wanyu/IteraTeR_v2 huggingface dataset:
Original:  We develop a model for observation uncertainty, and show that even a minor degradation in the assumption of perfect knowledge of the state of the infection drastically alters the landscape -- infections that could previously be cured in sublinear time , now may require exponential time  to cure.
Revised:  We develop a model for observation uncertainty, and show that even a minor degradation in the assumption of perfect knowledge of the state of the infection drastically alters the landscape . Infections that could previously be cured in sublinear time , now may require exponential time to cure.
Edit intent: fluency
Original:  We develop a model for observation uncertainty, and show that even a minor degradation in the assumption of perfect knowledge of the state of the infection drastically alters the landscape -- infections that could previously be cured in sublinear time , now may require exponential time  to cure.
Revised:  We develop a model for observation uncertainty, and show that even a minor degradation in the assumption of perfect knowledge of the state of the infection drastically alters the landscape -- infections that could previously be cured in sublinear time now may require exponential time to cure.
Edit intent: fluency
Original:  We discuss applications of our result to, among others, the problem of computing the maximum area centrally-symmetric convex body (MAC) inside a convex polygon, and the simplest case of the Heilbronn Triangle Problem.
Revised:  We discuss applications of our result to, among others, the problem of computing the maximum area centrally-symmetric convex body inside a convex polygon, and the simplest case of the Heilbronn Triangle Problem.
Edit intent: clarity
Original:  Our main result is an algorithm for computing the MAP in an n-sided polygon in O(n^2) time  .
Revised:  Our main result is an O(n^2) time .
Edit intent: clarity
Original:  Achieving this running time requires proving several new structural properties of the MAP.
Revised:  Achieving this running time requires proving several structural properties of the MAP.
Edit intent: clarity
Original:  In addition to the algorithm , we prove that the LMAPs interleave each other, thus  the number of LMAPs is bounded by O(n).
Revised:  In addition to the algorithm , we prove that the LMAPs interleave each other, thus we bound the number of LMAPs is bounded by O(n).
Edit intent: clarity
Original:  In addition to the algorithm , we prove that the LMAPs interleave each other, thus  the number of LMAPs is bounded by O(n).
Revised:  In addition to the algorithm , we prove that the LMAPs interleave each other, thus the number of LMAPs to be O(n).
Edit intent: clarity
Original:  Our main result is an O(n^2) time algorithm , where n denotes the number of edges of the given polygon.
Revised:  Our main result is an O(n^2) time , where n denotes the number of edges of the given polygon.
Edit intent: clarity
Original:  As an additional result , we prove that the LMAPs interleave each other , thus we bound the number of LMAPs to be O(n).
Revised:  As an additional result , we prove that the LMAPs interleave each other and thus the number of LMAPs to be O(n).
Edit intent: clarity
Original:  As an additional result , we prove that the LMAPs interleave each other , thus we bound the number of LMAPs to be O(n).
Revised:  As an additional result , we prove that the LMAPs interleave each other , thus we bound the number of LMAPs is O(n).
Edit intent: clarity
Original:  At each iteration during training , a graph is dynamically constructed based on  predictions of the teacher model, i.e., the implicit self-ensemble of models.
Revised:  At each iteration during training , a graph is constructed based on predictions of the teacher model, i.e., the implicit self-ensemble of models.
Edit intent: clarity
Original:  At each iteration during training , a graph is dynamically constructed based on  predictions of the teacher model, i.e., the implicit self-ensemble of models.
Revised:  At each iteration during training , a graph is dynamically constructed based on the predictions of the teacher model, i.e., the implicit self-ensemble of models.
Edit intent: fluency
Original:  Then the graph serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low dimensional manifold.
Revised:  Then the graph serves as a similarity measure with respect to which the representations of "similar" neighboring points are learned to be smooth on the low-dimensional manifold.
Edit intent: fluency
Original:  Our method is also effective under noisy supervision and shows robustness to incorrect labels.
