wanyu/IteraTeR_v2 huggingface dataset:
Original:   To show the efficiency and practicality, we test our algorithm on random datasets and also apply it to solve outlier recognition on benchmark image datasets.
Revised:  To show the efficiency and practicality, we test our algorithms on random datasets and also apply it to solve outlier recognition on benchmark image datasets.
Edit intent: fluency
Original:  To our best knowledge, this is the first algorithm yielding both solid theoretical quality guarantee and promising practical performance for MEB with outliers .
Revised:  To our best knowledge, this is the first result yielding both solid theoretical quality guarantee and promising practical performance for MEB with outliers .
Edit intent: clarity
Original:  To our best knowledge, this is the first algorithm yielding both solid theoretical quality guarantee and promising practical performance for MEB with outliers .
Revised:  To our best knowledge, this is the first algorithm yielding both solid theoretical quality guarantee and promising practical performance for the problems .
Edit intent: clarity
Original:  \mbox{ DBLP:journals/jea/KumarMY03 proposed the open problem " Are there practical methods for computing an MEB with outliers?
Revised:  proposed the open problem " Are there practical methods for computing an MEB with outliers?
Edit intent: coherence
Original:  \mbox{ DBLP:journals/jea/KumarMY03 proposed the open problem " Are there practical methods for computing an MEB with outliers?
Revised:  \mbox{ DBLP:journals/jea/KumarMY03 proposed the open problem ` Are there practical methods for computing an MEB with outliers?
Edit intent: fluency
Original:  " To answer this question, we  present a randomized algorithm for MEB with outliers in high dimension.
Revised:  ' To answer this question, we present a randomized algorithm for MEB with outliers in high dimension.
Edit intent: fluency
Original:  In particular, we provide a more "robust" analysis for the core-set construction of MEB, and propose a couple of novel improvements to further reduce the time complexity.
Revised:  In particular, we provide a more `robust' analysis for the core-set construction of MEB, and propose a couple of novel improvements to further reduce the time complexity.
Edit intent: fluency
Original:  We also extend our method to the problem of k-center clustering with outliers.
Edit intent: coherence
Original:  To show the efficiency and practicality, we test our algorithms on random datasets and also apply it to solve outlier recognition on benchmark image datasets.
Revised:  To show the efficiency and practicality, we test our algorithm on random datasets and also apply it to solve outlier recognition on benchmark image datasets.
Edit intent: fluency
Original:  To show the efficiency and practicality, we test our algorithms on random datasets and also apply it to solve outlier recognition on benchmark image datasets.
Revised:  To show the efficiency and practicality, we test our algorithms on random datasets and also apply it to handle outlier recognition on benchmark image datasets.
Edit intent: clarity
Original:  To show the efficiency and practicality, we test our algorithms on random datasets and also apply it to solve outlier recognition on benchmark image datasets.
Revised:  To show the efficiency and practicality, we test our algorithms on random datasets and also apply it to solve outlier recognition on benchmark datasets.
Edit intent: clarity
Original:  To our best knowledge, this is the first result yielding both solid theoretical quality guarantee and promising practical performance for the problems .
Revised:  To our best knowledge, this is the first algorithm yielding both solid theoretical quality guarantee and promising practical performance for the problems .
Edit intent: clarity
Original: Motivated by the arising realistic issues in big data , the problem of Minimum Enclosing Ball (MEB) with outliers has attracted a great deal of attention in recent years.
Revised: Outlier recognition is a fundamental problem in data analysis and has attracted a great deal of attention in recent years.
Edit intent: clarity
Original:  Though several methods have been proposed in both theory and practice, most of them still suffer from the issues such as high time  complexities or unstable performances for different datasets.
Revised:  Though several methods have been proposed in both theory and practice, most of them still suffer from several issues such as high time complexities or unstable performances for different datasets.
Edit intent: clarity
Original:  Here, we construct single layer memristor crossbars with a  density up to 4.5 terabits per inch square, much denser than the state-of-the-art 64-layer triple level cell NAND flash technology.
Revised:  Here, we construct memristor crossbars with a density up to 4.5 terabits per inch square, much denser than the state-of-the-art 64-layer triple level cell NAND flash technology.
Edit intent: clarity
Original:  Here, we construct single layer memristor crossbars with a  density up to 4.5 terabits per inch square, much denser than the state-of-the-art 64-layer triple level cell NAND flash technology.
Revised:  Here, we construct single layer memristor crossbars with a density up to 4.5 terabits per inch square, an order of magnitude denser than the state-of-the-art 64-layer triple level cell NAND flash technology.
