wanyu/IteraTeR_v2 huggingface dataset:
Original:  The grids are organized in modules with spatial periods  clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical self-organization in the mEC that can produce this modular structure  .
Revised:  The grids are organized in modules with spatial periods clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism that produces this modular structure .
Edit intent: clarity
Original:  The grids are organized in modules with spatial periods  clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical self-organization in the mEC that can produce this modular structure  .
Revised:  The grids are organized in modules with spatial periods clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical self-organization in the mEC that can produce this modular structure through dynamical self-organization in the MEC .
Edit intent: clarity
Original:  In attractor network models of grid formation, the period of a single module is set by the length scale of recurrent inhibition between neurons.
Revised:  In attractor network models of grid formation, the grid scale of a single module is set by the length scale of recurrent inhibition between neurons.
Edit intent: clarity
Original:  In attractor network models of grid formation, the period of a single module is set by the length scale of recurrent inhibition between neurons.
Revised:  In attractor network models of grid formation, the period of a single module is set by the distance of recurrent inhibition between neurons.
Edit intent: clarity
Original:  We show that grid cells will instead form a hierarchy of discrete modules if a continuous increase in inhibition distance along the dorso-ventral axis of the mEC is accompanied by excitatory interactions along this axis.
Revised:  We show that the MEC forms a hierarchy of discrete modules if a continuous increase in inhibition distance along the dorso-ventral axis of the mEC is accompanied by excitatory interactions along this axis.
Edit intent: clarity
Original:  We show that grid cells will instead form a hierarchy of discrete modules if a continuous increase in inhibition distance along the dorso-ventral axis of the mEC is accompanied by excitatory interactions along this axis.
Revised:  We show that grid cells will instead form a hierarchy of discrete modules if a continuous increase in inhibition distance along the dorso-ventral axis is accompanied by excitatory interactions along this axis.
Edit intent: clarity
Original:  We discuss how the interactions required by our model might be tested experimentally and realized by circuits in the mEC .
Revised:  We discuss how interactions required by our model might be tested experimentally and realized by circuits in the mEC .
Edit intent: fluency
Original:  We discuss how the interactions required by our model might be tested experimentally and realized by circuits in the mEC .
Revised:  We discuss how the interactions required by our model might be tested experimentally .
Edit intent: clarity
Original:  The upper bound is obtained by modifying the previously known smallest solution with 88 hexahedra.
Revised:  The upper bound is obtained by modifying a prior solution with 88 hexahedra.
Edit intent: clarity
Original:  Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: ensuring that each ethnic or social group receives its fair share in the outcome of classifiers and rankings .
Revised:  Research on how to incorporate fairness into such tasks has prevalently pursued the paradigm of group fairness: giving adequate success rates to specifically protected groups .
Edit intent: clarity
Original:  This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models .
Revised:  This paper introduces a method for probabilistically mapping user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models .
Edit intent: clarity
Original:  This paper introduces a method for probabilistically clustering user records into a low-rank representation that captures individual fairness yet also achieves high accuracy in classification and regression models .
Revised:  This paper introduces a method for probabilistically clustering user records into a low-rank representation that reconciles individual fairness and the utility of classifiers and rankings in downstream applications .
Edit intent: clarity
Original:  Since the case for fairness is ubiquitous across many tasks, we aim to learn general representations that can be applied to arbitrary downstream use-cases.
Edit intent: coherence
Original:  We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on two real-world datasets.
Revised:  We demonstrate the versatility of our method by applying it to classification and learning-to-rank tasks on a variety of real-world datasets.
Edit intent: clarity
Original:  In addition to our theoretical framework, we explain its causal interpretation and motivation, provide an efficient estimation procedure and prove identifiability of the unmixing matrix under mild assumptions.
Revised:  In addition to our theoretical framework, we provide an efficient estimation procedure and prove identifiability of the unmixing matrix under mild assumptions.
Edit intent: coherence
Original:  We provide a scikit-learn compatible pip-installable Python package groupICA as well as R and Matlab implementations accompanied by a documentation and an audible example at URL
Revised:  We provide a scikit-learn compatible pip-installable Python package groupICA as well as R and Matlab implementations accompanied by a documentation at URL
Edit intent: clarity
Original:  We show that the noise model has a natural relation to causality and  explain how it can be applied in the context of causal inference.
Revised:  Our proposed noise model has a natural relation to causality and explain how it can be applied in the context of causal inference.
Edit intent: clarity
Original:  We show that the noise model has a natural relation to causality and  explain how it can be applied in the context of causal inference.
Revised:  We show that the noise model has a natural relation to causality and we explain how it can be applied in the context of causal inference.