Revised:  Our method also shows robustness to incorrect labels.
Edit intent: coherence
Original:  Our method is also effective under noisy supervision and shows robustness to incorrect labels.
Revised:  Our method is also effective under noisy supervision and shows robustness to noisy labels.
Edit intent: clarity
Original: Mesh redistribution methods provide meshes with increased resolution in particular regions of interest, while requiring that the connectivity of the mesh remains fixed.
Revised: Mesh redistribution methods provide meshes with increased resolution in particular regions of interest, without modifying the connectivity of the mesh remains fixed.
Edit intent: clarity
Original:  Some state of the art methods attempt to explicitly prescribe both the local cell size and their alignment to features of the solution.
Revised:  Some state of the art methods attempt to explicitly prescribe both the local cell size and the alignment to features of the solution.
Edit intent: clarity
Original:  An alternative approach, extolled by the current authors , is to prescribe only the local cell size and augment this with an optimal transport condition .
Revised:  An alternative approach, described in this paper , is to prescribe only the local cell size and augment this with an optimal transport condition .
Edit intent: clarity
Original:  An alternative approach, extolled by the current authors , is to prescribe only the local cell size and augment this with an optimal transport condition .
Revised:  An alternative approach, extolled by the current authors , is to prescribe only the local cell size and augment this an optimal transport condition .
Edit intent: clarity
Original:  Although the optimal transport approach abandons explicit control over mesh anisotropy, previous work has shown that , on the plane, the meshes produced are naturally aligned to various simple features.
Revised:  Although the optimal transport approach have good a-priori regularity and that the meshes produced are naturally aligned to various simple features.
Edit intent: clarity
Original:  We provide a wide range of examples to showcase the behaviour of optimally transported meshes on the sphere , from axisymmetric cases that can be solved analytically to more general examples that are tackled numerically.
Revised:  We provide a wide range of examples to showcase the behaviour of optimally transported meshes on the sphere . These range from axisymmetric cases that can be solved analytically to more general examples that are tackled numerically.
Edit intent: clarity
Original:  Evaluation of the singular values and singular vectors of the mesh transformation provides a quantitative measure of the mesh anisotropy, and this is shown to match analytic predictions .
Revised:  Evaluation of the singular values and singular vectors of the mesh transformation provides a quantitative measure of the mesh aniso.. .
Edit intent: coherence
Original: Nearly all previous work on small-footprint keyword spotting with neural networks quantify model footprint in terms of the number of parameters and multiply operations for an inference pass.
Revised: Nearly all previous work on small-footprint keyword spotting with neural networks quantify model footprint in terms of the number of parameters and multiply operations for a feedforward inference pass.
Edit intent: clarity
Original:  We find that both proxies are good predictors of energy usage, although the number of multiplies is more predictive than the number of  parameters.
Revised:  We find that both proxies are good predictors of energy usage, although the number of multiplies is more predictive than the number of model parameters.
Edit intent: clarity
Original:  We show that once properly normalized, the fractional Brownian motion converges to a Gaussian random distribution which is very close to a log-correlated random field .12
Revised:  We show that once properly normalized, the fractional Brownian motion converges to a Gaussian random distribution which is very close to a log-correlated random field .
Edit intent: coherence
Original:  Motivated by this finding , we wish to define a natural and relevant limit for the fractional Brownian motion when H goes to zero.
Revised:  Motivated by this , we wish to define a natural and relevant limit for the fractional Brownian motion when H goes to zero.
Edit intent: clarity
Original: We explore efficient neural architecture search methods and present a simple yet powerful evolutionary algorithm that can discover new architectures achieving state of the art results .
Revised: We explore efficient neural architecture search methods and show that a simple yet powerful evolutionary algorithm that can discover new architectures achieving state of the art results .
Edit intent: coherence
Original: We explore efficient neural architecture search methods and present a simple yet powerful evolutionary algorithm that can discover new architectures achieving state of the art results .