Edit intent: clarity
Original:  Here, we construct single layer memristor crossbars with a  density up to 4.5 terabits per inch square, much denser than the state-of-the-art 64-layer triple level cell NAND flash technology.
Revised:  Here, we construct single layer memristor crossbars with a density up to 4.5 terabits per inch square, much denser than the state- of-the-art 64-layer triple level cell NAND flash technology.
Edit intent: fluency
Original: Convolutional neural networks have shown tremendous performance  in computer vision tasks, but their excessive amount of weights and operations prevent them from being adopted in embedded environments.
Revised: Convolutional neural networks have shown tremendous performance capabilities in computer vision tasks, but their excessive amount of weights and operations prevent them from being adopted in embedded environments.
Edit intent: clarity
Original:  One of the solutions involves pruning, where some unimportant weights are forced to be zero.
Revised:  One of the solutions involves pruning, where certain unimportant weights are forced to be zero.
Edit intent: fluency
Original:  One of the solutions involves pruning, where some unimportant weights are forced to be zero.
Revised:  One of the solutions involves pruning, where some unimportant weights are forced to have a value of zero.
Edit intent: clarity
Original:  Many pruning schemes have been proposed, but have focused mainly on the number of pruned weights.
Revised:  Many pruning schemes have been proposed, but these have mainly focused on the number of pruned weights.
Edit intent: clarity
Original:  The previous pruning schemes hardly considered ASIC or FPGA accelerator architectures.
Revised:  Previous pruning schemes scarcely considered ASIC or FPGA accelerator architectures.
Edit intent: fluency
Original:  When the pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems  including internal buffer mis-alignment and load imbalance .
Revised:  When these pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems including internal buffer mis-alignment and load imbalance .
Edit intent: fluency
Original:  When the pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems  including internal buffer mis-alignment and load imbalance .
Revised:  When the pruned networks are run on accelerators, the lack of architecture consideration casues some inefficiency problems including internal buffer mis-alignment and load imbalance .
Edit intent: fluency
Original:  When the pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems  including internal buffer mis-alignment and load imbalance .
Revised:  When the pruned networks are run on the accelerators, the lack of consideration of the architecture causes some inefficiency problems including internal buffer mis-alignment and load imbalance .
Edit intent: fluency
Original:  When the pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems  including internal buffer mis-alignment and load imbalance .
Revised:  When the pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems , including internal buffer mis-alignment and load imbalance .
Edit intent: fluency
Original:  When the pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems  including internal buffer mis-alignment and load imbalance .
Revised:  When the pruned networks are run on the accelerators, the lack of architecture consideration casues some inefficiency problems including internal buffer misalignments and load imbalances .
Edit intent: fluency
Original:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet.
Revised:  Even with this constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet.
Edit intent: fluency
Original:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet.
Revised:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet.
Edit intent: fluency
Original:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet.
Revised:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes , not only on AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet.
Edit intent: clarity
Original:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet.
Revised:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also on state-of-the-art very-deep networks like ResNet.
Edit intent: clarity
Original:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very-deep networks like ResNet.
Revised:  Even with the constraint, the proposed pruning scheme reached a pruning ratio similar to that of the previous unconstrained pruning schemes not only in AlexNet and VGG16 but also in the state-of-the-art very deep networks such as ResNet.
Edit intent: clarity
Original:  Furthermore, the proposed scheme demonstrated a comparable pruning ratio in slimmed networks that were already pruned channel-wisely .
Revised:  Furthermore, the proposed scheme demonstrated a comparable pruning ratio in slimmed networks that were already pruned in a channel-wise manner .
Edit intent: clarity
Original:  VoxelCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network.
Revised:  PMCNN can model spatiotemporal coherence to effectively perform predictive coding inside the learning network.
Edit intent: coherence
Original:  On the basis of VoxelCNN , we further explore a learning based framework for video compression with additional components of iterative analysis/synthesis, binarization, etc.
Revised:  On the basis of VoxelCNN , we further explore a learning-based framework for video compression with additional components of iterative analysis/synthesis, binarization, etc.
Edit intent: fluency
Original:  Experiment results demonstrate the effectiveness of the proposed scheme.
Revised:  Experimental results demonstrate the effectiveness of the proposed scheme.
Edit intent: fluency
Original:  The proposed learning based scheme provides a possible new direction to further improve compression efficiency and functionalities of future video coding.
Revised:  The proposed learning-based scheme provides a possible new direction to further improve compression efficiency and functionalities of future video coding.
