wanyu/IteraTeR_v2 huggingface dataset:
Original:  However, these two conditions are too strict even unrealistic in the traditional data environment, because it is hard to model the explicit distribution of natural image.
Revised:  However, these two conditions are too strict even unrealistic in the traditional data environment, e.g. the distribution of natural image.
Edit intent: clarity
Original:  With the development of deep learning , generative models bring new vitality to provably secure steganography, which can serve as the black-box sampler or provide the explicit distribution of generative media.
Revised:  With the development of deep learning , generative models bring new vitality to distribution-preserving steganography, which can serve as the black-box sampler or provide the explicit distribution of generative media.
Edit intent: clarity
Original:  With the development of deep learning , generative models bring new vitality to provably secure steganography, which can serve as the black-box sampler or provide the explicit distribution of generative media.
Revised:  With the development of deep learning , generative models bring new vitality to provably secure steganography, which can serve as the perfect sampler or provide the explicit distribution of generative media.
Edit intent: clarity
Original:  We will refer to this new paradigm as " Mobile Edge Learning (MEL) " . The problem of dynamic task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computing iterations at the heterogeneous nodes, are bounded by a preset duration.
Revised:  We will refer to this new paradigm as ` Mobile Edge Learning (MEL) " . The problem of dynamic task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computing iterations at the heterogeneous nodes, are bounded by a preset duration.
Edit intent: fluency
Original:  We will refer to this new paradigm as " Mobile Edge Learning (MEL) " . The problem of dynamic task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computing iterations at the heterogeneous nodes, are bounded by a preset duration.
Revised:  We will refer to this new paradigm as " Mobile Edge Learning (MEL) ' . The problem of dynamic task allocation for MEL is considered in this paper with the aim to maximize the learning accuracy, while guaranteeing that the total times of data distribution/aggregation over heterogeneous channels, and local computing iterations at the heterogeneous nodes, are bounded by a preset duration.
Edit intent: fluency
Original: The inverse geodesic length (IGL) of a graph G  = (V, E) is the sum of inverse distances between every two vertices : IGL(G ) = \{u,v\ \subseteq V} \frac{1 MinIGL is W[1]-hard for parameter treewidth, but the complexity status of the problem remains open in the case where G is a tree .
Revised: The inverse geodesic length (IGL) of a graph G = \{u,v\ \subseteq V} \frac{1 MinIGL is W[1]-hard for parameter treewidth, but the complexity status of the problem remains open in the case where G is a tree .
Edit intent: clarity
Original: The inverse geodesic length (IGL) of a graph G  = (V, E) is the sum of inverse distances between every two vertices : IGL(G ) = \{u,v\ \subseteq V} \frac{1 MinIGL is W[1]-hard for parameter treewidth, but the complexity status of the problem remains open in the case where G is a tree .
Revised: The inverse geodesic length (IGL) of a graph G = (V, E) is the sum of inverse distances between every two vertices : IGL(G ) = \{u,v\ \subseteq V} \frac{1 MinIGL is W[1]-hard for parameter treewidth, but the complexity status of the problem remains open in the case where G is a tree .
Edit intent: fluency
Original: The inverse geodesic length (IGL) of a graph G  = (V, E) is the sum of inverse distances between every two vertices : IGL(G ) = \{u,v\ \subseteq V} \frac{1 MinIGL is W[1]-hard for parameter treewidth, but the complexity status of the problem remains open in the case where G is a tree .
Revised: The inverse geodesic length (IGL) of a graph G = (V, E) is the sum of inverse distances between every two vertices : IGL(G ) = \{u,v\ \subseteq V} \frac{1 MinIGL is W[1]-hard for parameter treewidth, but the complexity status of the problem remains open on trees .
Edit intent: clarity
Original:  We show that MinIGL on treesis subexponential by giving a 2^{O %DIFDELCMD < \left(%%% (  n\log n)^{5/6} %DIFDELCMD < \right)%%%  } time, polynomial space algorithm , where n is the number of vertices of the input graph .
