wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Setting the maximum  rate to 0.30-0.40 (as in U.S. currently) leads to an exponent of 1.5-1.8, while counterfactually setting it to 0.8 (as suggested by Piketty, 2014) would lead to an exponent of 2.6.
Revised:  Setting the maximum tax rate to 0.30-0.40 (as in U.S. currently) leads to an exponent of 1.5-1.8, while counterfactually setting it to 0.8 (as suggested by Piketty, 2014) would lead to an exponent of 2.6.
Edit intent: clarity
Original:  Setting the maximum  rate to 0.30-0.40 (as in U.S. currently) leads to an exponent of 1.5-1.8, while counterfactually setting it to 0.8 (as suggested by Piketty, 2014) would lead to an exponent of 2.6.
Revised:  Setting the maximum rate to 0.30-0.40 (as in U.S. currently) leads to a Pareto exponent of 1.5-1.8, while counterfactually setting it to 0.8 (as suggested by Piketty, 2014) would lead to an exponent of 2.6.
Edit intent: meaning-changed
Original:  Setting the maximum  rate to 0.30-0.40 (as in U.S. currently) leads to an exponent of 1.5-1.8, while counterfactually setting it to 0.8 (as suggested by Piketty, 2014) would lead to an exponent of 2.6.
Revised:  Setting the maximum rate to 0.30-0.40 (as in U.S. currently) leads to an exponent of 1.5-1.8, while counterfactually setting it to 0.80 (as suggested by Piketty, 2014) would lead to an exponent of 2.6.
Edit intent: meaning-changed
Original:  Setting the maximum  rate to 0.30-0.40 (as in U.S. currently) leads to an exponent of 1.5-1.8, while counterfactually setting it to 0.8 (as suggested by Piketty, 2014) would lead to an exponent of 2.6.
Revised:  Setting the maximum rate to 0.30-0.40 (as in U.S. currently) leads to an exponent of 1.5-1.8, while counterfactually setting it to 0.8 (as suggested by Piketty, 2014) would lead to a Pareto exponent of 2.6.
Edit intent: meaning-changed
Original:  The labels were given to paroxysmal discharges visually observed in both ictal (seizure) and interictal (nonseizure) intervals . Results showed that the mEEGNet detected abnormal EEGs with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs. Moreover, the number of parameters is much smaller than other CNN models.
Revised:  The labels were given to paroxysmal discharges visually observed in both ictal (seizure) and interictal (nonseizure) durations . Results showed that the mEEGNet detected abnormal EEGs with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs. Moreover, the number of parameters is much smaller than other CNN models.
Edit intent: clarity
Original:  The labels were given to paroxysmal discharges visually observed in both ictal (seizure) and interictal (nonseizure) intervals . Results showed that the mEEGNet detected abnormal EEGs with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs. Moreover, the number of parameters is much smaller than other CNN models.
Revised:  The labels were given to paroxysmal discharges visually observed in both ictal (seizure) and interictal (nonseizure) intervals . Results showed that the mEEGNet detected abnormalities with the area under the curve, F1-values, and sensitivity equivalent to or higher than those of existing CNNs. Moreover, the number of parameters is much smaller than other CNN models.
Edit intent: clarity
Original:  Motivated by applications in the integration of multiple (point-wise mutual information) PMI matrices , we propose the model {\bf B} lockwise missing {\bf E}mbedding {\bf L}earning {\bf T}ransformer (BELT) to treat row-wise/column-wise missingness.
Revised:  Motivated by applications in the integration of multiple Electronic Health Record (EHR) datasets , we propose the model {\bf B} lockwise missing {\bf E}mbedding {\bf L}earning {\bf T}ransformer (BELT) to treat row-wise/column-wise missingness.
Edit intent: meaning-changed
Original:  Motivated by applications in the integration of multiple (point-wise mutual information) PMI matrices , we propose the model {\bf B} lockwise missing {\bf E}mbedding {\bf L}earning {\bf T}ransformer (BELT) to treat row-wise/column-wise missingness.
