wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense  methods such as DPR \mbox{%DIFAUXCMD karpukhin2020dense .
Revised:  We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense retrieval methods such as DPR \mbox{%DIFAUXCMD karpukhin2020dense .
Edit intent: clarity
Original:  We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense  methods such as DPR \mbox{%DIFAUXCMD karpukhin2020dense .
Revised:  We demonstrate on open-domain question answering that the generated contexts significantly enrich the semantics of the queries and thus GAR with sparse representations (BM25) achieves comparable or better performance than the state-of-the-art dense methods such as DPR .
Edit intent: meaning-changed
Original:  Furthermore, GAR achieves the state-of-the-art performance on the Natural Questions and TriviaQA datasets under the extractive setting when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
Revised:  GAR achieves state-of-the-art performance on the Natural Questions and TriviaQA datasets under the extractive setting when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
Edit intent: coherence
Original:  Furthermore, GAR achieves the state-of-the-art performance on the Natural Questions and TriviaQA datasets under the extractive setting when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
Revised:  Furthermore, GAR achieves the state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive setting when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
Edit intent: fluency
Original:  Furthermore, GAR achieves the state-of-the-art performance on the Natural Questions and TriviaQA datasets under the extractive setting when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
Revised:  Furthermore, GAR achieves the state-of-the-art performance on the Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader, and consistently outperforms other retrieval methods when the same generative reader is used.
Edit intent: clarity
Original:  Different from the traditional sparse reward function , we present three dense reward functions in this paper. Firstly, posture reward function is proposed to accelerate the learning process with a more reasonable trajectory by modeling the distance and direction constraints, which can reduce the blindness of exploration.
Revised:  Different from the traditional sparse reward function , we present three dense reward functions . These rewards differ from the traditional sparse reward. First, a posture reward function is proposed to accelerate the learning process with a more reasonable trajectory by modeling the distance and direction constraints, which can reduce the blindness of exploration.
Edit intent: meaning-changed
Original:  Secondly, to improve the stability, a reward function at stride reward is proposed by modeling the distance and movement distance of joints constraints, it can make the learning process more stable. In order to further improve learning efficiency, we are inspired by the cognitive process of human behavior and propose a stage incentive mechanism, including  hard stage incentive reward function and  soft stage incentive reward function.
Revised:  Secondly, to improve the stability, a reward function at stride reward is proposed by modeling the distance and movement distance of joint constraints. Finally, in order to further improve learning efficiency, we are inspired by the cognitive process of human behavior and propose a stage incentive mechanism, including hard stage incentive reward function and soft stage incentive reward function.
Edit intent: coherence
Original:  The percentage increase in  convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation  , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Revised:  The percentage increase in the convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Edit intent: fluency
Original:  The percentage increase in  convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation  , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Revised:  The percentage increase in convergence mean reward was 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Edit intent: fluency
Original:  The percentage increase in  convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation  , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Revised:  The percentage increase in convergence mean reward is 4.4 -15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Edit intent: meaning-changed
Original:  The percentage increase in  convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation  , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Revised:  The percentage increase in convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation were 21.9 \%~63.2\% . In the evaluation , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Edit intent: clarity
Original:  The percentage increase in  convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation  , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Revised:  The percentage increase in convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 -63.2\% . In the evaluation , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Edit intent: meaning-changed
Original:  The percentage increase in  convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation  , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Revised:  The percentage increase in convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation experiments , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Edit intent: meaning-changed
Original:  The percentage increase in  convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation  , the success rate of trajectory planning for robot manipulator is up to 99.6\%.
Revised:  The percentage increase in convergence mean reward is 4.4 \%~15.5\% and the percentage decreases with respect to standard deviation by 21.9 \%~63.2\% . In the evaluation , the success rate of trajectory planning for a robot manipulator reached 99.6\%.
Edit intent: clarity
Original:  A model of the stock price movement has been developed to explain such phenomena based on the Institutional fund flow and financial antifragility , which represents the financial indicator of a company.
Revised:  A model of the stock price movement has been developed to explain such phenomena based on the net-fund-flow (\Psi_t) due to institutional investors, and financial antifragility , which represents the financial indicator of a company.
Edit intent: meaning-changed
Original:  While the recent past has seen rapid growth in scientific and technological knowledge, relatively little is known about the manner in which science and technology develop and coalesce knowledge into larger structures that enable or constrain future breakthroughs.
Revised:  While the recent past has seen rapid growth in scientific and technological knowledge, relatively little is known about the manner in which scientific and technological knowledge develop and coalesce knowledge into larger structures that enable or constrain future breakthroughs.