Revised: We explore efficient neural architecture search methods and present a simple yet powerful evolutionary algorithm can discover new architectures achieving state of the art results .
Edit intent: fluency
Original: We explore efficient neural architecture search methods and present a simple yet powerful evolutionary algorithm that can discover new architectures achieving state of the art results .
Revised: We explore efficient neural architecture search methods and present a simple yet powerful evolutionary algorithm that can discover new architectures with excellent performance .
Edit intent: clarity
Original:  Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6\% on CIFAR-10 and 20.3\% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches and represents the new state of the art for evolutionary strategies on this task .
Revised:  Our algorithm efficiently discovers architectures that outperform a large number of manually designed models for image classification, obtaining top-1 error of 3.6\% on CIFAR-10 and 20.3\% when transferred to ImageNet, which is competitive with the best existing neural architecture search approaches .
Edit intent: coherence
Original:  We also present results using random search, achieving 0.3\% less top-1 accuracy on CIFAR-10 and 0.1\% less on ImageNet whilst reducing the architecture search time from 36 hours down to 1 hour.
Revised:  We also present results using random search, achieving 0.3\% less top-1 accuracy on CIFAR-10 and 0.1\% less on ImageNet whilst reducing the search time from 36 hours down to 1 hour.
Edit intent: clarity
Original:  Our new algorithm thereby provides a quadratic improvement in the complexity of computing their gradients .
Revised:  Our new algorithm thereby provides a quadratic improvement in the complexity of computing their gradient .
Edit intent: fluency
Original:  Our algorithm is an improved version of  Jordan's gradient calculation algorithm, providing an approximation of the gradient \nabla f with quadratically better dependence on the evaluation accuracy of f, for an important class of smooth functions.
Revised:  Our algorithm is an improved version of Jordan's gradient computation algorithm, providing an approximation of the gradient \nabla f with quadratically better dependence on the evaluation accuracy of f, for an important class of smooth functions.
Edit intent: fluency
Original:  We provide simple a O(|G|^2 + m|G|)-time algorithm to determine whether such homology relations are satisfiable and, in the positive case, to construct event-labeled gene trees containing speciation, duplication and horizontal gene transfer (HGT) events that can explain the relations.
Revised:  We provide a simple O(|G|^2 + m|G|)-time algorithm to determine whether such homology relations are satisfiable and, in the positive case, to construct event-labeled gene trees containing speciation, duplication and horizontal gene transfer (HGT) events that can explain the relations.
Edit intent: fluency
Original:  A variety of methods based on sequence similarity, reconciliation, synteny or functional characteristics, can be used to infer homology relations, that is, orthology, paralogy and xenologyrelations between genesof a given gene family G. The (inferred )  homology relations might not cover each pair of genes and thus, provide only partial knowledge on the full set of homology relations.
Revised:  A variety of methods based on pairwise sequence comparisons can be used to infer homology relations, that is, orthology, paralogy and xenologyrelations between genesof a given gene family G. The (inferred ) homology relations might not cover each pair of genes and thus, provide only partial knowledge on the full set of homology relations.
Edit intent: clarity
Original:  A variety of methods based on sequence similarity, reconciliation, synteny or functional characteristics, can be used to infer homology relations, that is, orthology, paralogy and xenologyrelations between genesof a given gene family G. The (inferred )  homology relations might not cover each pair of genes and thus, provide only partial knowledge on the full set of homology relations.
Revised:  A variety of methods based on sequence similarity, reconciliation, synteny or functional characteristics, can be used to infer such homology relations (e.g.\ orthology, paralogy and xenologyrelations between genesof a given gene family G. The (inferred ) homology relations might not cover each pair of genes and thus, provide only partial knowledge on the full set of homology relations.
Edit intent: clarity
Original:  A variety of methods based on sequence similarity, reconciliation, synteny or functional characteristics, can be used to infer homology relations, that is, orthology, paralogy and xenologyrelations between genesof a given gene family G. The (inferred )  homology relations might not cover each pair of genes and thus, provide only partial knowledge on the full set of homology relations.