Revised:  Motivated by applications in the integration of multiple (point-wise mutual information) PMI matrices , we propose the method {\bf B} lockwise missing {\bf E}mbedding {\bf L}earning {\bf T}ransformer (BELT) to treat row-wise/column-wise missingness.
Edit intent: clarity
Original:  Motivated by applications in the integration of multiple (point-wise mutual information) PMI matrices , we propose the model {\bf B} lockwise missing {\bf E}mbedding {\bf L}earning {\bf T}ransformer (BELT) to treat row-wise/column-wise missingness.
Revised:  Motivated by applications in the integration of multiple (point-wise mutual information) PMI matrices , we propose the model {\bf B} lock-wise missing {\bf E}mbedding {\bf L}earning {\bf T}ransformer (BELT) to treat row-wise/column-wise missingness.
Edit intent: fluency
Original:  We provide theoretical justification for the proposed BELT method . Simulation studies show that the method performs well in finite sample under a variety of configurations.
Revised:  Our idea is to exploit the orthogonal Procrustes problem to align the eigenspace of the two sub-matrices using their overlap, then complete the missing blocks by the inner product of the two low-rank components. Besides, we prove the statistical rate for the eigenspace of the underlying matrix, which is comparable to the rate under the independently missing assumption . Simulation studies show that the method performs well in finite sample under a variety of configurations.
Edit intent: meaning-changed
Original:  The method is applied to integrate several PMI matrices built by EHR data and Chinese medical text data, which enables us to construct a comprehensive embedding set for CUI and Chinese with high quality .
Revised:  In the real data analysis, the method is applied to integrate several PMI matrices built by EHR data and Chinese medical text data, which enables us to construct a comprehensive embedding set for CUI and Chinese with high quality .
Edit intent: meaning-changed
Original:  The method is applied to integrate several PMI matrices built by EHR data and Chinese medical text data, which enables us to construct a comprehensive embedding set for CUI and Chinese with high quality .
Revised:  The method is applied to two tasks: (i) the integrating of several point-wise mutual information matrices built by EHR data and Chinese medical text data, which enables us to construct a comprehensive embedding set for CUI and Chinese with high quality .
Edit intent: meaning-changed
Original:  The deep layered architectures, linear and nonlinear operators, and even parameters of the network are all explicitly constructed layer-by-layer via forward propagation, instead of learned via back propagation.
Revised:  The deep layered architectures, linear and nonlinear operators, and even parameters of the network are all explicitly constructed layer-by-layer via forward propagation, although they are amenable to fine-tuning via back propagation.
Edit intent: clarity
Original:  For networks with an architecure similar to VGG, the after kernel is more "global", in the sense that it is less invariant to transformations of input images that disrupt the global structure of the image while leaving the local statistics largely intact.
Revised:  For networks with an architecture similar to VGG, the after kernel is more "global", in the sense that it is less invariant to transformations of input images that disrupt the global structure of the image while leaving the local statistics largely intact.
Edit intent: fluency
Original:  We found that both  negative cross-entropy and  Kullback-Leibler divergence play a crucial role in constructing explainable latent space and the variability of the generated samples from our proposed model depends on a specific subspace, called `activated latent subspace'.
Revised:  We found that both the negative cross-entropy and Kullback-Leibler divergence play a crucial role in constructing explainable latent space and the variability of the generated samples from our proposed model depends on a specific subspace, called `activated latent subspace'.
Edit intent: fluency
Original:  We found that both  negative cross-entropy and  Kullback-Leibler divergence play a crucial role in constructing explainable latent space and the variability of the generated samples from our proposed model depends on a specific subspace, called `activated latent subspace'.
Revised:  We found that both negative cross-entropy and the Kullback-Leibler divergence play a crucial role in constructing explainable latent space and the variability of the generated samples from our proposed model depends on a specific subspace, called `activated latent subspace'.
Edit intent: coherence
Original:  We found that both  negative cross-entropy and  Kullback-Leibler divergence play a crucial role in constructing explainable latent space and the variability of the generated samples from our proposed model depends on a specific subspace, called `activated latent subspace'.
