wanyu/IteraTeR_v2 huggingface dataset:
Original:  Some of the most successful algorithms for solving this problem use the branch-and-bound or branch-and-reduce paradigms.
Revised:  Some of the most successful algorithms for this problem are based on the branch-and-bound or branch-and-reduce paradigms.
Edit intent: clarity
Original:  Results on branch-and-reduce algorithms  were to a large part achieved by developing new, more practical reduction rules.
Revised:  In particular, branch-and-reduce algorithms were to a large part achieved by developing new, more practical reduction rules.
Edit intent: clarity
Original:  However, other components that have been shown to have a significant impact on the performance of these algorithms have not received as much attention.
Revised:  However, other components that have been shown to have an impact on the performance of these algorithms have not received as much attention.
Edit intent: clarity
Original:  Even now, the most commonly used strategy selects vertices solely based on their degree and does not take into account other factors that contribute to the performance of the algorithm .
Revised:  The most commonly used strategy selects vertices solely based on their degree and does not take into account other factors that contribute to the performance of the algorithm .
Edit intent: coherence
Original:  Even now, the most commonly used strategy selects vertices solely based on their degree and does not take into account other factors that contribute to the performance of the algorithm .
Revised:  Even now, the most commonly used strategy selects vertices based on their degree and does not take into account other factors that contribute to the performance of the algorithm .
Edit intent: clarity
Original:  Even now, the most commonly used strategy selects vertices solely based on their degree and does not take into account other factors that contribute to the performance of the algorithm .
Revised:  Even now, the most commonly used strategy selects vertices solely based on their degree and does not take into account other factors that contribute to the performance .
Edit intent: clarity
Original:  Our strategies are based on one of two approaches which are motivated by existing research .
Revised:  Our strategies are based on one of two approaches .
Edit intent: clarity
Original:  Our experimental evaluation on a large set of real-world instances indicates that our strategies are able to improve the performance of the state-of-the-art branch-and-reduce algorithm by Akiba and Iwata .
Revised:  Our experimental evaluation on a large set of real-world instances indicates that our strategies are able to improve the performance of the state-of-the-art branch-and-reduce algorithms .
Edit intent: coherence
Original: In this paper, we present a new open source, production first  and production ready  end-to-end (E2E) speech recognition toolkit named WeNet .
Revised: In this paper, we propose an open source, production first and production ready end-to-end (E2E) speech recognition toolkit named WeNet .
Edit intent: clarity
Original: In this paper, we present a new open source, production first  and production ready  end-to-end (E2E) speech recognition toolkit named WeNet .
Revised: In this paper, we present a new open source, production first , and production ready end-to-end (E2E) speech recognition toolkit named WeNet .
Edit intent: fluency
Original:  The main motivation of WeNet is to close the gap between the research and the production of E2E speech recognition models.
Revised:  The main motivation of WeNet is to close the gap between the research and the production of E2E speechrecognition models.
Edit intent: fluency
Original:  Our experiments on  AISHELL-1 using WeNet, not only give a promising\\%DIF > relative character error rate (CER) on a unified streaming and non-streaming two pass (U2) E2E model but also show reasonable RTF and latency , both of these aspects are favored for production adoption.
Revised:  Our experiments on the AISHELL-1 using WeNet, not only give a promising\\%DIF > relative character error rate (CER) on a unified streaming and non-streaming two pass (U2) E2E model but also show reasonable RTF and latency , both of these aspects are favored for production adoption.
Edit intent: fluency
Original:  given the Gram matrix of a collection of m random k-sparse Boolean vectors in 0, 1%DIFDELCMD < }%%% ^r, recover the vectors (up to the trivial symmetries) . Equivalently, this can be thought of as a sparse, symmetric variant of the well-studied problem of Boolean factor analysis, or as an average-case version of the classic problem of recovering a k-uniform hypergraph from its line graph.
