wanyu/IteraTeR_v2 huggingface dataset:
Original:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in number of update messages , at the tagging AS , but also at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Revised:  Further 25\% update neither . Using beacon prefixes, we demonstrate that communities lead to an increase in number of update messages , at the tagging AS , but also at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Edit intent: clarity
Original:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in number of update messages , at the tagging AS , but also at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Revised:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in update messages both at the tagging AS , but also at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Edit intent: clarity
Original:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in number of update messages , at the tagging AS , but also at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Revised:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in number of update messages , at the tagging AS and at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Edit intent: coherence
Original:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in number of update messages , at the tagging AS , but also at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Revised:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in number of update messages , at the tagging AS , but also at neighboring ASes that neither add nor filter communities . This effect is prominent for geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Edit intent: clarity
Original:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in number of update messages , at the tagging AS , but also at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration events: On a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Revised:  Further 25\% update neither . Using beacon prefixes, we find that communities can lead to an increase in number of update messages , at the tagging AS , but also at neighboring ASes that neither add nor filter communities - primarily geolocation communities during path exploration : on a single day, 63\% of all unique community attributes are revealed exclusively due to global withdrawals .
Edit intent: clarity
Original: We describe a new approach to automatically repairing broken proofs in response to changes in type definitions in the Coq proof assistant  .
Revised: We describe a new approach to automatically repairing broken proofs in the Coq proof assistant .
Edit intent: coherence
Original:  Our approach combines a configurable proof term transformation with a proof term to tactic script decompiler .
Revised:  Our approach combines a configurable proof term transformation with a decompiler from proof terms to tactic scripts .
Edit intent: clarity
Original:  The proof term transformation implements transport across certain equivalences in a way that is suitable for repair and does not rely on axioms beyond those Coq assumes.
Revised:  The proof term transformation implements transport across equivalences in a way that is suitable for repair and does not rely on axioms beyond those Coq assumes.
Edit intent: clarity
Original:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools  .
Revised:  We have used PUMPKIN Pito support a benchmark from a user study, easing development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools .
Edit intent: fluency
Original:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools  .
Revised:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, porting functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools .
Edit intent: clarity
Original:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools  .
Revised:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools .
Edit intent: clarity
Original:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools  .
Revised:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and supporting an industrial proof engineer to more easily interoperate between Coq and other verification tools .
Edit intent: fluency
Original:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools  .
Revised:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to interoperate between Coq and other verification tools .
Edit intent: clarity
Original:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools  .
Revised:  We have used PUMPKIN Pito support a benchmark from a user study, ease development with dependent types, port functions and proofs between unary and binary natural numbers, and support an industrial proof engineer to more easily interoperate between Coq and other verification tools more easily .
Edit intent: clarity
Original:  This paper theoretically demonstrates that this is indeed the case : decomposition of learning rules makes it possible to characterize the training dynamics and show that  loss functions evolved through TaylorGLO regularize both in the beginning and end of learning, and maintain an invariant in between.
Revised:  This paper demonstrates theoretically that this is indeed the case : decomposition of learning rules makes it possible to characterize the training dynamics and show that loss functions evolved through TaylorGLO regularize both in the beginning and end of learning, and maintain an invariant in between.
Edit intent: fluency
Original:  This paper theoretically demonstrates that this is indeed the case : decomposition of learning rules makes it possible to characterize the training dynamics and show that  loss functions evolved through TaylorGLO regularize both in the beginning and end of learning, and maintain an invariant in between.
Revised:  This paper theoretically demonstrates that this is indeed the case for TaylorGLO: Decomposition of learning rules makes it possible to characterize the training dynamics and show that loss functions evolved through TaylorGLO regularize both in the beginning and end of learning, and maintain an invariant in between.
Edit intent: clarity
Original:  This paper theoretically demonstrates that this is indeed the case : decomposition of learning rules makes it possible to characterize the training dynamics and show that  loss functions evolved through TaylorGLO regularize both in the beginning and end of learning, and maintain an invariant in between.
Revised:  This paper theoretically demonstrates that this is indeed the case : decomposition of learning rules makes it possible to characterize the training dynamics and show that the loss functions evolved through TaylorGLO regularize both in the beginning and end of learning, and maintain an invariant in between.
Edit intent: fluency
Original:  The invariant can be utilized to make the metalearning process more efficient in practice , and the regularization can train networks that are robust against adversarial attacks.
Revised:  The invariant can be utilized to make the metalearning process more efficient in practice ; the mechanism also results in networks that are robust against adversarial attacks.
Edit intent: clarity
Original:  Widely shared reports indicate that gender differences may be specific to each country .
Revised:  Widely shared reports indicate that gender differences may be specific to each region .