Revised:  We found that both negative cross-entropy and Kullback-Leibler divergence play a crucial role in constructing explainable latent space and the variability of generated samples from our proposed model depends on a specific subspace, called `activated latent subspace'.
Edit intent: fluency
Original: Non-intrusive load monitoring (NILM) decomposes the total load reading into appliance-level load signals. Many deep learning-based methods have been developed to accomplish NILM, and the training of deep neural networks (DNN) requires massive load data containing different types of appliances.
Revised: Non-intrusive load monitoring (NILM) is essential for understanding customer's power consumption patterns and may find wide applications like carbon emission reduction and energy conservation. The training of NILM models requires massive load data containing different types of appliances.
Edit intent: meaning-changed
Original:  While during the cooperation of local data owners, data exchange and centralized data storage may increase the risk of power consumer privacy breaches . To eliminate the potential risks, a novel NILM method named Fed-NILM ap-plying Federated Learning (FL) is proposed in this paper.
Revised:  While during the cooperation of local data owners, data exchange and centralized data storage may increase the risk of power consumer privacy breaches may be encountered by local data owners during the NILM model training. To prevent such potential risks, a novel NILM method named Fed-NILM ap-plying Federated Learning (FL) is proposed in this paper.
Edit intent: clarity
Original:  In the experiments, Fed-NILM is validated on two real-world datasets . Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained one is conductedin both residential and industrial scenarios .
Revised:  Experiments based on two measured load datasets are conducted to explore the generalization ability of Fed-NILM is validated on two real-world datasets . Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained one is conductedin both residential and industrial scenarios .
Edit intent: meaning-changed
Original:  In the experiments, Fed-NILM is validated on two real-world datasets . Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained one is conductedin both residential and industrial scenarios .
Revised:  In the experiments, Fed-NILM . Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained one is conductedin both residential and industrial scenarios .
Edit intent: clarity
Original:  In the experiments, Fed-NILM is validated on two real-world datasets . Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained one is conductedin both residential and industrial scenarios .
Revised:  In the experiments, Fed-NILM is validated on two real-world datasets . Besides, a comparison of Fed-NILM with locally-trained NILMs and the centrally-trained NILM is conducted .
Edit intent: clarity
Original:  The experimental results show that Fed-NILM  outperforms locally-trained NILMs and approximate the centrally-trained NILM which is trained on the entire load dataset without privacy preservation .
Revised:  The experimental results show that Fed-NILM has superior performance in scalability and convergence. Fed-NILM outperforms locally-trained NILMs and approximate the centrally-trained NILM which is trained on the entire load dataset without privacy preservation .
Edit intent: meaning-changed
Original:  The experimental results show that Fed-NILM  outperforms locally-trained NILMs and approximate the centrally-trained NILM which is trained on the entire load dataset without privacy preservation .
Revised:  The experimental results show that Fed-NILM outperforms locally-trained NILMs operated by local data owners and approximates the centrally-trained NILM which is trained on the entire load dataset without privacy preservation .
Edit intent: meaning-changed
Original:  The experimental results show that Fed-NILM  outperforms locally-trained NILMs and approximate the centrally-trained NILM which is trained on the entire load dataset without privacy preservation .
Revised:  The experimental results show that Fed-NILM outperforms locally-trained NILMs and approximate the centrally-trained NILM which is trained on the entire load dataset without privacy protection. The proposed Fed-NILM significantly improves the co-modeling capabilities of local data owners while protecting power consumers' privacy .
Edit intent: meaning-changed
Original: The theory of Wasserstein gradient flows in the space of probability measures provides a powerful framework to study dissipative partial differential equations (PDE). It can be used to prove well-posedness, regularity, stability and quantitative convergence to the equilibrium. However, many PDE are not gradient flows, and hence the theory is not immediately applicable. In this work we develop a straightforward entropy regularised splitting scheme for degenerate non-local non-gradient systems.