Edit intent: clarity
Original:  While the recent past has seen rapid growth in scientific and technological knowledge, relatively little is known about the manner in which science and technology develop and coalesce knowledge into larger structures that enable or constrain future breakthroughs.
Revised:  While the recent past has seen rapid growth in scientific and technological knowledge, relatively little is known about the manner in which science and technology develop and coalesce into larger structures that enable or constrain future breakthroughs.
Edit intent: fluency
Original:  We observe rapid and varied growth in the high-dimensional structure in many fields of science and technology, and find this high-dimensional growth coincides with decline in lower-dimensional structure.
Revised:  We observe rapid and varied growth in the higher-order structure of knowledge in many scientific and technological fields. This growth is not observable using traditional network measures. We further demonstrate that the emergence of higher-order structure coincides with decline in lower-dimensional structure.
Edit intent: meaning-changed
Original:  We observe rapid and varied growth in the high-dimensional structure in many fields of science and technology, and find this high-dimensional growth coincides with decline in lower-dimensional structure. This higher-order growth in knowledge networks has historically far outpaced the growth in scientific and technological collaboration networks.
Revised:  We observe rapid and varied growth in the high-dimensional structure in many fields of science and technology, and find this high-dimensional growth coincides with decline in lower-order structure, and has historically far outpaced the growth in scientific and technological collaboration networks.
Edit intent: coherence
Original:  We also characterize the relationship between higher-order structure and the nature of the science and technology produced within these structural environments and find a positive relationship between the abstractness of language used within fields and increasing high-dimensional structure.
Revised:  Up to a point, increases in higher-order structure and the nature of the science and technology produced within these structural environments and find a positive relationship between the abstractness of language used within fields and increasing high-dimensional structure.
Edit intent: meaning-changed
Original:  We also characterize the relationship between higher-order structure and the nature of the science and technology produced within these structural environments and find a positive relationship between the abstractness of language used within fields and increasing high-dimensional structure.
Revised:  We also characterize the relationship between higher-order structure are associated with better outcomes, as measured by the novelty and impact of papers and patents. However, the nature of the science and technology produced within these structural environments and find a positive relationship between the abstractness of language used within fields and increasing high-dimensional structure.
Edit intent: meaning-changed
Original:  We also characterize the relationship between higher-order structure and the nature of the science and technology produced within these structural environments and find a positive relationship between the abstractness of language used within fields and increasing high-dimensional structure.
Revised:  We also characterize the relationship between higher-order structure and the nature of science and technology produced within these structural environments and find a positive relationship between the abstractness of language used within fields and increasing high-dimensional structure.
Edit intent: fluency
Original:  We also characterize the relationship between higher-order structure and the nature of the science and technology produced within these structural environments and find a positive relationship between the abstractness of language used within fields and increasing high-dimensional structure. We also find a robust relationship between high-dimensional structure and number of metrics for publication success, implying this high-dimensional structure may be linked to discovery and invention .
Revised:  We also characterize the relationship between higher-order structure and the nature of the science and technology produced under higher-order regimes also appears to be qualitatively different from that produced under lower-order ones, with the former exhibiting greater linguistic abstractness and greater tendencies for building upon prior streams of knowledge .
Edit intent: clarity
Original:  For this aim , this paper provides a new graph convolutional neural network architecture. This architecture benefits efficiently from the revealed training nodes, the node features, and the graph structure. On the other hand, in many applications, non-graph observations (  side information) exist beside a given graph realization .
Revised:  For this aim , this paper provides a method for extracting side information from a graph realization. Then a new GCN architecture is presented that combines the output of traditional GCN and the extracted side information. Another contribution of this paper is relevant to non-graph observations ( side information) exist beside a given graph realization .
Edit intent: meaning-changed
Original:  On the other hand, in many applications, non-graph observations (  side information) exist beside a given graph realization . The non-graph observations are usually independent of the graph structure.
Revised:  On the other hand, in many applications, non-graph observations ( side information) that exists beside a graph realization in many applications. Indeed, the extracted side information can be replaced by a sequence of side information that is independent of the graph structure.
Edit intent: meaning-changed
Original:  For both cases, the experiments on the synthetic and real-world datasets demonstrate that our proposed architecture achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for the node classification task.
Revised:  For both cases, the experiments on synthetic and real-world datasets demonstrate that our proposed architecture achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for the node classification task.
Edit intent: fluency
Original:  For both cases, the experiments on the synthetic and real-world datasets demonstrate that our proposed architecture achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for the node classification task.
Revised:  For both cases, the experiments on the synthetic and real-world datasets demonstrate that the proposed model achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for the node classification task.