Edit intent: fluency
Original:  In this paper, we uncover how both inherent properties and vulnerabilities to adversarial manipulation of these rankings may affect the conclusions of security studies .
Revised:  In this paper, we uncover how both inherent properties and vulnerabilities to adversarial manipulation of these rankings .
Edit intent: clarity
Original:  To that end, we compare four main rankings used in recent studies in terms of their agreement with each other, stability, representation bias and potential impact on research cases.
Revised:  To that end, we show for four main rankings used in recent studies in terms of their agreement with each other, stability, representation bias and potential impact on research cases.
Edit intent: clarity
Original:  Overall, our study reveals significant shortcomings of current rankingsand calls for a more cautious approach from the research community  .
Revised:  Finally, to overcome the limitations of such rankings, we propose improvements to reduce the fluctuations in list composition and guarantee better defenses against manipulation. To allow the research community .
Edit intent: coherence
Original:  To that end, we show for four main rankings how the choice of a list affects which domains are included, whether these are representative of real sites and if they are malicious .
Revised:  To that end, we show for four main rankings how their inherent properties (similarity, stability, representativeness, responsiveness and benignness) affect their composition and therefore potentially skew the conclusions made in studies .
Edit intent: clarity
Original:  Finally, to overcome the limitations of such rankings, we propose improvements to reduce the fluctuations in list composition and guarantee better defenses against manipulation.
Revised:  To overcome the limitations of such rankings, we propose improvements to reduce the fluctuations in list composition and guarantee better defenses against manipulation.
Edit intent: coherence
Original:  These interactions can also change the shape of the cell, analogous to liquid droplet wetting on adhesive surfaces .
Revised:  These interactions can also change the shape of the cell, analogous to liquid droplet wetting on adhesive substrates .
Edit intent: fluency
Original:  The numerical and analytical results are confirmed in experiments of motile eukaryotic cells.
Revised:  The numerical and analytical results are confirmed in experiments on motile eukaryotic cells.
Edit intent: fluency
Original: Deep reinforcement learning on Atari gamesmaps pixel directly to actions;
Revised: Deep reinforcement learning , applied to vision-based problems like Atari games, maps pixels directly to actions;
Edit intent: clarity
Original:  This enables networks of only 6 to 18 neurons to learn to play a selection of Atari games with performance comparable---and occasionally superior---to state-of-the-art techniques using evolution strategies on deep networks two orders of magnitude larger .
Revised:  This enables networks of only 6 to 18 neurons (depending on the game's controls). These are still capable of achieving results comparable---and occasionally superior---to state-of-the-art techniques using evolution strategies on deep networks two orders of magnitude larger .
Edit intent: clarity
Original:  This enables networks of only 6 to 18 neurons to learn to play a selection of Atari games with performance comparable---and occasionally superior---to state-of-the-art techniques using evolution strategies on deep networks two orders of magnitude larger .
Revised:  This enables networks of only 6 to 18 neurons to learn to play a selection of Atari games with performance comparable---and occasionally superior---to state-of-the-art techniques which use two orders of magnitude larger .
Edit intent: clarity
Original:  This enables networks of only 6 to 18 neurons to learn to play a selection of Atari games with performance comparable---and occasionally superior---to state-of-the-art techniques using evolution strategies on deep networks two orders of magnitude larger .
Revised:  This enables networks of only 6 to 18 neurons to learn to play a selection of Atari games with performance comparable---and occasionally superior---to state-of-the-art techniques using evolution strategies on deep networks two orders of magnitude more neurons .
Edit intent: clarity
Original: Learning vector embeddings of complex networks is a powerful approach used to predict missing or unobserved edges in network data .
Revised: Learning vector embeddings of complex networks is a powerful approach used to predict unobserved or missing edges between entities .
Edit intent: clarity
Original:  However, an open challenge in this area is developing techniques that can reason aboutsubgraphs in network data, which can involve the logical conjunction of several edge relationships.
Revised:  However, an open challenge in this area is developing techniques that can subgraphs in network data, which can involve the logical conjunction of several edge relationships.
Edit intent: clarity
Original:  However, an open challenge in this area is developing techniques that can reason aboutsubgraphs in network data, which can involve the logical conjunction of several edge relationships.
Revised:  However, an open challenge in this area is developing techniques that can reason about in network data, which can involve the logical conjunction of several edge relationships.
Edit intent: clarity
Original: {\em   Here we introduce a framework to  make predictions about conjunctive logical queries---i. e., subgraph relationships---on heterogeneous network data.
Revised: {\em might Here we introduce a framework to make predictions about conjunctive logical queries---i. e., subgraph relationships---on heterogeneous network data.
Edit intent: fluency
Original:  We demonstrate the utility of this framework in two application studies on networks with millions of edges: predicting unobserved subgraphs in a network of drug-gene-disease interactions and in a network of social interactions derived from a popular web forum .