Edit intent: clarity
Original:  Since 2014 , there has been a small and statistically significant improvement of gender diversity among software project contributors in Northern America and South-Eastern Asia but negligible change elsewhere .
Revised:  Since 2014 , there has been statistically significant improvement of gender diversity among software project contributors in Northern America and South-Eastern Asia but negligible change elsewhere .
Edit intent: clarity
Original:  We also find that most motivations and barriers to contributions (e.g.  lack of resources to contribute and poor working environment) were shared across regions, however, some insightful differences, such as how to make projects more inclusive, did arise.
Revised:  We also find that most motivations and barriers to contributions (e.g. , lack of resources to contribute and poor working environment) were shared across regions, however, some insightful differences, such as how to make projects more inclusive, did arise.
Edit intent: fluency
Original:  It minimizes overall system costs, including fixed and variable costs of various generation, storage and sector coupling options.
Revised:  It minimizes overall system costs, including fixed and variable costs of various generation, flexibility and sector coupling options.
Edit intent: clarity
Original:  For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per  article).
Revised:  For instance, Encyclopedias such as Wikipedia are structured by entities (e.g., one per Wikipedia article).
Edit intent: fluency
Original:  One way to understand current approaches is as classifiers among atomic labels, one for each entity.
Revised:  Current approaches can be understood as classifiers among atomic labels, one for each entity.
Edit intent: clarity
Original:  Their weight vectors are dense entity representations produced by encoding entity  information such as  descriptions.
Revised:  Their weight vectors are dense entity representations produced by encoding entity information such as their descriptions.
Edit intent: clarity
Original:  This approach leads to several shortcomings:  i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions between the two;
Revised:  This approach has several shortcomings: i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions between the two;
Edit intent: clarity
Original:  This approach leads to several shortcomings:  i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions between the two;
Revised:  This approach leads to several shortcomings: ( i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions between the two;
Edit intent: fluency
Original:  This approach leads to several shortcomings:  i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions between the two;
Revised:  This approach leads to several shortcomings: i) context and entity affinity is mainly captured through a vector dot product, potentially missing fine-grained interactions ; (
Edit intent: clarity
Original:   iii) an appropriately hard set of negative data has to be subsampled at training time.
Revised:  ( iii) an appropriately hard set of negative data has to be subsampled at training time.
Edit intent: fluency
Original:  We propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion , and conditioned on the context.
Revised:  In this work, we propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion , and conditioned on the context.
Edit intent: clarity
Original:  This enables to mitigate the aforementioned technical issues : i) the autoregressive formulation allows us to directly capture relations between context and entity name, effectively cross encoding both;
Revised:  This enables to mitigate the aforementioned technical issues since: ( i) the autoregressive formulation allows us to directly capture relations between context and entity name, effectively cross encoding both;
Edit intent: coherence
Original:  This enables to mitigate the aforementioned technical issues : i) the autoregressive formulation allows us to directly capture relations between context and entity name, effectively cross encoding both;
Revised:  This enables to mitigate the aforementioned technical issues : i) the autoregressive formulation directly captures relations between context and entity name, effectively cross encoding both;
Edit intent: clarity
Original:   ii) the memory footprint is greatly reduced because the parameters of our encoder-decoder architecture scale with vocabulary size, not entity count;
Revised:  ( ii) the memory footprint is greatly reduced because the parameters of our encoder-decoder architecture scale with vocabulary size, not entity count;
Edit intent: fluency
Original:   iii) the exact softmax loss can be efficiently computed without the need to subsample negative data.
Revised:  ( iii) the exact softmax loss can be efficiently computed without the need to subsample negative data.
Edit intent: fluency
Original:   iii) the exact softmax loss can be efficiently computed without the need to subsample negative data.
Revised:  iii) the softmax loss is computed without subsampling negative data.
Edit intent: clarity
Original:  It is a classic result that the greedy algorithm for this problem achieves an optimal approximation ratio of ( 1-e^{-1} ) . In this work we consider a generalization of this problem wherein an element a can contribute by an amount that depends on the number of times it is covered.
Revised:  It is a classic result that the greedy algorithm for this problem achieves an optimal approximation ratio of 1-e^{-1} ) . In this work we consider a generalization of this problem wherein an element a can contribute by an amount that depends on the number of times it is covered.
Edit intent: coherence
Original:  It is a classic result that the greedy algorithm for this problem achieves an optimal approximation ratio of ( 1-e^{-1} ) . In this work we consider a generalization of this problem wherein an element a can contribute by an amount that depends on the number of times it is covered.
Revised:  It is a classic result that the greedy algorithm for this problem achieves an optimal approximation ratio of ( 1-e^{-1} . In this work we consider a generalization of this problem wherein an element a can contribute by an amount that depends on the number of times it is covered.
