wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We introduce two approaches based on MLE ( Maximal Likelihood Estimation) and its weighted variants, and ME(Maximal Entropy) principals to recover the empirical rank distribution, and then utilize them for metrics estimation.
Revised:  We introduce two approaches based on MLE ( MaximalLikelihood Estimation) and its weighted variants, and ME(Maximal Entropy) principals to recover the empirical rank distribution, and then utilize them for metrics estimation.
Edit intent: fluency
Original:  We then introduce a statistical method to decompose, through the lens of GPT2 's activations, the brain activity of 345 subjects recorded with functional magnetic resonance imaging (fMRI) during the listening of ~4.6 hours of narrated text.
Revised:  We then introduce a statistical method to decompose, through the lens of GPT-2 's activations, the brain activity of 345 subjects recorded with functional magnetic resonance imaging (fMRI) during the listening of ~4.6 hours of narrated text.
Edit intent: fluency
Original: Measuring preferential attachment in growing networks is an important topic in network science, since the experimental confirmation of assumptions about the generative processes that give rise to heavy-tail degree distributions characteristic of real-world networks depends on it.
Revised: Measuring preferential attachment in growing networks is an important topic in network science, since the experimental confirmation of assumptions about the generative processes that give rise to heavy-tail degree distributions emerge in real-world networks depends on it.
Edit intent: clarity
Original: Measuring preferential attachment in growing networks is an important topic in network science, since the experimental confirmation of assumptions about the generative processes that give rise to heavy-tail degree distributions characteristic of real-world networks depends on it. Multiple methods have been devised for measuring preferential attachment in time-resolved networks. However, many real-world network datasets areavailable as only single snapshots .
Revised: Measuring preferential attachment in growing networks is an important topic in network science, since the experimental confirmation of assumptions about the generative processes that give rise to heavy-tail degree distributions characteristic of real-world networks , but also for gaining insight into various rich-get-richer effects that arise in diverse fields. Conventional estimation methods require that a network be observed across at least two snapshots in time. Numerous real-world network datasets areavailable as only single snapshots .
Edit intent: meaning-changed
Original:  The PAFit-oneshot method corrects for a  bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot. This bias, which had previously gone unnoticed, has a connection with a recently developed conditional inference approach called post-selection inference. Extensive experiments show that our method recovers the true preferential attachment function in simulated as well as real-world networks. Our work opens up a new path for network scientists to measure preferential attachment in a large number of  one-snapshot networks that have been out-of-reach until now .
Revised:  The PAFit-oneshot method corrects for a bias that arises when estimating preferential attachment values only for degrees observed in the snapshot. Our work provides scientists with a means of measuring preferential attachment in a large number of one-snapshot networks that have been out-of-reach until now .
Edit intent: clarity
Original: Measuring preferential attachment in growing networks has received considerable attention, not only for understanding how heavy-tail degree distributions emerge in real-world networks, but also for gaining insight into various rich-get-richer effects that arise in diverse fields.
Revised: Preferential attachment is commonly invoked to explain the emergence of those heavy-tailed degree distributions characteristic of growing network representations of divers real-world phenomena. Experimentally confirming this hypothesis in real-world networks, but also for gaining insight into various rich-get-richer effects that arise in diverse fields.
Edit intent: clarity
Original: Measuring preferential attachment in growing networks has received considerable attention, not only for understanding how heavy-tail degree distributions emerge in real-world networks, but also for gaining insight into various rich-get-richer effects that arise in diverse fields. Conventional estimation methods require that a  network be observed across at least two snapshots in time.
Revised: Measuring preferential attachment in growing networks has received considerable attention, not only for understanding how heavy-tail degree distributions emerge in real-world growing networks is an important frontier in network science research. Conventional preferential attachment estimation methods require that a network be observed across at least two snapshots in time.
Edit intent: meaning-changed
Original: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, of the COVID-19 pandemic by looking at the excess mortality trends of  one of the areas with the highest death toll in the world  , Northern Italy.
Revised: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, associated with the COVID-19 pandemic by looking at the excess mortality trends of one of the areas with the highest death toll in the world , Northern Italy.
Edit intent: clarity
Original: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, of the COVID-19 pandemic by looking at the excess mortality trends of  one of the areas with the highest death toll in the world  , Northern Italy.
Revised: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, of the COVID-19 pandemic by looking at the excess mortality trends of an area that registered one of the areas with the highest death toll in the world , Northern Italy.
Edit intent: clarity
Original: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, of the COVID-19 pandemic by looking at the excess mortality trends of  one of the areas with the highest death toll in the world  , Northern Italy.
Revised: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, of the COVID-19 pandemic by looking at the excess mortality trends of one of the highest death tolls in the world , Northern Italy.
Edit intent: clarity
Original: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, of the COVID-19 pandemic by looking at the excess mortality trends of  one of the areas with the highest death toll in the world  , Northern Italy.
