wanyu/IteraTeR_v2 huggingface dataset:
Original:  This formulation appears as a more natural one, particularly in cases where one needs to strike a balance between the coverage achieved and the cost being paid .
Revised:  This formulation appears as a more natural one, particularly in cases where one needs to strike a balance between the value of the objective function and the cost being paid .
Edit intent: clarity
Original: Graphics processor units (GPUs ) are prevalent in modern computing systems at all scales.
Revised: GPUs are prevalent in modern computing systems at all scales.
Edit intent: clarity
Original:  This work should give GPU architects and developers a more concrete understanding of these representative NVIDIA GPUs' microarchitecture.
Revised:  This work should aid in understanding NVIDIA GPUs' microarchitecture.
Edit intent: clarity
Original: We study dynamic graph algorithms in the Massively Parallel Computation model (MPC), inspired by practical data processing systems.
Revised: We study dynamic graph algorithms in the Massively Parallel Computation model , which was inspired by practical data processing systems.
Edit intent: coherence
Original:  ( i ) Every n-vertex unit disk graph has a 5-hop spanner with at most 5.5n edges.
Revised:  ( I ) Every n-vertex unit disk graph has a 5-hop spanner with at most 5.5n edges.
Edit intent: fluency
Original:  ( iii ) Every n-vertex unit disk graph has a 2-hop spanner with O(n ^{3/2 ) edges.
Revised:  ( III ) Every n-vertex unit disk graph has a 2-hop spanner with O(n ^{3/2 ) edges.
Edit intent: fluency
Original:  ( v ) For every point set on a circle, there exists a plane 4-hop spanner.
Revised:  ( V ) For every point set on a circle, there exists a plane 4-hop spanner.
Edit intent: fluency
Original:  ( vi ) The maximum degree of k-hop spanners cannot be bounded  above by a function of  k.
Revised:  ( VI ) The maximum degree of k-hop spanners cannot be bounded above by a function of k.
Edit intent: fluency
Original:  ( vi ) The maximum degree of k-hop spanners cannot be bounded  above by a function of  k.
Revised:  ( vi ) The maximum degree of k-hop spanners cannot be bounded from above by a function of k.
Edit intent: clarity
Original:  Current Long Term Evolution (LTE) system has been designed based on grant-based (GB)  random access, which can hardly support the URLLC requirements.
Revised:  Current Long Term Evolution (LTE) system has been designed based on grantbased (GB) random access, which can hardly support the URLLC requirements.
Edit intent: fluency
Original:  Grant-free (GF)  access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively save the time of requesting/waiting for a grant.
Revised:  Grant-free (GF) access is proposed as a feasible and promising technology to meet such requirements, especially for uplink transmissions, which effectively saves the time of requesting/waiting for a grant.
Edit intent: fluency
Original:  While some basic grant-free features have been proposed and standardized in NR Release-15, there is still much space to improve.
Revised:  While some basic GF access features have been proposed and standardized in NR Release-15, there is still much space to improve.
Edit intent: clarity
Original:  Being proposed as 3GPP study items, three grant-free access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this paper.
Revised:  Being proposed as 3GPP study items, three GF access schemes with Hybrid Automatic Repeat reQuest (HARQ) retransmissions including Reactive, K-repetition, and Proactive, are analyzed in this paper.
Edit intent: clarity
Original:  Specifically, we present a spatio-temporal analytical framework for the contention-based GF access analysis.
Revised:  Specifically, we present a spatiotemporal analytical framework for the contention-based GF access analysis.
Edit intent: fluency
Original:  Our results show that  the Reactive scheme and the Proactive scheme can provide the lowest latent access failure probability under shorter latency constraints, whereas the K-repetition scheme achieves the best performance under longer latency constraints, except for the scenario of lower SINR threshold and higher UE density .
Revised:  Our results show that the lowest latent access failure probability under shorter latency constraints, whereas the K-repetition scheme achieves the best performance under longer latency constraints, except for the scenario of lower SINR threshold and higher UE density .
Edit intent: clarity
Original:  Our results show that  the Reactive scheme and the Proactive scheme can provide the lowest latent access failure probability under shorter latency constraints, whereas the K-repetition scheme achieves the best performance under longer latency constraints, except for the scenario of lower SINR threshold and higher UE density .
Revised:  Our results show that the Reactive scheme and the Proactive scheme can provide the lowest latent access failure probability , whereas, under longer latency constraints, whereas the K-repetition scheme achieves the best performance under longer latency constraints, except for the scenario of lower SINR threshold and higher UE density .
Edit intent: coherence
Original:  Our results show that  the Reactive scheme and the Proactive scheme can provide the lowest latent access failure probability under shorter latency constraints, whereas the K-repetition scheme achieves the best performance under longer latency constraints, except for the scenario of lower SINR threshold and higher UE density .
