wanyu/IteraTeR_v2 huggingface dataset:
Original:  We put forward a range of  quasiorders that allow us to systematically design decision procedures for different language inclusion problems such as context-free languages into regular languages and regular languages into trace sets of one-counter nets .
Revised:  We put forward a range of such quasiorders that allow us to systematically design decision procedures for different language inclusion problems such as context-free languages into regular languages and regular languages into trace sets of one-counter nets .
Edit intent: clarity
Original:  We put forward a range of  quasiorders that allow us to systematically design decision procedures for different language inclusion problems such as context-free languages into regular languages and regular languages into trace sets of one-counter nets .
Revised:  We put forward a range of quasiorders that allow us to systematically design decision procedures for different language inclusion problems such as regular languages into regular languages and regular languages into trace sets of one-counter nets .
Edit intent: clarity
Original:  We put forward a range of  quasiorders that allow us to systematically design decision procedures for different language inclusion problems such as context-free languages into regular languages and regular languages into trace sets of one-counter nets .
Revised:  We put forward a range of quasiorders that allow us to systematically design decision procedures for different language inclusion problems such as context-free languages into regular languages or into trace sets of one-counter nets .
Edit intent: coherence
Original:  We also provide quasiorders for which the induced inclusion checking procedure corresponds to well-known state-of-the-art algorithms like the so-called antichain algorithms.
Revised:  We also provide quasiorders for which the induced inclusion checking procedures correspond to well-known state-of-the-art algorithms like the so-called antichain algorithms.
Edit intent: fluency
Original:  Finally, we provide an equivalent greatest fixpoint language inclusion check which relies on quotients of languages and, to the best of our knowledge, was not previously known.
Revised:  Finally, we provide an equivalent language inclusion checking algorithm based on a greatest fixpoint computation that relies on quotients of languages and, to the best of our knowledge, was not previously known.
Edit intent: clarity
Original:  This is the first rigorous result about the mixing time of Glauber dynamics for the six-vertex model in the ferroelectric phase.
Revised:  This is the first rigorous result bounding the mixing time of Glauber dynamics for the six-vertex model in the ferroelectric phase.
Edit intent: clarity
Original:  This is the first rigorous result about the mixing time of Glauber dynamics for the six-vertex model in the ferroelectric phase.
Revised:  This is the first rigorous result about the mixing time of Glauber dynamics in the ferroelectric phase.
Edit intent: clarity
Original:  Furthermore, our analysis demonstrates a fundamental connection between correlated random walks and the dynamics of intersecting lattice path models .
Revised:  Our analysis demonstrates a fundamental connection between correlated random walks and the dynamics of intersecting lattice path models .
Edit intent: coherence
Original:  Surprisingly, we show that the local variant has at least the same power as the original algorithm in terms of distinguishing non-isomorphic graphs.
Revised:  We show that the local variant has at least the same power as the original algorithm in terms of distinguishing non-isomorphic graphs.
Edit intent: coherence
Original:  In order to demonstrate the practical utility of our local variant, we apply it to supervised graph classification.
Revised:  To demonstrate the practical utility of our local variant, we apply it to supervised graph classification.
Edit intent: coherence
Original:  Here, we propose  a variant which considers a subset of the original neighborhood in each iteration step.
Revised:  Here, we propose a subset of the original neighborhood in each iteration step.
Edit intent: clarity
Original:  We show that the local variant has at least the same power as the original algorithm in terms of distinguishing non-isomorphic graphs.
Revised:  We show that the local variant has at least the same power as the original algorithm in terms of ability to distinguish non-isomorphic graphs.
Edit intent: clarity
Original:  To demonstrate the practical utility of our local variant, we apply it to supervised graph classification.
Edit intent: coherence
Original:  Upcoming persistent memory (PMem) technologies, like Intel's Optane DC Persistent Memory Modules, promise to bridge the gap between NAND-based flash (SSD) and DRAM  and thus eliminate the I/O bottleneck.
Revised:  Upcoming persistent memory (PMem) technologies, like Intel's Optane DC Persistent Memory Modules, promise to bridge the gap between NAND-based flash (SSD) and DRAM , and thus eliminate the I/O bottleneck.
