wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Both the Office for National Statistics's respective count of 18,757 death certificates which mention COVID-19, and the Department of Health and Social Care's count of 15,755 in-hospital COVID-19-associated deaths, are appreciably less, implying that their counting methods have underestimated rather than overestimated the pandemic's true death toll.
Revised:  Both the Office for National Statistics's respective count of 26,891 death certificates which mention COVID-19, and the Department of Health and Social Care's count of 15,755 in-hospital COVID-19-associated deaths, are appreciably less, implying that their counting methods have underestimated rather than overestimated the pandemic's true death toll.
Edit intent: meaning-changed
Original:  Both the Office for National Statistics's respective count of 18,757 death certificates which mention COVID-19, and the Department of Health and Social Care's count of 15,755 in-hospital COVID-19-associated deaths, are appreciably less, implying that their counting methods have underestimated rather than overestimated the pandemic's true death toll.
Revised:  Both the Office for National Statistics's respective count of 18,757 death certificates which mention COVID-19, and the Department of Health and Social Care's hospital-focused count of 21,222 deaths, are appreciably less, implying that their counting methods have underestimated rather than overestimated the pandemic's true death toll.
Edit intent: meaning-changed
Original:  Both the ONS's corresponding count of 36,870 death certificates which mention COVID-19, and the Department of Health and Social Care's hospital-focused count of 29,057 deaths, are appreciably less, implying that their counting methods have underestimated, not overestimated, the pandemic's true death toll.
Revised:  Both the ONS's corresponding count of 43,205 death certificates which mention COVID-19, and the Department of Health and Social Care's hospital-focused count of 29,057 deaths, are appreciably less, implying that their counting methods have underestimated, not overestimated, the pandemic's true death toll.
Edit intent: meaning-changed
Original:  Both the ONS's corresponding count of 36,870 death certificates which mention COVID-19, and the Department of Health and Social Care's hospital-focused count of 29,057 deaths, are appreciably less, implying that their counting methods have underestimated, not overestimated, the pandemic's true death toll.
Revised:  Both the ONS's corresponding count of 36,870 death certificates which mention COVID-19, and the Department of Health and Social Care's count of 33,671 deaths, are appreciably less, implying that their counting methods have underestimated, not overestimated, the pandemic's true death toll.
Edit intent: meaning-changed
Original:  We focus on choosing, for each user, the best algorithm for playlist generation in order to optimize engagement. We characterize the various methods proposed in the literature into three general approaches: learning models to predict outcomes, learning models to predict causal effects, and learning models to predict optimal treatment assignments.
Revised:  We characterize the various methods proposed in the literature into three general approaches: learning models to predict outcomes, learning models to predict causal effects, and learning models to predict optimal treatment assignments.
Edit intent: coherence
Original:  Our results show (i) that applying different algorithms to different users can improve streams substantially compared to deploying the same algorithm for everyone, (ii) that personalized assignments improve substantially with larger data sets, and (iii) that learning models by optimizing treatment assignments rather than outcome or causal-effect predictions can improve treatment assignment performance by more than 28\%  .
Revised:  Our results show (i) that applying different algorithms to different users can improve streams substantially compared to deploying the same algorithm for everyone, (ii) that personalized assignments improve substantially with larger data sets, and (iii) that learning models by optimizing for treatment assignment can increase engagement by 28\% .
Edit intent: clarity
Original:  Our results show (i) that applying different algorithms to different users can improve streams substantially compared to deploying the same algorithm for everyone, (ii) that personalized assignments improve substantially with larger data sets, and (iii) that learning models by optimizing treatment assignments rather than outcome or causal-effect predictions can improve treatment assignment performance by more than 28\%  .
Revised:  Our results show (i) that applying different algorithms to different users can improve streams substantially compared to deploying the same algorithm for everyone, (ii) that personalized assignments improve substantially with larger data sets, and (iii) that learning models by optimizing treatment assignments rather than outcome or causal-effect predictions can improve treatment assignment performance by more than 28\% more than when optimizing for outcome or causal effect predictions .
