wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The complexities of our enumeration algorithms are O(n \log \log (n/r)) time and  O(r ) words of working space for string length n and RLBWT size r .
Revised:  The complexities of our enumeration algorithms are O(n \log \log (n/r)) time and O(r ) words of working space for string length n and number r of runs in RLBWT, where r is expected to be significantly smaller than n for highly repetitive strings (i.e., strings with many repetitions). Experiments using a benchmark dataset of highly repetitive strings show that the results of r-enum are more space-efficient than the previous results. In addition, we demonstrate the applicability of r-enum to a huge string by performing experiments on a 300-gigabyte string of 100 human genomes .
Edit intent: meaning-changed
Original:  In particular, we implement the  t-distributed stochastic neighbor embedding (t-SNE) algorithm due to its beneficial properties as a data-driven dimension reduction and visualization tool in combination with spectral clustering to perform company classification .
Revised:  In particular, we implement the data-driven dimension reduction and visualization tool t-distributed stochastic neighbor embedding (t-SNE) algorithm due to its beneficial properties as a data-driven dimension reduction and visualization tool in combination with spectral clustering to perform company classification .
Edit intent: fluency
Original:  In particular, we implement the  t-distributed stochastic neighbor embedding (t-SNE) algorithm due to its beneficial properties as a data-driven dimension reduction and visualization tool in combination with spectral clustering to perform company classification .
Revised:  In particular, we implement the t-distributed stochastic neighbor embedding (t-SNE) algorithm due to its beneficial properties as a data-driven dimension reduction and visualization tool in combination with spectral clustering .
Edit intent: coherence
Original:  Recent work of Akbarpour and Li formalizes credibility as an auction desideratum, and prove that the only optimal, credible, strategyproof auction is the ascending price auction  (Akbarpour and Li, 2019).
Revised:  Recent work of Akbarpour and Li formalizes credibility as an auction desideratum, and prove that the only optimal, credible, strategyproof auction is the ascending price auction (Akbarpour and Li, 2019).
Edit intent: fluency
Original:  Recent work of Akbarpour and Li formalizes credibility as an auction desideratum, and prove that the only optimal, credible, strategyproof auction is the ascending price auction  (Akbarpour and Li, 2019).
Revised:  Recent work of Akbarpour and Li formalizes credibility as an auction desideratum, and prove that the only optimal, credible, strategyproof auction is the ascending price auction with reserves (Akbarpour and Li, 2019).
Edit intent: meaning-changed
Original:  In contrast, when buyers' valuations are MHR, we show that the mild additional assumption of a cryptographically secure commitment scheme suffices for a simple two-round auction which is optimal, credible, and strategyproof .
Revised:  In contrast, when buyers' valuations are MHR, we show that the mild additional assumption of a cryptographically secure commitment scheme suffices for a simple two-round auction which is optimal, credible, and strategyproof .
Edit intent: fluency
Original:  In contrast, when buyers' valuations are MHR, we show that the mild additional assumption of a cryptographically secure commitment scheme suffices for a simple two-round auction which is optimal, credible, and strategyproof .
Revised:  In contrast, when buyers' valuations are MHR, we show that the mild additional assumption of a cryptographically secure commitment scheme suffices for a simple two-round auction which is optimal, strategyproof, and credible (even when the number of bidders is only known by the auctioneer) .
Edit intent: meaning-changed
Original: Cross-impact, namely the fact that on average buy (sell) trades on a financial instrument induce positive (negative) price changes in other correlated assets, can be measured from abundant, although noisy, market data .
Revised: Cross-impact, namely the fact that on average buy (sell) trades on a financial instrument induces a price response on itself and on other correlated instruments, a phenomenon known as cross-impact. Unfortunately, empirical measures of cross-impact are affected by a large estimation error due to both the large number of interactions to infer and the strongly fluctuating nature of price returns .