Revised:  We show that MinIGL on treesis subexponential by giving a 2^{O %DIFDELCMD < \left(%%% ( ( n\log n)^{5/6} %DIFDELCMD < \right)%%% } time, polynomial space algorithm , where n is the number of vertices of the input graph .
Edit intent: fluency
Original:  We show that MinIGL on treesis subexponential by giving a 2^{O %DIFDELCMD < \left(%%% (  n\log n)^{5/6} %DIFDELCMD < \right)%%%  } time, polynomial space algorithm , where n is the number of vertices of the input graph .
Revised:  We show that MinIGL on treesis subexponential by giving a 2^{O %DIFDELCMD < \left(%%% ( n\log n)^{5/6} %DIFDELCMD < \right)%%% ) } time, polynomial space algorithm , where n is the number of vertices of the input graph .
Edit intent: fluency
Original:  We show that MinIGL on treesis subexponential by giving a 2^{O %DIFDELCMD < \left(%%% (  n\log n)^{5/6} %DIFDELCMD < \right)%%%  } time, polynomial space algorithm , where n is the number of vertices of the input graph .
Revised:  We show that MinIGL on treesis subexponential by giving a 2^{O %DIFDELCMD < \left(%%% ( n\log n)^{5/6} %DIFDELCMD < \right)%%% } time, polynomial space algorithm .
Edit intent: coherence
Original:  The distance distribution of a graph G is a sequence \{a_i\} describing the number of vertex pairs distance i apart in G: a_i=|\{\{u, v\}: d_G(u, v)=i\}|.
Revised:  The distance distribution of G is the sequence \{a_i\} describing the number of vertex pairs distance i apart in G: a_i=|\{\{u, v\}: d_G(u, v)=i\}|.
Edit intent: clarity
Original:  Given only the distance distribution, one can easily determine graph parameters such as diameter, Wiener index, and particularly, the IGL.
Edit intent: coherence
Original:  We also extend the result to graphs of bounded treewidth by showing that the first P values of the distance distribution can be computed in 2^{O( \text{tw (G))} n^{1+\varepsilon} \sqrt{P time, and the entire distance distribution can be computed in 2^{O(tw(G))} n^{1+\varepsilon} time, when the diameter of G is O(n^{\varepsilon'}) for every \varepsilon'>0.}
Revised:  We extend our result to graphs of bounded treewidth by showing that the first P values of the distance distribution can be computed in 2^{O( \text{tw (G))} n^{1+\varepsilon} \sqrt{P time, and the entire distance distribution can be computed in 2^{O(tw(G))} n^{1+\varepsilon} time, when the diameter of G is O(n^{\varepsilon'}) for every \varepsilon'>0.}
Edit intent: clarity
Original:  We also extend the result to graphs of bounded treewidth by showing that the first P values of the distance distribution can be computed in 2^{O( \text{tw (G))} n^{1+\varepsilon} \sqrt{P time, and the entire distance distribution can be computed in 2^{O(tw(G))} n^{1+\varepsilon} time, when the diameter of G is O(n^{\varepsilon'}) for every \varepsilon'>0.}
Revised:  We also extend the result to graphs of bounded treewidth by showing that the first p values of the distance distribution can be computed in 2^{O( \text{tw (G))} n^{1+\varepsilon} \sqrt{P time, and the entire distance distribution can be computed in 2^{O(tw(G))} n^{1+\varepsilon} time, when the diameter of G is O(n^{\varepsilon'}) for every \varepsilon'>0.}
Edit intent: fluency
Original:  We also extend the result to graphs of bounded treewidth by showing that the first P values of the distance distribution can be computed in 2^{O( \text{tw (G))} n^{1+\varepsilon} \sqrt{P time, and the entire distance distribution can be computed in 2^{O(tw(G))} n^{1+\varepsilon} time, when the diameter of G is O(n^{\varepsilon'}) for every \varepsilon'>0.}
Revised:  We also extend the result to graphs of bounded treewidth by showing that the first P values of the distance distribution can be computed in 2^{O( \text{tw (G))} n^{1+\varepsilon} \sqrt{p time, and the entire distance distribution can be computed in 2^{O(tw(G))} n^{1+\varepsilon} time, when the diameter of G is O(n^{\varepsilon'}) for every \varepsilon'>0.}
Edit intent: fluency
Original:  Our results provide strong evidence that QCA is indeed a suitable technology for breaking Landauer's limit .