Edit intent: clarity
Original: In the group testing problem one aims to infer a small set of k infected individuals out of a large population of size n. At our disposal we have a testing scheme which allows us to test a group of individuals, such that the outcome of the test is positive, if and only if at least one infected individual is part of the test. All tests are carried out in parallel. The overall goal is to find a test design and an inference algorithm requiring as few tests as possible, such that the infected individuals can be identified with high probability. As most relevant during the outbreak of pandemic diseases (Wang et al.,2011), our analysis focusses on the so-called sublinear regime of group testing, where k \sim n^\theta. The optimal group testing schemes require a test-size of \sim n/k and each individual has to take part in\sim \log n tests (Coja-Oghlan et. al, 2019). In real world applications, like testing many individuals in a short period of time during the outbreak of epidemics, pooling in such a way is not possible due to dilution effects. Evidence of those effects is known for important applications like HIV (Wein, 1996) or COVID-19 (Seifried and Ciesek, 2020). Our main contribution is the analysis of a group testing model, where we restrict the individuals-per-test to be finite. We present an easy to implement scheme to pool individuals into tests under these natural restrictions coming with the efficient decoding algorithm DD and present simulations which suggest that the decoding procedure succeeds for moderate population sizes. Furthermore, we show that our pooling schemerequires the fewest tests as possible under all pooling schemes (with \theta \in (0,1)), with n individuals among which k are infected.
Revised: Recent advances in noiseless non-adaptive group testing have led to a precise asymptotic characterization of the number of tests required for high-probability recovery in the sublinear regime k = n^{\theta (with \theta \in (0,1)), with n individuals among which k are infected.
Edit intent: meaning-changed
Original:  Finally, we apply our methods to the finitely many tests-per-individuals setting, where we provide a full understanding of the random regular test-design in this model by building up on work of (Gandikota et al., 2016) .
Revised:  Finally, for each of these two settings, we provide near-optimal adaptive algorithms based on sequential splitting, and provably demonstrate gaps between the performance of optimal adaptive and non-adaptive algorithms .
Edit intent: meaning-changed
Original: Two-dimensional partial covariance mass spectrometry ( 2D-pC-MS ) exploits the inherent fluctuations of fragment ion abundances across a series of tandem mass spectra, to identify correlated pairs of fragment ions produced along the same fragmentation pathway of the parent ion.
Revised: Two-dimensional partial covariance mass spectrometry ( 2D-PC-MS ) exploits the inherent fluctuations of fragment ion abundances across a series of tandem mass spectra, to identify correlated pairs of fragment ions produced along the same fragmentation pathway of the parent ion.
Edit intent: clarity
Original: Two-dimensional partial covariance mass spectrometry ( 2D-pC-MS ) exploits the inherent fluctuations of fragment ion abundances across a series of tandem mass spectra, to identify correlated pairs of fragment ions produced along the same fragmentation pathway of the parent ion.The technique has been applied so far to peptides and oligonucleotides.
Revised: Two-dimensional partial covariance mass spectrometry ( 2D-pC-MS ) exploits the inherent fluctuations of fragment ion abundances across a series of tandem mass spectra, to identify correlated pairs of fragment ions produced along the same fragmentation pathway of the same parent (e.g. peptide) ion.
Edit intent: clarity
Original:  Here, we apply 2D-pC-MS to the analysis of intact protein ions in a standard linear ion trap mass analyzer, using the fact that the fragment-fragment correlation signals are much more specific to biomolecular sequence than 1D MS/MS signals at a given mass accuracy and resolution.
Revised:  Here, we apply 2D-PC-MS to the analysis of intact protein ions in a standard linear ion trap mass analyzer, using the fact that the fragment-fragment correlation signals are much more specific to biomolecular sequence than 1D MS/MS signals at a given mass accuracy and resolution.