Edit intent: clarity
Original:  In this paper we propose a principled approach that allows to perform model selection for cross-impact models, showing that symmetriesand consistency requirements are particularly effective in reducing the universe of possible models to a much smaller set of viable candidates, thus mitigating the effect of noise on the properties of the inferred model.
Revised:  In this study we propose a principled approach that allows to perform model selection for cross-impact models, showing that symmetriesand consistency requirements are particularly effective in reducing the universe of possible models to a much smaller set of viable candidates, thus mitigating the effect of noise on the properties of the inferred model.
Edit intent: clarity
Original:  In this paper we propose a principled approach that allows to perform model selection for cross-impact models, showing that symmetriesand consistency requirements are particularly effective in reducing the universe of possible models to a much smaller set of viable candidates, thus mitigating the effect of noise on the properties of the inferred model.
Revised:  In this paper we propose a principled approach that leverages simple consistency criteria (symmetries, no-arbitrage conditions, correlation and liquidity limit-case properties) in order to impose ex-ante properties that might be required for practical applications. We validate our approach on empirical data for several asset classes, thus determining which properties are desirable across multiple markets. In particular, our results show that two cross-impact models, showing that symmetriesand consistency requirements are particularly effective in reducing the universe of possible models to a much smaller set of viable candidates, thus mitigating the effect of noise on the properties of the inferred model.
Edit intent: meaning-changed
Original:  In this paper we propose a principled approach that allows to perform model selection for cross-impact models, showing that symmetriesand consistency requirements are particularly effective in reducing the universe of possible models to a much smaller set of viable candidates, thus mitigating the effect of noise on the properties of the inferred model. We review the empirical performance of a large number of cross-impact models, comparing their strengths and weaknesses on a number of asset classes(futures, stocks, calendar spreads). Besides showing which models perform better, we argue that in presence of comparable statistical performance, which is often the case in a noisy world, it is relevant to favor models that provide ex-ante theoretical guarantees on their behavior in limit cases. From this perspective, we advocate that the empirical validation of universal properties (symmetries, invariances) should be regarded as holding a much deeper epistemological value than any measure of statistical performance on specific model instances .
Revised:  In this paper we propose a principled approach that allows to perform model selection for cross-impact models perform well in all markets studied but only one is suitable for other applications, such as optimal execution .
Edit intent: meaning-changed
Original:  Given a stream of n items from a data universe U ( equipped with a total order ) , the task is to compute a sketch (data structure) of size poly(\log(n), 1/\varepsilon).
Revised:  Given a stream of n items from a data universe U equipped with a total order ) , the task is to compute a sketch (data structure) of size poly(\log(n), 1/\varepsilon).
Edit intent: fluency
Original:  Given a stream of n items from a data universe U ( equipped with a total order ) , the task is to compute a sketch (data structure) of size poly(\log(n), 1/\varepsilon).
Revised:  Given a stream of n items from a data universe U ( equipped with a total order , the task is to compute a sketch (data structure) of size poly(\log(n), 1/\varepsilon).
Edit intent: fluency
Original: 5}(\varepsilon n)/\varepsilon) (ignoring \text{poly(\log\log n, \log(1/\varepsilon)) factors) } that achieves a 1\pm\varepsilon multiplicative error guarantee, without prior knowledge of the stream length or dependence on the size of the data universe.
Revised: 5}(\varepsilon n)/\varepsilon) (\log\log n, \log(1/\varepsilon)) factors) } that achieves a 1\pm\varepsilon multiplicative error guarantee, without prior knowledge of the stream length or dependence on the size of the data universe.
Edit intent: coherence
Original:  Given a stream of n items from a data universe U equipped with a total order, the task is to compute a sketch (data structure) of size \mathrm{poly (\log(n), 1/\varepsilon).
Revised:  Given a stream of n items from a data universe U equipped with a total order, the task is to compute a sketch (data structure) of size poly (\log(n), 1/\varepsilon).