Revised: In this paper, we develop a straightforward entropy regularised splitting scheme for degenerate non-local non-gradient systems.
Edit intent: coherence
Original:  In this work we develop a straightforward entropy regularised splitting scheme for degenerate non-local non-gradient systems. The approach is composed of two main stages: first we split the dynamics into the conservative and dissipative forces, secondly we perturb the problem so that the diffusionis no longer singular and perform a weighted Wasserstein ``JKO type'' descent step. Entropic regularisation of optimal transport problems opens the way for efficient numerical methods for solving these gradient flows. We illustrate the generality of our work by providing a number of examples, including the Regularized Vlasov-Poisson-Fokker-Planck equation , to which our results applicable .
Revised:  In this work we develop a straightforward entropy regularised splitting scheme for degenerate non-local , degenerate conservative-dissipative evolutionary equations. The splitting-scheme consists of two phases: a conservative (transport) phase and a dissipative (diffusion) phase. The first phase is solved exactly using the method of characteristic and DiPerna-Lions theory while the second phase is solved approximately using a JKO-type variational scheme that minimizes an energy functional with respect to a certain Kantorovich optimal transport cost functional. In addition, we also introduce an entropic-regularisation of the scheme. We prove the convergence of both schemes to a weak solution of the evolutionary equation. We illustrate the generality of our work by providing a number of examples, including the Regularized Vlasov-Poisson-Fokker-Planck equation , to which our results applicable .
Edit intent: meaning-changed
Original:  We illustrate the generality of our work by providing a number of examples, including the Regularized Vlasov-Poisson-Fokker-Planck equation , to which our results applicable .
Revised:  We illustrate the generality of our work by providing a number of examples, including the kinetic Fokker-Planck equation and the (regularized) Vlasov-Poisson-Fokker-Planck equation , to which our results applicable .
Edit intent: meaning-changed
Original:  In this work, we propose a GNP convolution layer called %DIFDELCMD < \methodnamebold%%% \ (%DIFDELCMD < \methodabv%%%  ) that uses the following mathematical property: when a matrix is {\it Skew-Symmetric}, its exponential function is an {\it orthogonal} matrix.
Revised:  In this work, we propose a GNP convolution layer called %DIFDELCMD < \methodnamebold%%% %DIFDELCMD < \methodabv%%% ) that uses the following mathematical property: when a matrix is {\it Skew-Symmetric}, its exponential function is an {\it orthogonal} matrix.
Edit intent: fluency
Original:  In this work, we propose a GNP convolution layer called %DIFDELCMD < \methodnamebold%%% \ (%DIFDELCMD < \methodabv%%%  ) that uses the following mathematical property: when a matrix is {\it Skew-Symmetric}, its exponential function is an {\it orthogonal} matrix.
Revised:  In this work, we propose a GNP convolution layer called %DIFDELCMD < \methodnamebold%%% \ (%DIFDELCMD < \methodabv%%% Skew Orthogonal Convolution (SOC ) that uses the following mathematical property: when a matrix is {\it Skew-Symmetric}, its exponential function is an {\it orthogonal} matrix.
Edit intent: meaning-changed
Original:  Our experiments on CIFAR-10 and CIFAR-100 show that %DIFDELCMD < \methodabv%%% \ allows us to train provably Lipschitz, large convolutional neural networks significantly faster than prior works while achieving significant improvements for both standard and certified robust accuracies.
Revised:  Our experiments on CIFAR-10 and CIFAR-100 show that %DIFDELCMD < \methodabv%%% SOC allows us to train provably Lipschitz, large convolutional neural networks significantly faster than prior works while achieving significant improvements for both standard and certified robust accuracies.
Edit intent: fluency
Original:  To achieve this goal, we apply a doubly randomized scheme (see, e.g.,~\mbox{%DIFAUXCMD ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \mbox{%DIFAUXCMD jacob2 .
Revised:  To achieve this goal, we apply a (see, e.g.,~\mbox{%DIFAUXCMD ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \mbox{%DIFAUXCMD jacob2 .
