wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We test the performance of our method on a variety of large and high-dimensional data sets .
Revised:  We test the performance of our method on data sets with up to 10 million nodes per layer as well as up to 104 dimensions resulting in graphs with up to 52 layers. While all presented numerical experiments can be run on an average laptop computer, the linear dependence per iteration step of the runtime on the network size makes the algorithm scalable to even larger and higher-dimensional problems .
Edit intent: meaning-changed
Original:  While all presented numerical experiments can be run on an average laptop computer, the linear dependence per iteration step of the runtime on the network size makes the algorithm scalable to even larger and higher-dimensional problems.
Revised:  While all presented numerical experiments can be run on an average laptop computer, the linear dependence per iteration step of the runtime on the network size in all stages of our algorithm makes it scalable to even larger and higher-dimensional problems.
Edit intent: clarity
Original: Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: Fault-detection inherits network uncertainties making a  faulty process indistinguishable from a slow process.
Revised: Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: fault detection inherits network uncertainties making a faulty process indistinguishable from a slow process.
Edit intent: fluency
Original: Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: Fault-detection inherits network uncertainties making a  faulty process indistinguishable from a slow process.
Revised: Large-scale decentralized systems of autonomous agents interacting via asynchronous communication often experience the following self-healing dilemma: Fault-detection inherits network uncertainties making a remote faulty process indistinguishable from a slow process.
Edit intent: meaning-changed
Original:  In particular, triggering an undesirable fault-correction results in new faults that could be prevented with fault-tolerance instead. Nevertheless, fault-tolerance alone without eventually correcting persistent faults makes systems underperformingas well .
Revised:  In particular, triggering an undesirable fault-correction results in new faults that could be prevented with fault tolerance that is a more proactive system maintenance. But in the case of an actual faulty process, fault tolerance alone without eventually correcting persistent faults makes systems underperformingas well .
Edit intent: meaning-changed
Original:  Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several application domains of energy, transport and health  .
Revised:  Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several energy, transport and health .
Edit intent: clarity
Original:  Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several application domains of energy, transport and health  .
Revised:  Measuring, understanding and resolving such self-healing dilemmas is a timely challenge and critical requirement given the rise of distributed ledgers, edge computing, the Internet of Things in several application domains of energy, transport and health applications .
Edit intent: clarity
Original:  This paper introduces a novel and general-purpose modeling of fault scenarios . They can accurately measure and predict inconsistencies generated by fault-correction and fault-tolerance when each node in a network can monitor the health status of another node, while both can defect.
Revised:  This paper introduces a novel and general-purpose modeling of fault scenarios during system runtime. They are used to accurately measure and predict inconsistencies generated by fault-correction and fault-tolerance when each node in a network can monitor the health status of another node, while both can defect.
Edit intent: meaning-changed
Original:  They can accurately measure and predict inconsistencies generated by fault-correction and fault-tolerance when each node in a network can monitor the health status of another node, while both can defect. In contrast to related work, no information about the computational/application scenario, overlying algorithms or application data is required .
Revised:  They can accurately measure and predict inconsistencies generated by the undesirable outcomes of fault correction and fault tolerance as the means to improve self-healing of large-scale decentralized systems at the design phase .
Edit intent: clarity
Original:  A rigorous experimental methodology is designed that evaluates 696 experimental settings of different fault scales, fault profiles and fault detection thresholds , each with almost 9M measurements of inconsistencies in a prototyped decentralized network of 3000 nodes.
Revised:  A rigorous experimental methodology is designed that evaluates 696 experimental settings of different fault scales, fault profiles and fault detection thresholds in a prototyped decentralized network of 3000 nodes.
Edit intent: coherence
Original:   The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network  aggregation using real-world data from a Smart Grid pilot project.
Revised:  Almost 9 million measurements of inconsistencies were collected in a network, where each node monitors the health status of another node, while both can defect. The prediction performance of the modeled fault scenarios is validated in a challenging application scenario of decentralized and dynamic in-network aggregation using real-world data from a Smart Grid pilot project.