Revised:  Our results provide strong evidence that QCA is indeed a suitable technology for near zero-energy computing .
Edit intent: clarity
Original:  Further, the first physically reversible design of an adder circuit is presented which serves as proof-of-concept for future fully reversible circuit realizations .
Revised:  Further, the first design of a logically and physically reversible adder circuit is presented which serves as proof-of-concept for future fully reversible circuit realizations .
Edit intent: clarity
Original:  Further, the first physically reversible design of an adder circuit is presented which serves as proof-of-concept for future fully reversible circuit realizations .
Revised:  Further, the first physically reversible design of an adder circuit is presented which serves as proof-of-concept for future circuits with the ability of near zero-energy computing .
Edit intent: clarity
Original:  In this paper, we show that by drastically reducing the uncertainties in the choice of the resource to use for SPS, we can significantly reduce the message collisions in the C-V2X sidelink Mode 4. Specifically, we propose the use of the ``lookahead,'' which contains the next starting resource location in the time-frequency plane.
Revised:  In this paper, we show that by drastically reducing the uncertainties in the choice of the resource to use for SPS, we can significantly reduce the message collisions in the C-V2X sidelink Mode 4. Specifically, we propose the use of the "lookahead," which contains the next starting resource location in the time-frequency plane.
Edit intent: fluency
Original:  We consider the complexity of the problem of deciding whether a graph G has a b-coloring with k colors , whenever the value of k is close to one of two upper bounds on \chi_b(G): The maximum degree \Delta(G) plus one, and the m-degree, denoted by m(G), which is defined as the maximum number i such that G has i vertices of degree at least i-1.
Revised:  We consider the complexity of the b-Coloring problem , whenever the value of k is close to one of two upper bounds on \chi_b(G): The maximum degree \Delta(G) plus one, and the m-degree, denoted by m(G), which is defined as the maximum number i such that G has i vertices of degree at least i-1.
Edit intent: clarity
Original:  We furthermore give an FPT-algorithm parameterized by k + \ell  , where \ell  denotes the number of vertices of degree at least k , while we observe that the problem is NP-complete whenever k is unbounded and \ell = 0.
Revised:  We furthermore give an FPT-algorithm parameterized by k + \ell , where \ell denotes the number of vertices of degree at least k .
Edit intent: coherence
Original:  Since these models are considerably similar, it has been suggested in recent years that they could be complementary , however, this statement has not yet been proven mathematically .
Revised:  Since these models are considerably similar, it has been suggested in recent years that they could be complementary .
Edit intent: clarity
Original:  In this manuscript we provide a rigorous analysis that shows that the heterogeneous bootstrap percolation is the complement of the heterogeneous k-core percolation in complex networks.
Revised:  In this manuscript we provide a rigorous analysis that shows that the heterogeneous bootstrap percolation can be mapped into heterogeneous k-core percolation in complex networks.
Edit intent: clarity
Original:  On the other hand, a problem that has been addressed in bootstrap percolation is the theoretical estimation of the relative size of the active nodes belonging to the giant component, P _{\infty , b %DIFDELCMD < \MBLOCKRIGHTBRACE%%% . However, we perform several stochastic simulations and find that they do not totally match these theoretical estimations.
Revised:  On the other hand, a problem that has been addressed in bootstrap percolation is the fraction of nodes belonging to the giant component, P _{\infty , b %DIFDELCMD < \MBLOCKRIGHTBRACE%%% . However, we perform several stochastic simulations and find that they do not totally match these theoretical estimations.
Edit intent: clarity
Original:  On the other hand, a problem that has been addressed in bootstrap percolation is the theoretical estimation of the relative size of the active nodes belonging to the giant component, P _{\infty , b %DIFDELCMD < \MBLOCKRIGHTBRACE%%% . However, we perform several stochastic simulations and find that they do not totally match these theoretical estimations.
