wanyu/IteraTeR_v2 huggingface dataset:
Original:  In response, a number of publishers have ramped up efforts to develop and deploy tools for detecting and/or counter-blocking adblockers ( tools we refer to as anti-adblockers), effectively escalating the online advertising arms race.
Revised:  In response, a number of publishers have ramped up efforts to develop and deploy tools for detecting and/or counter-blocking adblockers ( which we refer to as anti-adblockers), effectively escalating the online advertising arms race.
Edit intent: coherence
Original:  In this paper, we leverage a novel approach for identifying third-party services shared across multiple websites to present a first characterization of anti-adblocking across the Alexa Top-5K websites.
Revised:  In this paper, we leverage a novel approach for identifying third-party services shared across multiple web-sites and use it to provide a first characterization of anti-adblocking across the Alexa Top-5K websites.
Edit intent: clarity
Original:  The conjecture asserts that for each integers r,d> 0 and each continuous map f\colon\Delta\to\mathbb R^d of the (d+1)(r-1)-dimensional simplex \Delta there are pairwise disjoint subsimplices \sigma_1,\ldots,\sigma_r\subset\Delta such that f(\sigma_1)\cap \ldots \cap f(\sigma_r)\ne\emptyset.
Revised:  The conjecture asserts that for each integers r,d> 1 and each continuous map f\colon\Delta\to\mathbb R^d of the (d+1)(r-1)-dimensional simplex \Delta there are pairwise disjoint subsimplices \sigma_1,\ldots,\sigma_r\subset\Delta such that f(\sigma_1)\cap \ldots \cap f(\sigma_r)\ne\emptyset.
Edit intent: clarity
Original:  A counterexample was found in a series of papers by M. \"Ozaydin , M. Gromov, P. Blagojevi\'c , F. Frick, G. Ziegler, I. Mabillard and U. Wagner, most of them recent.
Revised:  A counterexample was found in a series of papers by M. Ozaydin , M. Gromov, P. Blagojevi\'c , F. Frick, G. Ziegler, I. Mabillard and U. Wagner, most of them recent.
Edit intent: fluency
Original:  A counterexample was found in a series of papers by M. \"Ozaydin , M. Gromov, P. Blagojevi\'c , F. Frick, G. Ziegler, I. Mabillard and U. Wagner, most of them recent.
Revised:  A counterexample was found in a series of papers by M. \"Ozaydin , M. Gromov, P. Blagojevic , F. Frick, G. Ziegler, I. Mabillard and U. Wagner, most of them recent.
Edit intent: fluency
Original:  The proof is a beautiful and fruitful interplay between combinatorics, algebra and topology.
Revised:  The arguments form a beautiful and fruitful interplay between combinatorics, algebra and topology.
Edit intent: clarity
Original:  In this expository note we present a simplified explanation of easier parts of the counterexample , accessible to non-specialists in the area, and give reference to more complicated parts.
Revised:  In this expository note we present a simplified explanation of easier parts of the arguments , accessible to non-specialists in the area, and give reference to more complicated parts.
Edit intent: clarity
Original:  In this expository note we present a simplified explanation of easier parts of the arguments, accessible to non-specialists in the area, and give reference to more complicated parts.
Revised:  In this expository paper we present a simplified explanation of easier parts of the arguments, accessible to non-specialists in the area, and give reference to more complicated parts.
Edit intent: clarity
Original: While the availability of services such as Amazon AWS make computing power abundantly available, adding more computing nodes can incur high costs in, for instance, pay-as-you-go plans while not always significantly improving the net running time (aka wall-clock time) of queries.
Revised: While services such as Amazon AWS make computing power abundantly available, adding more computing nodes can incur high costs in, for instance, pay-as-you-go plans while not always significantly improving the net running time (aka wall-clock time) of queries.
Edit intent: clarity
Original:  Building upon those results, we adapt this framework and apply it to a delayed feedforward spiking network to demonstrate how these models can qualitatively capture the modulation of firing rate heterogeneity in data from sensory neurons.
Revised:  Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network to demonstrate how these models can qualitatively capture the modulation of firing rate heterogeneity in data from sensory neurons.
Edit intent: clarity
Original:  Building upon those results, we adapt this framework and apply it to a delayed feedforward spiking network to demonstrate how these models can qualitatively capture the modulation of firing rate heterogeneity in data from sensory neurons.
Revised:  Building upon those results, we adapt this framework and apply it to a delayed feedforward spiking network that qualitatively capture the modulation of firing rate heterogeneity in data from sensory neurons.