Revised:  given the Gram matrix of a collection of m random k-sparse Boolean vectors in %DIFDELCMD < }%%% ^r, recover the vectors (up to the trivial symmetries) . Equivalently, this can be thought of as a sparse, symmetric variant of the well-studied problem of Boolean factor analysis, or as an average-case version of the classic problem of recovering a k-uniform hypergraph from its line graph.
Edit intent: coherence
Original:  given the Gram matrix of a collection of m random k-sparse Boolean vectors in 0, 1%DIFDELCMD < }%%% ^r, recover the vectors (up to the trivial symmetries) . Equivalently, this can be thought of as a sparse, symmetric variant of the well-studied problem of Boolean factor analysis, or as an average-case version of the classic problem of recovering a k-uniform hypergraph from its line graph.
Revised:  given the Gram matrix of a collection of m random k-sparse Boolean vectors in 0, 1%DIFDELCMD < }%%% ^r, recover the vectors (up to the trivial symmetries) . Equivalently, this can be thought of as recovering a uniformly random k-uniform hypergraph from its line graph.
Edit intent: clarity
Original:  Our central conditions concern whether actions and links are strategic complements or substitutes , as well as whether links create positive or negative payoff spillovers .
Revised:  Our first condition concerns whether links create positive or negative payoff spillovers. Our second condition concerns whether actions and links are strategic complements or substitutes , as well as whether links create positive or negative payoff spillovers .
Edit intent: clarity
Original:  The endogenous outcomes include both the strategic actions (e.g., effort levels) and the network in which spillovers occur .
Revised:  The endogenous outcomes include both the strategic actions (e.g., effort ) that creates spillovers for others and the partners with whom they associate .
Edit intent: clarity
Original:  We introduce a framework and two solution concepts that extend standard approaches -- Nash equilibrium in actions and pairwise (Nash) stability in links.
Revised:  We introduce a framework and two solution concepts that extend standard approaches -- Nash equilibrium in actions and pairwise stability in links.
Edit intent: clarity
Original:  Our main results show that  under suitable monotonicity assumptions on incentives, stable networks take simple forms.
Revised:  Our main results show that , under suitable monotonicity assumptions on incentives, stable networks take simple forms.
Edit intent: fluency
Original:  Our main results show that  under suitable monotonicity assumptions on incentives, stable networks take simple forms.
Revised:  Our main results show that under suitable monotonicity conditions on incentives, stable networks take simple forms.
Edit intent: clarity
Original:  Our first condition concerns whether links create positive or negative payoff spillovers.
Revised:  The first condition concerns whether links create positive or negative payoff spillovers.
Edit intent: fluency
Original:  Our second condition concerns whether actions and links are strategic complements or substitutes.
Revised:  The second condition concerns whether actions and links are strategic complements or substitutes.
Edit intent: fluency
Original:  Together, these conditions allow for a taxonomy of how network structure depends on economic primitives.
Revised:  Together, these conditions allow a taxonomy of how network structure depends on economic primitives.
Edit intent: fluency
Original:   A common theme in the proof of such results is the fact that one-hidden-layer  fail to approximate high-energy functions whose Fourier representation is spread in the domain.
Revised:  A common theme in the proof of such results is the fact that one-hidden-layer networks fail to approximate high-energy functions whose Fourier representation is spread in the domain.
Edit intent: clarity
Original:  Focusing on a fixed approximation domain, namely the sphere S^{d-1} in dimension d, we provide a characterization of both functions which are efficiently approximable by one-hidden-layer networks and of functions which are provably not, in terms of their Fourier expansion.
Revised:  Focusing on a fixed approximation domain, namely the sphere S^{d-1} in dimension d, we provide a characterisation of both functions which are efficiently approximable by one-hidden-layer networks and of functions which are provably not, in terms of their Fourier expansion.