Edit intent: fluency
Original:  ITCS 2012 introduced the notion of individual fairness (IF): given a task-specific similarity metric, every pair of similar individuals should receive similar outcomes.
Revised:  introduced the notion of individual fairness (IF): given a task-specific similarity metric, every pair of similar individuals should receive similar outcomes.
Edit intent: coherence
Original:  ITCS 2012 introduced the notion of individual fairness (IF): given a task-specific similarity metric, every pair of similar individuals should receive similar outcomes.
Revised:  ITCS 2012 which introduced a notion of individual fairness (IF): given a task-specific similarity metric, every pair of similar individuals should receive similar outcomes.
Edit intent: coherence
Original:  ITCS 2012 introduced the notion of individual fairness (IF): given a task-specific similarity metric, every pair of similar individuals should receive similar outcomes.
Revised:  ITCS 2012 introduced the notion of individual fairness (IF): given a task-specific similarity metric, every pair of individuals who are similarly qualified according to the metric should receive similar outcomes.
Edit intent: clarity
Original:  In this work, we study fairness when individuals have diverse preferences over the possible outcomes.
Revised:  We show that when individuals have diverse preferences over the possible outcomes.
Edit intent: clarity
Original:  We introduce and study a new notion of preference-informed individual fairness (PIIF) , a relaxation of individual fairness that allows for outcomes that deviate from IF, provided the deviations are in line with individuals' preferences.
Revised:  We introduce and study a new notion of preference-informed individual fairness (PIIF) that is a relaxation of individual fairness that allows for outcomes that deviate from IF, provided the deviations are in line with individuals' preferences.
Edit intent: clarity
Original:  We show that PIIF can allow for solutions that are considerably more beneficial to individuals than the best IF solution.
Revised:  We show that PIIF can permit outcomes that are more favorable to individuals than the best IF solution.
Edit intent: clarity
Original:  We further show how to efficiently optimize any convex objective over the outcomes subject to PIIF , for a rich class of individual preferences.
Revised:  We further show how to efficiently optimize any convex objective over the outcomes subject to PIIF for a rich class of individual preferences.
Edit intent: fluency
Original:  Motivated by fairness concerns in targeted advertising, we apply this new fairness notion to the multiple-task  setting introduced by Dwork and Ilvento ITCS 2019 . We show that, in this setting too, PIIF can allow for considerably more beneficial solutions, and we extend our efficient optimization algorithm to this setting .
Revised:  Motivated by fairness concerns in targeted advertising, we apply this new fairness notion to the multiple-task targeted advertising setting introduced by Dwork and Ilvento ITCS 2019 . We show that, in this setting too, PIIF can allow for considerably more beneficial solutions, and we extend our efficient optimization algorithm to this setting .
Edit intent: clarity
Original:  Motivated by fairness concerns in targeted advertising, we apply this new fairness notion to the multiple-task  setting introduced by Dwork and Ilvento ITCS 2019 . We show that, in this setting too, PIIF can allow for considerably more beneficial solutions, and we extend our efficient optimization algorithm to this setting .
Revised:  Motivated by fairness concerns in targeted advertising, we apply this new fairness notion to the multiple-task setting introduced by Dwork and Ilvento . We show that, in this setting too, PIIF can allow for considerably more beneficial solutions, and we extend our efficient optimization algorithm to this setting .
Edit intent: coherence
Original:  Motivated by fairness concerns in targeted advertising, we apply this new fairness notion to the multiple-task  setting introduced by Dwork and Ilvento ITCS 2019 . We show that, in this setting too, PIIF can allow for considerably more beneficial solutions, and we extend our efficient optimization algorithm to this setting .
Revised:  Motivated by fairness concerns in targeted advertising, we apply this new fairness notion to the multiple-task setting introduced by Dwork and Ilvento ITCS 2019 .
Edit intent: coherence
Original:  Meanwhile , the peak workload of inference service could be 10x higher than the average cases, with even unpredictable extreme cases.
Revised:  Meanwhile , the peak workload of inference service could be 10x higher than the average cases, with the presence of unpredictable extreme cases.
Edit intent: clarity
Original:  Supporting such deep learning service with dynamic workload cost-efficiently remains to be a challenging problem.
Revised:  Supporting such deep learning service with dynamic workload cost-efficiently remains a challenging problem.