Edit intent: clarity
Original:  The most space-efficient algorithms that can be derived from prior work store either O(\log(\varepsilon^2 n)/\varepsilon^2) or O(\log^3(\varepsilon n)/\varepsilon) universe items.
Revised:  The most space-efficient algorithms due to prior work store either O(\log(\varepsilon^2 n)/\varepsilon^2) or O(\log^3(\varepsilon n)/\varepsilon) universe items.
Edit intent: clarity
Original: 5}(\varepsilon n)/\varepsilon) that achieves a 1%DIFDELCMD < \pm%%% \varepsilon multiplicative error guarantee, without prior knowledge of the stream length or dependence on the size of the data universe.
Revised: 5}(\varepsilon n)/\varepsilon) %DIFDELCMD < \pm%%% \varepsilon multiplicative error guarantee, without prior knowledge of the stream length or dependence on the size of the data universe.
Edit intent: clarity
Original: 5}(\varepsilon n)/\varepsilon) that achieves a 1%DIFDELCMD < \pm%%% \varepsilon multiplicative error guarantee, without prior knowledge of the stream length or dependence on the size of the data universe. This is within a O(\log(\varepsilon n)) factor of optimal.
Revised: 5}(\varepsilon n)/\varepsilon) that achieves a 1%DIFDELCMD < \pm%%% items, which is within an O(\log(\varepsilon n)) factor of optimal.
Edit intent: clarity
Original:  By setting the end of containment as the day when hospital capacity is reached, numerical simulations showed that the duration can be reduced (up to only 84 days for France if the capacity is 1000 patients).
Revised:  By setting the end of intervention as the day when hospital capacity is reached, numerical simulations showed that the duration can be reduced (up to only 84 days for France if the capacity is 1000 patients).
Edit intent: clarity
Original:  By setting the end of containment as the day when hospital capacity is reached, numerical simulations showed that the duration can be reduced (up to only 84 days for France if the capacity is 1000 patients).
Revised:  By setting the end of containment as the day when hospital capacity is reached, numerical simulations showed that the duration can be reduced (up to only 39 days for France if the capacity is 1000 patients).
Edit intent: meaning-changed
Original: We provide a data-driven analysis of how investor expectations about economic growth and stock market returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic  .
Revised: We provide a data-driven analysis of how investor expectations about economic growth and stock market returns changed during the February-March 2020 stock market crash induced by the COVID-19 pandemic , as well as during the subsequent partial stock market recovery .
Edit intent: meaning-changed
Original:  We surveyed wealthy retail investors who are clients of Vanguard in mid-February 2020 , around the all-time stock market high, and then again on March 11 and 12 , after the stock market had collapsed by over 20 \%.
Revised:  We surveyed retail investors who are clients of Vanguard in mid-February 2020 , around the all-time stock market high, and then again on March 11 and 12 , after the stock market had collapsed by over 20 \%.
Edit intent: clarity
Original:  We surveyed wealthy retail investors who are clients of Vanguard in mid-February 2020 , around the all-time stock market high, and then again on March 11 and 12 , after the stock market had collapsed by over 20 \%.
Revised:  We surveyed wealthy retail investors who are clients of Vanguard at three points in time: (i) on February 11-12 , around the all-time stock market high, and then again on March 11 and 12 , after the stock market had collapsed by over 20 \%.
Edit intent: meaning-changed
Original:  We surveyed wealthy retail investors who are clients of Vanguard in mid-February 2020 , around the all-time stock market high, and then again on March 11 and 12 , after the stock market had collapsed by over 20 \%.
Revised:  We surveyed wealthy retail investors who are clients of Vanguard in mid-February 2020 , around the all-time stock market high, (ii) on March 11-12 , after the stock market had collapsed by over 20 \%.
