wanyu/IteraTeR_v2 huggingface dataset:
Original: We propose LandLayer , a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit  underlying topological features of the input data structure.
Revised: We propose LandLayer , a novel topological layer for general deep learning models based on persistence landscapes, in which we can efficiently exploit the underlying topological features of the input data structure.
Edit intent: fluency
Original:  We show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration.
Revised:  In this work, we show differentiability with respect to layer inputs, for a general persistent homology with arbitrary filtration.
Edit intent: clarity
Original:  We provide novel stability results, including an adaptation for the robust DTM filtration function , and show that the proposed layer is robust against noise and outliers  .
Revised:  We provide a novel adaptation for the robust DTM filtration function , and show that the proposed layer is robust against noise and outliers .
Edit intent: clarity
Original:  We provide novel stability results, including an adaptation for the robust DTM filtration function , and show that the proposed layer is robust against noise and outliers  .
Revised:  We provide novel stability results, including an adaptation for the DTM function-based filtration , and show that the proposed layer is robust against noise and outliers .
Edit intent: clarity
Original: Fair division has emerged as a very hot topic in AI , and envy-freeness is arguably the most compelling fairness concept .
Revised: Fair division has emerged as a very hot topic in multiagent systems , and envy-freeness is arguably the most compelling fairness concept .
Edit intent: clarity
Original: Fair division has emerged as a very hot topic in AI , and envy-freeness is arguably the most compelling fairness concept .
Revised: Fair division has emerged as a very hot topic in AI , and envy-freeness is among the most compelling fairness concept .
Edit intent: clarity
Original: Fair division has emerged as a very hot topic in AI , and envy-freeness is arguably the most compelling fairness concept .
Revised: Fair division has emerged as a very hot topic in AI , and envy-freeness is arguably the most compelling fairness concepts .
Edit intent: fluency
Original:  On the positive side, we present an algorithm which , for a constant number of agents, approximates the minimum amount of subsidies within any required accuracy, at the expense of a graceful increase in the running time.
Revised:  On the positive side, we present an algorithm that , for a constant number of agents, approximates the minimum amount of subsidies within any required accuracy, at the expense of a graceful increase in the running time.
Edit intent: fluency
Original:  On the negative side, we show that, for a superconstant number of agents, the problem of minimizing subsidies for envy-freeness is not only hard to compute exactly (as a folklore argument shows) but also, more importantly, hard to approximate.
Revised:  On the negative side, we show that, for a super-constant number of agents, the problem of minimizing subsidies for envy-freeness is not only hard to compute exactly (as a folklore argument shows) but also, more importantly, hard to approximate.
Edit intent: fluency
Original: We propose a deep learning strategy to compute the mean curvature of an implicit level-set representation of an interface .
Revised: We propose a deep learning strategy to estimate the mean curvature of an implicit level-set representation of an interface .
Edit intent: clarity
Original: We propose a deep learning strategy to compute the mean curvature of an implicit level-set representation of an interface .
Revised: We propose a deep learning strategy to compute the mean curvature of an implicit level-set method .
Edit intent: clarity
Original:  Our approach is based on fitting  neural networks to synthetic datasets of pairs of nodal \phi values and curvatures obtained from circular interfaces immersed in different uniform resolutions.
Revised:  Our approach is based on fitting neural networks to synthetic data sets constructed from circular interfaces immersed in different uniform resolutions.
Edit intent: clarity
Original:  Our approach is based on fitting  neural networks to synthetic datasets of pairs of nodal \phi values and curvatures obtained from circular interfaces immersed in different uniform resolutions.
Revised:  Our approach is based on fitting neural networks to synthetic datasets of pairs of nodal \phi values and curvatures obtained from circular interfaces immersed in uniform grids of various resolutions.
Edit intent: clarity
Original:  These neural networks are multilayer perceptrons that ingest sample level-set values of grid points along a free boundary and output the dimensionless curvature at the center vertices of each sampled neighborhood.
Revised:  These multilayer perceptrons process the level-set values of grid points along a free boundary and output the dimensionless curvature at the center vertices of each sampled neighborhood.
Edit intent: clarity
Original:  These neural networks are multilayer perceptrons that ingest sample level-set values of grid points along a free boundary and output the dimensionless curvature at the center vertices of each sampled neighborhood.
Revised:  These neural networks are multilayer perceptrons that ingest sample level-set values from mesh points next to the free boundary and output the dimensionless curvature at the center vertices of each sampled neighborhood.