Revised:  We demonstrate the utility of this framework in two application studies on real-world datasets with millions of edges: predicting unobserved subgraphs in a network of drug-gene-disease interactions and in a network of social interactions derived from a popular web forum .
Edit intent: clarity
Original:  We demonstrate the utility of this framework in two application studies on networks with millions of edges: predicting unobserved subgraphs in a network of drug-gene-disease interactions and in a network of social interactions derived from a popular web forum .
Revised:  We demonstrate the utility of this framework in two application studies on networks with millions of edges: predicting unobserved subgraphs in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum .
Edit intent: clarity
Original:  \left\{\right\}  Algorithms for learning both the original time series of graphs and the eigennetworksare presented and discussed \left\{\right\} . Experiments on simulated and real time series data   demonstrate the performance of the learning and the interpretation of the eigennetworks.
Revised:  \left\{\right\} Algorithms for learning the time series of graphs and the eigennetworksare presented and discussed \left\{\right\} . Experiments on simulated and real time series data demonstrate the performance of the learning and the interpretation of the eigennetworks.
Edit intent: clarity
Original:  \left\{\right\}  Algorithms for learning both the original time series of graphs and the eigennetworksare presented and discussed \left\{\right\} . Experiments on simulated and real time series data   demonstrate the performance of the learning and the interpretation of the eigennetworks.
Revised:  \left\{\right\} Algorithms for learning both the original time series of graphs \left\{\right\} . Experiments on simulated and real time series data demonstrate the performance of the learning and the interpretation of the eigennetworks.
Edit intent: clarity
Original:  \left\{\right\}  Algorithms for learning both the original time series of graphs and the eigennetworksare presented and discussed \left\{\right\} . Experiments on simulated and real time series data   demonstrate the performance of the learning and the interpretation of the eigennetworks.
Revised:  \left\{\right\} Algorithms for learning both the original time series of graphs and the eigennetworksare presented and discussed \left\{\right\} . Experiments on simulated and real time series data demonstrate the performance of the learning and provide interesting interpretations of the eigennetworks.
Edit intent: clarity
Original:  We show that the probability of these cycles appearing in a vertex is O%DIFDELCMD < \left(%%% \log^{-1n}%DIFDELCMD < \right)%%% . This property allows the long-range edges removing through the detection of lattice patterns on combinations of 4-cycles, and the running of a breadth-first search in the resulting graph.
Revised:  We show that %DIFDELCMD < \left(%%% \log^{-1n}%DIFDELCMD < \right)%%% . This property allows the long-range edges removing through the detection of lattice patterns on combinations of 4-cycles, and the running of a breadth-first search in the resulting graph.
Edit intent: clarity
Original: We study the long time error behaviour of correction procedure via reconstruction / flux reconstruction methods to hyperbolic conservation laws.
Revised: We study the long-time error behavior of correction procedure via reconstruction / flux reconstruction methods to hyperbolic conservation laws.
Edit intent: fluency
Original:  We show that not only the choice of the numerical flux (upwind or central) affects the growth rate and asymptotic value of the error, but also that the selection of bases (Gau{\ss}-Lobatto or Gau{\ss}-Legendre) is even more important.
Revised:  We show that not only the choice of the numerical flux (upwind or central) affects the growth rate and asymptotic value of the error, but that the selection of bases (Gau{\ss}-Lobatto or Gau{\ss}-Legendre) is even more important.
Edit intent: fluency
Original:  Using  Gau{\ss}-Legendre basis, the error reaches the asymptotic value faster and to a lower value than by using Gau{\ss}-Lobatto basis.
Revised:  Using a Gau{\ss}-Legendre basis, the error reaches the asymptotic value faster and to a lower value than by using Gau{\ss}-Lobatto basis.
Edit intent: fluency
Original:  Using  Gau{\ss}-Legendre basis, the error reaches the asymptotic value faster and to a lower value than by using Gau{\ss}-Lobatto basis.
Revised:  Using Gau{\ss}-Legendre basis, the error reaches the asymptotic value faster and to a lower value than when using a Gau{\ss}-Lobatto basis.
Edit intent: clarity
Original:  Also  the differences in the error caused by the numerical flux is not essential for low resolution computations in the Gau{\ss}-Legendre case.
Revised:  Also , the differences in the error caused by the numerical flux is not essential for low resolution computations in the Gau{\ss}-Legendre case.
Edit intent: fluency
Original:  Also  the differences in the error caused by the numerical flux is not essential for low resolution computations in the Gau{\ss}-Legendre case.
Revised:  Also the differences in the error caused by the numerical flux are not essential for low resolution computations in the Gau{\ss}-Legendre case.
