wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we propose a novel framework named DKPLM to dynamically select  and embed knowledge context according to textual context for PLMs, which can avoid the effect of redundant and ambiguous knowledge in KGs that cannot match the input text.
Revised:  In this paper, we propose a novel framework named Coke to dynamically select and embed knowledge context according to textual context for PLMs, which can avoid the effect of redundant and ambiguous knowledge in KGs that cannot match the input text.
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Original:  In this paper, we propose a novel framework named DKPLM to dynamically select  and embed knowledge context according to textual context for PLMs, which can avoid the effect of redundant and ambiguous knowledge in KGs that cannot match the input text.
Revised:  In this paper, we propose a novel framework named DKPLM to dynamically select contextual knowledge and embed knowledge context according to textual context for PLMs, which can avoid the effect of redundant and ambiguous knowledge in KGs that cannot match the input text.
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Original:  Besides the performance improvements, the dynamically selected knowledge in DKPLM can describe the semantics of text-related knowledge in a more interpretable form than the conventional PLMs. Our source code and datasets will be available to provide more details for DKPLM .
Revised:  Besides the performance improvements, the dynamically selected knowledge in Coke can describe the semantics of text-related knowledge in a more interpretable form than the conventional PLMs. Our source code and datasets will be available to provide more details for DKPLM .
Edit intent: meaning-changed
Original:  Besides the performance improvements, the dynamically selected knowledge in DKPLM can describe the semantics of text-related knowledge in a more interpretable form than the conventional PLMs. Our source code and datasets will be available to provide more details for DKPLM .
Revised:  Besides the performance improvements, the dynamically selected knowledge in DKPLM can describe the semantics of text-related knowledge in a more interpretable form than the conventional PLMs. Our source code and datasets will be available to provide more details for Coke .
Edit intent: clarity
Original:  At a high level, the algorithm maintains \ell solutions and greedily updates them in a simultaneous fashion , rather than a sequential one . SimultaneousGreedys achieves the tightest known approximation guarantees for both k-extendible systems and the more general k-systems, which are (k+1)^2/k = k + O(1) and (1 + k+2)^2 = k + O(k), respectively.
Revised:  At a high level, the algorithm maintains \ell solutions and greedily updates them in a simultaneous fashion . SimultaneousGreedys achieves the tightest known approximation guarantees for both k-extendible systems and the more general k-systems, which are (k+1)^2/k = k + O(1) and (1 + k+2)^2 = k + O(k), respectively.
Edit intent: coherence
Original:  This leads to the first nearly linear time algorithm for submodular maximization over k-extendible systems and k-systems. Finally, the technique is flexible enough to incorporate the intersection of m additional knapsack constraints, while retaining similar approximation guarantees , which are roughly k + 2m + \mathcal{O}(\sqrt{k+m}) for k-systems and  k+2m + \mathcal{O}(\sqrt{m}) for k-extendible systems.
Revised:  Both SimultaneousGreedys and RepeatedGreedy are flexible enough to incorporate the intersection of m additional knapsack constraints, while retaining similar approximation guarantees , which are roughly k + 2m + \mathcal{O}(\sqrt{k+m}) for k-systems and k+2m + \mathcal{O}(\sqrt{m}) for k-extendible systems.
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Original:  Finally, the technique is flexible enough to incorporate the intersection of m additional knapsack constraints, while retaining similar approximation guarantees , which are roughly k + 2m + \mathcal{O}(\sqrt{k+m}) for k-systems and  k+2m + \mathcal{O}(\sqrt{m}) for k-extendible systems.
Revised:  Finally, the technique is flexible enough to incorporate the intersection of m additional knapsack constraints, while retaining similar approximation guarantees : both algorithms yield an approximation guarantee of roughly k + 2m + \mathcal{O}(\sqrt{k+m}) for k-systems and k+2m + \mathcal{O}(\sqrt{m}) for k-extendible systems.
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Original:  Finally, the technique is flexible enough to incorporate the intersection of m additional knapsack constraints, while retaining similar approximation guarantees , which are roughly k + 2m + \mathcal{O}(\sqrt{k+m}) for k-systems and  k+2m + \mathcal{O}(\sqrt{m}) for k-extendible systems.
Revised:  Finally, the technique is flexible enough to incorporate the intersection of m additional knapsack constraints, while retaining similar approximation guarantees , which are roughly k + 2m + \mathcal{O}(\sqrt{k+m}) for k-systems and SimultaneousGreedys enjoys an improved approximation guarantee of k+2m + \mathcal{O}(\sqrt{m}) for k-extendible systems.