Revised:  Our results show that the Reactive scheme and the Proactive scheme can provide the lowest latent access failure probability under shorter latency constraints, the K-repetition scheme achieves the best performance under longer latency constraints, except for the scenario of lower SINR threshold and higher UE density .
Edit intent: coherence
Original:   This strong law requires a square integrable integrand and it holds along a sequence of sample sizes of the form n=mb^k for m= 1 ,%DIFDELCMD < \dots%%% ,M and k%DIFDELCMD < \ge0 %%% and a base b%DIFDELCMD < \ge2%%% .
Revised:  This strong law requires a square integrable integrand and it holds along a sequence of sample sizes of the form n=mb^k for m= 1 %DIFDELCMD < \dots%%% ,M and k%DIFDELCMD < \ge0 %%% and a base b%DIFDELCMD < \ge2%%% .
Edit intent: fluency
Original:   This strong law requires a square integrable integrand and it holds along a sequence of sample sizes of the form n=mb^k for m= 1 ,%DIFDELCMD < \dots%%% ,M and k%DIFDELCMD < \ge0 %%% and a base b%DIFDELCMD < \ge2%%% .
Revised:  This strong law requires a square integrable integrand and it holds along a sequence of sample sizes of the form n=mb^k for m= 1 ,%DIFDELCMD < \dots%%% %DIFDELCMD < \ge0 %%% and a base b%DIFDELCMD < \ge2%%% .
Edit intent: coherence
Original:   This strong law requires a square integrable integrand and it holds along a sequence of sample sizes of the form n=mb^k for m= 1 ,%DIFDELCMD < \dots%%% ,M and k%DIFDELCMD < \ge0 %%% and a base b%DIFDELCMD < \ge2%%% .
Revised:  This strong law requires a square integrable integrand and it holds along a sequence of sample sizes of the form n=mb^k for m= 1 ,%DIFDELCMD < \dots%%% ,M and k%DIFDELCMD < \ge0 %%% %DIFDELCMD < \ge2%%% .
Edit intent: coherence
Original:  The novelty here is an alternative way of constructing the projections without using M-decomposition as a middle step.
Revised:  The novelty here is an alternative way of constructing the projections without using M-decompositions as a middle step.
Edit intent: fluency
Original:  In this paper  we present new algorithms that improve the state-of-the-art for provable classical/quantum algorithms for SVP.
Revised:  In this paper , we present new algorithms that improve the state-of-the-art for provable classical/quantum algorithms for SVP.
Edit intent: fluency
Original:   This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ ADRSD15 ] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{1.741n+o(n)} time and 2^{0.5n+o(n)} space.
Revised:  This improves over the previously fastest classical (which is also the fastest quantum) algorithm due to [ ADRS15 ] that has a time and space complexity 2^{n+o(n)}. \bullet A classical algorithm for SVP that runs in time 2^{1.741n+o(n)} time and 2^{0.5n+o(n)} space.
Edit intent: clarity
Original:  In practice most lattices have a much smaller kissing number which is often 2^{o(n)}.
Revised:  We conjecture that for most lattices this quantity is a 2^{o(n)}.
Edit intent: clarity
Original:  In that case, our classical algorithm runs in time 2^{1.292n  }  and our quantum algorithm  runs in time 2^{ 0.750 n  }.
Revised:  Assuming that this is the case, our classical algorithm runs in time 2^{1.292n } and our quantum algorithm runs in time 2^{ 0.750 n }.
Edit intent: coherence
Original:  Through both theoretical study and numerical study , we show  the R-G method solves this particular problem by a piecewise linear function because R-G method considers the discrete sampling points as linear combinations of Dirac delta functions .
Revised:  Through both theoretical and numerical studies , we show the R-G method solves this particular problem by a piecewise linear function because R-G method considers the discrete sampling points as linear combinations of Dirac delta functions .
Edit intent: coherence
Original:  Through both theoretical study and numerical study , we show  the R-G method solves this particular problem by a piecewise linear function because R-G method considers the discrete sampling points as linear combinations of Dirac delta functions .
Revised:  Through both theoretical study and numerical study , we show that solution of the R-G method solves this particular problem by a piecewise linear function because R-G method considers the discrete sampling points as linear combinations of Dirac delta functions .
Edit intent: clarity
Original:  Through both theoretical study and numerical study , we show  the R-G method solves this particular problem by a piecewise linear function because R-G method considers the discrete sampling points as linear combinations of Dirac delta functions .
Revised:  Through both theoretical study and numerical study , we show the R-G method converges to a piecewise linear function because R-G method considers the discrete sampling points as linear combinations of Dirac delta functions .