Edit intent: fluency
Original:  The inclusion of R/S in SMILES representation allowed simplify the assignment of the respective information based on SMILES representation, but did not  show advantages on regression or classification tasks.
Revised:  The inclusion of R/S in SMILES representation allowed simplify the assignment of the respective information based on SMILES representation, but did not always show advantages on regression or classification tasks.
Edit intent: coherence
Original:  Interestingly, we did not see degradation of the performance of Transformer CNN models when the stereochemical information was not present in SMILES.
Revised:  Interestingly, we did not see degradation of the performance of Transformer-CNN models when the stereochemical information was not present in SMILES.
Edit intent: clarity
Original:  An open challenge remains whether Tranformer-CNN can efficiently embed 3D knowledge from SMILES input and whether a better representation could further increase the accuracy of this approach.
Revised:  An open challenge remains whether Transformer-CNN can efficiently embed 3D knowledge from SMILES input and whether a better representation could further increase the accuracy of this approach.
Edit intent: fluency
Original: Modern machine learning models are typically highly accurate but have been shown to be vulnerable to small, adversarially-chosen perturbations of the input.
Edit intent: coherence
Original:  There are two main models of attacks considered in the  literature: black-box and white-box.
Revised:  There are two main attack models considered in the literature: black-box and white-box.
Edit intent: clarity
Original:  Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the epidemic .
Revised:  Moreover, the negative correlation gets stronger after the global COVID-19 outbreak, which shows the crash risk of the US stock market will be more affected by EPU during the pandemic .
Edit intent: clarity
Original:  An accompanying paper [Gilbert and Scheichl, 2020 ], focusses on practical extensions of the MLQMC algorithm to improve efficiency, and presents numerical results.
Revised:  An accompanying paper [Gilbert and Scheichl, 2021 ], focusses on practical extensions of the MLQMC algorithm to improve efficiency, and presents numerical results.
Edit intent: fluency
Original:  It substantially improves the ability of communicating MARL systems to learn across a variety of cooperative and competitive tasks.
Revised:  We find in our experiments that it substantially improves the ability of communicating MARL systems to learn across a variety of cooperative and competitive tasks.
Edit intent: clarity
Original:  It verifies the correctness of a range of a quantum algorithms including Simon's algorithm, Grover's algorithm  and quantum phase estimation, a key component of Shor's algorithm.
Revised:  It verifies the correctness of a range of a quantum algorithms including Simon's algorithm, Grover's algorithm , and quantum phase estimation, a key component of Shor's algorithm.
Edit intent: fluency
Original: As quantum computing steadily progresses from theory to practice, programmers are faced with a common problem: How can they be sure that their code does what they intend it to do?
Revised: As quantum computing progresses steadily from theory into practice, programmers are faced with a common problem: How can they be sure that their code does what they intend it to do?
Edit intent: fluency
Original: As quantum computing steadily progresses from theory to practice, programmers are faced with a common problem: How can they be sure that their code does what they intend it to do?
Revised: As quantum computing steadily progresses from theory to practice, programmers will face a common problem: How can they be sure that their code does what they intend it to do?
Edit intent: clarity
Original:  It verifies the correctness of a range of a quantum algorithms including Simon's algorithm, Grover's algorithm , and quantum phase estimation, a key component of Shor's algorithm.
Revised:  It verifies the correctness of a range of a quantum algorithms including Grover's algorithm , and quantum phase estimation, a key component of Shor's algorithm.
Edit intent: clarity
Original:  It verifies the correctness of a range of a quantum algorithms including Simon's algorithm, Grover's algorithm , and quantum phase estimation, a key component of Shor's algorithm.
Revised:  It verifies the correctness of a range of a quantum algorithms including Simon's algorithm, Grover's algorithm and quantum phase estimation, a key component of Shor's algorithm.
Edit intent: fluency
Original:  These results may be used to quantify the memory content of resistive memories  and tune their Fourier spectrum according to the excitation signal.
Revised:  These results may be used to quantify the memory content of resistive memories , and tune their Fourier spectrum according to the excitation signal.
Edit intent: fluency
Original:  Furthermore, the proposed scheme in that work can be stuck in local minima , especially for a complex solution structure and longer terminal time.
Revised:  Furthermore, those schemes can be stuck in local minima , especially for a complex solution structure and longer terminal time.
Edit intent: clarity
Original:  We also investigate recent improvements to CBS and adapt them to EECBS.
Revised:  We also investigate recent improvements of CBS and adapt them to EECBS.
Edit intent: fluency
Original: As the communication infrastructure of the blockchain system, the underlying peer-to-peer (P2P) network has a crucial impact on the efficiency and security of the upper-layer blockchain such as Bitcoin and Ethereum .
Revised: The peer-to-peer (P2P) network has a crucial impact on the efficiency and security of the upper-layer blockchain such as Bitcoin and Ethereum .
Edit intent: clarity