wanyu/IteraTeR_v2 huggingface dataset:
Original:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands  and the allocated bandwidth as the environment state and action, respectively .
Revised:  Deep reinforcement learning (DRL) is leveraged to solve this problem by considering the varying service demands and the allocated bandwidth as the environment state and action, respectively .
Edit intent: fluency
Original:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands  and the allocated bandwidth as the environment state and action, respectively .
Revised:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands as the environment state and the allocated bandwidth as the environment state and action, respectively .
Edit intent: fluency
Original:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands  and the allocated bandwidth as the environment state and action, respectively .
Revised:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands and the allocated resources as the environment state and action, respectively .
Edit intent: clarity
Original:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands  and the allocated bandwidth as the environment state and action, respectively .
Revised:  Deep reinforcement learning (DRL) is leveraged to solve this problem by regarding the varying service demands and the allocated bandwidth as the environment action .
Edit intent: clarity
Original:  In order to tackle the annoying randomness and noise embedded in the received quality of experience (QoE ) satisfaction ratio  and spectrum efficiency (SE), we  propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of action values.
Revised:  In order to reduce the effects of the annoying randomness and noise embedded in the received quality of experience (QoE ) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of action values.
Edit intent: clarity
Original:  In order to tackle the annoying randomness and noise embedded in the received quality of experience (QoE ) satisfaction ratio  and spectrum efficiency (SE), we  propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of action values.
Revised:  In order to tackle the annoying randomness and noise embedded in the received quality of experience (QoE ) satisfaction ratio and spectrum efficiency (SE), we primarily propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of action values.
Edit intent: coherence
Original:  In order to tackle the annoying randomness and noise embedded in the received quality of experience (QoE ) satisfaction ratio  and spectrum efficiency (SE), we  propose generative adversarial network (GAN) based deep distributional Q network (GAN-DDQN) to learn the distribution of action values.
Revised:  In order to tackle the annoying randomness and noise embedded in the received quality of experience (QoE ) satisfaction ratio and spectrum efficiency (SE), we propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the distribution of action values.
Edit intent: clarity
Original:  For the sake of protecting the stability of GAN-DDQN 's training process from the widely-spanning utility values , we also put forward a reward-clipping mechanism .
Revised:  For the sake of protecting the stability of GAN-DDQN training against the effects of widely-spanning utility values , we also put forward a reward-clipping mechanism .
Edit intent: clarity
Original:   Then, we present a numerical optimization algorithm for estimating each PSF from multiple radiographs acquired at different X-ray source to object (SOD) and object to detector distances (ODD).
Revised:  Then, we present a numerical optimization algorithm for estimating the source and detector PSFs from multiple radiographs acquired at different X-ray source to object (SOD) and object to detector distances (ODD).
Edit intent: clarity
Original: With businesses deploying a growing number of  Internet of Things ( IoTs) , battery maintenance has become a limiting factor for realizing a sustainable IoT infrastructure.
Revised: With the growing number of Internet of Things ( IoTs) , battery maintenance has become a limiting factor for realizing a sustainable IoT infrastructure.
Edit intent: clarity
Original: With businesses deploying a growing number of  Internet of Things ( IoTs) , battery maintenance has become a limiting factor for realizing a sustainable IoT infrastructure.
Revised: With businesses deploying a growing number of deployments of Internet of Things ( IoTs) , battery maintenance has become a limiting factor for realizing a sustainable IoT infrastructure.
Edit intent: fluency
Original: With businesses deploying a growing number of  Internet of Things ( IoTs) , battery maintenance has become a limiting factor for realizing a sustainable IoT infrastructure.
Revised: With businesses deploying a growing number of Internet of Things ( IoTs) , battery maintenance has become a major limitation for the sustainability of such infrastructure.
Edit intent: clarity
Original:  To overcome battery limitations, industry is now considering energy harvesting as a viable alternative to autonomously power IoT devices as much as possible, which has resulted in a number of batteryless energy harvesting IoTs (  EH-IoTs) appearing in the market in recent years.
Revised:  To overcome this problem, energy harvesting offers a viable alternative to autonomously power IoT devices as much as possible, which has resulted in a number of batteryless energy harvesting IoTs ( EH-IoTs) appearing in the market in recent years.
Edit intent: clarity
Original:  To overcome battery limitations, industry is now considering energy harvesting as a viable alternative to autonomously power IoT devices as much as possible, which has resulted in a number of batteryless energy harvesting IoTs (  EH-IoTs) appearing in the market in recent years.
Revised:  To overcome battery limitations, industry is now considering energy harvesting as a viable alternative to autonomously power IoT devices , resulting in a number of batteryless energy harvesting IoTs ( EH-IoTs) appearing in the market in recent years.
Edit intent: coherence
Original:  To overcome battery limitations, industry is now considering energy harvesting as a viable alternative to autonomously power IoT devices as much as possible, which has resulted in a number of batteryless energy harvesting IoTs (  EH-IoTs) appearing in the market in recent years.