Edit intent: clarity
Original:  However, we show that DNNs solve the problem with a much smoother function based on previous study of F-Principle (Xu et al., (2019) [ 15 ] and Zhang et al., (2019) [ 17 ]) , that is, DNN methods implicitly impose regularity on the function that interpolates the discrete sampling points.
Revised:  However, we show that DNNs solve the problem with a much smoother function based on previous study of F-Principle (Xu et al., (2019) [ 17 ] and Zhang et al., (2019) [ 17 ]) , that is, DNN methods implicitly impose regularity on the function that interpolates the discrete sampling points.
Edit intent: coherence
Original:  Our work shows that with implicit bias of DNNs, the traditional methods, e.g., FEM, could provide insights into understanding DNNs.
Revised:  Our work shows that the implicit bias in the learning process, which is different from traditional numerical methods, could help better understand the characteristics of DNNs.
Edit intent: clarity
Original: Envisioned to be the future of  distributed systems, blockchain networks have received increasing attentions from both industry and academic research in recent years.
Revised: Envisioned to be the future of distributed systems, blockchain networks have received increasing attention from both the industry and academia in recent years.
Edit intent: clarity
Original:  However, the blockchain mining process consumes vast amounts of energy , and studies have shown that the amount of energy consumed in Bitcoin mining is almost the same as  electricity used in Ireland  .
Revised:  However, the blockchain mining process consumes vast amounts of energy , and studies have shown that the amount of energy consumed in Bitcoin mining is almost the same as the electricity used in Ireland .
Edit intent: fluency
Original:  However, the blockchain mining process consumes vast amounts of energy , and studies have shown that the amount of energy consumed in Bitcoin mining is almost the same as  electricity used in Ireland  .
Revised:  However, the blockchain mining process consumes vast amounts of energy , and studies have shown that the amount of energy consumed in Bitcoin mining is almost the same as electricity used in Ireland ) .
Edit intent: fluency
Original:  To address the high mining energy cost problem of blockchain networks, in this paper, we propose a blockchain mining resources allocation algorithm to reduce the mining cost in PoW-based (proof-of-work-based) blockchain networks.
Revised:  To address the high mining cost problem of blockchain networks, in this paper, we propose a blockchain mining resources allocation algorithm to reduce the mining cost in PoW-based (proof-of-work-based) blockchain networks.
Edit intent: clarity
Original:  We first provide a systematic study on general blockchain queueing model .
Revised:  We first propose an analytical queueing model for general blockchain networks .
Edit intent: clarity
Original:  In our queueing model, transactions arrive randomly to the queue and  served in a batch manner with unknown  probability distribution and agnostic to any priority mechanism.
Revised:  In our queueing model, transactions arrive randomly to the queue and are served in a batch manner with unknown probability distribution and agnostic to any priority mechanism.
Edit intent: clarity
Original:  Then, we leverage  Lyapunov optimization techniques to propose a dynamic mining resources allocation algorithm (DMRA), which is parameterized by a tuning parameter K>0.
Revised:  Then, we leverage the Lyapunov optimization techniques to propose a dynamic mining resources allocation algorithm (DMRA), which is parameterized by a tuning parameter K>0.
Edit intent: fluency
Original:  We show that our algorithm achieves performance-delay tradeoff as [O(1/K), O(K)] . The simulation results also demonstrate the effectiveness of DMRA in reducing the mining cost .
Revised:  We show that our algorithm achieves an [O(1/K), O(K)] . The simulation results also demonstrate the effectiveness of DMRA in reducing the mining cost .
Edit intent: clarity
Original:  We show that our algorithm achieves performance-delay tradeoff as [O(1/K), O(K)] . The simulation results also demonstrate the effectiveness of DMRA in reducing the mining cost .
Revised:  We show that our algorithm achieves performance-delay tradeoff as [O(1/K), O(K)] . The simulation results also demonstrate the effectiveness of DMRA in reducing mining costs .
Edit intent: fluency
Original:  Inspired by this, the team learning performance in multiagent settings can be potentially promoted with agents reusing knowledge between each other when all agents interact with the environment and learn simultaneously .
Revised:  Inspired by this, the team learning performance in multiagent settings , the learning performance can also be promoted if agents can share knowledge between each other when all agents interact with the environment and learn simultaneously .
Edit intent: clarity
Original:  Inspired by this, the team learning performance in multiagent settings can be potentially promoted with agents reusing knowledge between each other when all agents interact with the environment and learn simultaneously .
Revised:  Inspired by this, the team learning performance in multiagent settings can be potentially promoted with agents reusing knowledge between each other .
Edit intent: clarity
Original:  However, how each independent agent selectively learns from other agents' knowledge is still a problem .
Revised:  However, it remains an open question of how an agent should learn from other agents' knowledge is still a problem .
Edit intent: clarity
Original:  However, how each independent agent selectively learns from other agents' knowledge is still a problem .