Edit intent: clarity
Original:  We address this conflict with a general and novel training scheme called model slicing, which enables deep learning models to provide predictions within  prescribed computational resource budget dynamically.
Revised:  In this paper, we address the challenge with a general and novel training scheme called model slicing, which enables deep learning models to provide predictions within prescribed computational resource budget dynamically.
Edit intent: clarity
Original:  We address this conflict with a general and novel training scheme called model slicing, which enables deep learning models to provide predictions within  prescribed computational resource budget dynamically.
Revised:  We address this conflict with a general and novel training scheme called model slicing, which enables deep learning models to provide predictions within the prescribed computational resource budget dynamically.
Edit intent: fluency
Original:  Dynamically trained under such structural constraint, we can slice a narrower sub-model during inference whose run-time memory and computational operation consumption is roughly quadratic to the width controlled by a single parameter slice rate.
Revised:  Dynamically trained under such structural constraint, we can slice a narrower sub-model during inference whose run-time memory and computational operation consumption is roughly quadratic to the width controlled by the slice rate.
Edit intent: clarity
Original:  Extensive experiments show that models trained with model slicing can support elastic inference cost effectively with minimum performance loss .
Revised:  Extensive experiments show that models trained with model slicing can effectively support on-demand workload with elastic inference cost effectively with minimum performance loss .
Edit intent: clarity
Original:  Extensive experiments show that models trained with model slicing can support elastic inference cost effectively with minimum performance loss .
Revised:  Extensive experiments show that models trained with model slicing can support elastic inference cost .
Edit intent: clarity
Original:  How to support deep learning services with  dynamic workload cost-efficiently remains a challenging problem.
Revised:  How to support deep learning services with a dynamic workload cost-efficiently remains a challenging problem.
Edit intent: fluency
Original: We present a highly effective unsupervised method for detecting the stance of  Twitter users with respect to controversial topics.
Revised: We present a highly effective unsupervised framework for detecting the stance of Twitter users with respect to controversial topics.
Edit intent: clarity
Original:  Our method has three major advantages over current state-of-the-art methods, which are based on supervised or semi-supervised classification.
Revised:  Our framework has three major advantages over current state-of-the-art methods, which are based on supervised or semi-supervised classification.
Edit intent: clarity
Original:  Our method has three major advantages over current state-of-the-art methods, which are based on supervised or semi-supervised classification.
Revised:  Our method has three major advantages over pre-existing methods, which are based on supervised or semi-supervised classification.
Edit intent: clarity
Original:  Third, our method is robust in the face of data skewness, e.g., when some users or some stances have greater representation in the data.
Revised:  Third, our framework is robust in the face of data skewness, e.g., when some users or some stances have greater representation in the data.
Edit intent: clarity
Original:   Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality user clusters, typically just 2-3 , with more than 98 %DIFDELCMD < \\%%% %DIF < purity.
Revised:  Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality user clusters, typically just 2--3 , with more than 98 %DIFDELCMD < \\%%% %DIF < purity.
Edit intent: fluency
Original:   Moreover, our method is robust to variations in the parameter values and also with respect to random initialization.
Revised:  Moreover, our framework is robust to variations in the parameter values and also with respect to random initialization.
Edit intent: clarity
Original:  Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality user clusters, typically just 2--3, with more than 98 \% \\%DIF > purity.
Revised:  Our best combination in terms of effectiveness and efficiency uses retweeted accounts as features, UMAP for dimensionality reduction, and Mean Shift for clustering, and yields a small number of high-quality user clusters, typically just 2--3, with more than 98 \\%DIF > purity.
Edit intent: fluency
Original:  The resultant user clusters can be used to train downstream classification .
Revised:  The resulting user clusters can be used to train downstream classification .
Edit intent: fluency
Original:  The resultant user clusters can be used to train downstream classification .
Revised:  The resultant user clusters can be used to train downstream classifiers .
Edit intent: clarity
Original:   Glynn's formula is modified for computation of probabilities of output configurations m=(m_1,\ldots,m_n,0,\ldots,0)  with n<N output ports occupied by bosons, requiring only  C_m\equiv O\Bigl(N\prod_{l=1^n (m_l+1)}{min(m_l+1)}\Bigr) computations.