Edit intent: meaning-changed
Original:  We surveyed wealthy retail investors who are clients of Vanguard in mid-February 2020 , around the all-time stock market high, and then again on March 11 and 12 , after the stock market had collapsed by over 20 \%. The average investor turned more pessimistic about the short-run performance of both stock markets and the economy. Investors also perceived higher probability of both further extreme stock market declines and large declines in short-run real economic activity. In contrast, investors' expectations about the long run remained largely unchanged, and if anything improved. Disagreement among investors about economic and stock market outcomes also increased substantially. Our analysis is an input in both the design of the ongoing economic policy response and in further advancing economic theories.
Revised:  We surveyed wealthy retail investors who are clients of Vanguard in mid-February 2020 , around the all-time stock market high, and then again on March 11 and 12 , after the stock market had collapsed by over 20 
Edit intent: clarity
Original:  The SEIR model is a well-known method used in computational simulations of infectious viral diseases . It has been widely used to model other epidemics such as Ebola, MERS (Middle East Respiratory Syndrome) , and influenza . Ageneral SEIR model represents an epidemic using four compartments in cascade;
Revised:  The SEIR model is a well-known method used in computational simulations of infectious viral diseases and it has been widely used to model other epidemics such as Ebola, MERS (Middle East Respiratory Syndrome) , and influenza . Ageneral SEIR model represents an epidemic using four compartments in cascade;
Edit intent: coherence
Original:  It has been widely used to model other epidemics such as Ebola, MERS (Middle East Respiratory Syndrome) , and influenza . Ageneral SEIR model represents an epidemic using four compartments in cascade;
Revised:  It has been widely used to model other epidemics such as Ebola, SARS, MERS , and influenza . Ageneral SEIR model represents an epidemic using four compartments in cascade;
Edit intent: meaning-changed
Original:  It has been widely used to model other epidemics such as Ebola, MERS (Middle East Respiratory Syndrome) , and influenza . Ageneral SEIR model represents an epidemic using four compartments in cascade; susceptible (S), exposed (E), infected (I), and recovery/removed (R), where the model assumes that the epidemic exits with the infected population either recovered or removed due to immunity to the virus. In this work, we present a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we tuned the exit conditions in the model to extend predictions on the projection of the current pandemic into three  outcomes;
Revised:  It has been widely used to model other epidemics such as Ebola, MERS (Middle East Respiratory Syndrome) , and influenza A. This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we tuned the exit conditions in the model to extend predictions on the projection of the current pandemic into three outcomes;
Edit intent: clarity
Original:  In this work, we present a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we tuned the exit conditions in the model to extend predictions on the projection of the current pandemic into three  outcomes;
Revised:  In this work, we present a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we can tune the exit conditions in the model to extend predictions on the projection of the current pandemic into three outcomes;
Edit intent: clarity
Original:  In this work, we present a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we tuned the exit conditions in the model to extend predictions on the projection of the current pandemic into three  outcomes;
Revised:  In this work, we present a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we tuned the exit conditions in the model to extend prediction on the current projections of the pandemic into three outcomes;
Edit intent: fluency
Original:  In this work, we present a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we tuned the exit conditions in the model to extend predictions on the projection of the current pandemic into three  outcomes;
Revised:  In this work, we present a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy, where we tuned the exit conditions in the model to extend predictions on the projection of the current pandemic into three possible outcomes;
Edit intent: clarity
Original:   The model is verified with a case study using the real-world data in South Korea  .
Revised:  Owing to huge variations in clinical symptoms exhibited by COVID-19, the proposed model aims to reflect better on the current scenario and case data reported, such that the spread of the disease and the efficiency of the control action taken can be better understood. The model is verified with a case study using the real-world data in South Korea .
Edit intent: meaning-changed
Original:  This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibiliy , where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes;
Revised:  This paper presents a modified SEIRS model with additional exit conditions in the form of death rates and resusceptibility , where we can tune the exit conditions in the model to extend prediction on the current projections of the pandemic into three possible outcomes;
Edit intent: fluency
Original:  As such, we believe that it is an important first step towards discovering the structural properties behind the tractability of arbitrary k-CSPs for constraint densities up to the algorithmic barrier \ge  .
