wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We specifically propose a needs-driven multi-robot cooperation mechanism represented through a Behavior Tree structure and evaluate the performance of the system in terms of the group utility and energy cost to achieve the rescue mission in a limited time.
Revised:  We specifically propose a needs-driven multi-robot cooperation mechanism represented through a Behavior Tree structure and evaluate the system's performance in terms of the group utility and energy cost to achieve the rescue mission in a limited time.
Edit intent: clarity
Original:   We find that when the method is properly applied, variance reductions by factors in the thousands can be obtained.
Revised:  The task is to estimate the expectation of a function of molecule copy numbers at a given future time T by the sample average over n sample paths, and the goal is to reduce the variance of this sample-average estimator. We find that when the method is properly applied, variance reductions by factors in the thousands can be obtained.
Edit intent: meaning-changed
Original: In this article we apply the methods of quantum mechanics to the study of the financial markets. Specifically, we discuss the Pseudo-Hermiticity of the Hamiltonian operators associated to the typical partial differential equations of Mathematical Finance (such as the Black-Scholes equation) and how this relates to the non-arbitrage condition. We propose that one can use a Schrodinger equation to derive the probabilistic behaviour of the financial market , and discuss the benefits of doing so.
Revised: Financial models based on the Wick product, and White Noise formalism have previously been suggested in order to incorporate integrals with respect to fractional Brownian motion. It has also been pointed out that this leads naturally to a quantum mechanical interpretation of the financial market , and discuss the benefits of doing so.
Edit intent: meaning-changed
Original:  We propose that one can use a Schrodinger equation to derive the probabilistic behaviour of the financial market , and discuss the benefits of doing so. This presents an alternative approach to replace the use of standard diffusion processes (for example a Brownian motion or Wiener process) .
Revised:  We propose that one can use a Schrodinger equation to derive the probabilistic behaviour of the financial market . In this article we pursue this idea further, and in particular show how the framework of quantum probability can be used to construct Martingales, without relying on Brownian integrals .
Edit intent: meaning-changed
Original:  More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many Sub-Saharan African countries and other low- and middle-income countries.
Revised:  More importantly, our cross-country estimation enables the creation of poverty maps when it is not practical or cost-effective to field new national household surveys, as is the case with many low- and middle-income countries.
Edit intent: clarity
Original:  We provide several important results which were missing in the early derivation: i) a rigorous proof of the converge of the SINC formula to the correct option price when the support growths and the number of Fourier frequencies increases;
Revised:  We provide several results which were missing in the early derivation: i) a rigorous proof of the converge of the SINC formula to the correct option price when the support growths and the number of Fourier frequencies increases;
Edit intent: clarity
Original:  We provide several important results which were missing in the early derivation: i) a rigorous proof of the converge of the SINC formula to the correct option price when the support growths and the number of Fourier frequencies increases;
Revised:  We provide several important results which were missing in the early derivation: i) a rigorous proof of the convergence of the SINC formula to the correct option price when the support growths and the number of Fourier frequencies increases;
Edit intent: fluency
Original:  We provide several important results which were missing in the early derivation: i) a rigorous proof of the converge of the SINC formula to the correct option price when the support growths and the number of Fourier frequencies increases;
Revised:  We provide several important results which were missing in the early derivation: i) a rigorous proof of the converge of the SINC formula to the correct option price when the support grows and the number of Fourier frequencies increases;
Edit intent: fluency
Original:  This makes our simulator extremely fast: rendering achieves speeds of up to 230 Hz, while physics simulation of up to 200,000 Hz  . In addition, Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene;
Revised:  This makes our simulator extremely fast: rendering achieves speeds of up to 230 Hz, while physics simulation of up to 200,000 Hz on a laptop . In addition, Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene;
Edit intent: meaning-changed
Original:  We demonstrate the flexibility of Flightmare by using it for two completely different robotic tasks: learning a sensorimotor control policy for a quadrotor and path-planning in a complex 3D environment.
Revised:  We demonstrate the flexibility of Flightmare by using it for two different robotic tasks: learning a sensorimotor control policy for a quadrotor and path-planning in a complex 3D environment.
Edit intent: clarity
Original:  We demonstrate the flexibility of Flightmare by using it for two completely different robotic tasks: learning a sensorimotor control policy for a quadrotor and path-planning in a complex 3D environment.