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Original:  To complement our algorithmic contributions, we provide a hardness result which states that no algorithm making polynomially many queries to the value and independence oracles can achieve an approximation better than k + 1/2 + \varepsilon  .
Revised:  To complement our algorithmic contributions, we provide a hardness result which states that no algorithm making polynomially many oracle queries can achieve an approximation better than k + 1/2 + \varepsilon .
Edit intent: clarity
Original:  To complement our algorithmic contributions, we provide a hardness result which states that no algorithm making polynomially many queries to the value and independence oracles can achieve an approximation better than k + 1/2 + \varepsilon  .
Revised:  To complement our algorithmic contributions, we provide a hardness result which states that no algorithm making polynomially many queries to the value and independence oracles can achieve an approximation better than k + 1/2 + \varepsilon . We also present SubmodularGreedy.jl, a Julia package which implements these algorithms and may be downloaded at URL . Finally, we test the effectiveness of these algorithms on real datasets .
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Original:  Our results suggest that before the OuAA campaign stay-at-home orders were effective in decreasing time spent at retail recreation places and in increasing time spent at home. These stay-at-home orders were less effective in more conservative counties. We further find that the OuAA campaign significantly increased time spent at retail recreation places and decreased time spent at home particularly in conservative counties .
Revised:  Our results suggest that before the OuAA campaign stay-at-home orders were effective in decreasing time spent at retail brought down time spent in retail and recreation businesses by about 27\% for typical conservative and liberal counties. However, after the launch of OuAA campaign, the time spent at retail recreation places and decreased time spent at home particularly in conservative counties .
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Original:  We further find that the OuAA campaign significantly increased time spent at retail recreation places and decreased time spent at home particularly in conservative counties .
Revised:  We further find that the OuAA campaign significantly increased time spent at retail and recreational businesses in a typical conservative county increased significantly more than in liberal counties (15\% increase in a typical conservative county Vs. 9\% increase in a typical liberal county). We also found that in conservative counties .
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Original:  We further find that the OuAA campaign significantly increased time spent at retail recreation places and decreased time spent at home particularly in conservative counties . However, in conservative counties with stay-at-home orders in place, OuAA campaign was less effective when compared to conservative counties without stay-at-home orders.
Revised:  We further find that the OuAA campaign significantly increased time spent at retail recreation places and decreased time spent at home particularly in conservative counties with stay-at-home orders in place, OuAA campaign was less effective when compared to conservative counties without stay-at-home orders.
Edit intent: coherence
Original:  These findings signal promising news for local (county and state) authorities. That is , even when the federal government is reopening the country, the local authorities that enforced stay-at-home restrictions were to some extent effective.
Revised:  These findings illuminate to what extent residents' political ideology could determine to what extent they follow local orders and to what extent the White House's OuAA campaign polarized the obedience between liberal and conservative counties. The silver lining in our study is that even when the federal government is reopening the country, the local authorities that enforced stay-at-home restrictions were to some extent effective.
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Original:  We propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning to arrive at optimal shape changes for two swimming tasks: swimming in a desired direction and swimming towards a known target.
Revised:  To address the problem of underwater motion planning, we propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning to arrive at optimal shape changes for two swimming tasks: swimming in a desired direction and swimming towards a known target.
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Original:  We propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning to arrive at optimal shape changes for two swimming tasks: swimming in a desired direction and swimming towards a known target.
Revised:  We propose a simple model of a three-link fish swimming in a potential flow environment and we use model-free reinforcement learning for shape control. We arrive at optimal shape changes for two swimming tasks: swimming in a desired direction and swimming towards a known target.
Edit intent: clarity
Original:  These FLOW portraits provide an intuitive map of dynamic calcium activity, including sources, sinks, and traveling fronts in the data, using the finite-time Lyapunov exponent (FTLE) technique developed to analyze time-varying manifolds in unsteady fluids.
Revised:  These FLOW portraits provide an intuitive map of dynamic calcium activity, including regions of initiation and termination, as well as the direction and extent of activity spread. To extract these structures, we use the finite-time Lyapunov exponent (FTLE) technique developed to analyze time-varying manifolds in unsteady fluids.