Revised: We investigate the possibility of a harvesting effect, i.e. a temporary forward shift in mortality, of the COVID-19 pandemic by looking at the excess mortality trends of one of the areas with the highest death toll in the world during the first wave , Northern Italy.
Edit intent: meaning-changed
Original:  According to our estimates, only a minor share of the total excess deaths detected in Northern Italy over the entire period under scrutiny (February - November 2020) can be attributed to an anticipatory role of COVID-19.
Revised:  According to our estimates, only a minor share of the total excess deaths detected in Northern Italian municipalities over the entire period under scrutiny (February - November 2020) can be attributed to an anticipatory role of COVID-19.
Edit intent: meaning-changed
Original:  A somewhat more sizable harvesting effect is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect only accounts for less than 20\% of excess deaths.
Revised:  A slightly higher share is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect only accounts for less than 20\% of excess deaths.
Edit intent: clarity
Original:  A somewhat more sizable harvesting effect is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect only accounts for less than 20\% of excess deaths.
Revised:  A somewhat more sizable harvesting effect is detected for the most severely affected areas (the provinces of Bergamo and Brescia, in particular), but even in these territories, the harvesting effect can only account for less than 20\% of excess deaths.
Edit intent: clarity
Original:  Computation of a multi-robot optimal control policy is challenging not only because of the complexity of incorporating dynamic uncertainties while planning, but also because of the exponential growth in problem size as a function of the number of robots.
Revised:  Computation of a multi-robot optimal control policy is challenging not only because of the complexity of incorporating dynamic uncertainties while planning, but also because of the exponential growth in problem size as a function of number of robots.
Edit intent: fluency
Original:  We also find fair in-processors to deliver a good balance between profit and fairness . More generally, we show that algorithmic discrimination can be reduced to a reasonable level at a relatively low cost  .
Revised:  We also find fair in-processors to deliver a good balance between profit and fairness . More generally, we show that algorithmic discrimination can be reduced to a reasonable level at a relatively low cost . The codes corresponding to the paper are available on GitHub .
Edit intent: meaning-changed
Original: Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent  problems.
Revised: Proximal Policy Optimization (PPO) is a popular on-policy reinforcement learning algorithm but is significantly less utilized than off-policy learning algorithms in multi-agent settings. This is often due the belief that on-policy methods are significantly less sample efficient than their off-policy counterparts in multi-agent problems.
Edit intent: coherence
Original:  In this work, we investigate Multi-Agent PPO (MAPPO), a  multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Revised:  In this work, we investigate Multi-Agent PPO (MAPPO), a variant of PPO which is specialized for multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Edit intent: meaning-changed
Original:  In this work, we investigate Multi-Agent PPO (MAPPO), a  multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Revised:  In this work, we investigate Multi-Agent PPO (MAPPO), a multi-agent settings . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Edit intent: clarity
Original:  In this work, we investigate Multi-Agent PPO (MAPPO), a  multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Revised:  In this work, we investigate Multi-Agent PPO (MAPPO), a multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves surprisingly strong performance in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Edit intent: clarity
Original:  In this work, we investigate Multi-Agent PPO (MAPPO), a  multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Revised:  In this work, we investigate Multi-Agent PPO (MAPPO), a multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the particle-world environments, the Starcraft multi-agent challenge , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Edit intent: clarity
Original:  In this work, we investigate Multi-Agent PPO (MAPPO), a  multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi Challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Revised:  In this work, we investigate Multi-Agent PPO (MAPPO), a multi-agent PPO variant which adopts a centralized value function . Using a 1-GPU desktop, we show that MAPPO achieves performance comparable to the state-of-the-art in three popular multi-agent testbeds: the Particle World environments, Starcraft II Micromanagement Tasks , and the Hanabi challenge , with minimal hyperparameter tuning and without any domain-specific algorithmic modifications or architectures.
Edit intent: fluency
Original:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k-secretary problem and propose%DIFDELCMD < \algoname%%%  , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Revised:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied secretary problem and propose%DIFDELCMD < \algoname%%% , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Edit intent: fluency
Original:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k-secretary problem and propose%DIFDELCMD < \algoname%%%  , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Revised:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k- and propose%DIFDELCMD < \algoname%%% , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Edit intent: clarity
Original:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k-secretary problem and propose%DIFDELCMD < \algoname%%%  , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Revised:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k-secretary problem %DIFDELCMD < \algoname%%% , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Edit intent: coherence
Original:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k-secretary problem and propose%DIFDELCMD < \algoname%%%  , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Revised:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k-secretary problem and propose%DIFDELCMD < \algoname%%% k-secretary problem in theoretical computer science and propose Virtual+ , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Edit intent: meaning-changed
Original:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k-secretary problem and propose%DIFDELCMD < \algoname%%%  , a simple yet practical algorithm yielding a provably better competitive ratio for k=2 over the current best single threshold algorithm .
