wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Following the convention in many data mining and machine learning applications such as data summarization, recommender systems, and social network analysis , the problem is formulated as maximizing a monotone submodular function subject to a cardinality constraint -- i.e., the size of the selected subset is restricted to be smaller than or equal to an input integer k.
Revised:  Following the convention in many data mining and machine learning applications such as social network analysis , the problem is formulated as maximizing a monotone submodular function subject to a cardinality constraint -- i.e., the size of the selected subset is restricted to be smaller than or equal to an input integer k.
Edit intent: clarity
Original:  Following the convention in many data mining and machine learning applications such as data summarization, recommender systems, and social network analysis , the problem is formulated as maximizing a monotone submodular function subject to a cardinality constraint -- i.e., the size of the selected subset is restricted to be smaller than or equal to an input integer k.
Revised:  Following the convention in many data mining and machine learning applications such as data summarization, recommender systems, and social network analysis and recommender systems, this problem can be formulated as maximizing a monotone submodular function subject to a cardinality constraint -- i.e., the size of the selected subset is restricted to be smaller than or equal to an input integer k.
Edit intent: clarity
Original:  Following the convention in many data mining and machine learning applications such as data summarization, recommender systems, and social network analysis , the problem is formulated as maximizing a monotone submodular function subject to a cardinality constraint -- i.e., the size of the selected subset is restricted to be smaller than or equal to an input integer k.
Revised:  Following the convention in many data mining and machine learning applications such as data summarization, recommender systems, and social network analysis , the problem is formulated as maximizing a monotone submodular function subject to a cardinality constraint k.
Edit intent: clarity
Original:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the  number of items selected from each group to a given number.
Revised:  In this paper , we consider the problem with additionalfairness constraints setting where data items in the stream belong to one of several disjoint groups and investigate the optimization problem with an additional number of items selected from each group to a given number.
Edit intent: meaning-changed
Original:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the  number of items selected from each group to a given number.
Revised:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits thefairness number of items selected from each group to a given number.
Edit intent: meaning-changed
Original:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the  number of items selected from each group to a given number.
Revised:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the constraint that limits selection to a given number of items selected from each group to a given number.
Edit intent: clarity
Original:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the  number of items selected from each group to a given number. We propose efficient algorithms for this fairness-aware variant of the streaming submodular maximization problem.
Revised:  In this paper , we consider the problem with additionalfairness constraints , which takes into account the group membership of data items and limits the number of items selected from each group . We then propose efficient algorithms for this fairness-aware variant of the streaming submodular maximization problem.
Edit intent: clarity
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first give a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: clarity
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: meaning-changed
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . Moreover, we give a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: clarity
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same approximation ratio of (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: meaning-changed
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Edit intent: clarity
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer sizes and post-processing time is permitted\emph{ \emph{ .
Edit intent: fluency
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time are permitted, and discuss how to adapt it to more practical settings where the buffer sizes are bounded. Finally, we demonstrate the efficiency and effectiveness of our proposed algorithms on two real-world applications, namely\emph{ \emph{ .
Edit intent: meaning-changed
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{maximum coverage on large graphs \emph{ .
Edit intent: meaning-changed
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ and\emph{ .
Edit intent: coherence
Original:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same  (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{ .
Revised:  In particular, we first provide a (\frac{1}{2}-\varepsilon) -approximation algorithm that requires O(\frac{1}{\varepsilon} \cdot \log \frac{k}{\varepsilon}) passes over the stream for any constant \varepsilon>0 . In addition, we design a single-pass streaming algorithm that has the same (\frac{1}{2}-\varepsilon) approximation ratio when unlimited buffer size and post-processing time is permitted\emph{ \emph{personalized recommendation .
Edit intent: meaning-changed
Original:  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions .
Revised:  To achieve this, we propose to (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions .
Edit intent: clarity
Original:  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions .
Revised:  To achieve this, we designed a method that (i) decompose the multi-modal dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions .
Edit intent: clarity
Original:  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions .
Revised:  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions .
Edit intent: fluency
Original:  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions .
Revised:  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterize each primitive using a recurrent graph neural network to capture interactions .
Edit intent: fluency
Original:  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions . Our model is a deep, hierarchical, modular architecture with a high-level planner that learns to compose primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control.
Revised:  To achieve this, we designed a method that (i) uses a multi-model framework to decomposes complex dynamics into elemental movement primitives, and (ii) parameterizes each primitive using a recurrent graph neural network to capture interactions , and (iii) integrate a high-level planner that learns to compose primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control.
Edit intent: coherence
Original:  Our model is a deep, hierarchical, modular architecture with a high-level planner that learns to compose primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control.
Revised:  Our model is a deep, hierarchical, modular architecture with a high-level planner that composes primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control.
Edit intent: clarity
Original:  Our model is a deep, hierarchical, modular architecture with a high-level planner that learns to compose primitives sequentially and a low-level controller which integrates primitive dynamics modules and inverse kinematics control.
Revised:  Our model is a deep, hierarchical, modular architecture with a high-level planner that learns to compose primitives sequentially and a low-level controller to combine primitive dynamics and inverse kinematics control.
Edit intent: clarity
Original:  We demonstrate the effectiveness using several simulated bimanual robotic manipulation tasks . Compared to models based on previous imitation learning studies , our model generalizes better and achieves higher success rates in the simulated tasks.