Revised:  As such, we believe that it is an important first step towards discovering the structural properties behind the tractability of arbitrary k-CSPs for constraint densities up to the algorithmic barrier . Our main technical contribution is to prove that, for every k\ge .
Edit intent: meaning-changed
Original:  As such, we believe that it is an important first step towards discovering the structural properties behind the tractability of arbitrary k-CSPs for constraint densities up to the algorithmic barrier \ge  .
Revised:  As such, we believe that it is an important first step towards discovering the structural properties behind the tractability of arbitrary k-CSPs for constraint densities up to the algorithmic barrier \ge 2, every k-uniform hypergaph of degree \Delta and girth at least 5 is efficiently (1+o(1) )(k-1) (\Delta / \ln \Delta )^{1/(k-1) .
Edit intent: meaning-changed
Original: All known efficient algorithms for constraint satisfaction problems are stymied by random instances. For example, no efficient algorithm is known that can q-color a random graph with average degree (1 + \epsilon)q \ln q, even though random graphs remain q-colorable for average degree up to (2-o(1)) q \ln q. Similar failure to find solutions at relatively low constraint densities is known for random CSPs such as random k-SAT and other hypergraph-based problems. The constraint density where algorithms break down for each CSP is known as the "algorithmic barrier" and provably corresponds to a phase transition in the geometry of the space of solutions Achlioptas and Coja-Oghlan 2008%DIFDELCMD < ]%%% . In this paper we aim to shed light on the following question: Can algorithmic success up to the barrier for each CSP be ascribed to some simple structural property of the inputs?
Revised:  Achlioptas and Coja-Oghlan 2008%DIFDELCMD < ]%%% . In this paper we aim to shed light on the following question: Can algorithmic success up to the barrier for each CSP be ascribed to some simple structural property of the inputs?
Edit intent: meaning-changed
Original:  The constraint density where algorithms break down for each CSP is known as the "algorithmic barrier" and provably corresponds to a phase transition in the geometry of the space of solutions Achlioptas and Coja-Oghlan 2008%DIFDELCMD < ]%%% . In this paper we aim to shed light on the following question: Can algorithmic success up to the barrier for each CSP be ascribed to some simple structural property of the inputs?
Revised:  The constraint density where algorithms break down for each CSP is known as the "algorithmic barrier" and provably corresponds to a phase transition in the geometry of the space of solutions %DIFDELCMD < ]%%% . In this paper we aim to shed light on the following question: Can algorithmic success up to the barrier for each CSP be ascribed to some simple structural property of the inputs?
Edit intent: meaning-changed
Original:  The constraint density where algorithms break down for each CSP is known as the "algorithmic barrier" and provably corresponds to a phase transition in the geometry of the space of solutions Achlioptas and Coja-Oghlan 2008%DIFDELCMD < ]%%% . In this paper we aim to shed light on the following question: Can algorithmic success up to the barrier for each CSP be ascribed to some simple structural property of the inputs? We answer this question positively for graph coloring by identifying the property ofgirth-reducibility .
Revised:  The constraint density where algorithms break down for each CSP is known as the "algorithmic barrier" and provably corresponds to a phase transition in the geometry of the space of solutions Achlioptas and Coja-Oghlan 2008%DIFDELCMD < ]%%% girth-reducibility .
Edit intent: coherence
Original:  We answer this question positively for graph coloring by identifying the property ofgirth-reducibility . We prove that every girth-reducible graph of average degree (1-o(1) )q \ln q is efficiently q-colorable and that the threshold for girth reducibility of random graphs coincides with the algorithmic barrier.
Revised:  We answer this question positively for graph coloring by identifying the property ofgirth-reducibility We show that, for every girth-reducible graph of average degree (1-o(1) )q \ln q is efficiently q-colorable and that the threshold for girth reducibility of random graphs coincides with the algorithmic barrier.
Edit intent: coherence