Edit intent: clarity
Original:  Here, we apply 2D-pC-MS to the analysis of intact protein ions in a standard linear ion trap mass analyzer, using the fact that the fragment-fragment correlation signals are much more specific to biomolecular sequence than 1D MS/MS signals at a given mass accuracy and resolution.
Revised:  Here, we apply 2D-pC-MS to the analysis of intact protein ions in a standard linear ion trap mass analyzer, using the fact that the fragment-fragment correlation signals are much more specific to bio-molecular sequence than 1D MS/MS signals at a given mass accuracy and resolution.
Edit intent: fluency
Original:  Here, we apply 2D-pC-MS to the analysis of intact protein ions in a standard linear ion trap mass analyzer, using the fact that the fragment-fragment correlation signals are much more specific to biomolecular sequence than 1D MS/MS signals at a given mass accuracy and resolution.
Revised:  Here, we apply 2D-pC-MS to the analysis of intact protein ions in a standard linear ion trap mass analyzer, using the fact that the fragment-fragment correlation signals are much more specific to biomolecular sequence than 1D MS/MS signals at the same mass accuracy and resolution.
Edit intent: clarity
Original:  Using this technique we also perform the in silico deconvolution of the overlapping fragment ion signals from two co-isolated and co-fragmented intact protein molecules, demonstrating a viable new method for the concurrent mass spectrometric identification of multiple intact protein ions from the same fragment ion spectrum.
Revised:  Using this technique we also perform the in silico deconvolution of the overlapping fragment ion signals from two co-isolated and co-fragmented intact protein molecules, demonstrating a viable new method for the concurrent mass spectrometric identification of a mixture of intact protein ions from the same fragment ion spectrum.
Edit intent: clarity
Original: We model the limit order book (LOB) as a continuous Markov process and develop an algebra to describe its dynamics based on the fundamental events of the book : order arrivals and cancellations.
Revised: We model the limit order book (LOB) as a continuous Markov process and develop an algebra to describe its dynamics based on the fundamental events in the limit order book (LOB) : order arrivals and cancellations.
Edit intent: clarity
Original:  We show how all observables (prices , returns, and liquidity measures) are governed by the same variables which also drive arrival and cancellation rates. The sensitivity of our model is evaluated in a simulation study and an empirical analysis.
Revised:  It is based on an operator algebra for individual orders and describes their effect on the LOB. The model inputs are arrival and cancellation rate distributions that emerge from individual behavior of traders, and we show how prices and liquidity arise from the LOB dynamics. In a simulation study and an empirical analysis.
Edit intent: meaning-changed
Original:  The sensitivity of our model is evaluated in a simulation study and an empirical analysis. We estimate several linearized model specifications based on the theoretical description of the LOB and conduct in- and out-of-sample forecasts on several frequencies .
Revised:  The sensitivity of our model is evaluated in a simulation study we illustrate how the model works and highlight its sensitivity with respect to assumptions regarding the collective behavior of market participants. Empirically, we test the model on a LOB snapshot of XETRA, estimate several linearized model specifications based on the theoretical description of the LOB and conduct in- and out-of-sample forecasts on several frequencies .
Edit intent: meaning-changed
Original: The in-sample results based on contemporaneous information suggest that our model describes up to 90\% of the variation of close-to-close returns , the adjusted R^2 still ranges at around 80\%.
Revised: The in-sample results based on contemporaneous information suggest that our model describes up to 90\% of the variation of close-to-close returns , the adjusted R^2 of roughly 80\%.
Edit intent: clarity
Original:  Out-of-sample, on average, we obtain R^2 values for the Mincer-Zarnowitz regression of around 2-3\% and an RMSPE that is 10 times lower than values documented in the literature.
Revised:  On average, we obtain R^2 values for the Mincer-Zarnowitz regression of around 2-3\% and an RMSPE that is 10 times lower than values documented in the literature.