Edit intent: fluency
Original:  Our computable error estimates show that this yields a good coarse-scale approximation of the solution for small p . Moreover, extensive numerical experiments illustrate that relative errors of a few percent are achieved up to at least p=0.1 . This makes the proposed technique attractive already for moderate sample sizes in a Monte Carlo simulation.
Revised:  Our computable error estimates show that this yields a good coarse-scale approximation of the solution for small p , which is illustrated by extensive numerical experiments illustrate that relative errors of a few percent are achieved up to at least p=0.1 . This makes the proposed technique attractive already for moderate sample sizes in a Monte Carlo simulation.
Edit intent: coherence
Original:  Our computable error estimates show that this yields a good coarse-scale approximation of the solution for small p . Moreover, extensive numerical experiments illustrate that relative errors of a few percent are achieved up to at least p=0.1 . This makes the proposed technique attractive already for moderate sample sizes in a Monte Carlo simulation.
Revised:  Our computable error estimates show that this yields a good coarse-scale approximation of the solution for small p . Moreover, extensive numerical experiments . This makes the proposed technique attractive already for moderate sample sizes in a Monte Carlo simulation.
Edit intent: coherence
Original: In this paper, we analyze the causes and discuss potential consequences of perceived privatization of AIresearch, particularly the transition of AI  researchers from academia to industry .
Revised: The private sector is playing an increasingly important role in basic Artificial Intelligence (AI) R researchers from academia to industry .
Edit intent: clarity
Original:  We find that researchers working within the field of deep learning as well as those with higher average impact tend to transition into industry.
Revised:  We find that researchers working within the field of deep learning as well as those with higher average impact are more likely to transition into industry.
Edit intent: clarity
Original:  These findings highlight the importance of strengthening academic research in public organizations within AI to balance a potential dominance of private companies and to maintain public supervision of the development and application of this technology .
Revised:  These findings highlight the importance of strengthening the public AI research sphere in order to ensure that the future of this powerful technology is not dominated by private interests .
Edit intent: clarity
Original: We analyze greedy algorithms for the Hierarchical Aggregation (HAG) problem, a strategy introduced in [Jia et . al., KDD 2020] for speeding up learning on Graph Neural Networks (GNNs).
Revised: We analyze greedy algorithms for the Hierarchical Aggregation (HAG) problem, a strategy introduced in [Jia et al., KDD 2020] for speeding up learning on Graph Neural Networks (GNNs).
Edit intent: fluency
Original: Understanding the power of quantum supremacy experiments is one of the most pressing topics in the NISQ era .
Revised: Noise is the defining feature of the NISQ era, but it remains unclear if noisy quantum devices are capable of quantum speedups. Quantum supremacy experiments have been a major step forward, but gaps remain between the theory behind these experiments and their actual implementations .
Edit intent: coherence
Original:  For random circuit sampling , we show that computing the output probability of an m-gate random quantum circuit to within additive imprecision 2^{-O(m\log m) is \#P-hard, an exponential improvement over the prior hardness results of Bouland et al. and Movassagh which were resistant to imprecision 2^{-O(m^3)}.
Revised:  For random circuit sampling , we show that is \#P-hard, an exponential improvement over the prior hardness results of Bouland et al. and Movassagh which were resistant to imprecision 2^{-O(m^3)}.
Edit intent: clarity
Original:  For BosonSampling, we show \#P-hardness of computing output probabilities of n-photon m=poly(n) mode BosonSamplingexperiments to additive error 2^{-O(n\log n) within a constant factor in the exponent of the robustness required for hardness of sampling .
Revised:  In the latter case we bring the proven hardness within a constant factor in the exponent of the robustness required for hardness of sampling .
Edit intent: coherence
Original:  Interestingly, we then show that our results are in tension with one another , and the latter result implies the former is essentially optimal with respect to additive imprecision , even with generalizations of our techniques.
Revised:  Interestingly, we then show that our results are in tension with one another , and the latter result implies the former is essentially optimal , even with generalizations of our techniques.
