wanyu/IteraTeR_v2 huggingface dataset:
Original:  The prediction is possible because of two facts (a) The structure of complex networks plays an essential role in the spreading processes on networks . (b) Availability of non-linear regression techniques with excellent accuracy for prediction of a quantity even for a datawhich is highly non-linear .
Revised:  The prediction is possible because : (a) The structure of complex networks plays an essential role in the spreading processes on networks . (b) Availability of non-linear regression techniques with excellent accuracy for prediction of a quantity even for a datawhich is highly non-linear .
Edit intent: clarity
Original:  The prediction is possible because of two facts (a) The structure of complex networks plays an essential role in the spreading processes on networks . (b) Availability of non-linear regression techniques with excellent accuracy for prediction of a quantity even for a datawhich is highly non-linear .
Revised:  The prediction is possible because of two facts (a) The structure of complex networks plays an essential role in the spreading processes on networks (b) Availability of non-linear regression techniques with excellent accuracy for prediction of a quantity even for a datawhich is highly non-linear .
Edit intent: fluency
Original: Seagrass meadows are among the most important coastal ecosystems  in terms of both spatial extent and ecosystem services but are declining worldwide.
Revised: Seagrass meadows are among the most important coastal ecosystems , in terms of both spatial extent and ecosystem services but are declining worldwide.
Edit intent: fluency
Original: Seagrass meadows are among the most important coastal ecosystems  in terms of both spatial extent and ecosystem services but are declining worldwide.
Revised: Seagrass meadows are among the most important coastal ecosystems in terms of both spatial extent and ecosystem services , but they are also declining worldwide.
Edit intent: clarity
Original:  Understanding the drivers of seagrass meadow dynamics is essential for designing sound management, conservation  and restoration strategies.
Revised:  Understanding the drivers of seagrass meadow dynamics is essential for designing sound management, conservation , and restoration strategies.
Edit intent: fluency
Original:  However, the  effect of clonality on the population genetics of natural populations understood, severely limiting our comprehension of the dynamics and connectivity of meadows.
Revised:  However, the effect of clonality on the population genetics of natural populations severely limits our understanding of the dynamics and connectivity of meadows.
Edit intent: clarity
Original:  Here, we revisited population genetics data on 165 meadows of four seagrass species in light of recent theoretical developments,
Revised:  Here, we revisited population genetic data for 165 meadows of four seagrass species in light of recent theoretical developments,
Edit intent: fluency
Original:  This meta-analysis provides a nested validation of this hypothesis at the species scale but also at the meadow scale through the emergence of a significant relationship between Fis and R within each species.
Revised:  This meta-analysis provides a nested validation of this hypothesis at both the species and meadow scales through a significant relationship between Fis and R within each species.
Edit intent: clarity
Original:  We also emphasize the need to revise our perception of the influence of clonality on the dynamics, connectivity and evolutionary trajectory of seagrass meadows to develop the most accurate conservation strategies.
Revised:  The results also emphasize the need to revise our perception of the influence of clonality on the dynamics, connectivity and evolutionary trajectory of seagrass meadows to develop the most accurate conservation strategies.
Edit intent: clarity
Original:  We also emphasize the need to revise our perception of the influence of clonality on the dynamics, connectivity and evolutionary trajectory of seagrass meadows to develop the most accurate conservation strategies.
Revised:  We also emphasize the need to revise our perception of the influence of clonality on the dynamics, connectivity and evolutionary trajectory of seagrass meadows to develop the most accurate management strategies.
Edit intent: clarity
Original:  Our method learns a (single) mapping from discretized PDEs to prolongation operators for a broad class of 2D diffusion problems .
Revised:  Our method learns a (single) mapping from discretized PDEs to prolongation operators .
Edit intent: clarity
Original:  Our goal is to estimate \alpha in a small amount of space, given that the input is received as a one-pass turnstile stream.
Revised:  Our goal is to estimate \alpha in a small amount of space, given that the input is received as a one-pass stream.
Edit intent: clarity
Original:  We  provide the first algorithms for estimating \alpha and \beta in turnstile streams .