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Original:  We demonstrate the application of FLOW portraits on  two widefield calcium imaging datasets : activity of the developing mouse cortex during the first 8 postnatal days, and spontaneous cortical activity in an adult mouse.
Revised:  We demonstrate the application of FLOW portraits on three simple synthetic datasets and two widefield calcium imaging datasets : activity of the developing mouse cortex during the first 8 postnatal days, and spontaneous cortical activity in an adult mouse.
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Original:  We demonstrate the application of FLOW portraits on  two widefield calcium imaging datasets : activity of the developing mouse cortex during the first 8 postnatal days, and spontaneous cortical activity in an adult mouse.
Revised:  We demonstrate the application of FLOW portraits on two widefield calcium imaging datasets , including cortical waves in the developing mouse cortex during the first 8 postnatal days, and spontaneous cortical activity in an adult mouse.
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Original: The recent proliferation of computing technologies , e.g., sensors, computer vision, machine learning, hardware acceleration , and the broad deployment of communication mechanisms , e.g., DSRC, C-V2X, 5G , have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Revised: The recent proliferation of computing technologies ( e.g., sensors, computer vision, machine learning, hardware acceleration , and the broad deployment of communication mechanisms , e.g., DSRC, C-V2X, 5G , have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Edit intent: fluency
Original: The recent proliferation of computing technologies , e.g., sensors, computer vision, machine learning, hardware acceleration , and the broad deployment of communication mechanisms , e.g., DSRC, C-V2X, 5G , have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Revised: The recent proliferation of computing technologies , e.g., sensors, computer vision, machine learning, and hardware acceleration) , and the broad deployment of communication mechanisms , e.g., DSRC, C-V2X, 5G , have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Edit intent: coherence
Original: The recent proliferation of computing technologies , e.g., sensors, computer vision, machine learning, hardware acceleration , and the broad deployment of communication mechanisms , e.g., DSRC, C-V2X, 5G , have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Revised: The recent proliferation of computing technologies , e.g., sensors, computer vision, machine learning, hardware acceleration , and the broad deployment of communication mechanisms ( e.g., DSRC, C-V2X, 5G , have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Edit intent: fluency
Original: The recent proliferation of computing technologies , e.g., sensors, computer vision, machine learning, hardware acceleration , and the broad deployment of communication mechanisms , e.g., DSRC, C-V2X, 5G , have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Revised: The recent proliferation of computing technologies , e.g., sensors, computer vision, machine learning, hardware acceleration , and the broad deployment of communication mechanisms , e.g., DSRC, C-V2X, 5G ) have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors.
Edit intent: fluency
Original:  We find that this simple extension of the game allows cooperation to flourish in well-mixed populations that adhere to either direct or indirect reciprocity. In combination with network reciprocity , however, the exit option is less conducive to cooperation.
Revised:  We find that this simple extension of the game allows cooperation to flourish in well-mixed populations when iterations or reputation are added. In networked populations , however, the exit option is less conducive to cooperation.
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Original: Modern deep reinforcement learning plays an important role to solve a wide range of complex decision-making tasks. However, due to the use of deep neural networks, the trained models are lacking transparency which causes distrust from their user and hard to be used in the critical field such as self-driving car and unmanned aerial vehicles. In this paper, an explainable deep reinforcement learning method is proposed to deal with the multirotor obstacle avoidance and navigation problem.
Revised: Autonomous navigation in unknown complex environment is still a hard problem, especially for small Unmanned Aerial Vehicles (UAVs) with limited computation resources. In this paper, a neural network-based reactive controller is proposed for a quadrotor to fly autonomously in unknown outdoor environment. The navigation controller makes use of only current sensor data to generate the control signal without any optimization or configuration space searching, which reduces both memory and computation requirement. The navigation problem is modelled as a Markov Decision Process (MDP) and solved using deep reinforcement learning method is proposed to deal with the multirotor obstacle avoidance and navigation problem.
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Original:  In this paper, an explainable deep reinforcement learning method is proposed to deal with the multirotor obstacle avoidance and navigation problem. Both visual and textual explanation is provided to make the trained agent more transparency and comprehensible for humans. Our model can provide real-time decision explanation for non-expert users. Also , some global explanation results are provided for experts to diagnose the learned policy.
Revised:  In this paper, an explainable deep reinforcement learning (DRL) method. Specifically, to get better understanding of the trained network, some model explanation methods are proposed. Based on the feature attribution, each decision making result during flight is explained using both visual and texture explanation. Moreover , some global explanation results are provided for experts to diagnose the learned policy.