Revised:  We demonstrate the flexibility of Flightmare by using it for two completely different robotic tasks: quadrotor control using deep reinforcement learning and collision-free path planning in a complex 3D environment.
Edit intent: meaning-changed
Original:  It is complemented with an analysis of the Portuguese social network, which allows detecting changes in public opinion and provides a feedback to update the model parameters. With this , we apply  control theory to maximize the number of people returning to "normal life" and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations.
Revised:  After identification of the population readiness to follow social restrictions, by analyzing the social media, we incorporate this effect in a version of the model that allow us to check different scenarios. This is realized by considering a Monte Carlo discrete version of the previous model coupled via a complex network. Then , we apply control theory to maximize the number of people returning to "normal life" and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations.
Edit intent: meaning-changed
Original:  With this , we apply  control theory to maximize the number of people returning to "normal life" and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations.
Revised:  With this , we apply optimal control theory to maximize the number of people returning to "normal life" and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations.
Edit intent: clarity
Original: Motivation: RNAs are ubiquitous molecules involved in many regulatory and catalytic processes. Their ability to form complex structures is often key to support these functions. Remarkably, RNA 3D structures are articulated around smaller 3D sub-units referred as RNA 3D motifs that can be found in unrelated molecules.
Revised:  RNA 3D structures are articulated around smaller 3D sub-units referred as RNA 3D motifs that can be found in unrelated molecules.
Edit intent: coherence
Original:  Remarkably, RNA 3D structures are articulated around smaller 3D sub-units referred as RNA 3D motifs that can be found in unrelated molecules. The classification of these 3D motifs is thus essential to characterize RNA structures, but current methods can only retrieve motifs with identical base interaction patterns. Results: Here, we relax this constraint by posing the motif finding problem as a graph representation learning and clustering task.
Revised:  Remarkably, RNA 3D motifs are recurrent substructures, modelled as networks of base pair interactions, which are crucial for understanding structure-function relationships. The task of automatically identifying such motifs is computationally hard, and remains a key challenge in the field of RNA structural biology and network analysis. State of the art methods solve special cases of the motif problem by constraining the structural variability in occurrences of a motif, and narrowing the substructure search space. Here, we relax this constraint by posing the motif finding problem as a graph representation learning and clustering task.
Edit intent: clarity
Original:  SVM and RF achieved macro-averaged F1 scores of 0.45 and 0.6 while BERT_{base} and BERT_{rad} achieved 0.61 and 0.63. Knowledge distillation improved overall performance on the minority classes, achieving a F1 score of 0.66 . Additionally, by choosing the optimal threshold, the BERT models could classify over 50\% of test samples within 5\% error rate and potentially alleviate half of radiologist protocoling workload .
Revised:  SVM and RF achieved macro-averaged F1 scores of 0.45 and 0.6 while BERT_{base} and BERT_{rad} achieved 0.61 and 0.63. Knowledge distillation improved overall performance on the minority classes, achieving a F1 score of 0.66 .
Edit intent: coherence
Original: In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation  that addresses the limitation of effectively integrating local and global information in convolutional neural networks.
Revised: In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks.
Edit intent: meaning-changed
Original:  Extensive experiments reveal the high degree of capability that this bidirectional attention model presents over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets , KITTI and DIODE.
Revised:  Extensive experiments reveal the high degree of capability of this bidirectional attention model presents over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets , KITTI and DIODE.
Edit intent: fluency
Original:  Extensive experiments reveal the high degree of capability that this bidirectional attention model presents over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets , KITTI and DIODE.
Revised:  Extensive experiments reveal the high degree of capability that this bidirectional attention model over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets , KITTI and DIODE.
Edit intent: clarity
Original:  Extensive experiments reveal the high degree of capability that this bidirectional attention model presents over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets , KITTI and DIODE.
Revised:  Extensive experiments reveal the high degree of capability that this bidirectional attention model presents over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets -- KITTI and DIODE.
Edit intent: fluency
Original:  We propose a general approach, Text Modular Networks(TMNs) , where the system learns to decompose any complex task into the language of existing models. Specifically, we focus on Question Answering (QA) and learn to decompose complex questions into sub-questions answerable by existing QA models. TMNs treat these models as blackboxes and learn their textual input-output behavior (i.e., their language) through their task datasets.
Revised:  We propose a general approach, Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. To ensure solvability of simpler tasks, TMNs learn the textual input-output behavior (i.e., their language) through their task datasets.