Revised:  To overcome battery limitations, industry is now considering energy harvesting as a viable alternative to autonomously power IoT devices as much as possible, which has resulted in a number of battery-less energy harvesting IoTs ( EH-IoTs) appearing in the market in recent years.
Edit intent: fluency
Original:  To overcome battery limitations, industry is now considering energy harvesting as a viable alternative to autonomously power IoT devices as much as possible, which has resulted in a number of batteryless energy harvesting IoTs (  EH-IoTs) appearing in the market in recent years.
Revised:  To overcome battery limitations, industry is now considering energy harvesting as a viable alternative to autonomously power IoT devices as much as possible, which has resulted in a number of batteryless energy harvesting IoTs ( or EH-IoTs) appearing in the market in recent years.
Edit intent: clarity
Original:  Despite the early commercial and standards activities, IoT sensing, computing and communication under unpredictable power supply still faces significant research challenges and has become a topic of intense research .
Revised:  Despite the early commercial and standards activities, IoT sensing, computing and communications under unpredictable power supply still faces significant research challenges and has become a topic of intense research .
Edit intent: fluency
Original:  Despite the early commercial and standards activities, IoT sensing, computing and communication under unpredictable power supply still faces significant research challenges and has become a topic of intense research .
Revised:  Despite the early commercial and standards activities, IoT sensing, computing and communication under unpredictable power supply still face significant research challenges and has become a topic of intense research .
Edit intent: fluency
Original:  Despite the early commercial and standards activities, IoT sensing, computing and communication under unpredictable power supply still faces significant research challenges and has become a topic of intense research .
Revised:  Despite the early commercial and standards activities, IoT sensing, computing and communication under unpredictable power supply still faces significant research challenges .
Edit intent: clarity
Original:  This paper  surveys recent advances in EH-IoTs from several perspectives.
Revised:  This paper systematically surveys recent advances in EH-IoTs from several perspectives.
Edit intent: clarity
Original:  First, it surveys the recent commercial developments for EH-IoT in terms of both products and services, followed by a review of initial standards activities in this space.
Revised:  First, it reviews the recent commercial developments for EH-IoT in terms of both products and services, followed by a review of initial standards activities in this space.
Edit intent: fluency
Original:  First, it surveys the recent commercial developments for EH-IoT in terms of both products and services, followed by a review of initial standards activities in this space.
Revised:  First, it surveys the recent commercial developments for EH-IoT in terms of both products and services, followed by initial standards activities in this space.
Edit intent: clarity
Original:  Then it surveys methods that enable  use of energy harvesting hardware as a proxy for conventional sensors to detect contexts more power-efficiently.
Revised:  Then it surveys methods that enable the use of energy harvesting hardware as a proxy for conventional sensors to detect contexts more power-efficiently.
Edit intent: fluency
Original:  The advancements in efficient checkpointing and timekeeping for intermittently powered IoT devices are reviewed next .
Revised:  The advancements in efficient checkpointing and timekeeping for intermittently powered IoT devices .
Edit intent: clarity
Original:  We also survey recent research in novel wireless communication techniques for EH-IoTs, including applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions as well as packet-less IoT communication and backscatter communication techniques for energy impoverished environments.
Revised:  We also survey recent research in novel wireless communication techniques for EH-IoTs, such as the applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions as well as packet-less IoT communication and backscatter communication techniques for energy impoverished environments.
Edit intent: coherence
Original:  We also survey recent research in novel wireless communication techniques for EH-IoTs, including applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions as well as packet-less IoT communication and backscatter communication techniques for energy impoverished environments.
Revised:  We also survey recent research in novel wireless communication techniques for EH-IoTs, including applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions , and packet-less IoT communication and backscatter communication techniques for energy impoverished environments.
Edit intent: coherence
Original:  We also survey recent research in novel wireless communication techniques for EH-IoTs, including applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions as well as packet-less IoT communication and backscatter communication techniques for energy impoverished environments.
Revised:  We also survey recent research in novel wireless communication techniques for EH-IoTs, including applications of reinforcement learning to optimize power allocations on-the-fly under unpredictable energy productions as well as packet-less IoT communications and backscatter communication techniques for energy impoverished environments.
Edit intent: fluency
Original:  We conclude the paper with a discussion of future research directions.
Revised:  The paper is concluded with a discussion of future research directions.
Edit intent: clarity
Original:  A simpler task for the sweeping formation could be the confinement of the evaders to their initial domain.
Revised:  A simpler task for the sweeping formation is the confinement of the evaders to their initial domain.
Edit intent: clarity
Original:  The feasibility of completing these tasks depends on geometric and dynamic constraints that impose a critical velocity that the sweeper line formation must have.
Revised:  The feasibility of completing these tasks depends on geometric and dynamic constraints that impose a lower bound on the velocity that the sweeper line formation must have.
Edit intent: clarity
Original:  The critical velocity is derived to ensure the satisfaction of the confinement task.
Revised:  This critical velocity is derived to ensure the satisfaction of the confinement task.