Edit intent: clarity
Original:  This leads to a prediction about how the strength of the effective network connectivity is related to intrinsic heterogeneity  : the strength of the delayed feedforward is positively correlated with excitability (threshold value for spiking) with low firing rate heterogeneity and is negatively correlated with excitability with high firing rate heterogeneity.
Revised:  The model predicts how the strength of the effective network connectivity is related to intrinsic heterogeneity : the strength of the delayed feedforward is positively correlated with excitability (threshold value for spiking) with low firing rate heterogeneity and is negatively correlated with excitability with high firing rate heterogeneity.
Edit intent: clarity
Original:  This leads to a prediction about how the strength of the effective network connectivity is related to intrinsic heterogeneity  : the strength of the delayed feedforward is positively correlated with excitability (threshold value for spiking) with low firing rate heterogeneity and is negatively correlated with excitability with high firing rate heterogeneity.
Revised:  This leads to a prediction about how the strength of the effective network connectivity is related to intrinsic heterogeneity in such delayed feedforward networks : the strength of the delayed feedforward is positively correlated with excitability (threshold value for spiking) with low firing rate heterogeneity and is negatively correlated with excitability with high firing rate heterogeneity.
Edit intent: clarity
Original:  This leads to a prediction about how the strength of the effective network connectivity is related to intrinsic heterogeneity  : the strength of the delayed feedforward is positively correlated with excitability (threshold value for spiking) with low firing rate heterogeneity and is negatively correlated with excitability with high firing rate heterogeneity.
Revised:  This leads to a prediction about how the strength of the effective network connectivity is related to intrinsic heterogeneity : the strength of the feedforward input is positively correlated with excitability (threshold value for spiking) with low firing rate heterogeneity and is negatively correlated with excitability with high firing rate heterogeneity.
Edit intent: clarity
Original:  Recent experimental recordings in weakly electric fish indicate that the distribution of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population.
Revised:  Recent experimental recordings in weakly electric fish indicate that the distribution-width of superficial pyramidal cell firing rates (trial- and time-averaged) in the electrosensory lateral line lobe (ELL) depends on the stimulus, and also that network inputs can mediate changes in the firing rate distribution across the population.
Edit intent: clarity
Original:  Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the modulation of firing rate heterogeneity (across the population) in the data .
Revised:  Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the changes of firing rate heterogeneity (across the population) in the data .
Edit intent: clarity
Original:  Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the modulation of firing rate heterogeneity (across the population) in the data .
Revised:  Inspired by our experimental data, we extend these theoretical results to a delayed feedforward spiking network that qualitatively capture the modulation of firing rate heterogeneity observed in in-vivo recordings .
Edit intent: clarity
Original:  With generic computational models, we demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and to ultimately shape population codes.
Revised:  We demonstrate that neural attributes do not interact in a simple manner but rather in a complex stimulus-dependent fashion to control neural heterogeneity and to ultimately shape population codes.
Edit intent: clarity
Original:  Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning learning algorithms.
Revised:  Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning algorithms.
Edit intent: fluency
Original:  Skills are defined in terms of the  transitions between such abstract states.
Revised:  Skills are defined in terms of the sequence of actions that lead to transitions between such abstract states.
Edit intent: clarity
Original:  The skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over a common state space.
Revised:  These skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over a common state space.
Edit intent: fluency
Original:  Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories  such as faces, album covers, room interiors etc .
Revised:  Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories , such as faces, album covers, room interiors etc .
Edit intent: fluency
Original:  Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories  such as faces, album covers, room interiors etc .
Revised:  Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories such as faces, album covers, and room interiors .
Edit intent: clarity
Original:  In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling , translating visual concepts from characters to pixels.
Revised:  In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image model- ing , translating visual concepts from characters to pixels.
Edit intent: fluency
Original:  A natural competitor of the GMM kernel is the radial basis function (RBF) kernel, whose corresponding hashing method is known as the "random Fourier features" (RFF).
Revised:  A natural competitor of GMM is the radial basis function (RBF) kernel, whose corresponding hashing method is known as the "random Fourier features" (RFF).
Edit intent: fluency
Original:  Our classification experiments on public datasets illustrate  that both the GMM and RBF kernels can substantially improve linear classifiers.
Revised:  An extensive experimental study on classifications of that both the GMM and RBF kernels can substantially improve linear classifiers.
Edit intent: clarity
Original:  Our classification experiments on public datasets illustrate  that both the GMM and RBF kernels can substantially improve linear classifiers.
Revised:  Our classification experiments on public datasets illustrate that both the GMM and RBF kernels can often substantially improve over linear classifiers.