Edit intent: clarity
Original:  TMNs treat these models as blackboxes and learn their textual input-output behavior (i.e., their language) through their task datasets. Our next-question generator then learns to sequentially produce sub-questions that help answer a given complex question .
Revised:  TMNs treat these models as blackboxes and learn their textual input-output behavior (i.e., language) of existing models through their datasets. This differs from prior decomposition-based approaches which, besides being designed specifically for each complex task, produce decompositions independent of existing sub-models. Specifically, we focus on Question Answering (QA) and show how to train a next-question generator then learns to sequentially produce sub-questions that help answer a given complex question .
Edit intent: meaning-changed
Original:  These sub-questions are posed to different existing QA models and , together with their answers , provide a natural language explanation of the exact reasoning used by the model. We present the first system, incorporating a neural factoid QA model and a symbolic calculator , that uses decomposition for the DROP dataset, while also generalizing to the multi-hop HotpotQA dataset.
Revised:  These sub-questions are posed to different existing QA models and , together with their answers , provide a natural language explanation of the model's reasoning. We use this framework to build ModularQA, a system that can answer multi-hop reasoning questions by decomposing them into sub-questions answerable by a neural factoid single-span QA model and a symbolic calculator , that uses decomposition for the DROP dataset, while also generalizing to the multi-hop HotpotQA dataset.
Edit intent: meaning-changed
Original:  We present the first system, incorporating a neural factoid QA model and a symbolic calculator , that uses decomposition for the DROP dataset, while also generalizing to the multi-hop HotpotQA dataset. Our system, ModularQA, outperforms a cross-task baseline by 10-60 F1 points and performs comparable to task-specific systems, while also providing an easy-to-read explanation of its reasoning .
Revised:  We present the first system, incorporating a neural factoid QA model and a symbolic calculator . Our experiments show that ModularQA is more versatile than existing explainable systems for DROP and HotpotQA datasets, is more robust than state-of-the-art blackbox (uninterpretable) systems, and generates more understandable and trustworthy explanations compared to prior work .
Edit intent: clarity
Original:  The proposed variational principle is a generalization of the well-known Hu--Washizu variational principle, and various existing methods such as smoothed finite element methods can be expressed in terms of the proposed variational principle.
Revised:  The proposed variational principle is a generalization of the well-known Hu--Washizu variational principle, so that various existing strain smoothing methods can be expressed in terms of the proposed variational principle.
Edit intent: meaning-changed
Original:  The proposed variational principle is a generalization of the well-known Hu--Washizu variational principle , so that various existing strain smoothing methods can be expressed in terms of the proposed variational principle.
Revised:  The proposed variational principle is a generalization of the well-known Hu--Washizu variational principle ; thus, various existing strain smoothing methods can be expressed in terms of the proposed variational principle.
Edit intent: coherence
Original:  With a unified view to the SSE method and other existing ones through the proposed variational principle, we analyze the convergence behavior of the SSE method and explain the reason for  improved performance compared to others. Numerical experiments that support our theoretical results are presented .
Revised:  With a unified view to the SSE method and other existing ones through the proposed variational principle, we analyze the convergence behavior of the SSE method and explain the reason for improved performance compared to other methods. We also present numerical experiments that support our theoretical results are presented .
Edit intent: coherence
Original:  Through simulating the evolution of Prisoners' dilemma game with three different hierarchical distributions: uniform, exponential and power-law , we find that the levels of cooperation are enhanced in all cases, and the enhancement is optimal in the uniform case.
Revised:  Through simulating the evolution of Prisoners' dilemma game with three hierarchical distributions , we find that the levels of cooperation are enhanced in all cases, and the enhancement is optimal in the uniform case.
Edit intent: clarity
Original:  In this article , we measure the forces exerted by two robot arms (UR10e and Kuka LBR iiwa) on an impact measuring device in different positions in the robot workspace andwith various velocities.
Revised:  In this work , we measure the forces exerted by two robot arms (UR10e and Kuka LBR iiwa) on an impact measuring device in different positions in the robot workspace andwith various velocities.
Edit intent: clarity
Original:  In this article , we measure the forces exerted by two robot arms (UR10e and Kuka LBR iiwa) on an impact measuring device in different positions in the robot workspace andwith various velocities.