Edit intent: fluency
Original:  This behaviour is best seen if a particular FR scheme is investigated which has a strong connection to the Discontinious Galerkin framework , but holds also for other flux reconstruction schemes with low order resolution computations.
Revised:  This behavior is better seen on a particular FR scheme is investigated which has a strong connection to the Discontinious Galerkin framework , but holds also for other flux reconstruction schemes with low order resolution computations.
Edit intent: clarity
Original:  This behaviour is best seen if a particular FR scheme is investigated which has a strong connection to the Discontinious Galerkin framework , but holds also for other flux reconstruction schemes with low order resolution computations.
Revised:  This behaviour is best seen if a particular FR scheme which has a strong connection to the Discontinious Galerkin framework , but holds also for other flux reconstruction schemes with low order resolution computations.
Edit intent: clarity
Original:  This behaviour is best seen if a particular FR scheme is investigated which has a strong connection to the Discontinious Galerkin framework , but holds also for other flux reconstruction schemes with low order resolution computations.
Revised:  This behaviour is best seen if a particular FR scheme is investigated which has a strong connection with the discontinuous Galerkin framework but holds also for other flux reconstruction schemes with low order resolution computations.
Edit intent: fluency
Original:  However, training of autoencoders is typically complicated by the need for training of a separate encoder and decoder model that have to be enforced to be reciprocal to each other.
Revised:  However, training of autoencoders is typically complicated by the need to train a separate encoder and decoder model that have to be enforced to be reciprocal to each other.
Edit intent: fluency
Original:  We investigate the generative performance of RevNets on the CelebA dataset, showing that generative RevNets can indeed generate coherent faces with similar quality as Variational Autoencoders.
Revised:  We investigate the generative performance of RevNets on the CelebA dataset, showing that generative RevNets can generate coherent faces with similar quality as Variational Autoencoders.
Edit intent: coherence
Original:  This first attempt to use RevNets as a generative model still slightly underperformed relative to recent advanced generative models using an autoencoder component on CelebA, but this gap may diminish with further optimization of the training setup of generative RevNets.
Revised:  This first attempt to use RevNets as a generative model slightly underperformed relative to recent advanced generative models using an autoencoder component on CelebA, but this gap may diminish with further optimization of the training setup of generative RevNets.
Edit intent: clarity
Original:  In addition to the experiments on CelebA, we show a proof-of-principle experiment on the MNIST dataset suggesting that adversary-free trained RevNets can discover meaningful  dimensions without pre-specifying the number of latent dimensions of the  sampling distribution.
Revised:  In addition to the experiments on CelebA, we show a proof-of-principle experiment on the MNIST dataset suggesting that adversary-free trained RevNets can discover meaningful latent dimensions without pre-specifying the number of latent dimensions of the sampling distribution.
Edit intent: fluency
Original:  In addition to the experiments on CelebA, we show a proof-of-principle experiment on the MNIST dataset suggesting that adversary-free trained RevNets can discover meaningful  dimensions without pre-specifying the number of latent dimensions of the  sampling distribution.
Revised:  In addition to the experiments on CelebA, we show a proof-of-principle experiment on the MNIST dataset suggesting that adversary-free trained RevNets can discover meaningful dimensions without pre-specifying the number of dimensions of the sampling distribution.
Edit intent: clarity
Original:  In summary, this study shows that RevNets enable generative applications with an encoding component while overcoming the need of training separate encoder and decoder models .
Revised:  In summary, this study shows that RevNets enable generative applications with an encoding component while overcoming the need to train a separate encoder and decoder models .
Edit intent: fluency
Original:  In summary, this study shows that RevNets enable generative applications with an encoding component while overcoming the need of training separate encoder and decoder models .
Revised:  In summary, this study shows that RevNets enable generative applications with an encoding component while overcoming the need of training separate encoder and decoder model .
Edit intent: fluency
Original:  Here, we propose to use the by-design reversible neural networks (RevNets) as a new class of generative models .
Revised:  To overcome this problem, by-design reversible neural networks (RevNets) as a new class of generative models .
Edit intent: coherence
Original:  In summary, this study shows that RevNets enable generative applications with an encoding component while overcoming the need to train a separate encoder and decoder model .
Revised:  In summary, this study shows that RevNets can be employed in different generative training settings .
Edit intent: clarity
Original:  In recent years, several studies proposed to use target lists of IPv6 addresses, called  hitlists.
Revised:  In recent years, several studies have proposed the use of target lists of IPv6 addresses, called hitlists.
Edit intent: clarity
Original:  We present novel techniques that allow to push IPv6 hitlists  from quantity to quality.
Revised:  We present novel techniques that allow IPv6 hitlists from quantity to quality.
Edit intent: clarity