Revised:  On the other hand, a problem that has been addressed in bootstrap percolation is the theoretical estimation of the relative size of the active nodes belonging to their giant connected components P _{\infty , b %DIFDELCMD < \MBLOCKRIGHTBRACE%%% . However, we perform several stochastic simulations and find that they do not totally match these theoretical estimations.
Edit intent: clarity
Original:  On the other hand, a problem that has been addressed in bootstrap percolation is the theoretical estimation of the relative size of the active nodes belonging to the giant component, P _{\infty , b %DIFDELCMD < \MBLOCKRIGHTBRACE%%% . However, we perform several stochastic simulations and find that they do not totally match these theoretical estimations.
Revised:  On the other hand, a problem that has been addressed in bootstrap percolation is the theoretical estimation of the relative size of the active nodes belonging to the giant component, P _{\infty b %DIFDELCMD < \MBLOCKRIGHTBRACE%%% . However, we perform several stochastic simulations and find that they do not totally match these theoretical estimations.
Edit intent: fluency
Original: We introduce a two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space.
Revised: We introduce two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space.
Edit intent: fluency
Original:  These work by truncating infinite expansions of the Gaussian inner product , and carefully invoking the TensorSketch.
Revised:  These work by truncating infinite expansions of the Gaussian inner product , and carefully invoking the RecursiveTensorSketch
Edit intent: clarity
Original:  ] After providing concentration and approximation properties of these sketches, we demonstrate them to approximate the kernel distance between points sets.
Revised:  ]. After providing concentration and approximation properties of these sketches, we demonstrate them to approximate the kernel distance between points sets.
Edit intent: fluency
Original:  ] After providing concentration and approximation properties of these sketches, we demonstrate them to approximate the kernel distance between points sets.
Revised:  ] After providing concentration and approximation properties of these sketches, we use them to approximate the kernel distance between points sets.
Edit intent: clarity
Original:  In the first variants the dependence on 1/\alpha is logarithmic , but has a separate exponential dependence on the original dimension d.
Revised:  In the first variants the dependence on 1/\alpha is poly-logarithmic , but has a separate exponential dependence on the original dimension d.
Edit intent: clarity
Original: Modern ad-hoc retrieval models learned with implicit feedback have two problemsin general.
Revised: Ad-hoc retrieval models with implicit feedback have two problemsin general.
Edit intent: clarity
Original: First, there are usually much more non-clicked documents than clicked documents , and many of the non-clicked documents are not informational.
Revised: First, there are usually much more non-clicked documents are not informational.
Edit intent: coherence
Original:  Second, modern ad-hoc retrieval models are vulnerable to adversarial examples due to the linear nature in the models .
Revised:  Second, modern ad-hoc retrieval models are vulnerable to adversarial examples due to the linear nature in them .
Edit intent: clarity
Original:  To solve the problems at the same time, we propose adversarial training methods that can overcome those weaknesses .
Revised:  To solve the problems at the same time, we propose an adversarial sampling and training framework to learn ad-hoc retrieval models with implicit feedback .
Edit intent: clarity
Original:  Our key idea is  to combine adversarial training with adversarial sampling in order to obtain very difficult examples, which are informational and can attack the linear nature of the models.
Revised:  Our key idea is to obtain very difficult examples, which are informational and can attack the linear nature of the models.
Edit intent: coherence
Original:  Experiment results indicate that the proposed methods significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5\% of labeled data for the Web search task.
Revised:  Experiment results indicate that the proposed approaches significantly outperform strong baselines especially for high-ranked documents, and they outperform IRGAN in NDCG@5 using only 5\% of labeled data for the Web search task.
Edit intent: clarity
Original:  The recent developments of the edge computing paradigms have also enabled on-sight processing and managing capabilities of sensor networks.
Revised:  The recent developments of the edge computing paradigms have also enabled on-site processing and managing capabilities of sensor networks.
Edit intent: clarity
Original:  In this paper  we introduce a lightweight realization of the novel dew computing paradigm that helps to manage data processing tasks of a heterogeneous wireless sensor networks.