Edit intent: clarity
Original:  We give numerical results demonstrating the good conditioning and improved performance of the method in the presence of near-intersections  .
Revised:  We discretize the model and present numerical results demonstrating the good conditioning and improved performance of the method in the presence of near-intersections .
Edit intent: clarity
Original:  We also show the importance of tracking fast changes in synaptic weights, slow changes in synaptic weights, and unexplained variations in baseline firing simultaneously .
Revised:  We then apply our model to two experimentally recorded putative synaptic connections. We find that simultaneously tracking fast changes in synaptic weights, slow changes in synaptic weights, and unexplained variations in baseline firing simultaneously .
Edit intent: coherence
Original:  While still an area of active development, there are already a number of powerful models for interpreting large scale neural recordings even in complex experimental settings.
Edit intent: coherence
Original: Modern extracellular recording technologies now enable simultaneous recording from large numbers of neurons.
Revised: Modern recording technologies now enable simultaneous recording from large numbers of neurons.
Edit intent: clarity
Original:  This indicates that the latent space is constructed to reflect the proximity structure of the data set, and may provide a mechanism by which many aspects of cognition are spatially represented.
Revised:  This indicates that the latent space is self-organized to reflect the proximity structure of the data set, and may provide a mechanism by which many aspects of cognition are spatially represented.
Edit intent: clarity
Original:  This indicates that the latent space is constructed to reflect the proximity structure of the data set, and may provide a mechanism by which many aspects of cognition are spatially represented.
Revised:  This indicates that the latent space is constructed to reflect the proximity structure of the dataset and may provide a mechanism by which many aspects of cognition are spatially represented.
Edit intent: clarity
Original:  This indicates that the latent space is constructed to reflect the proximity structure of the data set, and may provide a mechanism by which many aspects of cognition are spatially represented.
Revised:  This indicates that the latent space is constructed to reflect the proximity structure of the data set, and may provide a mechanism through which many aspects of cognition are spatially represented.
Edit intent: fluency
Original:  The present study allows the network to internally generate temporal sequences analogous to hippocampal replay/pre-play  , where VAE produces only near-accurate replays of past experiences, but by introducing GANs, latent vectors of temporally close images are closely aligned and sequence acquired some instability.
Revised:  The present study allows the network to internally generate temporal sequences that are analogous to the hippocampal replay/pre-play , where VAE produces only near-accurate replays of past experiences, but by introducing GANs, latent vectors of temporally close images are closely aligned and sequence acquired some instability.
Edit intent: fluency
Original:  The present study allows the network to internally generate temporal sequences analogous to hippocampal replay/pre-play  , where VAE produces only near-accurate replays of past experiences, but by introducing GANs, latent vectors of temporally close images are closely aligned and sequence acquired some instability.
Revised:  The present study allows the network to internally generate temporal sequences analogous to hippocampal replay/pre-play ability , where VAE produces only near-accurate replays of past experiences, but by introducing GANs, latent vectors of temporally close images are closely aligned and sequence acquired some instability.
Edit intent: clarity
Original: In this work, we propose a state redistribution method for high order discontinuous Galerkin methods on curvilinear embedded boundary grids.
Revised: We propose a state redistribution method for high order discontinuous Galerkin methods on curvilinear embedded boundary grids.
Edit intent: clarity
Original:  State redistribution relaxes the overly restrictive CFL condition that results from arbitrarily small cut cells when explicit time steppers are used .
Revised:  State redistribution relaxes the overly restrictive CFL condition that results from arbitrarily small cut cells and explicit time stepping .
Edit intent: clarity
Original:  Thus, the scheme can take take time steps that are proportional to the size of cells in the background grid.
Revised:  Thus, the scheme can take time steps that are proportional to the size of cells in the background grid.
Edit intent: fluency
Original:  This is done by temporarily coarsening, or merging , the small cells into larger, possibly overlapping neighborhoods using a special weighted inner product.