Revised:  We provide the first algorithms for estimating \alpha and \beta .
Edit intent: clarity
Original:  For unit-radius disks, we obtain a%DIFDELCMD < \left(%%% \frac{8\sqrt{3}{\pi}}%DIFDELCMD < \right)%%% -approximation to \alpha and \beta inpoly %DIFDELCMD < \left(%%% \frac{\log(n){\epsilon}}%DIFDELCMD < \right) %%% space, which is closely related to the hexagonal circle packing constant .
Revised:  For unit-radius disks, we obtain a%DIFDELCMD < \left(%%% \frac{8\sqrt{3}{\pi}}%DIFDELCMD < \right)%%% -approximation to \alpha and \beta in %DIFDELCMD < \left(%%% \frac{\log(n){\epsilon}}%DIFDELCMD < \right) %%% space, which is closely related to the hexagonal circle packing constant .
Edit intent: clarity
Original: With the ubiquity of accelerators , such as FPGAs and GPUs, the complexity of high-performance programming is increasing beyond the skill-set of the average scientist in domains outside of computer science.
Revised: The ubiquity of accelerators , such as FPGAs and GPUs, the complexity of high-performance programming is increasing beyond the skill-set of the average scientist in domains outside of computer science.
Edit intent: fluency
Original: With the ubiquity of accelerators , such as FPGAs and GPUs, the complexity of high-performance programming is increasing beyond the skill-set of the average scientist in domains outside of computer science.
Revised: With the ubiquity of accelerators in high-performance programming is increasing beyond the skill-set of the average scientist in domains outside of computer science.
Edit intent: clarity
Original:  It is thus imperative to decouple programming paradigms and architecture-specific implementation from the underlying scientific computations.
Revised:  It is thus imperative to decouple architecture-specific programming paradigms from the underlying scientific computations.
Edit intent: clarity
Original:  We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that facilitates high performance application development and optimization.
Revised:  We present the Stateful DataFlow multiGraph (SDFG), a data-centric intermediate representation that enables separating program definition from its optimization.
Edit intent: clarity
Original:  These transformations are then applied to the SDFG in an interactive process, using extensible pattern matching and graph rewriting.
Revised:  These transformations are applied to the SDFG in an interactive process, using extensible pattern matching and graph rewriting.
Edit intent: coherence
Original:  To facilitate this process, we provide a graphical user interface that enables applying transformations, as well as creating custom optimizations, reusable across applications .
Revised:  To facilitate this process, we provide a graphical user interface .
Edit intent: coherence
Original:  We demonstrate SDFGs on CPUs, GPUs, and FPGAs , using a wide variety of applications and motifs --- from fundamental computational kernels , through polyhedral applications, to graph analytics.
Revised:  We demonstrate SDFGs on CPUs, GPUs, and FPGAs over various motifs --- from fundamental computational kernels , through polyhedral applications, to graph analytics.
Edit intent: clarity
Original:  We demonstrate SDFGs on CPUs, GPUs, and FPGAs , using a wide variety of applications and motifs --- from fundamental computational kernels , through polyhedral applications, to graph analytics.
Revised:  We demonstrate SDFGs on CPUs, GPUs, and FPGAs , using a wide variety of applications and motifs --- from fundamental computational kernels to graph analytics.
Edit intent: clarity
Original:  We show that the algorithm provides a provable approximation guarantee, which becomes constant under a mild assumption on the minimum number of points for each cluster.
Revised:  We show that the algorithm provides a provable approximation guarantee, which becomes constant under an assumption on the minimum number of points for each cluster.
Edit intent: clarity
Original: In robot swarms operating with severely constrained sensing and communication  , individuals may need to use direct physical proximity to facilitate information exchange , perform task-specific actions, or, crucially, both.
Revised: In robot swarms operating under highly restrictive sensing and communication , individuals may need to use direct physical proximity to facilitate information exchange , perform task-specific actions, or, crucially, both.