Edit intent: clarity
Original:  Furthermore, we show that GCWS typically requires substantially fewer samples than RFF . We expect that GMM and GCWS will be adopted in practice for large-scale  machine learning applications and  near neighbor search  .
Revised:  Furthermore, we show that GCWS typically requires substantially fewer samples than RFF . We expect that GMM and GCWS will be adopted in practice for large-scale machine learning applications and efficient near neighbor search .
Edit intent: clarity
Original:  An extensive experimental study on classifications of 50 publicly available datasets demonstrates that both the GMM and RBF kernels can often substantially improve over linear classifiers.
Revised:  An extensive experimental study on classifications of 50 publicly available datasets demonstrates that both the GMM and RBF kernels can often substantially improve over linear classifiers.
Edit intent: fluency
Original: We propose the "generalized min-max " (GMM)  kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS)  which linearizes this (nonlinear ) kernel.
Revised: We propose the "generalized min-max (GMM) kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS) which linearizes this (nonlinear ) kernel.
Edit intent: fluency
Original: We propose the "generalized min-max " (GMM)  kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS)  which linearizes this (nonlinear ) kernel.
Revised: We propose the "generalized min-max " (GMM) " kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS) which linearizes this (nonlinear ) kernel.
Edit intent: fluency
Original: We propose the "generalized min-max " (GMM)  kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS)  which linearizes this (nonlinear ) kernel.
Revised: We propose the "generalized min-max " (GMM) kernel as a measure of data similarity . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS) which linearizes this (nonlinear ) kernel.
Edit intent: coherence
Original: We propose the "generalized min-max " (GMM)  kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS)  which linearizes this (nonlinear ) kernel.
Revised: We propose the "generalized min-max " (GMM) kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associated hashing method named "generalized consistent weighted sampling " (GCWS) which linearizes this (nonlinear ) kernel.
Edit intent: fluency
Original: We propose the "generalized min-max " (GMM)  kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS)  which linearizes this (nonlinear ) kernel.
Revised: We propose the "generalized min-max " (GMM) kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling (GCWS) which linearizes this (nonlinear ) kernel.
Edit intent: fluency
Original: We propose the "generalized min-max " (GMM)  kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS)  which linearizes this (nonlinear ) kernel.
Revised: We propose the "generalized min-max " (GMM) kernel as a measure of data similarity , where data vectors can have both positive and negative entries . GMM is positive definite as there is an associate hashing method named "generalized consistent weighted sampling " (GCWS) " which linearizes this (nonlinear ) kernel.
Edit intent: fluency
Original:  Specifically, delay bounds are presented for  D/D/1, G / D/1, GI/ D/ 1 , and GI / GI / 1, all under multiclass FIFO .
Revised:  Specifically, delay bounds are presented for multiclass D/D/1, G / D/1, GI/ D/ 1 , and GI / GI / 1, all under multiclass FIFO .
Edit intent: fluency
Original:  Specifically, delay bounds are presented for  D/D/1, G / D/1, GI/ D/ 1 , and GI / GI / 1, all under multiclass FIFO .
Revised:  Specifically, delay bounds are presented for D/D/1, GI / D/1, GI/ D/ 1 , and GI / GI / 1, all under multiclass FIFO .
Edit intent: fluency
Original:  Specifically, delay bounds are presented for  D/D/1, G / D/1, GI/ D/ 1 , and GI / GI / 1, all under multiclass FIFO .
Revised:  Specifically, delay bounds are presented for D/D/1, G / GI/ D/ 1 , and GI / GI / 1, all under multiclass FIFO .
Edit intent: fluency
Original:  Specifically, delay bounds are presented for  D/D/1, G / D/1, GI/ D/ 1 , and GI / GI / 1, all under multiclass FIFO .
Revised:  Specifically, delay bounds are presented for D/D/1, G / D/1, GI/ 1 , and GI / GI / 1, all under multiclass FIFO .
Edit intent: fluency
Original:  Specifically, delay bounds are presented for  D/D/1, G / D/1, GI/ D/ 1 , and GI / GI / 1, all under multiclass FIFO .
Revised:  Specifically, delay bounds are presented for D/D/1, G / D/1, GI/ D/ 1 and G / GI / 1, all under multiclass FIFO .
Edit intent: fluency
Original: The AHP/ANP are multicriteria decision making theories that deal with both hierarchic structures when the criteria are independent of the alternatives and with networks when there is any dependence within and between elements of the decision.
Revised: The AHP/ANP are multicriteria decision-making theories that deal with both hierarchic structures when the criteria are independent of the alternatives and with networks when there is any dependence within and between elements of the decision.