Edit intent: clarity
Original:  To achieve this goal, we apply a doubly randomized scheme (see, e.g.,~\mbox{%DIFAUXCMD ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \mbox{%DIFAUXCMD jacob2 .
Revised:  To achieve this goal, we apply a doubly randomized scheme doubly randomized scheme, that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \mbox{%DIFAUXCMD jacob2 .
Edit intent: coherence
Original:  To achieve this goal, we apply a doubly randomized scheme (see, e.g.,~\mbox{%DIFAUXCMD ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization \mbox{%DIFAUXCMD jacob2 .
Revised:  To achieve this goal, we apply a doubly randomized scheme (see, e.g.,~\mbox{%DIFAUXCMD ub_mcmc, ub_grad that involves a novel coupled conditional particle filter (CCPF) on the second level of randomization .
Edit intent: meaning-changed
Original:  We illustrate our methodology in the context of stochastic gradient descent (SGD) in several numerical examples and compare with the Rhee \& Glynn estimator\mbox{%DIFAUXCMD rhee,vihola .
Revised:  We illustrate our methodology in the context of stochastic gradient descent (SGD) in several numerical examples and compare with the Rhee Glynn estimator .
Edit intent: clarity
Original:  It is applied on an autonomous robot (QOLO) in a static complex indoor environment  and also tested in simulations with dense crowds  .
Revised:  It is applied on an autonomous robot (QOLO) in a static complex indoor environment and also tested in simulations with dense crowds . The final proof of concept was performed in an outdoor environment in Lausanne. The QOLO-robot successfully traversed a marketplace in the center of town in presence of a diverse crowd with a non-uniform motion pattern .
Edit intent: meaning-changed
Original:  Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as thelocally optimal bandwidths (LOB) -- the bandwidth function that minimizes MISE in the asymptotic limit.
Revised:  Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as the locally optimal bandwidths (LOB) -- the bandwidth function that minimizes MISE in the asymptotic limit.
Edit intent: fluency
Original:  Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as thelocally optimal bandwidths (LOB) -- the bandwidth function that minimizes MISE in the asymptotic limit.
Revised:  Assuming that a small number of initial samples are available, we derive the optimal training density that minimizes the generalization error of local polynomial smoothing (LPS) with its kernel bandwidth tuned locally: We adopt the mean integrated squared error (MISE) as a generalization criterion, and use the asymptotic behavior of the MISE as well as thelocally optimal bandwidths (LOB) - the bandwidth function that minimizes MISE in the asymptotic limit.
Edit intent: fluency
Original:  In this work, we provide a simple  monotone CR scheme with a balancedness factor of 1 - e^{-k}k^k/k! for uniform matroids of rank k  , and show that this is optimal.
Revised:  In this work, we provide a simple and explicit monotone CR scheme with a balancedness factor of 1 - e^{-k}k^k/k! for uniform matroids of rank k , and show that this is optimal.
Edit intent: meaning-changed
Original:  In this work, we provide a simple  monotone CR scheme with a balancedness factor of 1 - e^{-k}k^k/k! for uniform matroids of rank k  , and show that this is optimal.
Revised:  In this work, we provide a simple monotone CR scheme with a balancedness factor of 1 - e^{-k}k^k/k! for uniform matroids of rank k (which matches the balancedness of 1-1/e for k=1) , and show that this is optimal.
Edit intent: meaning-changed
Original:  This result generalizes the 1-1/e optimal factor for the rank one (i. e. k=1) case, and improves it for any k>1.
Revised:  While this bound can be obtained by combining previously known results, these require defining an exponential-sized linear program and using random sampling and the ellipsoid algorithm. Our procedure, on the other hand, has the advantage of being simple and explicit.
Edit intent: meaning-changed
Original: The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of "lottery tickets" , and training a certain collection of them (i.e., a subnetwork) can match the performance of the full model.
Revised: The Lottery Ticket Hypothesis suggests that an over-parametrized network consists of ``lottery tickets'' , and training a certain collection of them (i.e., a subnetwork) can match the performance of the full model.
Edit intent: fluency