wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In our computational studies of the bidomain FitzHugh--Nagumo pulse solution, we show that the pulses can also become unstable much like the bidomain Allen--Cahn fronts.
Revised:  In our computational studies of the bidomain FitzHugh--Nagumo pulse solution, we show that the pulses can also become unstable much like the bidomain Allen-Cahn fronts.
Edit intent: fluency
Original:   ] We also prove that for a prescribed edge e of G, it is NP-hard to obtain a 0-perfect forest containing e, but we can find a 0-perfect forest not containing e in polynomial time.
Revised:  Moreover, we show that to decide whether G has a 0-perfect forest with at least |V(G)|/2+k edges, where k is the parameter, is W ] We also prove that for a prescribed edge e of G, it is NP-hard to obtain a 0-perfect forest containing e, but we can find a 0-perfect forest not containing e in polynomial time.
Edit intent: meaning-changed
Original:   ] We also prove that for a prescribed edge e of G, it is NP-hard to obtain a 0-perfect forest containing e, but we can find a 0-perfect forest not containing e in polynomial time.
Revised:  1] We also prove that for a prescribed edge e of G, it is NP-hard to obtain a 0-perfect forest containing e, but we can find a 0-perfect forest not containing e in polynomial time.
Edit intent: fluency
Original:   ] We also prove that for a prescribed edge e of G, it is NP-hard to obtain a 0-perfect forest containing e, but we can find a 0-perfect forest not containing e in polynomial time.
Revised:  ]-hard. We also prove that for a prescribed edge e of G, it is NP-hard to obtain a 0-perfect forest containing e, but we can find a 0-perfect forest not containing e in polynomial time.
Edit intent: coherence
Original:   ] We also prove that for a prescribed edge e of G, it is NP-hard to obtain a 0-perfect forest containing e, but we can find a 0-perfect forest not containing e in polynomial time.
Revised:  ] We also prove that for a prescribed edge e of G, it is NP-hard to obtain a 0-perfect forest containing e, but one can decide if there existsa 0-perfect forest not containing e in polynomial time.
Edit intent: clarity
Original:  However, current ML-FFs typically ignore electronic degrees of freedom, such as the total charge or spin , when forming their prediction. In addition, they often assume chemical locality, which can be problematic in cases where nonlocal effects play a significant role.
Revised:  However, current ML-FFs typically ignore electronic degrees of freedom, such as the total charge or spin state, and assume chemical locality, which can be problematic in cases where nonlocal effects play a significant role.
Edit intent: clarity
Original:  Its predictions are further augmented with physically-motivated corrections to improve the description of long-ranged interactions and nuclear repulsion . SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets.
Revised:  Chemically meaningful inductive biases and analytical corrections built into the network architecture allow it to properly model physical limits . SpookyNet improves upon the current state-of-the-art (or achieves similar performance) on popular quantum chemistry data sets.
Edit intent: clarity
Original:  Notably, it  can leverage the learned chemical insights, e.g. by predicting unknown spin states or by properly modeling physical limits. Moreover, it is able to generalize across chemical and conformational space and thus close an important remaining gap for today's machine learning models in quantum chemistry.
Revised:  Notably, it can leverage the learned chemical insights, e.g. by predicting unknown spin states , thus helping to close a further important remaining gap for today's machine learning models in quantum chemistry.
Edit intent: clarity
Original:  The paired data structure in persistent homology as birth and death events of topological features add further complexity to conducting inference. To address these current problems, we propose to analyze the birth and death events using lattice paths .
Revised:  The paired data structure in persistent homology as birth and death in persistent diagrams, adds additional complexity to the development. In this paper, we present a new lattice path representation for persistent diagrams. A new exact statistical inference procedure is developed for lattice paths via combinatorial enumerations .
Edit intent: meaning-changed
Original:  The proposed lattice path method is implemented to characterize the topological features of the protein structures of corona viruses. This demonstrates new insights to building a coherent statistical inference procedure in persistent homology .
Revised:  The proposed lattice path method is implemented to characterize the topological features of the protein structures of COVID-19 viruse. We demonstrate that there are topological changes during the conformation change of spike proteins that are needed to initiate the infection of host cells .
Edit intent: meaning-changed
Original:  In this paper, we propose Synthesized Difference in Differences (SDD) that infers the correct (possibly non-parallel) slopes by linearly adjusting a conditional version of DD using additional RCT data.
Revised:  We instead propose Synthesized Difference in Differences (SDD) that infers the correct (possibly non-parallel) slopes by linearly adjusting a conditional version of DD using additional RCT data.