Edit intent: fluency
Original:  This paper presents a comprehensive survey about the latest research works on multiple aligned HSNs studies based on the broad learning setting, which covers 5 major research tasks  network alignment, link prediction, community detection, information diffusion and network embedding respectively.
Revised:  This paper presents a comprehensive survey about the latest research works on multiple aligned HSNs studies based on the broad learning setting, which covers 5 major research tasks , i.e., network alignment, link prediction, community detection, information diffusion and network embedding respectively.
Edit intent: coherence
Original:  The results indicate that our method compares well to existing methods, particularly for dense graphs, and is able to obtain some of the best known heuristic solutions to well-known instances.
Revised:  The results indicate that our method compares well to existing methods, and also that it is suitable for dense instances.
Edit intent: clarity
Original: Threshold factors govern the maximally allowable step-size at which positivity or contractivity preservation of explicit integration methods for initial value problems is guaranteed.
Revised: Threshold factors govern the maximally allowable step-size at which positivity or contractivity preservation of integration methods for initial value problems is guaranteed.
Edit intent: clarity
Original:  An optimal threshold factor, R_{m,n -R_{m,n,0}%DIFDELCMD < ] %%% and such that P(x)= \exp(x) + \mathcal{O(x^{n+1}) when x \rightarrow 0. } In this paper, we show that the task of computing optimal threshold factors is, to a certain extent, equivalent to the problem of characterizing positive quadratures with integer nodes with respect to Poisson-Charlier measures.
Revised:  -R_{m,n,0}%DIFDELCMD < ] %%% and such that P(x)= \exp(x) + \mathcal{O(x^{n+1}) when x \rightarrow 0. } In this paper, we show that the task of computing optimal threshold factors is, to a certain extent, equivalent to the problem of characterizing positive quadratures with integer nodes with respect to Poisson-Charlier measures.
Edit intent: coherence
Original:  An optimal threshold factor, R_{m,n -R_{m,n,0}%DIFDELCMD < ] %%% and such that P(x)= \exp(x) + \mathcal{O(x^{n+1}) when x \rightarrow 0. } In this paper, we show that the task of computing optimal threshold factors is, to a certain extent, equivalent to the problem of characterizing positive quadratures with integer nodes with respect to Poisson-Charlier measures.
Revised:  An optimal threshold factor, R_{m,n ,0}%DIFDELCMD < ] %%% and such that P(x)= \exp(x) + \mathcal{O(x^{n+1}) when x \rightarrow 0. } In this paper, we show that the task of computing optimal threshold factors is, to a certain extent, equivalent to the problem of characterizing positive quadratures with integer nodes with respect to Poisson-Charlier measures.
Edit intent: coherence
Original:  An optimal threshold factor, R_{m,n -R_{m,n,0}%DIFDELCMD < ] %%% and such that P(x)= \exp(x) + \mathcal{O(x^{n+1}) when x \rightarrow 0. } In this paper, we show that the task of computing optimal threshold factors is, to a certain extent, equivalent to the problem of characterizing positive quadratures with integer nodes with respect to Poisson-Charlier measures.
Revised:  An optimal threshold factor, R_{m,n -R_{m,n,0}%DIFDELCMD < ] %%% (x^{n+1}) when x \rightarrow 0. } In this paper, we show that the task of computing optimal threshold factors is, to a certain extent, equivalent to the problem of characterizing positive quadratures with integer nodes with respect to Poisson-Charlier measures.
Edit intent: coherence
Original:  The algorithm possesses the remarkable property that its complexity depends only on the order of approximation and thus is independent of the degree of the underlying polynomials .
Revised:  The algorithm possesses the remarkable property that its complexity depends only on the order of the method and thus is independent of the degree of the underlying polynomials .
Edit intent: clarity
Original:  The algorithm possesses the remarkable property that its complexity depends only on the order of approximation and thus is independent of the degree of the underlying polynomials .
Revised:  The algorithm possesses the remarkable property that its complexity depends only on the order of approximation and thus is independent of the number of stages .
Edit intent: clarity
Original:  RMA extend the expressive power of symbolic automata, by allowing Boolean formulas to be applied not only to the last element read from the input string, but to multiple elements, stored in their registers.
Revised:  RMA extend the expressive power of symbolic automata, by allowing formulas to be applied not only to the last element read from the input string, but to multiple elements, stored in their registers.
Edit intent: clarity
Original:  We study the closure properties of RMA under union, concatenation, Kleene closure , complement and determinization and show that RMA, contrary to symbolic automata, are not in general closed under determinization, but they are when a window operator, quintessential in Complex Event Processing, is used  .