Edit intent: clarity
Original: In robot swarms operating with severely constrained sensing and communication  , individuals may need to use direct physical proximity to facilitate information exchange , perform task-specific actions, or, crucially, both.
Revised: In robot swarms operating with severely constrained sensing and communication constraints , individuals may need to use direct physical proximity to facilitate information exchange , perform task-specific actions, or, crucially, both.
Edit intent: clarity
Original:  We envision a scenario where a swarm of vibration-driven robots - which sit atop bristles and achieve directed motion by vibrating them - move somewhat randomly in an environment while colliding with each other.
Revised:  We envision a scenario where a swarm of vibration-driven robots---which sit atop bristles and achieve directed motion by vibrating them - move somewhat randomly in an environment while colliding with each other.
Edit intent: fluency
Original:  We envision a scenario where a swarm of vibration-driven robots - which sit atop bristles and achieve directed motion by vibrating them - move somewhat randomly in an environment while colliding with each other.
Revised:  We envision a scenario where a swarm of vibration-driven robots - which sit atop bristles and achieve directed motion by vibrating them---move somewhat randomly in an environment while colliding with each other.
Edit intent: fluency
Original:  We demonstrate that such a swarm of brushbots can predictably form high-density robot clusters along with simultaneously co-existing regions with lower robot densities.
Edit intent: coherence
Original:  This deeply surprising connection is a formal one and is a relatively recent result in studies of motility induced phase separation;
Revised:  This connection is a formal one and is a relatively recent result in studies of motility induced phase separation;
Edit intent: clarity
Original:  Experiments are presented for a swarm of differential-drive like brushbots .
Revised:  Real robot experiments as well as simulations illustrate how inter-robot collisions can precipitate the formation of non-uniform robot densities in a closed and bounded region .
Edit intent: clarity
Original: Real world data often exhibit inhomogeneity - complexity of the target function , noise level , etc. are not uniform over the input space.
Revised: Real world data often exhibit inhomogeneity - complexity of the target function and uneven noise level over the input space.
Edit intent: clarity
Original:  We address the issue of estimating locally optimal kernel bandwidth as a way to describe inhomogeneity.
Revised:  In this paper, we cope with inhomogeneity by explicitly estimating the locally optimal kernel bandwidth as a way to describe inhomogeneity.
Edit intent: clarity
Original: Real world data often exhibit inhomogeneity, e.g., skewed distribution, non-uniform complexity of the target function and uneven noise level over the input space.
Revised: Real world data often exhibit inhomogeneity, e.g., skewed distribution, non-uniform complexity of the target function may change over the input space.
Edit intent: clarity
Original:  In this paper, we cope with inhomogeneity by explicitly estimating the locally optimal kernel bandwidth as a function . Specifically, we propose Spatially Adaptive Bandwidth Estimation in Regression (SABER), which employs the mixture of experts consisting of multinomial kernel logistic regression as a gate and Gaussian process regression models as experts.
Revised:  In this paper, we cope with inhomogeneity by explicitly estimating the locally optimal kernel bandwidth as a functional relationship . Specifically, we propose Spatially Adaptive Bandwidth Estimation in Regression (SABER), which employs the mixture of experts consisting of multinomial kernel logistic regression as a gate and Gaussian process regression models as experts.
Edit intent: clarity
Original:  Drawing parallels to the theory of locally linear smoothing , we derive an estimate to the local function complexity .
Revised:  Drawing parallels to the theory of locally linear smoothing . We demonstrate the usefulness of local function complexity .
Edit intent: clarity
Original:  It implies that  algorithms that are uniformly stable with \gamma = O(1/n) have essentially the same estimation error as algorithms that output a fixed function .
Revised:  It implies that algorithms that are uniformly stable with \gamma = O(1/n) , estimation error is essentially the same estimation error as algorithms that output a fixed function .
Edit intent: clarity
Original:  It implies that  algorithms that are uniformly stable with \gamma = O(1/n) have essentially the same estimation error as algorithms that output a fixed function .
Revised:  It implies that algorithms that are uniformly stable with \gamma = O(1/n) have essentially the same as the sampling error .