Revised:  First, Glynn's formula is modified for computation of probabilities of output configurations m=(m_1,\ldots,m_n,0,\ldots,0) with n<N output ports occupied by bosons, requiring only C_m\equiv O\Bigl(N\prod_{l=1^n (m_l+1)}{min(m_l+1)}\Bigr) computations.
Edit intent: fluency
Original:   Glynn's formula is modified for computation of probabilities of output configurations m=(m_1,\ldots,m_n,0,\ldots,0)  with n<N output ports occupied by bosons, requiring only  C_m\equiv O\Bigl(N\prod_{l=1^n (m_l+1)}{min(m_l+1)}\Bigr) computations.
Revised:  Glynn's formula is modified for computation of probabilities of output configurations m=(m_1,\ldots,m_n,0,\ldots,0) \; with n<N output ports occupied by bosons, requiring only C_m\equiv O\Bigl(N\prod_{l=1^n (m_l+1)}{min(m_l+1)}\Bigr) computations.
Edit intent: fluency
Original:   Glynn's formula is modified for computation of probabilities of output configurations m=(m_1,\ldots,m_n,0,\ldots,0)  with n<N output ports occupied by bosons, requiring only  C_m\equiv O\Bigl(N\prod_{l=1^n (m_l+1)}{min(m_l+1)}\Bigr) computations.
Revised:  Glynn's formula is modified for computation of probabilities of output configurations m=(m_1,\ldots,m_n,0,\ldots,0) with n<N output ports occupied by bosons, requiring only \; C_m\equiv O\Bigl(N\prod_{l=1^n (m_l+1)}{min(m_l+1)}\Bigr) computations.
Edit intent: fluency
Original:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations in the classical sampling algorithm of P. Clifford and R. Clifford scales as at least O\bigl( N 2^{1-\delta{1+\rho}N}\bigr) and at most  O\Bigl(N\left(1+r\right)^{N{r}}\Bigr), where \delta = \frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)}  and r = \mathrm{max}\left(1,\frac{1+\rho}{1+\delta}\right)  .
Revised:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations in the classical sampling algorithm of P. Clifford and R. Clifford scales as at least O\bigl( N 2^{1-\delta{1+\rho}N}\bigr) and at most as O\Bigl(N\left(1+r\right)^{N{r}}\Bigr), where \delta = \frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)} and r = \mathrm{max}\left(1,\frac{1+\rho}{1+\delta}\right) .
Edit intent: fluency
Original:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations in the classical sampling algorithm of P. Clifford and R. Clifford scales as at least O\bigl( N 2^{1-\delta{1+\rho}N}\bigr) and at most  O\Bigl(N\left(1+r\right)^{N{r}}\Bigr), where \delta = \frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)}  and r = \mathrm{max}\left(1,\frac{1+\rho}{1+\delta}\right)  .
Revised:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations in the classical sampling algorithm of P. Clifford and R. Clifford scales as at least O\bigl( N 2^{1-\delta{1+\rho}N}\bigr) and at most O\Bigl(N\left(1+r\right)^{N{r}}\Bigr), where \delta = \frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)} \; and r = \mathrm{max}\left(1,\frac{1+\rho}{1+\delta}\right) .
Edit intent: coherence
Original: The classical complexity of sampling from the probability distribution of quantum interference of N indistinguishable single bosons on unitary network with M input and output ports is studied with the focus on how boson density \rho =N{M} for M\ge N affects the number of computations required to produce a single sample.
Revised: The classical complexity of sampling from the probability distribution of quantum interference of N indistinguishable single bosons on unitary network with M input and output ports is studied with the focus on how density of bosons \rho =N{M} for M\ge N affects the number of computations required to produce a single sample.
Edit intent: fluency
Original:  These bounds apply also to the leading order of the number of classical computations in the sampling algorithm of P. Clifford and R. Clifford, which is based on  Glynn's formula and applies uniformly over the output configurations.
Revised:  These bounds apply also to the leading order of the number of classical computations in the sampling algorithm of P. Clifford and R. Clifford, which is based on using Glynn's formula and applies uniformly over the output configurations.