Revised:  In this paper , we introduce a lightweight realization of the novel dew computing paradigm that helps to manage data processing tasks of a heterogeneous wireless sensor networks.
Edit intent: fluency
Original:  The performance of proposed solution is analyzed by using the simulation results.
Revised:  The performance of proposed solution is analyzed by using simulation results.
Edit intent: fluency
Original:  Moreover, in order to demonstrate the practical usability of the given solution, the prototype implementation is presented using the commercial off-the-shelf devices.
Revised:  Moreover, in order to demonstrate the practical usability of the proposed solution, the prototype implementation is presented using the commercial off-the-shelf devices.
Edit intent: clarity
Original:  Moreover, in order to demonstrate the practical usability of the given solution, the prototype implementation is presented using the commercial off-the-shelf devices.
Revised:  Moreover, in order to demonstrate the practical usability of the given solution, the prototype implementation is presented using commercial off-the-shelf devices.
Edit intent: fluency
Original:  We transform unstructured point clouds into regular voxel grids and use subvoxels within each voxel to encode the local geometry using a VAE architecture .
Revised:  We transform unstructured point clouds into regular voxel grids and use subvoxels within each voxel to encode the local geometry within each voxel .
Edit intent: clarity
Original:  Traditionally, the voxel representation only comprises Boolean occupancy information , which fails to capture the sparsely distributed points within voxels in a compact manner.
Revised:  Traditionally, the voxel representation only comprises Boolean occupancy information which fails to capture the sparsely distributed points within voxels in a compact manner.
Edit intent: fluency
Original:  In order to handle sparse distribution of pointswithin each voxel , we further employ RBF to compute a local, continuous representation within each subvoxel.
Revised:  In order to handle sparse distributions of points , we further employ RBF to compute a local, continuous representation within each subvoxel.
Edit intent: fluency
Original:  The combination of RBF and VAE results in a good volumetric representation that can handle noisy point cloud datasets and is more robust for learning.
Revised:  The combination of RBF and VAE results in a good volumetric representation that effectively tackles noisy point cloud datasets and is more robust for learning.
Edit intent: clarity
Original:  Instead of deriving dense node-wise representations, the goal of latent network summarization is to summarize the structural properties of the graph in a latent space with dimensionality that is independent of the nodes or edges in the input graph .
Revised:  Instead of deriving dense node-wise representations, the goal of latent network summarization is to summarize the structural properties of the graph structure with dimensionality that is independent of the nodes or edges in the input graph .
Edit intent: clarity
Original: Genetic Programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way.
Revised: Genetic programming (GP) is an evolutionary computation technique to solve problems in an automated, domain-independent way.
Edit intent: fluency
Original:  A population of programs is evolved using variation operators inspired by Darwinian evolution (crossover and mutation) and natural selection principles to guide the search process towards better programs.
Edit intent: coherence
Original:  Compared to traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable length representation of candidate programs, and the difficulty of evaluating their quality efficiently.
Revised:  Compared with traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable length representation of candidate programs, and the difficulty of evaluating their quality efficiently.
Edit intent: fluency
Original:  Compared to traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable length representation of candidate programs, and the difficulty of evaluating their quality efficiently.
Revised:  Compared to traditional evolutionary algorithms for function optimization, GP applications are further complicated by two additional factors: the variable-length representation of candidate programs, and the difficulty of evaluating their quality efficiently.
Edit intent: fluency
Original:  Such difficulties considerably impact the runtime analysis of GP  where space complexity also comes into play.
Revised:  Such difficulties considerably impact the runtime analysis of GP , where space complexity also comes into play.
Edit intent: fluency
Original:  As a result  initial complexity analyses of GP  focused on restricted settings such as evolving trees with given structures or estimating the quality of solutions using only a small polynomial number of input/output examples.
Revised:  As a result , initial complexity analyses of GP focused on restricted settings such as evolving trees with given structures or estimating the quality of solutions using only a small polynomial number of input/output examples.
Edit intent: fluency