Revised:  A variety of methods based on sequence similarity, reconciliation, synteny or functional characteristics, can be used to infer homology relations, that is, orthology, paralogy , xenology). They are homology relations might not cover each pair of genes and thus, provide only partial knowledge on the full set of homology relations.
Edit intent: clarity
Original:  Moreover, for particular pairs of genes  it might be known with a high degree of certainty that they are not orthologs (resp.\ paralogs, xenologs) which yields forbidden pairs of genes.
Revised:  Moreover, for particular pairs of genes , it might be known with a high degree of certainty that they are not orthologs (resp.\ paralogs, xenologs) which yields forbidden pairs of genes.
Edit intent: fluency
Original:  The question arises as whether such sets of (partial)  homology relations with or without forbidden gene pairs are satisfiable, i.e., can they simultaneously co-exist in an evolutionary history for G. In this contribution, we characterize satisfiable homology relations.
Revised:  Motivated by this observation, we characterize (partial) homology relations with or without forbidden gene pairs are satisfiable, i.e., can they simultaneously co-exist in an evolutionary history for G. In this contribution, we characterize satisfiable homology relations.
Edit intent: clarity
Original: In vision science, cascades of Linear+Nonlinear transforms explain a number of perceptual experiences [Carandini&Heeger Nat.
Revised: In vision science, cascades of Linear+Nonlinear layers explain a number of perceptual experiences [Carandini&Heeger Nat.
Edit intent: clarity
Original:  Instead, in this work we present the mathematical details of such cascades beyond the forward transform, namely the derivatives and the inverse.
Revised:  Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the derivatives and the inverse.
Edit intent: clarity
Original:  These analytic results (usually omitted) are important for three reasons : (a) they are strictly necessary in new experimental methods based on the synthesis of visual stimuli with interesting geometrical properties , (b) they are convenient to analyze classical experiments for model fitting , and (c) they represent a promising way to include model information in blind visual decodingmethods.
Revised:  These analytic results are important because : (a) they are strictly necessary in new experimental methods based on the synthesis of visual stimuli with interesting geometrical properties , (b) they are convenient to analyze classical experiments for model fitting , and (c) they represent a promising way to include model information in blind visual decodingmethods.
Edit intent: coherence
Original:  These analytic results (usually omitted) are important for three reasons : (a) they are strictly necessary in new experimental methods based on the synthesis of visual stimuli with interesting geometrical properties , (b) they are convenient to analyze classical experiments for model fitting , and (c) they represent a promising way to include model information in blind visual decodingmethods.
Revised:  These analytic results (usually omitted) are important for three reasons : (a) they are strictly necessary in new experimental methods based on the synthesis of interesting visual stimuli , (b) they are convenient to analyze classical experiments for model fitting , and (c) they represent a promising way to include model information in blind visual decodingmethods.
Edit intent: clarity
Original:  As an example, we develop a &  derivable and invertible vision model consisting of a cascade of modules that account for brightness, contrast, energy masking, and wavelet masking.
Revised:  As an example, we develop a & . Our derivable and invertible vision model consisting of a cascade of modules that account for brightness, contrast, energy masking, and wavelet masking.
Edit intent: coherence
Original:  As an example, we develop a &  derivable and invertible vision model consisting of a cascade of modules that account for brightness, contrast, energy masking, and wavelet masking.
Revised:  As an example, we develop a & derivable and invertible model consists of a cascade of modules that account for brightness, contrast, energy masking, and wavelet masking.
Edit intent: clarity
Original:  To stress the generality of this modular setting we show examples where some of the canonical Divisive Normalizations Carandini%DIFDELCMD < &%%% Heeger Nat.
Revised:  To stress the generality of this modular setting we show examples where some of the canonical Divisive Carandini%DIFDELCMD < &%%% Heeger Nat.
