potentially irrelevant information seeking data relevant to their task. In machine learning, the related task of active search considers the automated discovery of rare, valuable items from large databases and has delivered massive speedups in discovery in areas including drug and materials discovery. However, there have yet to be any advances in integrating active search with an interactive interface to make use of an analyst's domain knowledge in the search process. We introduce and evaluate a technique we call Active Visual Analytics technique (ActiveVA), an augmentation of interactive visualizations with active search to aid data foragingand information extraction.
Edit intent: meaning-changed
Original: We propose the Active Visual Analytics technique (ActiveVA), an augmentation of interactive visualizations with active search to aid data foragingand information extraction. We accomplish this by integrating an active search algorithm into the visual interface, which leads users to explore promising data points on a visualization and updates suggestions upon observing user feedback.
Revised: We propose the Active Visual Analytics technique (ActiveVA), an augmentation of interactive visualizations with active search to accelerate data foraging. In this approach, underlying machine learning models automatically learn a user's latent interest by observing their interactions; these models then inform an active search algorithm into the visual interface, which leads users to explore promising data points on a visualization and updates suggestions upon observing user feedback.
Edit intent: meaning-changed
Original:  We accomplish this by integrating an active search algorithm into the visual interface, which leads users to explore promising data points on a visualization and updates suggestions upon observing user feedback. Using a fictitious epidemic dataset published by the VAST community, we conduct two evaluations. First, we present simulation results that demonstrate the effectiveness of active search in data discovery. Second, we show how a human-computer partnership based on ActiveVA can result in more meaningful interactions during interactive visual exploration and discovery with a crowd-sourced user study.
Revised:  We accomplish this by integrating an active search algorithm that leads the user toward the points judged most promising for exploration. Using the epidemic dataset from VAST Challenge 2011, we design and conduct a crowd-sourced user study to evaluate several aspects of this technique. We present evidence that a human--computer partnership based on ActiveVA can result in more meaningful interactions during interactive visual exploration and discovery with a crowd-sourced user study.
Edit intent: clarity
Original:  Second, we show how a human-computer partnership based on ActiveVA can result in more meaningful interactions during interactive visual exploration and discovery with a crowd-sourced user study. Finally, we outline open research questions regarding human factors in active search and the implications of this line of research in real-world scenarios such as visual drug discovery and intelligence analysis .
Revised:  Second, we show how a human-computer partnership based on ActiveVA can result in more meaningful interactions during interactive visual exploration and discovery without any undue effect on the user experience .
Edit intent: clarity
Original:  In this study, we obtain approximately 7.5M tweets , containing 1.4M users, based on prevalent hashtags related to US election 2020, as well as the related YouTube links, contained in the Twitter dataset, likes, dislikes and comments of the videos and conduct volume, sentiment and graph analysis on the communities formed.
Revised:  In this study, we obtain approximately 17.5 M tweets containing 3M users, based on prevalent hashtags related to US election 2020, as well as the related YouTube links, contained in the Twitter dataset, likes, dislikes and comments of the videos and conduct volume, sentiment and graph analysis on the communities formed.
Edit intent: meaning-changed
Original:  The results of sentiment analysis indicate that 45.7  express positive sentiment towards Trump in Twitter and 33.8  positive sentiment towards Biden, while 14.55  of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7  positive sentiment towards Biden  .
Revised:  Additionally, we gather the related YouTube links contained in the previous dataset and perform sentiment analysis. The results on sentiment analysis on the Twitter corpus and the YouTube metadata gathered, show the positive and negative sentiment for the two entities throughout this period. The results of sentiment analysis indicate that 45.7 express positive sentiment towards Trump in Twitter and 33.8 positive sentiment towards Biden, while 14.55 of users express positive sentiment in YouTube metadata gathered towards Trump and 8.7 positive sentiment towards Biden .
Edit intent: meaning-changed