Revised:  In this article , we measure the forces exerted by two collaborative manipulators (UR10e and Kuka LBR iiwa) on an impact measuring device in different positions in the robot workspace andwith various velocities.
Edit intent: meaning-changed
Original:  In this article , we measure the forces exerted by two robot arms (UR10e and Kuka LBR iiwa) on an impact measuring device in different positions in the robot workspace andwith various velocities.
Revised:  In this article , we measure the forces exerted by two robot arms (UR10e and KUKA LBR iiwa) on an impact measuring device in different positions in the robot workspace andwith various velocities.
Edit intent: fluency
Original:  In this article , we measure the forces exerted by two robot arms (UR10e and Kuka LBR iiwa) on an impact measuring device in different positions in the robot workspace andwith various velocities. Based on these measurements, we investigate the Power and Force Limiting regime presented in ISO/TS 15066.
Revised:  In this article , we measure the forces exerted by two robot arms (UR10e and Kuka LBR iiwa) on an impact measuring device . Our contributions are the following: (i) a new data-driven model predicting impact forces from velocity, distance from robot base, and, newly, the height in the workspace; (ii) demonstration that the model can be trained on a limited number of data points; (iii) analysis of the force evolution upon impact. The standard ISO/TS 15066.
Edit intent: meaning-changed
Original:  Based on these measurements, we investigate the Power and Force Limiting regime presented in ISO/TS 15066. Impact forces are in practice hard to calculate analytically as many properties of the robots are not available (e.g., proprietary control algorithms). This motivates the use of simple, yet reasonably accurate, approximations. Our results show that height of the impact location is also an important factor and that an accurate model of the robot can be created from a limited number of impact samples. Previous work predicted impact forces based on other factors (distance, velocity, weight), yet these predictions are less accurate. This would allow a fast estimation of the impact forcesin the robot's workspace and thus make it easier to design a safe human-robot collaboration setup .
Revised:  Based on these measurements, we investigate the Power and Force Limiting regime presented in ISO/TS 15066 defines the Power and Force Limiting operation mode and prescribes force thresholds that a moving robot is allowed to exert on human body parts during impact (for the back of the hand, 280 N for transient and 140 N for quasi-static contact), along with a simple formula to obtain maximum allowed speed of the robot in the whole workspace. Applying this to our scenario where the impact has a clamping nature, the allowed end effector speed on the whole robot workspace would be only 0.13 and 0.16 m/s for the UR10e and KUKA LBR iiwa, respectively. Our measurements reveal that if the task is performed for example 0.8 m away and 0.4 m above the robot base, speeds of 0.16 m/s (UR10e) and 0.20 m/s (KUKA LBR iiwa) can be safely operated, staying within the prescribed force limit. Furthermore, the force evolution during impact for the UR10e reveals that clamping never occurs. With the 280 N limit, 0.36 m/s will still be safe with the UR10e. The formulas relating robot mass, velocity, and impact forces from ISO/TS 15066 are insufficient---leading both to significant underestimation and overestimation of velocities/forces, and thus to unnecessarily long cycle times or even dangerous applications .
Edit intent: meaning-changed
Original:  In this work, we measure the forces exerted by two collaborative manipulators (UR10e and KUKA LBR iiwa) on an impact measuring device. Our contributions are the following: (i) a new data-driven model predicting impact forces from velocity, distance from robot base, and, newly, the height in the workspace; (ii) demonstration that the model can be trained on a limited number of data points; (iii) analysis of the force evolution upon impact.
Edit intent: coherence
Original:  The standard ISO/TS 15066 defines the Power and Force Limiting operation mode and prescribes force thresholds that a moving robot is allowed to exert on human body parts during impact (for the back of the hand, 280 N for transient and 140 N for quasi-static contact), along with a simple formula to obtain maximum allowed speed of the robot in the whole workspace.
Revised:  The standard ISO/TS 15066 defines the Power and Force Limiting operation mode and prescribes force thresholds that a moving robot is allowed to exert on human body parts during impact , along with a simple formula to obtain maximum allowed speed of the robot in the whole workspace.
Edit intent: coherence
Original:  Applying this to our scenario where the impact has a clamping nature, the allowed end effector speed on the whole robot workspace would be only 0.13 and 0.16 m/s for the UR10e and KUKA LBR iiwa , respectively.
Revised:  In this work, we measure the forces exerted by two collaborative manipulators ( UR10e and KUKA LBR iiwa , respectively.
Edit intent: clarity