Edit intent: meaning-changed
Original:  This random vector is augmented by a random regularization parameter for the linear part. A probability distribution function for this augmented random vector knowing the measurements is derived . We explain how this derivation is related to the maximum likelihood regularization parameter selection [ Galatsanos and Katsaggelos,
Revised:  A dilation parameter alpha is used to scale the regularity of the linear unknown and is also modeled as a random variable. A posterior probability distribution for (m; alpha) is derived following an approach related to the maximum likelihood regularization parameter selection [ Galatsanos and Katsaggelos,
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Original:  We explain how this derivation is related to the maximum likelihood regularization parameter selection [ Galatsanos and Katsaggelos, 1992 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter.
Revised:  We explain how this derivation is related to the maximum likelihood regularization parameter selection [ 5 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter.
Edit intent: meaning-changed
Original:  1992 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter.
Revised:  1992 ] . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter.
Edit intent: coherence
Original:  1992 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter.
Revised:  1992 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ 5 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter.
Edit intent: meaning-changed
Original:  1992 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter. Based on our new probability distribution function, we construct a choice sampling algorithm to compute the posterior expected value and covariance of the nonlinear parameter. This algorithm is greatly accelerated by using a parallel platform where we alternate computing proposals in parallel and combining proposals to accept or reject them as in [ Calderhead, 2014 ].
Revised:  1992 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the maximum likelihood value of alpha. We then derive a parallel sampling algorithm where we alternate computing proposals in parallel and combining proposals to accept or reject them as in [ Calderhead, 2014 ].
Edit intent: meaning-changed
Original:  We show how our algorithm performs in that example and how it is able to compute marginal posterior probability functions even in the presence of strong noise. We discuss why this problem can not be approached by using the Generalized Cross Validation method or the discrepancy principle .
Revised:  We show how our results compare favorably to those obtained from the Maximum Likelihood (ML), the Generalized Cross Validation method or the discrepancy principle .
Edit intent: meaning-changed
Original:  = 0. We show that there is an (n+1)\times(n+1) homeostasis matrix H(I) for which dx_o/dI = 0 if and only if \det(H) = 0. We  use combinatorial matrix theory to factor the polynomial \det(H) and thereby determine a menu of different types of possible homeostasis associated with each digraph G. Specifically, we prove that each factor corresponds to a subnetwork of G. The factors divide into two combinatorially defined classes: structural and appendage.
Revised:  = 0. We show that there is an (n+1)\times(n+1) homeostasis matrix H(I) for which dx_o/dI = 0 if and only if \det(H) = 0. We note that the entries in H are linearized couplings and \det(H) is a homogeneous polynomial of degree n+1 in these entries. We use combinatorial matrix theory to factor the polynomial \det(H) and thereby determine a menu of different types of possible homeostasis associated with each digraph G. Specifically, we prove that each factor corresponds to a subnetwork of G. The factors divide into two combinatorially defined classes: structural and appendage.
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Original:  Our results show that source-level EEG functional connectivity estimates and the derived network measures display a substantial dependency on the arbitrary choice of the selected connectivity metric . The observed variability reflects ambiguity and concern that should be always discussed when reporting findings based on any connectivity metric.
Revised:  Our results show that source-level EEG functional connectivity estimates and the derived network measures display a substantial dependency on the arbitrary choice of the selected connectivity metric and thresholding approach. In our opinion, the observed variability reflects ambiguity and concern that should be always discussed when reporting findings based on any connectivity metric.
Edit intent: meaning-changed
Original:  A free boundary in a region of negative source -- an ablation-caused margin -- turns out to be a barrier to exact balance for a numerical scheme ( in either a continuous- or discrete-space sense ). We propose computablea posteriori quantities which allow conservation-error accounting in finite volume and  element schemes.
Revised:  A free boundary in a region of negative source -- an ablation-caused margin -- turns out to be a barrier to exact balance for a numerical scheme ( in either a continuous- or discrete-space sense a posteriori quantities which allow conservation-error accounting in finite volume and element schemes.
Edit intent: clarity
Original:  Our analysis reveals that for COVID-19 close contact parameters, the storage space requires maintaining the contact graph of ten million users  having 14 days  close contact data in  PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list  .