Edit intent: clarity
Original:  Evaluations with irregular (smooth and sharp) interfaces, in both uniform and adaptive meshes , show that our deep learning approach is systematically superior to conventional numerical approximation in the L ^2 and L^\infty norms.
Revised:  Evaluations with irregular (smooth and sharp) interfaces, in both uniform and adaptive grids , show that our deep learning approach is systematically superior to conventional numerical approximation in the L ^2 and L^\infty norms.
Edit intent: clarity
Original:  Evaluations with irregular (smooth and sharp) interfaces, in both uniform and adaptive meshes , show that our deep learning approach is systematically superior to conventional numerical approximation in the L ^2 and L^\infty norms.
Revised:  Evaluations with irregular (smooth and sharp) interfaces, in both uniform and adaptive meshes , show that our models are competitive with traditional numerical schemes in the L ^2 and L^\infty norms.
Edit intent: clarity
Original:  Additionally, we show that an application-dependent map of local resolutions to neural networks can be constructed and employed to estimate interface curvatures more efficiently than using typically expensive numerical schemes while still attaining comparable or higher precision .
Revised:  Additionally, we show that an application-dependent map of local resolutions to neural models can be devised to estimate mean curvature more effectively than a universal neural network .
Edit intent: clarity
Original:  Accuracy analyses involving irregular interfaces, both in uniform and adaptive grids, show that our models are competitive with traditional numerical schemes in the L^1 and L^2 norms.
Revised:  Accuracy analyses involving irregular interfaces, in both uniform and adaptive grids, show that our models are competitive with traditional numerical schemes in the L^1 and L^2 norms.
Edit intent: fluency
Original:  By studying the topological properties of the binary and weighted versions of the three representations above, we find that the total volume of transacted bitcoins approximately grows as the square of the network size;
Revised:  By studying the topological properties of the three representations above, we find that the total volume of transacted bitcoins approximately grows as the square of the network size;
Edit intent: clarity
Original:  The LRSFN algorithm mitigates this issue by the use of Levenberg-Marquardt damping , but generally second order methods with stochastic Hessian and gradient information may need to take small steps, unlike in deterministic problems.
Revised:  The LRSFN method mitigates this stability issue via Levenberg-Marquardt damping , but generally second order methods with stochastic Hessian and gradient information may need to take small steps, unlike in deterministic problems.
Edit intent: clarity
Original:  The LRSFN algorithm mitigates this issue by the use of Levenberg-Marquardt damping , but generally second order methods with stochastic Hessian and gradient information may need to take small steps, unlike in deterministic problems.
Revised:  The LRSFN algorithm mitigates this issue by the use of Levenberg-Marquardt damping . However, generally the analysis shows that second order methods with stochastic Hessian and gradient information may need to take small steps, unlike in deterministic problems.
Edit intent: coherence
Original:  Numerical results show that  even under restrictive step-length conditions, LRSFN can outperform popular first order methods on nontrivial deep learning tasks in terms of generalizability for equivalent computational work.
Revised:  Numerical results show that even under restrictive step length conditions, LRSFN can outperform popular first order methods on nontrivial deep learning tasks in terms of generalizability for equivalent computational work.
Edit intent: fluency
Original:  Numerical results show that  even under restrictive step-length conditions, LRSFN can outperform popular first order methods on nontrivial deep learning tasks in terms of generalizability for equivalent computational work.
Revised:  Numerical results show that even under restrictive step-length conditions, LRSFN can outperform popular first order methods on large scale deep learning tasks in terms of generalizability for equivalent computational work.
Edit intent: clarity
Original:  As a result, continuous local changes may seriously affect the performance of a pre-trained global model generated by Federated Learning.
Revised:  As a result, continuous local changes may seriously affect the performance of a global model generated by Federated Learning.
Edit intent: clarity
Original: Phase retrieval is in general a non-convex and non-linear task and the corresponding algorithms struggle with the problem of local minima.
Revised: Phase retrieval is in general a non-convex and non-linear task and the corresponding algorithms struggle with the issue of local minima.
Edit intent: clarity
Original:  We consider the case where portions of the measurement samples  are coherently linked to each other - which is a reasonable assumption for our objective of antenna measurements.
Revised:  We consider the case where the measurement samples are coherently linked to each other - which is a reasonable assumption for our objective of antenna measurements.
Edit intent: clarity
Original:  The problem may even be reduced to a linear system of equations similar to an eigenvalue problem and to the search for a unique non-trivial null-space vector  .
Revised:  The problem may even be reduced to a linear system of equations similar to an eigenvalue problem where a unique non-trivial null-space vector .