Edit intent: clarity
Original:  Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found.
Revised: Motivation: Detailed mechanistic models of biological processes can pose significant challenges for analysis and parameter estimations due to the large number of equations used to track the dynamics of all distinct configurations in which each involved biochemical species can be found.
Edit intent: meaning-changed
Original: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS) that can be used to construct optimistic parameter estimates using significantly fewer samples from the posterior distributions compared to existing oversampling frameworks.
Revised: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS) that can be used to construct optimistic parameter estimates with significantly fewer samples from the posterior distributions compared to existing oversampling frameworks.
Edit intent: fluency
Original:  Combining CO with the oracle we designed for the offline problem, our online learning algorithm tackles the budget allocation, parameter learning, and reward maximization challenges simultaneously. We prove that our online learning algorithm achieves a scaled regret comparable to that of the  UCB-based algorithms for IM semi-bandits.
Revised:  Combining CO with the oracle we designed for the offline problem, our online learning algorithm tackles the budget allocation, parameter learning, and reward maximization . We show that for IM semi-bandits, our TS-based algorithm achieves a scaled regret comparable to that of the UCB-based algorithms for IM semi-bandits.
Edit intent: clarity
Original:  We prove that our online learning algorithm achieves a scaled regret comparable to that of the  UCB-based algorithms for IM semi-bandits. It is the first regret bound for TS-based algorithms for IM semi-bandits that does not depend linearly on the reciprocal of the minimum observation probability of an edge .
Revised:  We prove that our online learning algorithm achieves a scaled regret comparable to that of the UCB-based algorithms while significantly outperforming UCB-based alternatives in numerical experiments. Before this work, TS-based algorithms for IM semi-bandits that does not depend linearly on the reciprocal of the minimum observation probability of an edge .
Edit intent: meaning-changed
Original:  It is the first regret bound for TS-based algorithms for IM semi-bandits that does not depend linearly on the reciprocal of the minimum observation probability of an edge . In numerical experiments, our algorithm outperforms all UCB-based alternatives by a large margin .
Revised:  It is the first regret bound for TS-based algorithms for IM semi-bandits that does not depend linearly on the reciprocal of the minimum observation probability of an edge .
Edit intent: clarity
Original: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS)to construct optimistic parameter estimates with significantly fewer samples than existing oversampling frameworks .
Revised: We propose a cumulative oversampling (CO) method for online learning. Our key idea is to sample parameter estimations from the updated belief space once in each round (similar to Thompson Sampling), and utilize the cumulative samples up to the current round to construct optimistic parameter estimates with significantly fewer samples than existing oversampling frameworks .
Edit intent: meaning-changed
Original: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS)to construct optimistic parameter estimates with significantly fewer samples than existing oversampling frameworks .
Revised: We propose a cumulative oversampling (CO) technique for Thompson Sampling (TS)to construct optimistic parameter estimations that asymptotically concentrate around the true parameters as tighter upper confidence bounds compared to the ones constructed with standard UCB methods .
Edit intent: meaning-changed
Original:  We apply CO to a novel budgeted variant of the Influence Maximization (IM) semi-bandits with linear generalization of edge weights  . Combining CO with the oracle we design for the offline problem, our online learning algorithm simultaneously tackles budget allocation, parameter learning, and reward maximization.
Revised:  We apply CO to a novel budgeted variant of the Influence Maximization (IM) semi-bandits with linear generalization of edge weights , whose offline problem is NP-hard . Combining CO with the oracle we design for the offline problem, our online learning algorithm simultaneously tackles budget allocation, parameter learning, and reward maximization.
Edit intent: meaning-changed
Original:  We show that for IM semi-bandits, our TS-based algorithm achieves a scaled regret comparable to that of the best UCB-based algorithms while significantly outperforming UCB-based alternatives in numerical experiments.
Revised:  We show that for IM semi-bandits, our CO-based algorithm achieves a scaled regret comparable to that of the best UCB-based algorithms while significantly outperforming UCB-based alternatives in numerical experiments.