Edit intent: clarity
Original:  Furthermore, we no longer need the decay behaviour of the perturbation, relying on a variant of the classical quasi-reversibility regime.
Edit intent: coherence
Original:  By this way, Sobolev- and Gevrey-type source conditions are easily included, while we successfully maintain the logarithmic stability estimate of the corresponding stabilized operator, which is necessary for the error analysis.
Revised:  By this way, Gevrey-type source conditions are easily included, while we successfully maintain the logarithmic stability estimate of the corresponding stabilized operator, which is necessary for the error analysis.
Edit intent: coherence
Original:  By this way, Sobolev- and Gevrey-type source conditions are easily included, while we successfully maintain the logarithmic stability estimate of the corresponding stabilized operator, which is necessary for the error analysis.
Revised:  By this way, Sobolev- and Gevrey-type source conditions are included, while we successfully maintain the logarithmic stability estimate of the corresponding stabilized operator, which is necessary for the error analysis.
Edit intent: clarity
Original:  Compared to the classical error bounds in L^2 and H^1 that we obtained in the previous studies, our analysis here needs a higher smoothness of the true terminal data to ensure their reconstructions from the stochastic fashion . Especially, using finer energy estimates our method is valid for any finite time .
Revised:  Compared to the classical error bounds in L^2 and H^1 that we obtained in the previous studies, our analysis here needs a higher smoothness of the true terminal data to ensure their reconstructions from the stochastic fashion .
Edit intent: coherence
Original:  Classically, we are tasked with producing a clustering that minimizes the number of disagreements: an edge is in disagreement if it is a "similar" edge and is present across clusters or if it is a "dissimilar" edge and is present within a cluster.
Revised:  Classically, we are tasked with producing a clustering that minimizes the number of disagreements: an edge is in disagreement if it is a ``similar'' edge and is present across clusters or if it is a "dissimilar" edge and is present within a cluster.
Edit intent: fluency
Original:  Classically, we are tasked with producing a clustering that minimizes the number of disagreements: an edge is in disagreement if it is a "similar" edge and is present across clusters or if it is a "dissimilar" edge and is present within a cluster.
Revised:  Classically, we are tasked with producing a clustering that minimizes the number of disagreements: an edge is in disagreement if it is a "similar" edge and is present across clusters or if it is a ``dissimilar'' edge and is present within a cluster.
Edit intent: fluency
Original:  Our technique is the first work to effectively disable covert communications and transactions that use deep-learning steganography.
Revised:  Our technique is the first to effectively disable covert communications and transactions that use deep-learning steganography.
Edit intent: clarity
Original:  We address the problem by exploiting sophisticated pixel distributions and edge areas of images using a deep neural network.
Revised:  We address the problem by exploiting sophisticated pixel distributions and an edge distribution of images by using a deep neural network.
Edit intent: clarity
Original:  Based on the given information, we adaptively remove secret information at the pixel level.
Revised:  Based on the given information, we remove the hidden secret information at the pixel level.
Edit intent: clarity
Original:  We also introduce a new quantitative metric called destruction rate since the decoding method of deep-learning steganography is approximate (lossy) , which is different from conventional steganography.
Revised:  We also introduce a new quantitative metric called destruction rate since the decoding method of deep-learning steganography is approximate (lossy) and is different from conventional steganography.
Edit intent: coherence
Original:  In combination with tightly-packed shifts and advanced deflation techniques such as aggressive early deflation  we obtain an efficient method for the dense generalized eigenvalue problem.
Revised:  In combination with optimally packed shifts and aggressive early deflation we obtain an efficient method for the dense generalized eigenvalue problem.
Edit intent: clarity
Original:  Numerical experiments demonstrate the level 3 BLAS performance and compare the results with LAPACK routines for the generalized eigenvalue problem .
Revised:  In the numerical experiments we compare the results with LAPACK routines for the generalized eigenvalue problem .
Edit intent: clarity
Original:  Numerical experiments demonstrate the level 3 BLAS performance and compare the results with LAPACK routines for the generalized eigenvalue problem .