Revised:  However, how each independent agent selectively learns from other agents' knowledge .
Edit intent: coherence
Original:  In this paper, we propose a novel multi-agent transfer learning framework to improve the learning efficiencyof multiagent systems .
Revised:  In this paper, we propose a novel multi-agent transfer learning framework to improve multiagent learning efficiency .
Edit intent: clarity
Original:  Our framework learns when and what advice to give to each agent and when to terminate it by modeling multi-agent transfer as the option learning problem.
Revised:  Our framework learns what advice to give to each agent and when to terminate it by modeling multi-agent transfer as the option learning problem.
Edit intent: coherence
Original:  Our framework learns when and what advice to give to each agent and when to terminate it by modeling multi-agent transfer as the option learning problem.
Revised:  Our framework learns when and what advice to give to each agent and when to terminate it by modeling multiagent policy transfer as the option learning problem.
Edit intent: clarity
Original:  The proposed framework can be easily combined with existing deep RL approaches.
Revised:  MAOPT can be easily combined with existing deep RL approaches.
Edit intent: clarity
Original:  Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art deep RL methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.
Revised:  Experimental results show it significantly boosts the performance of existing deep RL methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.
Edit intent: clarity
Original:  Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art deep RL methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.
Revised:  Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art deep RL methods in both discrete and continuous action spaces.
Edit intent: clarity
Original:  Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art deep RL methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.
Revised:  Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art deep RL methods in terms of learning efficiency and final performance in both discrete and continuous state spaces.
Edit intent: clarity
Original: Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency , by sharing learned policies of previous tasks . Similarly, in multiagent settings, the learning performance can also be promoted if agents can share knowledge between each other.
Revised: Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency . Similarly, in multiagent settings, the learning performance can also be promoted if agents can share knowledge between each other.
Edit intent: clarity
Original: Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency , by sharing learned policies of previous tasks . Similarly, in multiagent settings, the learning performance can also be promoted if agents can share knowledge between each other.
Revised: Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency , by sharing learned policies of previous tasks . Similarly, Multiagent RL (MARL) can also be promoted if agents can share knowledge between each other.
Edit intent: clarity
Original: Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency , by sharing learned policies of previous tasks . Similarly, in multiagent settings, the learning performance can also be promoted if agents can share knowledge between each other.
Revised: Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency , by sharing learned policies of previous tasks . Similarly, in multiagent settings, the learning performance can also be accelerated if agents can share knowledge between each other.
Edit intent: clarity
Original: Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency , by sharing learned policies of previous tasks . Similarly, in multiagent settings, the learning performance can also be promoted if agents can share knowledge between each other.
Revised: Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency , by sharing learned policies of previous tasks . Similarly, in multiagent settings, the learning performance can also be promoted if agents can share knowledge with each other.
Edit intent: fluency
Original:  However, it remains an open question of how an agent should learn from other agents ' knowledge .
Revised:  However, it remains a problem of how an agent should learn from other agents ' knowledge .
Edit intent: clarity
Original:  However, it remains an open question of how an agent should learn from other agents ' knowledge .
Revised:  However, it remains an open question of how an agent should learn from other agents .
Edit intent: clarity
Original:  In this paper, we propose a novel multiagent option-based policy transfer (MAOPT) framework to improve multiagent learning efficiency.
Revised:  In this paper, we propose a novel Multiagent Option-based Policy Transfer (MAOPT) framework to improve multiagent learning efficiency.
Edit intent: fluency
Original:  Our framework learns what advice to give to each agent and when to terminate it  by modeling multiagent policy transfer as the option learning problem.
Revised:  Our framework learns what advice to provide and when to terminate it by modeling multiagent policy transfer as the option learning problem.
Edit intent: clarity
Original:  MAOPT provides different kinds of variants which can be classified into two types in terms of the experience used during training.
Revised:  Our framework provides two kinds of option learning methods in terms of the experience used during training.
Edit intent: clarity
Original:  MAOPT provides different kinds of variants which can be classified into two types in terms of the experience used during training.
Revised:  MAOPT provides different kinds of variants which can be classified into two types in terms of what experience is used during training.
Edit intent: clarity
Original:  In many cases , each agent's experience is inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate.
Revised:  In many cases , each agent's experience may be inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate.
Edit intent: clarity
Original:  In many cases , each agent's experience is inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate.
Revised:  In many cases , each agent's experience is inconsistent with each other , which may cause the inaccuracy and oscillation of the option-value estimation to oscillate and to become inaccurate.
Edit intent: clarity
Original:  SRO is used to handle the experience inconsistency by decoupling the dynamics of the environment from the rewards to learn the option-value function under each agent's preference.
Revised:  SRO is used to handle the experience inconsistency by decoupling the environment dynamics from rewards and learning the option-value function under each agent's preference.
Edit intent: clarity