Edit intent: clarity
Original:  To stress the generality of this modular setting we show examples where some of the canonical Divisive Normalizations Carandini%DIFDELCMD < &%%% Heeger Nat.
Revised:  To stress the generality of this modular setting we show examples where some of the canonical Divisive Normalizations %DIFDELCMD < &%%% Heeger Nat.
Edit intent: fluency
Original: Rev.Neur.12 are substituted by equivalent modules such as the Wilson-Cowan interaction [Wilson&Cowan J.Biophys.
Revised: Rev.Neur.12 are substituted by equivalent modules such as the Wilson-Cowan model [Wilson&Cowan J.Biophys.
Edit intent: clarity
Original: 72] (at the V1 cortex) or a tone-mapping  [Cyriac et al.
Revised: 72] (at the V1 cortex) or a tone-mapping model [Cyriac et al.
Edit intent: clarity
Original: 72] (at the V1 cortex) or a tone-mapping  [Cyriac et al.
Revised: 72] (at the V1 cortex) or a tone-mapping [Cyriac 
Edit intent: clarity
Original:  First, we show how the Jacobian  w.r.t. the input plays a major role in setting the model by allowing the use of novel psychophysics based on the geometry of the neural representation (as in [Malo&Simoncelli SPIE 15]) .
Revised:  First, we show how the Jacobian ( w.r.t. the input plays a major role in setting the model by allowing the use of novel psychophysics based on the geometry of the neural representation (as in [Malo&Simoncelli SPIE 15]) .
Edit intent: fluency
Original:  First, we show how the Jacobian  w.r.t. the input plays a major role in setting the model by allowing the use of novel psychophysics based on the geometry of the neural representation (as in [Malo&Simoncelli SPIE 15]) .
Revised:  First, we show how the Jacobian w.r.t. the input ) allows novel psychophysics based on the geometry of the neural representation (as in [Malo&Simoncelli SPIE 15]) .
Edit intent: clarity
Original:  Second, we show how the Jacobian  w.r.t. the parameters  can be used to find the model that better reproduces  image distortion psychophysics .
Revised:  Second, we show how the Jacobian ( w.r.t. the parameters can be used to find the model that better reproduces image distortion psychophysics .
Edit intent: fluency
Original:  Second, we show how the Jacobian  w.r.t. the parameters  can be used to find the model that better reproduces  image distortion psychophysics .
Revised:  Second, we show how the Jacobian w.r.t. the parameters ) can be used to find the model that better reproduces image distortion psychophysics .
Edit intent: fluency
Original:  Third, we show that analytic inverse may improve regression-based brain decoding techniques .
Revised:  Third, we show that analytic inverse may improve regression-based brain decoding .
Edit intent: clarity
Original: In vision science, cascades of Linear+Nonlinear layers explain a number of perceptual experiences [Carandini& Heeger Nat.
Revised: In vision science, cascades of Linear+Nonlinear transforms are very successful in modeling a number of perceptual experiences [Carandini& Heeger Nat.
Edit intent: clarity
Original:  Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse.
Revised:  Instead, here we present the mathematics of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse.
Edit intent: clarity
Original:  Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse.
Revised:  Instead, in this work we present the maths of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse.
Edit intent: fluency
Original:  Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the Jacobian matrices and the inverse.
Revised:  Instead, in this work we present the mathematics of such cascades beyond the forward transform, namely the Jacobians and the inverse.
Edit intent: clarity
Original:  These analytic results are important because: (a) they are strictly necessary in new experimental methods based on the synthesis of interesting visual stimuli , (b) they are convenient to learn the model from classical experiments or alternative goal optimization , and (c) they are a promising model-based alternative to blind regression for neural decoding .
Revised:  These analytic results are important because: (a) they are strictly necessary in new experimental methods based on the synthesis of interesting visual stimuli , (b) they are convenient to learn the model from classical experiments or alternative optimization goals , and (c) they are a promising model-based alternative to blind regression for neural decoding .
Edit intent: fluency