Edit intent: coherence
Original:  OpTorch python package is available at available at URLabstract 
Revised:  OpTorch python package is available at available at URLabstract \%. We also explore the effect of weights on total memory usage in deep learning pipelines. In our experiments, parallel encoding-decoding along with sequential checkpoints results in much improved memory and time usage while keeping the accuracy similar to existing pipelines. OpTorch python package is available at available at URL
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Original: A novel approach of adapting quarantine consideration into a SEIR model is presented, where susceptible, exposed and unidentified compartments are collated under the umbrella of the quarantine compartment.
Revised: A novel approach of adapting quarantine consideration into a SEIR model is presented, where susceptible, exposed and unidentified compartments are collated under the umbrella of the social-distanced compartment.
Edit intent: meaning-changed
Original: One of the early ideas started from the 2004 workshop is to regard third order tensors as tubal matrices such as matrix theory can be naturally extended to third order tensors. We revisit this approach in this paper. We define the value of a tubal scalar to remedy the gap between this approach and the practical needs to have singular values as real scalars. We define the transpose of a tubal scalar such that the transpose of a tubal matrix is a natural extension of the transpose of a matrix and still corresponding to the transpose of a third order tensor. We define positive semi-definite symmetric tubal scalars such that s-diagonal tubal matrices can be defined meaningfully and are still corresponding to third order s-diagonal tensors, which were introduced recently for studying the T-SVD factorizations.
Revised: It was shown recently that the f-diagonal tensor in the T-SVD factorizations.
Edit intent: meaning-changed
Original:  We define positive semi-definite symmetric tubal scalars such that s-diagonal tubal matrices can be defined meaningfully and are still corresponding to third order s-diagonal tensors, which were introduced recently for studying the T-SVD factorizations. We show that the 2-norm of a tubal matrix is equal to its largest T-singular value, multiplied with a coefficient, which is 1 in the case of matrices. This shows that T-singular values of tubal matrices are natural extensions of singular values of matrices. Further studyon tubal matrices may reveal more links between matrix theory and tensor theory .
Revised:  We define positive semi-definite symmetric tubal scalars such that s-diagonal tubal matrices can be defined meaningfully and are still corresponding to third order s-diagonal tensors, which were introduced recently for studying the T-SVD factorization must satisfy some special properties. Such f-diagonal tensors are called s-diagonal tensors. In this paper, we show that such a discussion can be extended to any real invertible linear transformation. We show that two Eckart-Young like theorems hold for a third order real tensor, under any doubly real-preserving unitary transformation. The Discrete Fourier Transformation matrix and any orthogonal matrices are doubly real-preserving unitary transformations. In particular, Discrete Cosine Transformation is in this category. We use tubal matrices as a tool for our study. We feel that the tubal matrix language makes this approach more natural .
Edit intent: meaning-changed
Original:  Then, we consider two strategies to build confidence intervals on the AME. In the first, we estimate the sharp bounds with a semiparametric two-step estimator involving a first-step nonparametric estimator. We derive the asymptotic distributions of these bounds under, mostly, a restriction on the cardinality of the support of the unobserved heterogeneity.
Revised:  Then, we consider two strategies to build confidence intervals on the AME. In the first, we estimate the sharp bounds with a semiparametric two-step estimator .
Edit intent: coherence
Original:  With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb for our case study, completing non-trivial patterns with as few as 400 interactions.
Revised:  With properly tuned parameters, RL-IoT learns how to perform actions with the target device, a Yeelight smart bulb in our case study, completing non-trivial patterns with as few as 400 interactions.
Edit intent: clarity
Original:  The proposed model is robust to occlusion, scale  and illumination, thereby outperforms the state-of-the-art models in terms of improvement over baseline.
Revised:  The proposed model is robust to occlusion, scale and illumination, thereby outperforms the state-of-the-art GAN based models in 4 benchmark datasets. It also surpasses the state-of-the-art models that report higher re-ID accuracy in terms of improvement over baseline.
Edit intent: meaning-changed
Original: Classical graph modeling approaches such as Erdos-Renyis R\'{e}nyi } (ER) random graphs or Barabasi-Albert (BA) graphs, here referred to as stylized models, aim to reproduce properties of real-world graphs in an interpretable way.
Revised: Classical graph modeling approaches such as Erd\H{os R\'{e}nyi } (ER) random graphs or Barabasi-Albert (BA) graphs, here referred to as stylized models, aim to reproduce properties of real-world graphs in an interpretable way.
Edit intent: fluency
Original: Classical graph modeling approaches such as Erdos-Renyis R\'{e}nyi } (ER) random graphs or Barabasi-Albert (BA) graphs, here referred to as stylized models, aim to reproduce properties of real-world graphs in an interpretable way.