Edit intent: clarity
Original:  Accurate phase reconstruction for partially coherent observations is, thus, possible by a reliable solution process where we are able to judge the solution quality.
Revised:  Accurate phase reconstruction for partially coherent observations is, thus, possible by a reliable solution process and with judgment of the solution quality.
Edit intent: clarity
Original: The process of neural network pruning with suitable fine-tuning and retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy.
Revised: Neural network pruning with suitable fine-tuning and retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy.
Edit intent: clarity
Original: The process of neural network pruning with suitable fine-tuning and retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy.
Revised: The process of neural network pruning with suitable retraining can yield networks with considerably fewer parameters than the original with comparable degrees of accuracy.
Edit intent: clarity
Original:  Typically, pruning methods require large, pre-trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining algorithm .
Revised:  Typical pruning methods require large, pre-trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining algorithm .
Edit intent: fluency
Original:  Typically, pruning methods require large, pre-trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining algorithm .
Revised:  Typically, pruning methods require large, fully trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining algorithm .
Edit intent: clarity
Original:  Typically, pruning methods require large, pre-trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining algorithm .
Revised:  Typically, pruning methods require large, pre-trained networks as a starting point from which they perform a time-intensive iterative pruning and retraining procedure to regain the original accuracy .
Edit intent: clarity
Original:  We propose a novel pruning in-training method that prunes a network real-time during training, reducing the overall training time to achieve an optimal compressed network.
Revised:  We propose a novel pruning method that prunes a network real-time during training, reducing the overall training time to achieve an optimal compressed network.
Edit intent: clarity
Original:  We propose a novel pruning in-training method that prunes a network real-time during training, reducing the overall training time to achieve an optimal compressed network.
Revised:  We propose a novel pruning in-training method that prunes a network real-time during training, reducing the overall training time to achieve an efficient compressed network.
Edit intent: clarity
Original:  To do so, we introduce an activation density based analysis that identifies the optimal relative sizing or compression for each layer of the network.
Revised:  We introduce an activation density based analysis that identifies the optimal relative sizing or compression for each layer of the network.
Edit intent: clarity
Original:  To do so, we introduce an activation density based analysis that identifies the optimal relative sizing or compression for each layer of the network.
Revised:  To do so, we introduce an activation density based analysis to identify the optimal relative sizing or compression for each layer of the network.
Edit intent: fluency
Original:  Our method removes the need for pre-training and is architecture agnostic, allowing it to be employed on a wide variety of systems.
Revised:  Our method is architecture agnostic, allowing it to be employed on a wide variety of systems.
Edit intent: clarity
Original:  For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to 200x reduction in parameters and >60x reduction in inference compute operations in the best case) with comparable accuracies (up to 2\%-3\% loss with respect to the baseline network ) . By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods.
Revised:  For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to 200 \times reduction in parameters and >60x reduction in inference compute operations in the best case) with comparable accuracies (up to 2\%-3\% loss with respect to the baseline network ) . By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods.
Edit intent: fluency
Original:  For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to 200x reduction in parameters and >60x reduction in inference compute operations in the best case) with comparable accuracies (up to 2\%-3\% loss with respect to the baseline network ) . By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods.
Revised:  For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to 200x reduction in parameters and over 60 \times reduction in inference compute operations in the best case) with comparable accuracies (up to 2\%-3\% loss with respect to the baseline network ) . By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods.
Edit intent: clarity
Original:  For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to 200x reduction in parameters and >60x reduction in inference compute operations in the best case) with comparable accuracies (up to 2\%-3\% loss with respect to the baseline network ) . By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods.
Revised:  For VGG-19 and ResNet18 on CIFAR-10, CIFAR-100, and TinyImageNet, we obtain exceedingly sparse networks (up to 200x reduction in parameters and >60x reduction in inference compute operations in the best case) with comparable accuracies (up to 2\%-3\% loss with respect to the baseline network . By reducing the network size periodically during training, we achieve total training times that are shorter than those of previously proposed pruning methods.
Edit intent: fluency
Original:  Furthermore, training compressed networks at different epochs with our proposed method yields considerable reduction in training compute complexity (1.6 x -3.2x lower) at near iso-accuracy as compared to a baseline network trained entirely from scratch.
Revised:  Furthermore, training compressed networks at different epochs with our proposed method yields considerable reduction in training compute complexity (1.6 \times to 3.2\times lower) at near iso-accuracy as compared to a baseline network trained entirely from scratch.