Revised:  Our model is a deep, hierarchical, modular architecture . Compared to models based on previous imitation learning studies , our model generalizes better and achieves higher success rates in the simulated tasks.
Edit intent: meaning-changed
Original:  We demonstrate the effectiveness using several simulated bimanual robotic manipulation tasks . Compared to models based on previous imitation learning studies , our model generalizes better and achieves higher success rates in the simulated tasks.
Revised:  We demonstrate the effectiveness using several simulated bimanual robotic manipulation tasks . Compared to models based on previous imitation learning studies , our model generalizes better and achieves higher success rates on several simulated bimanual robotic manipulation tasks. We open source the code for simulation, data, and models at: URL
Edit intent: meaning-changed
Original:  Due to the real-time joint velocity and ground reaction force optimization based on a kinodynamic model , our approachaccurately captures the real robot's dynamics and automatically discovers complex and dynamic motions cumbersome to hand-craft through heuristics.
Revised:  This online joint velocity and ground reaction force optimization based on a kinodynamic model , our approachaccurately captures the real robot's dynamics and automatically discovers complex and dynamic motions cumbersome to hand-craft through heuristics.
Edit intent: clarity
Original:  Due to the real-time joint velocity and ground reaction force optimization based on a kinodynamic model , our approachaccurately captures the real robot's dynamics and automatically discovers complex and dynamic motions cumbersome to hand-craft through heuristics.
Revised:  Due to the real-time joint velocity and ground reaction force optimization integrates a kinodynamic model , our approachaccurately captures the real robot's dynamics and automatically discovers complex and dynamic motions cumbersome to hand-craft through heuristics.
Edit intent: coherence
Original:  Due to the real-time joint velocity and ground reaction force optimization based on a kinodynamic model , our approachaccurately captures the real robot's dynamics and automatically discovers complex and dynamic motions cumbersome to hand-craft through heuristics.
Revised:  Due to the real-time joint velocity and ground reaction force optimization based on a kinodynamic model of a wheeled quadrupedal robot. It defines the single rigid body dynamics along with the robot's kinematics while treating the wheels as moving ground contacts. With this approach, we can accurately capture the robot's dynamics and automatically discovers complex and dynamic motions cumbersome to hand-craft through heuristics.
Edit intent: meaning-changed
Original:  Due to the real-time joint velocity and ground reaction force optimization based on a kinodynamic model , our approachaccurately captures the real robot's dynamics and automatically discovers complex and dynamic motions cumbersome to hand-craft through heuristics. Thanks to the single set of parameters for all behaviors, whole-body optimization makes online gait sequence adaptation possible.
Revised:  Due to the real-time joint velocity and ground reaction force optimization based on a kinodynamic model , our approachaccurately captures the real robot's rolling constraint and dynamics, enabling automatic discovery of hybrid maneuvers without needless motion heuristics. The formulation's generality through the simultaneous optimization over the robot's whole-body variables allows for a single set of parameters for all behaviors, whole-body optimization makes online gait sequence adaptation possible.
Edit intent: meaning-changed
Original:  Aperiodic gait sequences are automatically found through kinematic leg utilities without the need for predefined contact and lift-off timings . Also, this enables us to reduce the cost of transport of wheeled-legged robots significantly .
Revised:  Aperiodic gait sequences are automatically found through kinematic leg utilities without the need for predefined contact and lift-off timings , reducing the cost of transport of wheeled-legged robots significantly .
Edit intent: coherence
Original:  Herewith, we verify that a single task formulation is key to reveal the full potential of wheeled-legged robots  .
Revised:  The paper's findings contribute to evaluating a decomposed, i.e., sequential optimization of wheel and torso motion, and single-task motion planner with a novel quantity, the prediction error, which describes how well a receding horizon planner can predict the robot's future state. To this end, we report an improvement of up to 71\% using our proposed single-task approach, making fast locomotion feasible and revealing wheeled-legged robots .
Edit intent: meaning-changed
Original: China's pledge to reach carbon neutrality by 2060 is ambitious and could provide the world with much-needed leadership on how to achieve a +1.5 degC warming target above pre-industrial levels by the end of the century.
Revised: China's pledge to reach carbon neutrality before 2060 is ambitious and could provide the world with much-needed leadership on how to achieve a +1.5 degC warming target above pre-industrial levels by the end of the century.
Edit intent: clarity
Original: China's pledge to reach carbon neutrality by 2060 is ambitious and could provide the world with much-needed leadership on how to achieve a +1.5 degC warming target above pre-industrial levels by the end of the century.
Revised: China's pledge to reach carbon neutrality by 2060 is an ambitious goal and could provide the world with much-needed leadership on how to achieve a +1.5 degC warming target above pre-industrial levels by the end of the century.
Edit intent: clarity
Original: China's pledge to reach carbon neutrality by 2060 is ambitious and could provide the world with much-needed leadership on how to achieve a +1.5 degC warming target above pre-industrial levels by the end of the century.
Revised: China's pledge to reach carbon neutrality by 2060 is ambitious and could provide the world with much-needed leadership on how to limit warming to +1.5 degC warming target above pre-industrial levels by the end of the century.
Edit intent: clarity