Revised:  Numerical experiments demonstrate the level 3 BLAS performance and compare the results with state-of-the-art routines for the generalized eigenvalue problem .
Edit intent: clarity
Original:  We show that our methods can outperform all in terms of speed and accuracy and observe an empirical time complexity significantly lower than O(n^3) .
Revised:  We show that our methods can outperform all in terms of speed and accuracy .
Edit intent: coherence
Original:  For wireless links, rate selection is used to select the optimal data transmission rate that maximizes the link throughput subject to an application-defined latency constraint.
Revised:  For wireless links, rate selection is used to select the optimal data transmission rate that maximizes the link throughput subject to an application-defined latency constraint.
Edit intent: fluency
Original:  We show that Con-TS outperforms OLLA in simulations, further, it can elegantly incorporate information from the offline link models to substantially improve performance .
Revised:  We show that Con-TS significantly outperforms state-of-the-art bandit schemes for rate selection .
Edit intent: clarity
Original: We introduce KL-hardness , a new notion of hardness for search problems which on the one hand is satisfied by all one-way functions and on the other hand implies both next-block pseudoentropy and inaccessible-entropy , two forms of computational entropy used in recent constructions of pseudorandom generators and statistically hiding commitment schemes, respectively.
Revised: We introduce KL-hardness , a new notion of hardness for search problems which on the one hand is satisfied by all one-way functions and on the other hand implies both next-block pseudoentropy and inaccessible entropy , two forms of computational entropy used in recent constructions of pseudorandom generators and statistically hiding commitment schemes, respectively.
Edit intent: fluency
Original: Antifragility is a property to not only resist stress and but also to benefit from it.
Revised: Antifragility is a property from which systems are able to resist stress and but also to benefit from it.
Edit intent: clarity
Original: Antifragility is a property to not only resist stress and but also to benefit from it.
Revised: Antifragility is a property to not only resist stress and furthermore benefit from it.
Edit intent: coherence
Original:  Our measure and findings can be utilized for many applications from understanding properties of biological systems with multilayer structures to designing more antifragile engineered systems.
Revised:  Our measure and findings will be useful for various applications such as exploring properties of biological systems with multilayer structures to designing more antifragile engineered systems.
Edit intent: clarity
Original:  Our measure and findings can be utilized for many applications from understanding properties of biological systems with multilayer structures to designing more antifragile engineered systems.
Revised:  Our measure and findings can be utilized for many applications from understanding properties of biological systems with multilayer structures and creating more antifragile engineered systems.
Edit intent: clarity
Original: We model " fair " dimensionality reduction as an optimization problem .
Revised: Dimensionality reduction is a classical technique widely used for data analysis. One foundational instantiation is Principal Component Analysis (PCA), which minimizes the average reconstruction error. In this paper, we introduce the " fair " dimensionality reduction as an optimization problem .
Edit intent: coherence
Original:  As an application, our model captures several fairness criteria for dimensionality reduction such as the Fair-PCA problem introduced by Samadi, et. al. 2018 and the Nash Social Welfare (NSW) problem.
Revised:  As an application, our model captures several fairness criteria for dimensionality reduction such as the Fair-PCA problem and the Nash Social Welfare (NSW) problem.
Edit intent: coherence
Original:  In the Fair-PCA problem , the input data is divided into k groups, and the goal is to find a single d-dimensional representation for all groups for which the maximum reconstruction error of any one group is minimized .
Revised:  In Fair-PCA problem , the input data is divided into k groups, and the goal is to find a single d-dimensional representation for all groups for which the maximum reconstruction error of any one group is minimized .
Edit intent: fluency
Original:  In the Fair-PCA problem , the input data is divided into k groups, and the goal is to find a single d-dimensional representation for all groups for which the maximum reconstruction error of any one group is minimized .
Revised:  In the Fair-PCA , the input data is divided into k groups, and the goal is to find a single d-dimensional representation for all groups for which the maximum reconstruction error of any one group is minimized .
Edit intent: clarity