Revised: Classical graph modeling approaches such as Erdos-Renyis R\'{e}nyi } (ER) random graphs or Barab\'asi-Albert (BA) graphs, here referred to as stylized models, aim to reproduce properties of real-world graphs in an interpretable way.
Edit intent: fluency
Original:  Previous work by Stoehr et al. (2019) addresses these issues by learning the generation process from graph data, using a disentanglement-focused deep autoencoding framework, more specifically, a beta-Variational Autoencoder (beta-VAE ).
Revised:  Previous work by Stoehr et al. (2019) addresses these issues by learning the generation process from graph data, using a disentanglement-focused deep autoencoding framework, more specifically, a \beta-Variational Autoencoder (\beta-VAE ).
Edit intent: fluency
Original:  A series of treatments aimed at identifying mechanisms suggests that  the behavior of other people are often "ambiguous" to a decision maker who observes their choices . So, the decision maker behaves as if she has limited ability to infer the relationship between what other people choose and what they know .
Revised:  A series of treatments aimed at identifying mechanisms suggests that a decision maker is often uncertain about the behavior of other people are often "ambiguous" to a decision maker who observes their choices . So, the decision maker behaves as if she has limited ability to infer the relationship between what other people choose and what they know .
Edit intent: meaning-changed
Original:  A series of treatments aimed at identifying mechanisms suggests that  the behavior of other people are often "ambiguous" to a decision maker who observes their choices . So, the decision maker behaves as if she has limited ability to infer the relationship between what other people choose and what they know .
Revised:  A series of treatments aimed at identifying mechanisms suggests that the behavior of other people . So, the decision maker behaves as if she has limited ability to infer the relationship between what other people choose and what they know .
Edit intent: clarity
Original:  A series of treatments aimed at identifying mechanisms suggests that  the behavior of other people are often "ambiguous" to a decision maker who observes their choices . So, the decision maker behaves as if she has limited ability to infer the relationship between what other people choose and what they know .
Revised:  A series of treatments aimed at identifying mechanisms suggests that the behavior of other people are often "ambiguous" to a decision maker who observes their choices . So, she might have difficulty in inferring the information contained in others' choices. A general decision-making process is then non-parametrically estimated and sheds light on the identification of three types of error in subjects' behavior .
Edit intent: meaning-changed
Original:  In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension ( ViCoQA ), consisting of 10,000 questions with answers over 2,000 conversations about health news articles.
Revised:  In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension ( UIT-ViCoQA ), consisting of 10,000 questions with answers over 2,000 conversations about health news articles.
Edit intent: clarity
Original:  In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension (UIT-ViCoQA), consisting of 10,000 questions with answers  over 2,000 conversations about health news articles.
Revised:  In practice, conversation is an essential way to communicate and transfer information. To help machines understand conversation texts, we present UIT-ViCoQA - a new corpus for conversational machine reading comprehension (UIT-ViCoQA), consisting of 10,000 questions with answers over 2,000 conversations about health news articles.
Edit intent: coherence
Original:  In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension (UIT-ViCoQA), consisting of 10,000 questions with answers  over 2,000 conversations about health news articles.
Revised:  In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension in the Vietnamese language. This corpus consists of 10,000 questions with answers over 2,000 conversations about health news articles.
Edit intent: meaning-changed
Original:  In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension (UIT-ViCoQA), consisting of 10,000 questions with answers  over 2,000 conversations about health news articles.
Revised:  In this paper, we present a new Vietnamese corpus for conversational machine reading comprehension (UIT-ViCoQA), consisting of 10,000 questions with answers to over 2,000 conversations about health news articles.
Edit intent: fluency
Original: We propose two algorithms for episodic stochastic shortest path problems with linear function approximation. The first is computationally expensive but provably obtains (\sqrt{B_\star^3 d^3 K/c_{min , where B_\star is a (known)upper bound on the optimal cost-to-go function, d is the feature dimension, K is the number of episodes, and c_{min .
Revised: We propose two algorithms for episodic stochastic shortest path (\sqrt{B_\star^3 d^3 K/c_{min , where B_\star is a (known)upper bound on the optimal cost-to-go function, d is the feature dimension, K is the number of episodes, and c_{min .
Edit intent: clarity
Original:  The first is computationally expensive but provably obtains (\sqrt{B_\star^3 d^3 K/c_{min , where B_\star is a (known)upper bound on the optimal cost-to-go function, d is the feature dimension, K is the number of episodes, and c_{min .
Revised:  The first is computationally expensive but provably obtains (SSP) and bound their regret over K episodes. When all stationary policies are proper, our first algorithm obtains sublinear regret (K^{3/4 , where B_\star is a (known)upper bound on the optimal cost-to-go function, d is the feature dimension, K is the number of episodes, and c_{min .