Edit intent: clarity
Original:  Items of size smaller or equal to 1 are presented in an iterative fashion and an algorithm has to decide whether to permanently include or reject each item into the knapsack without any knowledge about the rest of the instance.
Revised:  Items of size smaller or equal to 1 are presented in an iterative way and an algorithm has to decide whether to permanently include or reject each item into the knapsack without any knowledge about the rest of the instance.
Edit intent: clarity
Original:  While the classical Online Simple Knapsack Problem does not admit any constantly bounded competitive ratio in the deterministic setting, we find that adding the possibility of reservation makes the problem constantly competitive , with varying competitive ratios depending on the value of \alpha.
Revised:  While the classical Online Simple Knapsack Problem does not admit any constantly bounded competitive ratio in the deterministic setting, we find that adding the possibility of reservation makes the problem constantly competitive with varying competitive ratios depending on the value of \alpha.
Edit intent: fluency
Original:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive---and for costs between 2-1 and 0.5---an area that is  (2+\alpha)-competitive .
Revised:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6 -- an area that is strictly 2-competitive---and for costs between 2-1 and 0.5---an area that is (2+\alpha)-competitive .
Edit intent: fluency
Original:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive---and for costs between 2-1 and 0.5---an area that is  (2+\alpha)-competitive .
Revised:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive, for costs between 2-1 and 0.5---an area that is (2+\alpha)-competitive .
Edit intent: coherence
Original:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive---and for costs between 2-1 and 0.5---an area that is  (2+\alpha)-competitive .
Revised:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive---and for costs between 2-1 and 1 -- an area that is (2+\alpha)-competitive .
Edit intent: meaning-changed
Original:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive---and for costs between 2-1 and 0.5---an area that is  (2+\alpha)-competitive .
Revised:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive---and for costs between 2-1 and 0.5---an area that is strictly (2+\alpha)-competitive .
Edit intent: fluency
Original:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive---and for costs between 2-1 and 0.5---an area that is  (2+\alpha)-competitive . With our analysis, we find a counterintuitive characteristic of the problem: Intuitively, one would expect that the possibility of rejecting items becomes more and more helpful for an online algorithm with growing reservation costs.
Revised:  We give upper and lower bounds for the whole range of reservation costs, with tight bounds for costs up to 1/ 6---an area that is strictly 2-competitive---and for costs between 2-1 and 0.5---an area that is (2+\alpha)-competitive up to \phi -1, and strictly 1/(1-\alpha)-competitive above \phi-1, where \phi is the golden ratio. We find a counterintuitive characteristic of the problem: Intuitively, one would expect that the possibility of rejecting items becomes more and more helpful for an online algorithm with growing reservation costs.
Edit intent: meaning-changed
Original:  With our analysis, we find a counterintuitive characteristic of the problem: Intuitively, one would expect that the possibility of rejecting items becomes more and more helpful for an online algorithm with growing reservation costs.
Revised:  With our analysis, we find a counterintuitive characteristic of the problem: Intuitively, one may expect that the possibility of rejecting items becomes more and more helpful for an online algorithm with growing reservation costs.
Edit intent: clarity
Original:  With our analysis, we find a counterintuitive characteristic of the problem: Intuitively, one would expect that the possibility of rejecting items becomes more and more helpful for an online algorithm with growing reservation costs.
Revised:  With our analysis, we find a counterintuitive characteristic of the problem: Intuitively, one would expect that the possibility of rejecting items becomes more helpful for an online algorithm with growing reservation costs.
Edit intent: clarity
Original:  However, for higher reservation costs between \sqrt{2}-1 and 0.5 , an algorithm that is unable to reject any items tightly matches the lower bound and is thus the best possible.
Revised:  However, for higher reservation costs above \sqrt{2}-1 and 0.5 , an algorithm that is unable to reject any items tightly matches the lower bound and is thus the best possible.
Edit intent: meaning-changed
Original:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as  nonlinear dynamical systems such as a network of neural mass models  . Finally, we apply our method to real-world observational microelectrode array  data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Revised:  By applying this method to numerical simulations of coupled linear stochastic processes as well as nonlinear dynamical systems such as a network of neural mass models . Finally, we apply our method to real-world observational microelectrode array data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Edit intent: clarity
Original:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as  nonlinear dynamical systems such as a network of neural mass models  . Finally, we apply our method to real-world observational microelectrode array  data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Revised:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as two examples of interacting nonlinear dynamical systems such as a network of neural mass models . Finally, we apply our method to real-world observational microelectrode array data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Edit intent: meaning-changed