Edit intent: fluency
Original:  The recently introduced Trade-Off Game (TOG) - in which a decision-maker has to  choose between being equitable or being efficient - offers a unique opportunity to fill this gap.
Revised:  The recently introduced Trade-Off Game (TOG) - in which a decision-maker has to unilaterally choose between being equitable or being efficient - offers a unique opportunity to fill this gap.
Edit intent: clarity
Original:  A hardware-accelerated controller packet comparison procedure results in an average ~ 96.4\% decrease in processing delay per request compared to existing software approaches.
Revised:  A hardware-accelerated controller packet comparison procedure results in an average 96.4\% decrease in processing delay per request compared to existing software approaches.
Edit intent: fluency
Original:  However, direct application of these models leads to a limited estimator that is prohibitively expensive to evaluate for range and wildcard predicates.
Revised:  However, direct application of these models leads to a limited estimator that is prohibitively expensive to evaluate for range or wildcard predicates.
Edit intent: fluency
Original:  To make a truly usable estimator, we develop a Monte Carlo integration scheme on top of likelihood models that can efficiently handle range queries with dozens of filters or more.
Revised:  To produce a truly usable estimator, we develop a Monte Carlo integration scheme on top of likelihood models that can efficiently handle range queries with dozens of filters or more.
Edit intent: clarity
Original:  To make a truly usable estimator, we develop a Monte Carlo integration scheme on top of likelihood models that can efficiently handle range queries with dozens of filters or more.
Revised:  To make a truly usable estimator, we develop a Monte Carlo integration scheme on top of autoregressive models that can efficiently handle range queries with dozens of filters or more.
Edit intent: clarity
Original:  To make a truly usable estimator, we develop a Monte Carlo integration scheme on top of likelihood models that can efficiently handle range queries with dozens of filters or more.
Revised:  To make a truly usable estimator, we develop a Monte Carlo integration scheme on top of likelihood models that can efficiently handle range queries with dozens of dimensions or more.
Edit intent: clarity
Original:  Unlike previous solutions, our estimator approximates the joint data distribution without any independence assumptions.
Revised:  Unlike previous solutions, we approximate the joint data distribution without any independence assumptions.
Edit intent: clarity
Original:  When evaluated on real-world datasets and compared against real systems and dominant families of techniques, our likelihood model based estimator achieves single-digit multiplicative error at tail, a 40-200 \times accuracy improvement over the second best method, and is space- and runtime-efficient.
Revised:  Evaluated on real-world datasets and compared against real systems and dominant families of techniques, our likelihood model based estimator achieves single-digit multiplicative error at tail, a 40-200 \times accuracy improvement over the second best method, and is space- and runtime-efficient.
Edit intent: clarity
Original:  When evaluated on real-world datasets and compared against real systems and dominant families of techniques, our likelihood model based estimator achieves single-digit multiplicative error at tail, a 40-200 \times accuracy improvement over the second best method, and is space- and runtime-efficient.
Revised:  When evaluated on real-world datasets and compared against real systems and dominant families of techniques, our estimator achieves single-digit multiplicative error at tail, a 40-200 \times accuracy improvement over the second best method, and is space- and runtime-efficient.
Edit intent: clarity
Original:  While the choice of the best seed set, known as influence maximization , is a computationally hard problem, many computationally efficient algorithms have been proposed to approximate the optimal seed sets with provable guarantees .
Revised:  While the choice of the best seed set, known as influence maximization ) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees .
Edit intent: clarity
Original:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Revised:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using %DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Edit intent: fluency
Original:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Revised:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Edit intent: fluency
Original:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Revised:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Edit intent: fluency
Original:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Revised:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Edit intent: coherence
Original:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Revised:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( %DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Edit intent: clarity
Original:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^2 + \sqrt{p n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Revised:  We propose a ''probe-and-seed'' algorithm that provides almost tight approximation guarantees using (%DIFDELCMD < {%%% p n^2 + \sqrt{p n^{1.5}) edge queries in }%DIFDELCMD < O%%% ( p%DIFDELCMD < } %%% n^{1.5}) time, where n is the network size and p is the probability of spreading through an edge.
Edit intent: fluency
Original:  Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, assuming the knowledge of the entire network graph . However, in practice, obtaining full knowledge of the network structure is very costly.
Revised:  Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, while assuming knowledge of the entire network graph . However, in practice, obtaining full knowledge of the network structure is very costly.
Edit intent: coherence
Original:  Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, assuming the knowledge of the entire network graph . However, in practice, obtaining full knowledge of the network structure is very costly.
Revised:  Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, assuming the knowledge of the entire network . However, in practice, obtaining full knowledge of the network structure is very costly.
Edit intent: clarity
Original:  Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, assuming the knowledge of the entire network graph . However, in practice, obtaining full knowledge of the network structure is very costly.
Revised:  Most of the previous work on this problem (known as influence maximization) focuses on efficient algorithms to approximate the optimal seed sets with provable guarantees, assuming the knowledge of the entire network graph . However, in practice, obtaining full knowledge of the network is very costly.
Edit intent: clarity