Edit intent: meaning-changed
Original:  The first is computationally expensive but provably obtains (\sqrt{B_\star^3 d^3 K/c_{min , where B_\star is a (known)upper bound on the optimal cost-to-go function, d is the feature dimension, K is the number of episodes, and c_{min .
Revised:  The first is computationally expensive but provably obtains (\sqrt{B_\star^3 d^3 K/c_{min , is computationally efficient, and uses stationary policies. This is the first LFA algorithm with these three properties, to the best of our knowledge. Our second algorithm improves the regret to \sqrt{K .
Edit intent: meaning-changed
Original:  Both algorithms are based on an optimistic least-squares version of value iteration analogous to the finite-horizon backward induction approach from Jin et al. 2020. To the best of our knowledge, these are the first regret bounds for stochastic shortest path that are independent of the size of the state and action spaces .
Revised:  Both algorithms are based on an optimistic least-squares version of value iteration analogous to the first for SSP with function approximation .
Edit intent: meaning-changed
Original:  In this work, we  show that feed-forward networks trained via supervised policy distillation and reinforcement learning can be pruned to the same level of sparsity .
Revised:  In this work, we address this question by comparing sparse agents who have to address the non-stationarity of the exploration-exploitation problem with supervised agents trained to imitate an expert. We show that feed-forward networks trained via supervised policy distillation and reinforcement learning can be pruned to the same level of sparsity .
Edit intent: meaning-changed
Original:  In this work, we  show that feed-forward networks trained via supervised policy distillation and reinforcement learning can be pruned to the same level of sparsity . Furthermore, we establish the existence of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask .
Revised:  In this work, we show that feed-forward networks trained via supervised policy distillation and reinforcement learning can be pruned to the same level of sparsity , suggesting that the distributional shift has a limited impact on the size of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask .
Edit intent: meaning-changed
Original:  Furthermore, we establish the existence of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask .
Revised:  Furthermore, we establish the existence of winning tickets . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask .
Edit intent: coherence
Original:  Furthermore, we establish the existence of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask .
Revised:  Furthermore, we establish the existence of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in both learning paradigms can be attributed to the identified mask .
Edit intent: clarity
Original:  Furthermore, we establish the existence of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask . The resulting masked observation space eliminates redundant information and yields minimal task-relevant representations. The mask identified by iterative magnitude pruning provides an interpretable inductive bias.
Revised:  Furthermore, we establish the existence of winning tickets for both on- and off-policy methods in a visual navigation and classic control task . Using a set of carefully designed baseline conditions, we find that the majority of the lottery ticket effect in reinforcement learning can be attributed to the identified mask rather than the weight initialization. The input layer mask selectively prunes entire input dimensions that turn out to be irrelevant for the task at hand. At a moderate level of sparsity the mask identified by iterative magnitude pruning provides an interpretable inductive bias.
Edit intent: meaning-changed
Original:  Our results build on a recent work of Chou, Golovnev, Sudan,  and Velusamy who show tight  inapproximability results for some constraint satisfaction problems over arbitrary (finite) alphabets.
Revised:  Our results build on recent works of Chou, Golovnev, Sudan, and Velusamy who show tight inapproximability results for some constraint satisfaction problems over arbitrary (finite) alphabets.
Edit intent: fluency
Original:  Our results build on a recent work of Chou, Golovnev, Sudan,  and Velusamy who show tight  inapproximability results for some constraint satisfaction problems over arbitrary (finite) alphabets.
Revised:  Our results build on a recent work of Chou, Golovnev, Sudan, Velingker, and Velusamy who show tight inapproximability results for some constraint satisfaction problems over arbitrary (finite) alphabets.
Edit intent: meaning-changed
Original:  Our results build on a recent work of Chou, Golovnev, Sudan,  and Velusamy who show tight  inapproximability results for some constraint satisfaction problems over arbitrary (finite) alphabets.
Revised:  Our results build on a recent work of Chou, Golovnev, Sudan, and Velusamy who show tight , linear-space inapproximability results for some constraint satisfaction problems over arbitrary (finite) alphabets.
Edit intent: meaning-changed
Original:  Our results build on a recent work of Chou, Golovnev, Sudan,  and Velusamy who show tight  inapproximability results for some constraint satisfaction problems over arbitrary (finite) alphabets.
Revised:  Our results build on a recent work of Chou, Golovnev, Sudan, and Velusamy who show tight inapproximability results for a broad class of (non-ordering) constraint satisfaction problems over arbitrary (finite) alphabets.
Edit intent: meaning-changed