Edit intent: clarity
Original: The classical complexity of sampling from the probability distribution of quantum interference of N indistinguishable single bosons on unitary network with M input and output ports is studied with the focus on how density of bosons \rho =N{M for M}%DIFDELCMD < \ge %%% N affects the number of computations required to produce a single sample.
Revised:  for M}%DIFDELCMD < \ge %%% N affects the number of computations required to produce a single sample.
Edit intent: coherence
Original:  Second   ,  it is found that in a unitary network chosen according to the Haar probability measure the tails of the distribution of the total number n of output ports occupied by bosons are bounded by those of a binomial distribution.
Revised:  Secondet al , it is found that in a unitary network chosen according to the Haar probability measure the tails of the distribution of the total number n of output ports occupied by bosons are bounded by those of a binomial distribution.
Edit intent: clarity
Original:  Second   ,  it is found that in a unitary network chosen according to the Haar probability measure the tails of the distribution of the total number n of output ports occupied by bosons are bounded by those of a binomial distribution.
Revised:  Second, , it is found that in a unitary network chosen according to the Haar probability measure the tails of the distribution of the total number n of output ports occupied by bosons are bounded by those of a binomial distribution.
Edit intent: fluency
Original:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations C_m scales at least as O%DIFDELCMD < \bigl( %%% N 2^{\frac{1-\delta{1+\rho}N}}%DIFDELCMD < \bigr) %%% and at most as O%DIFDELCMD < \Bigl(%%% N%DIFDELCMD < \left(%%%  1 +r%DIFDELCMD < \right)%%% ^{\frac{N{r}}}%DIFDELCMD < \Bigr)%%% , where \delta = \sqrt{\frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)}\;
Revised:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations C_m scales at least as O%DIFDELCMD < \bigl( %%% N 2^{\frac{1-\delta{1+\rho}N}}%DIFDELCMD < \bigr) %%% and at most as O%DIFDELCMD < \Bigl(%%% %DIFDELCMD < \left(%%% 1 +r%DIFDELCMD < \right)%%% ^{\frac{N{r}}}%DIFDELCMD < \Bigr)%%% , where \delta = \sqrt{\frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)}\;
Edit intent: coherence
Original:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations C_m scales at least as O%DIFDELCMD < \bigl( %%% N 2^{\frac{1-\delta{1+\rho}N}}%DIFDELCMD < \bigr) %%% and at most as O%DIFDELCMD < \Bigl(%%% N%DIFDELCMD < \left(%%%  1 +r%DIFDELCMD < \right)%%% ^{\frac{N{r}}}%DIFDELCMD < \Bigr)%%% , where \delta = \sqrt{\frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)}\;
Revised:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations C_m scales at least as O%DIFDELCMD < \bigl( %%% N 2^{\frac{1-\delta{1+\rho}N}}%DIFDELCMD < \bigr) %%% and at most as O%DIFDELCMD < \Bigl(%%% N%DIFDELCMD < \left(%%% 1 %DIFDELCMD < \right)%%% ^{\frac{N{r}}}%DIFDELCMD < \Bigr)%%% , where \delta = \sqrt{\frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)}\;
Edit intent: coherence
Original:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations C_m scales at least as O%DIFDELCMD < \bigl( %%% N 2^{\frac{1-\delta{1+\rho}N}}%DIFDELCMD < \bigr) %%% and at most as O%DIFDELCMD < \Bigl(%%% N%DIFDELCMD < \left(%%%  1 +r%DIFDELCMD < \right)%%% ^{\frac{N{r}}}%DIFDELCMD < \Bigr)%%% , where \delta = \sqrt{\frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)}\;
Revised:  This fact allows to prove that for any \epsilon>0 with probability 1-\epsilon the number of computations C_m scales at least as O%DIFDELCMD < \bigl( %%% N 2^{\frac{1-\delta{1+\rho}N}}%DIFDELCMD < \bigr) %%% and at most as O%DIFDELCMD < \Bigl(%%% N%DIFDELCMD < \left(%%% 1 +r%DIFDELCMD < \right)%%% {r}}}%DIFDELCMD < \Bigr)%%% , where \delta = \sqrt{\frac{4(1+\rho){N}\ln\left(\frac{2}{\epsilon}\right)}\;
Edit intent: fluency