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Original:  Also , some global explanation results are provided for experts to diagnose the learned policy. Our method is validated in the simulation environment. The simulation result shows our proposed method can get useful explanations to increase the user's trust to the network and also improve the network performance .
Revised:  Also , some global explanation results are provided for experts to evaluate and improve the trained neural network. The simulation results illustrated the proposed method can get useful explanations to increase the user's trust to the network and also improve the network performance .
Edit intent: clarity
Original:  The simulation result shows our proposed method can get useful explanations to increase the user's trust to the network and also improve the network performance .
Revised:  The simulation result shows our proposed method can make useful and reasonable explanation for the trained model, which is beneficial for both non-expert users and controller designer. Finally, the real world tests shown the proposed controller can navigate the quadrotor to goal position successfully and the reactive controller performs much faster than some conventional approach under the same computation resource .
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Original:  We show a generalization of this result on Hausdorff distances and middle shapes, and various related properties. We also show that a generalization of such middle shapes implies a morph with a bounded rate of change  .
Revised:  We show that our result can be generalised to give an interpolated shape between A and B for any interpolation variable \alpha between 0 and 1, and prove that the resulting morph has a bounded rate of change .
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Original:  Finally, we explore a generalization of the concept of a Hausdorff middle to more than two sets and show how to approximate or compute it .
Revised:  Finally, we explore a generalization of the concept of a Hausdorff middle to more than two sets and show how to approximate or compute this middle shape, and that the properties relating to the connectedness of the Hausdorff middle extend from the case with two input sets. We also give bounds on the Hausdorff distance between the middle set and the input .
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Original:  Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics in  tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics.
Revised:  Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics in these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics.
Edit intent: clarity
Original:  First, we examine the spectral gaps of tie-decay networks from empirical data .
Revised:  First, we examine the spectral gaps of tie-decay networks from empirical data with those for corresponding randomized and aggregate networks. We find that the spectral gaps of the empirical networks tend to be smaller than those of the randomized and aggregate networks. Second, we study the spectral gap as a function of the tie-decay rate and time.
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Original:  Second, we compare the spectral gaps of these empirical networks with corresponding randomized and aggregate networks. We find that the spectral gaps of the empirical networks tend to be smaller than those of the randomized and aggregate networks. This highlights the importance of the interplay between the times that edges arise and decay in temporal networks.
Revised:  Our results highlight the importance of the interplay between the times that edges arise and decay in temporal networks.
Edit intent: clarity
Original:  Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics in these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics.
Revised:  Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics on these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics.
Edit intent: clarity
Original:  We propose a labeling scheme based on graphemes (linguistic segments of word formation) that makes segmentation inside alpha-syllabary words linear and present the first dataset of Bengali handwritten graphemes that are commonly used in an everyday context.
Revised:  We propose a labeling scheme based on graphemes (linguistic segments of word formation) that makes segmentation in-side alpha-syllabary words linear and present the first dataset of Bengali handwritten graphemes that are commonly used in an everyday context.
Edit intent: fluency
Original:  The dataset  is open-sourced as a part of the BengaliAI Handwritten Grapheme Classification Challenge on Kaggle to benchmark vision algorithms for multi-label grapheme classification.
Revised:  The dataset contains 411k curated samples of 1295 unique commonly used Bengali graphemes. Additionally, the test set contains 900 uncommon Bengali graphemes for out of dictionary performance evaluation. The dataset is open-sourced as a part of the BengaliAI Handwritten Grapheme Classification Challenge on Kaggle to benchmark vision algorithms for multi-label grapheme classification.
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Original:   Next, we introduce a  space-time (tensorproduct) discretization using finite elements in space and piecewise linear functions in time .
Revised:  We propose to utilize a well-posed inf-sup stable framework of the PDE in appropriate Lebesgue-Bochner spaces. Next, we introduce a space-time (tensorproduct) discretization using finite elements in space and piecewise linear functions in time .
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Original:   Next, we introduce a  space-time (tensorproduct) discretization using finite elements in space and piecewise linear functions in time . This setting is known to be equivalent to a Crank-Nicolson time stepping scheme for parabolic problems.
Revised:  Next, we introduce a space-time (tensorproduct) discretization using finite elements in space and piecewise linear functions in time , this setting is known to be equivalent to a Crank-Nicolson time stepping scheme for parabolic problems.
Edit intent: fluency