Edit intent: fluency
Original:  From the resulting pairwise comparisons matrix one then derives the principal eigenvector whose entries represent priorities and are measures on a normalized absolute scale [1 ,2,3 ].
Revised:  From the resulting pairwise comparisons matrix one then derives the principal eigenvector whose entries represent priorities and are measures on a normalized absolute scale [1 ].
Edit intent: coherence
Original: Many modern applications collect large sample size and highly imbalanced categorical data, with some categories being relatively rare.
Revised: Many modern applications collect highly imbalanced categorical data, with some categories being relatively rare.
Edit intent: clarity
Original: Many modern applications collect large sample size and highly imbalanced categorical data, with some categories being relatively rare.
Revised: Many modern applications collect large sample size and highly imbalanced categorical data, with some categories relatively rare.
Edit intent: clarity
Original:  Bayesian hierarchical models are well motivated in such settings in providing an approach to borrow information to combat data sparsity , while quantifying uncertainty in estimation .
Revised:  Bayesian hierarchical models combat data sparsity , while quantifying uncertainty in estimation .
Edit intent: coherence
Original:  Bayesian hierarchical models are well motivated in such settings in providing an approach to borrow information to combat data sparsity , while quantifying uncertainty in estimation .
Revised:  Bayesian hierarchical models are well motivated in such settings in providing an approach to borrow information to combat data sparsity by borrowing information, while also quantifying uncertainty .
Edit intent: clarity
Original:  We previously speculated that the mechanism of DNA to DNA transcription might be similar to that of DNA to RNA transcription, but there is another mechanism that we think is most likely to be true.
Revised:  We once speculated that the mechanism of DNA to DNA transcription might be similar to that of DNA to RNA transcription, but there is another mechanism that we think is most likely to be true.
Edit intent: fluency
Original:  Occasionally, multiple copies of ssDNA transcripts might be produced through multiple rounds of cutout-repair-cutout cycle.
Revised:  Occasionally, multiple copies of ssDNA transcripts could be produced through multiple rounds of cutout-repair-cutout cycle.
Edit intent: clarity
Original:  The gap in the double-stranded DNA will be filled through DNA repair mechanism.
Revised:  The gap in the dsDNA will be filled through DNA repair mechanism.
Edit intent: clarity
Original:  Scott's monograph uses a formulation of domains called %DIFDELCMD < {\it %%% neighborhood systems in which finite elements are selected subsets of a master set of objects called "tokens".
Revised:  Scott's monograph uses a formulation of domains called %DIFDELCMD < {\it %%% neighborhood systems in which finite elements are selected subsets of a master set of objects called "tokens".
Edit intent: fluency
Original:  Since tokens have little intuitive significance, Scott has discarded neighborhood systems in favor of an equivalent formulation of domains called %DIFDELCMD < {\it %%% information systems .
Revised:  Since tokens have little intuitive significance, Scott has discarded neighborhood systems in favor of an equivalent formulation of domains called %DIFDELCMD < {\it %%% information systems .
Edit intent: fluency
Original:  We have rewritten Scott's monograph in terms of %DIFDELCMD < {\it %%% finitary bases instead of%DIFDELCMD < {\it %%% information systems .
Revised:  We have rewritten Scott's monograph in terms of %DIFDELCMD < {\it %%% instead of%DIFDELCMD < {\it %%% information systems .
Edit intent: clarity
Original:  We have rewritten Scott's monograph in terms of %DIFDELCMD < {\it %%% finitary bases instead of%DIFDELCMD < {\it %%% information systems .
Revised:  We have rewritten Scott's monograph in terms of %DIFDELCMD < {\it %%% finitary bases %DIFDELCMD < {\it %%% information systems .
Edit intent: coherence
Original:  We have rewritten Scott's monograph in terms of %DIFDELCMD < {\it %%% finitary bases instead of%DIFDELCMD < {\it %%% information systems .
Revised:  We have rewritten Scott's monograph in terms of %DIFDELCMD < {\it %%% finitary bases instead of%DIFDELCMD < {\it %%% finitary bases instead of information systems .
Edit intent: coherence
Original: We present Montre, a monitoring tool to search patterns specified by timed regular expressions in symbolic behaviors of real-time systems .
Revised: We present Montre, a monitoring tool to search patterns specified by timed regular expressions over real-time systems .
Edit intent: clarity
Original: We present Montre, a monitoring tool to search patterns specified by timed regular expressions in symbolic behaviors of real-time systems .
Revised: We present Montre, a monitoring tool to search patterns specified by timed regular expressions in symbolic behaviors of real-time behaviors .
Edit intent: clarity