Edit intent: clarity
Original: We developed a new scalable evolution strategy with directional Gaussian smoothing (DGS-ES) for high-dimensional blackbox optimization.
Revised: We propose an improved evolution strategy (ES) using a novel nonlocal gradient operator for high-dimensional blackbox optimization.
Edit intent: clarity
Original: We developed a new scalable evolution strategy with directional Gaussian smoothing (DGS-ES) for high-dimensional blackbox optimization.
Revised: We developed a new scalable evolution strategy with directional Gaussian smoothing (DGS-ES) for high-dimensional black-box optimization.
Edit intent: fluency
Original:  Standard ES methods have been proved to suffer from the curse of dimensionality , due to the random directional search and low accuracy of Monte Carlo estimation.
Revised:  Standard ES methods have been proved to suffer from the curse of dimensionality due to the random directional search and low accuracy of Monte Carlo estimation.
Edit intent: fluency
Original:  As such, the averaged partial derivatives can be approximated using the Gauss-Hermite quadrature rule, as opposed to MC , which significantly improves the accuracy of the averaged gradients.
Revised:  As such, the averaged partial derivatives can be approximated using the Gauss-Hermite quadrature , which significantly improves the accuracy of the averaged gradients.
Edit intent: clarity
Original:  We provide three sets of examples to demonstrate the performance of our method  , including benchmark functions for global optimization, and a rocket shell design problem .
Revised:  We provide three sets of examples to demonstrate the performance of our method in three sets of examples , including benchmark functions for global optimization, and a rocket shell design problem .
Edit intent: clarity
Original: The Bloom tree is a probabilistic data structure that  combines the idea of Bloom filters with that of Merkle trees.
Revised: We introduce a data structure that combines the idea of Bloom filters with that of Merkle trees.
Edit intent: clarity
Original:  Amrita Vishwa Vidyapeetham, a small institution with less than 200 papers, has performed reasonably well. ....
Revised:  Amrita Vishwa Vidyapeetham, a small institution with less than 200 papers, has performed reasonably well. 
Edit intent: fluency
Original:  We present an improvement of Koksma's inequality that is also applicable for functions with infinite total variation .
Revised:  We present an extension of Koksma's inequality that is also applicable for functions with infinite total variation .
Edit intent: clarity
Original: With more than 1.3 Billion in the cumulative number of downloads reported, the top six applications compete in the niche of Wi-Fi Direct D2D file sharing on Android.
Revised: The number of active users of Wi-Fi Direct D2D file sharing on Android.
Edit intent: clarity
Original:  For its end users, such type of direct file transfer does not result in cellular data charges and allows to keep their primary Wi-Fi interface up concurrently with a dedicated Wi-Fi P2P interface, which is commonly provided by the default wireless module of the mobile phone .
Revised:  For its end users, such type of direct file transfer does not result in cellular data charges .
Edit intent: coherence
Original:  However, despite the popularity of these solutions demonstrated by Google Play download statistics , we observe that the software vendors tend to prioritize the ease of user flow over the security of the implementation, introducing serious security flaws.
Revised:  However, despite the popularity of such applications , we observe that the software vendors tend to prioritize the ease of user flow over the security of the implementation, introducing serious security flaws.
Edit intent: clarity
Original: Density Peaks Clustering (DPC), a novel density-based clustering approach, has received considerable attention of the research community primarily due to its simplicity and less parameter requirement.
Revised: Density Peak Clustering (DPC), a novel density-based clustering approach, has received considerable attention of the research community primarily due to its simplicity and less parameter requirement.
Edit intent: fluency
Original: Density Peaks Clustering (DPC), a novel density-based clustering approach, has received considerable attention of the research community primarily due to its simplicity and less parameter requirement.
Revised: Density Peaks Clustering (DPC), a novel density-based clustering approach, has received considerable attention from the research community primarily due to its simplicity and less parameter requirement.
Edit intent: clarity
Original: Density Peaks Clustering (DPC), a novel density-based clustering approach, has received considerable attention of the research community primarily due to its simplicity and less parameter requirement.
Revised: Density Peaks Clustering (DPC), a novel density-based clustering approach, has received considerable attention of the research community primarily due to its simplicity and fewer-parameter requirement.
Edit intent: clarity
Original:  However, the resultant clusters obtained using DPC are influenced by its sensitive parameter dc which depends upon data distribution and requirements of different users.
Revised:  However, the resultant clusters obtained using DPC are influenced by the sensitive parameter d_c, which depends on data distribution and requirements of different users.
Edit intent: clarity