Edit intent: meaning-changed
Original:  We show that for IM semi-bandits, our TS-based algorithm achieves a scaled regret comparable to that of the best UCB-based algorithms while significantly outperforming UCB-based alternatives in numerical experiments.
Revised:  We show that for IM semi-bandits, our TS-based algorithm achieves a scaled regret comparable to that of the UCB-based algorithms while significantly outperforming UCB-based alternatives in numerical experiments.
Edit intent: clarity
Original:  We show that for IM semi-bandits, our TS-based algorithm achieves a scaled regret comparable to that of the best UCB-based algorithms while significantly outperforming UCB-based alternatives in numerical experiments. Before this work, TS-based algorithms for IM semi-bandits had larger regret bounds that were linearly dependent on the reciprocal of the minimum observation probability of an edge .
Revised:  We show that for IM semi-bandits, our TS-based algorithm achieves a scaled regret comparable to that of the best UCB-based algorithms in theory, and performs on par with Thompson Sampling in numerical experiments .
Edit intent: meaning-changed
Original:  It is shown  that during the fast phase, the number of new infected cases in several countries depends on the total number of cases by a power-law relation with a scaling exponent equal to 0.82.
Revised:  It is shown by the first model that during the fast phase, the number of new infected cases in several countries depends on the total number of cases by a power-law relation with a scaling exponent equal to 0.82.
Edit intent: meaning-changed
Original:  During this phase, the duplication time is 1-3 days. The duplication time together with another parameter that controls the phase change from the fast phase to the slow and plateau phases, varying in the range of 0.1-0.5 , may be used for data interpretation and for guiding predictions regarding this disease, e.g. the onset of the maximum in the number of new cases.
Revised:  The second model gives a duplication time in the range 1 to 3 days early in the start of the epidemic, and another parameter alpha = 0.1-0.5 , may be used for data interpretation and for guiding predictions regarding this disease, e.g. the onset of the maximum in the number of new cases.
Edit intent: clarity
Original:  The duplication time together with another parameter that controls the phase change from the fast phase to the slow and plateau phases, varying in the range of 0.1-0.5 , may be used for data interpretation and for guiding predictions regarding this disease, e.g. the onset of the maximum in the number of new cases.
Revised:  The duplication time together with another parameter that controls the phase change from the fast phase to the slow and plateau phases, varying in the range of 0.1-0.5 ) that deviates the progress of the epidemic from an exponential growth. Our models may be used for data interpretation and for guiding predictions regarding this disease, e.g. the onset of the maximum in the number of new cases.
Edit intent: meaning-changed
Original:  Even when the listing can be done in polynomial delay, that is, spending no more than polynomial time to go from one solution to the next, this can be costly as the number of solutions themselves may be huge, including sometimes exponential.
Revised:  Even when the enumeration can be done in polynomial delay, that is, spending no more than polynomial time to go from one solution to the next, this can be costly as the number of solutions themselves may be huge, including sometimes exponential.
Edit intent: clarity
Original:  This paper addresses this problem by proposing what we called alazy listing algorithm. By this we mean that, instead of listing all solutions , we list, in an efficient way, directly only the equivalence classes or  one representative per class . Besides the need to then provide ana priori relation of equivalence between solutions, we place ourselves in the special context where the problem to be addressed , either after some preliminary polynomial time dynamic programming computation or directly from the start, leads to a decomposable tropical circuit .
Revised:  This paper addresses this problem by proposing what we called alazy listing Furthermore, depending on the application, many of these solutions can be considered equivalent. The problem of an efficient enumeration of the equivalence classes or one representative per class . Besides the need to then provide ana priori relation of equivalence between solutions, we place ourselves in the special context where the problem to be addressed , either after some preliminary polynomial time dynamic programming computation or directly from the start, leads to a decomposable tropical circuit .
Edit intent: meaning-changed