Revised:  Our analysis reveals that for COVID-19 close contact parameters, the storage space requires maintaining the contact graph of ten million users ; having 14 days close contact data in PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list .
Edit intent: fluency
Original:  Our analysis reveals that for COVID-19 close contact parameters, the storage space requires maintaining the contact graph of ten million users  having 14 days  close contact data in  PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list  .
Revised:  Our analysis reveals that for COVID-19 close contact parameters, the storage space requires maintaining the contact graph of ten million users having 14 days of close contact data in PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list .
Edit intent: clarity
Original:  Our analysis reveals that for COVID-19 close contact parameters, the storage space requires maintaining the contact graph of ten million users  having 14 days  close contact data in  PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list  .
Revised:  Our analysis reveals that for COVID-19 close contact parameters, the storage space requires maintaining the contact graph of ten million users having 14 days close contact data in the PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list .
Edit intent: fluency
Original:  Our analysis reveals that for COVID-19 close contact parameters, the storage space requires maintaining the contact graph of ten million users  having 14 days  close contact data in  PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list  .
Revised:  Our analysis reveals that for COVID-19 close contact parameters, the storage space requires maintaining the contact graph of ten million users having 14 days close contact data in PHA server takes 55 Gigabytes of memory and preparation of the contact list for a given set of the infected person depends on the size of the infected list . Our centralized digital contact tracing framework can also be applicable for other relevant diseases parameterized by an incubation period and proximity duration of contacts .
Edit intent: meaning-changed
Original: Nematode species are well-known for their invariant cell lineage pattern , including reproducible division timing, volume segregation, fate specification and migration trajectory for each and every cell during embryonic development. Here, we study the fundamental principle optimizing cell lineage pattern with Caenorhabditis elegans. Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume  , we propose a model to simulate lineage  by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Revised: Nematode species are well-known for their invariant cell lineage pattern during development. Combining knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume , we propose a model to simulate lineage by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Edit intent: coherence
Original:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume  , we propose a model to simulate lineage  by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Revised:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume in Caenorhabditis elegans , we propose a model to simulate lineage by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Edit intent: meaning-changed
Original:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume  , we propose a model to simulate lineage  by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Revised:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume , we propose a model to simulate lineage initiation by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Edit intent: meaning-changed
Original:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume  , we propose a model to simulate lineage  by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Revised:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume , we propose a model to simulate lineage by altering cell volume segregation ratio in each division, and quantify the derived pattern 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Edit intent: clarity
Original:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume  , we propose a model to simulate lineage  by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Revised:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume , we propose a model to simulate lineage by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation speed , fate diversity and space robustness (PFS Model) .
Edit intent: clarity
Original:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume  , we propose a model to simulate lineage  by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness (PFS Model) .
Revised:  Combining previous knowledge about the fate specification induced by asymmetric division and the anti-correlation between cell cycle length and cell volume , we propose a model to simulate lineage by altering cell volume segregation ratio in each division, and quantify the derived lineage 's performance in proliferation rapidity , fate diversity and space robustness .
Edit intent: coherence
Original:  The stereotypic pattern in early C. elegans embryo is  one of the most optimal solutions taking minimum time to achieve the cell number before gastrulation . Our methods lay a foundation for deciphering principles of development and guiding designs of bio-system .
Revised:  The stereotypic pattern in early C. elegans embryo is one of the most optimal solutions taking minimum time to achieve the cell number before gastrulation , by programming asymmetric division as a strategy .
Edit intent: meaning-changed
Original: As SARS-Cov 2 spreads worldwide, governments struggle to keep people safe without collapsing the economy. Social distancing and quarantines have proven to be effective measures to save lives, yet their impact on the economy is becoming apparent. The major challenge faced by many countriesat this point of the pandemic, is to find a way to keep their critical industries such as health, telecommunications, national security, transportation, food and energy functioning while having a safe environment for their workers .
Revised: The SARS-CoV-2 pandemic has transformed the way that the world functions. Health issues and population safety have driven countries' economies to a critical state; therefore, sustaining economic activity while keeping workers safe has become a worldwide goal .
Edit intent: clarity