Revised:  We study the closure properties of RMA under union, concatenation, Kleene closure , complement and determinization and show that RMA, contrary to symbolic automata, are not in general closed under determinization, but they are when a window operator, a quintessential feature in Complex Event Processing, is used .
Edit intent: clarity
Original:  For post-selected quantum query algorithms, a similar relationship was shown by Mahadev and de Wolf 15 , where the rational approximations are allowed to have negative coefficients.
Revised:  For post-selected quantum query algorithms, a similar relationship was shown by Mahadev and de Wolf 15 , where the rational approximations are allowed to have negative coefficients.
Edit intent: fluency
Original:  For post-selected quantum query algorithms, a similar relationship was shown by Mahadev and de Wolf 15 , where the rational approximations are allowed to have negative coefficients.
Revised:  For post-selected quantum query algorithms, a similar relationship was shown by Mahadev and de Wolf , where the rational approximations are allowed to have negative coefficients.
Edit intent: clarity
Original:  Using the radial distribution, the axial distribution is then derived accordingly.
Revised:  Using the radial distribution, the axial distribution is then approximated accordingly.
Edit intent: clarity
Original:  This article introduces an implementation of adaptive mesh refinement for analog mesh computers , which reduces the energy expended during high-resolution computations of partial differential equations.
Revised:  This article introduces an implementation of refinement for analog mesh computers , which reduces the energy expended during high-resolution computations of partial differential equations.
Edit intent: clarity
Original:  With these implementations at hand, our code generator can generate code for any tensor algebra expression on any combination of the aforementioned formats.
Revised:  With these implementations at hand, our code generator can generate code to compute any tensor algebra expression on any combination of the aforementioned formats.
Edit intent: clarity
Original:  To demonstrate our modular code generator design , we have implemented it in the open-source taco tensor algebra compiler.
Revised:  To demonstrate our technique , we have implemented it in the open-source taco tensor algebra compiler.
Edit intent: clarity
Original:  To demonstrate our modular code generator design , we have implemented it in the open-source taco tensor algebra compiler.
Revised:  To demonstrate our modular code generator design , we have implemented it in the taco tensor algebra compiler.
Edit intent: clarity
Original:  Our evaluation shows that we get better performance by supporting more formats specialized to different tensor structures, and our plugins makes it easy to add new formats.
Revised:  Our modular code generator design makes it simple to add support for new tensor formats, and the performance of the generated code is competitive with hand-optimized implementations. Furthermore, by extending taco to support a wider range of formats specialized for different application and data characteristics, we can improve end-user application performance.
Edit intent: coherence
Original:  For example, when data is provided in the COO format,  computing a single matrix-vector multiplication with COO is up to 3.6\times faster than with CSR.
Revised:  For example, if input data is provided in the COO format, computing a single matrix-vector multiplication with COO is up to 3.6\times faster than with CSR.
Edit intent: clarity
Original:  For example, when data is provided in the COO format,  computing a single matrix-vector multiplication with COO is up to 3.6\times faster than with CSR.
Revised:  For example, when data is provided in the COO format, our technique allows computing a single matrix-vector multiplication with COO is up to 3.6\times faster than with CSR.
Edit intent: clarity
Original:  Com- pared to the existing residual-type a posteriori error estimators for the virtual element methods, the recovery-type a posteriori error estimator based on the proposed gradient recovery technique is much simpler in implementation and asymptotically exact.
Revised:  Compared to the existing residual-type a posteriori error estimators for the virtual element methods, the recovery-type a posteriori error estimator based on the proposed gradient recovery technique is much simpler in implementation and asymptotically exact.
Edit intent: fluency
Original:  Since Mantegnaseminal paper the linear Pearson's correlation coefficient between pairs of stocks is used to determine network edges.
Revised:  Since Mantegnaseminal paper the linear Pearson's correlation coefficient between pairs of stocks has been the usual way to determine network edges.
Edit intent: clarity
Original:  Further , strong nonstationarity in a few specific stocks may play a role.
Revised:  Furthermore , strong nonstationarity in a few specific stocks may play a role.
Edit intent: fluency
Original:  In particular, the sharp decrease of some stocks during the global finance crisis in 2008 gives rise to apparent nonlinear dependences among stocks.
Revised:  In particular, the sharp decrease in some stocks during the global finance crisis in 2008 gives rise to apparent nonlinear dependences among stocks.
Edit intent: fluency
Original:  In particular, the sharp decrease of some stocks during the global finance crisis in 2008 gives rise to apparent nonlinear dependences among stocks.
Revised:  In particular, the sharp decrease of some stocks during the global financial crisis of 2008 gives rise to apparent nonlinear dependences among stocks.
Edit intent: clarity