Revised:  This is done by temporarily merging the small cells into larger, possibly overlapping neighborhoods using a special weighted inner product.
Edit intent: clarity
Original:  Then, the numerical solution on the neighborhoods is refined back onto the base grid in a conservative fashion.
Revised:  Then, the numerical solution on the neighborhoods is returned to the base grid in a conservative fashion.
Edit intent: clarity
Original:  We prove that state redistribution is conservative and p-exact.
Edit intent: coherence
Original:  Finally, we solve a number of test problems that demonstrate the encouraging potential of this technique for applications on curvilinear embedded geometries.
Revised:  Finally, we present a number of test problems that demonstrate the encouraging potential of this technique for applications on curvilinear embedded geometries.
Edit intent: clarity
Original:  However, advancing from correlative learning to causal learning is an important problem, since causal/counterfactual modeling helps us to go beyond the observational data for user modeling and personalized .
Revised:  However, advancing from correlative learning to causal learning is an important problem, because causal/counterfactual modeling helps us to go beyond the observational data for user modeling and personalized .
Edit intent: coherence
Original:  However, advancing from correlative learning to causal learning is an important problem, since causal/counterfactual modeling helps us to go beyond the observational data for user modeling and personalized .
Revised:  However, advancing from correlative learning to causal learning is an important problem, since causal/counterfactual modeling can help us to think outside of the observational data for user modeling and personalized .
Edit intent: clarity
Original:  However, advancing from correlative learning to causal learning is an important problem, since causal/counterfactual modeling helps us to go beyond the observational data for user modeling and personalized .
Revised:  However, advancing from correlative learning to causal learning is an important problem, since causal/counterfactual modeling helps us to go beyond the observational data for user modeling and personalization .
Edit intent: fluency
Original:  In this work , we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommender systems .
Revised:  In this paper , we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommender systems .
Edit intent: clarity
Original:  In this work , we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommender systems .
Revised:  In this work , we propose Causal Collaborative Filtering (CCF) -- a general framework for modeling causality in collaborative filtering and recommendation .
Edit intent: clarity
Original:  We first provide a unified causal view of collaborative filtering and mathematically show that many of the traditional CF algorithms are actually special cases of CCF under simplified causal graphs.
Revised:  We first provide a unified causal view of CF and mathematically show that many of the traditional CF algorithms are actually special cases of CCF under simplified causal graphs.
Edit intent: clarity
Original:  In this study , a novel approach is developed to derive an analytical solution for the DSO problem  .
Revised:  Specifically , a novel approach is developed to derive an analytical solution for the DSO problem .
Edit intent: clarity
Original:  In this study , a novel approach is developed to derive an analytical solution for the DSO problem  .
Revised:  In this study , a novel approach to derive an analytical solution for the DSO problem .
Edit intent: clarity
Original:  By utilizing the analytical solution, we prove that the queuing delay at a bottleneck in a DUE solution is equal to an optimal toll that eliminates the queue in a DSO solution under certain conditions of a schedule delay function.
Revised:  By utilizing this solution, we prove that the queuing delay at a bottleneck in a DUE solution is equal to an optimal toll that eliminates the queue in a DSO solution under certain conditions of a schedule delay function.
Edit intent: clarity
Original:   Numerical examples are provided to illustrate and verify  analytical results.
Revised:  Numerical examples are provided to illustrate and verify the analytical results.
Edit intent: fluency
Original:  Finally, the model was tested with the unfamiliar "illusory contour" configuration: inducer shapes oriented to form an illusory square.
Revised:  Finally, the model was tested with the unfamiliar ''illusory contour'' configuration: inducer shapes oriented to form an illusory square.
Edit intent: fluency
Original:  Compared with feedforward baselines, the iterative "predictive coding" feedback resulted in more illusory contours being classified as physical squares.
Revised:  Compared with feedforward baselines, the iterative ''predictive coding'' feedback resulted in more illusory contours being classified as physical squares.
Edit intent: fluency