Revised:  We first rigorously analyze a deterministic variant of the online threat model by drawing parallels to the well-studied k-secretary problem and propose%DIFDELCMD < \algoname%%% , a simple yet practical online algorithm. Our main theoretical result show Virtual+ yields provably the best competitive ratio over all single-threshold algorithms for k<5 -- extending previous analysis of the k-secretary problem .
Edit intent: meaning-changed
Original:  We also introduce the stochastic k-secretary -- effectively reducing online blackbox  attacks to a k-secretary problem under noise -- and prove theoretical bounds on the competitive ratios of any online algorithms adapted to this setting.
Revised:  We also introduce the stochastic k-secretary -- effectively reducing online blackbox transfer attacks to a k-secretary problem under noise -- and prove theoretical bounds on the competitive ratios of any online algorithms adapted to this setting.
Edit intent: meaning-changed
Original:  Finally, we complement our theoretical results by conducting a systematic suite of experiments on MNIST and CIFAR-10 with both vanilla and robust classifiers, revealing that, by leveraging online secretary algorithms , like%DIFDELCMD < \algoname%%% , we can get an online attack success rate close to the one achieved by the optimal offline solution .
Revised:  Finally, we complement our theoretical results by conducting experiments on both MNIST and CIFAR-10 with both vanilla and robust classifiers, revealing that, by leveraging online secretary algorithms , like%DIFDELCMD < \algoname%%% , we can get an online attack success rate close to the one achieved by the optimal offline solution .
Edit intent: clarity
Original:  Finally, we complement our theoretical results by conducting a systematic suite of experiments on MNIST and CIFAR-10 with both vanilla and robust classifiers, revealing that, by leveraging online secretary algorithms , like%DIFDELCMD < \algoname%%% , we can get an online attack success rate close to the one achieved by the optimal offline solution .
Revised:  Finally, we complement our theoretical results by conducting a systematic suite of experiments on MNIST and CIFAR-10 with both vanilla and robust classifiers, revealing %DIFDELCMD < \algoname%%% , we can get an online attack success rate close to the one achieved by the optimal offline solution .
Edit intent: clarity
Original:  Finally, we complement our theoretical results by conducting a systematic suite of experiments on MNIST and CIFAR-10 with both vanilla and robust classifiers, revealing that, by leveraging online secretary algorithms , like%DIFDELCMD < \algoname%%% , we can get an online attack success rate close to the one achieved by the optimal offline solution .
Revised:  Finally, we complement our theoretical results by conducting a systematic suite of experiments on MNIST and CIFAR-10 with both vanilla and robust classifiers, revealing that, by leveraging online secretary algorithms , like%DIFDELCMD < \algoname%%% not only the necessity of online algorithms in achieving near-optimal performance but also the rich interplay of a given attack strategy towards online attack selection, enabling simple strategies like FGSM to outperform classically strong whitebox adversaries .
Edit intent: meaning-changed
Original:  We prove that when a PDE's coefficients are representable by small neural networks, the parameters required to approximate its solution scale polynomially with the input dimension d and are proportional to the parameter counts of the coefficient neural networks.
Revised:  We prove that when a PDE's coefficients are representable by small neural networks, the parameters required to approximate its solution scale polynomially with the input dimension d and proportionally to the parameter counts of the coefficient neural networks.
Edit intent: clarity
Original:  We prove that when a PDE's coefficients are representable by small neural networks, the parameters required to approximate its solution scale polynomially with the input dimension d and are proportional to the parameter counts of the coefficient neural networks. Our proof is based on constructing a neural network which simulates gradient descent  in an appropriate Hilbert space which converges to the solution of the PDE.
Revised:  We prove that when a PDE's coefficients are representable by small neural networks, the parameters required to approximate its solution scale polynomially with the input dimension d and are proportional to the parameter counts of the coefficient networks. To this we end, we develop a proof technique that simulates gradient descent in an appropriate Hilbert space which converges to the solution of the PDE.
Edit intent: clarity
Original:  Our proof is based on constructing a neural network which simulates gradient descent  in an appropriate Hilbert space which converges to the solution of the PDE.
Revised:  Our proof is based on constructing a neural network which simulates gradient descent in an appropriate Hilbert space ) by growing a neural network architecture whose iterates each participate as sub-networks in their (slightly larger) successors, and converge to the solution of the PDE.
Edit intent: meaning-changed
Original:  Moreover, we bound the size of the neural network needed to represent each iterate in terms of the neural network representing the previous iterate, resulting in a final network whose parameters depend polynomially on d and does not depend on the volume of the domain.
Revised:  We bound the size of the neural network needed to represent each iterate in terms of the neural network representing the previous iterate, resulting in a final network whose parameters depend polynomially on d and does not depend on the volume of the domain.
Edit intent: coherence