wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We trace  movements of scholars between Mexican states  and provide key demographic measures of internal migration for the period 1996-2018  . From a methodological perspective, we develop a new framework for enhancing data quality, inferring states from affiliations, and detecting moves from modal states for the purposes of studying internal migration between researchers.
Revised:  We trace movements of scholars between Mexican states and provide key demographic measures of internal migration for the period 1996-2018 . From a methodological perspective, we develop a new framework for enhancing data quality, inferring states from affiliations, and detecting moves from modal states for the purposes of studying internal migration among researchers.
Edit intent: fluency
Original:  However, while many scholars remain in their regions, there seems to be a preference for Mexico City and the surrounding states as a destination. Over the past two decades, we observed a general decreasing trend in the crude migration intensity.
Revised:  However, while many scholars remain in their regions, there seems to be a preference for Mexico City and the surrounding states as migration destinations. We observed that over the past two decades, we observed a general decreasing trend in the crude migration intensity.
Edit intent: coherence
Original:  Recently, Ban et . al. [BCF+19] studied the problem where the unknown distribution over n-bit strings is known to be \ell-sparse for some fixed \ell, and the noise is induced through the deletion channel.
Revised:  Recently, Ban et al. [BCF+19] studied the problem where the unknown distribution over n-bit strings is known to be \ell-sparse for some fixed \ell, and the noise is induced through the deletion channel.
Edit intent: fluency
Original:  The deletion channel is a noise model where each bit of the string is independently deleted with some fixed probability, and the retained bits are concatenated. We note that if \ell = 1, i.e., we are trying to learn a single string , learning the distribution is equivalent to the famous trace reconstruction problem.
Revised:  This problem generalizes the famous trace reconstruction problem, where one wishes to learn a single string , learning the distribution is equivalent to the famous trace reconstruction problem.
Edit intent: meaning-changed
Original:  We note that if \ell = 1, i.e., we are trying to learn a single string , learning the distribution is equivalent to the famous trace reconstruction problem. The best known algorithms for trace reconstruction require \exp%DIFDELCMD < \left(%%% O(n^{1/3)}%DIFDELCMD < \right) %%% samples.
Revised:  We note that if \ell = 1, i.e., we are trying to learn a single string %DIFDELCMD < \left(%%% O(n^{1/3)}%DIFDELCMD < \right) %%% samples.
Edit intent: coherence
Original:  The best known algorithms for trace reconstruction require \exp%DIFDELCMD < \left(%%% O(n^{1/3)}%DIFDELCMD < \right) %%% samples. For population recovery under the deletion channel , Ban et . al. provided an algorithm that could learn \ell-sparse distributions over strings using \exp %DIFDELCMD < \left(%%% \big( n^{1/2} \cdot (\log n)^{O(\ell)} %DIFDELCMD < \right) %%% \big) samples.
Revised:  The best known algorithms for trace reconstruction require \exp%DIFDELCMD < \left(%%% O(n^{1/3)}%DIFDELCMD < \right) %%% under the deletion channel , Ban et . al. provided an algorithm that could learn \ell-sparse distributions over strings using \exp %DIFDELCMD < \left(%%% \big( n^{1/2} \cdot (\log n)^{O(\ell)} %DIFDELCMD < \right) %%% \big) samples.
Edit intent: clarity
Original:  For population recovery under the deletion channel , Ban et . al. provided an algorithm that could learn \ell-sparse distributions over strings using \exp %DIFDELCMD < \left(%%% \big( n^{1/2} \cdot (\log n)^{O(\ell)} %DIFDELCMD < \right) %%% \big) samples.
Revised:  For population recovery under the deletion channel . Ban et al. showed how to learn \ell-sparse distributions over strings using \exp %DIFDELCMD < \left(%%% \big( n^{1/2} \cdot (\log n)^{O(\ell)} %DIFDELCMD < \right) %%% \big) samples.
Edit intent: clarity
Original:  In this work, we provide an algorithm that learns the distribution using only \exp\big(O(n^{1/3}) \cdot \ell^2\big) samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17] \big(O)}\big)  .
Revised:  In this work, we learn the distribution using only \exp\big(O(n^{1/3}) \cdot \ell^2\big) samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17] \big(O)}\big) .
Edit intent: clarity
Original:  In this work, we provide an algorithm that learns the distribution using only \exp\big(O(n^{1/3}) \cdot \ell^2\big) samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17] \big(O)}\big)  .
Revised:  In this work, we provide an algorithm that learns the distribution using only \exp\big(O(n^{1/3}) \cdot \ell^2\big) samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17] , which solve trace reconstruction in \exp\big(O)}\big) .
Edit intent: meaning-changed
Original:  In this work, we provide an algorithm that learns the distribution using only \exp\big(O(n^{1/3}) \cdot \ell^2\big) samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17] \big(O)}\big)  .
Revised:  In this work, we provide an algorithm that learns the distribution using only \exp\big(O(n^{1/3}) \cdot \ell^2\big) samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17] \big(O(n^{1/3)}\big) .
Edit intent: fluency
Original:  In this work, we provide an algorithm that learns the distribution using only \exp\big(O(n^{1/3}) \cdot \ell^2\big) samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17] \big(O)}\big)  .
Revised:  In this work, we provide an algorithm that learns the distribution using only \exp\big(O(n^{1/3}) \cdot \ell^2\big) samples, by developing a higher-moment analog of the algorithms of [DOS17, NP17] \big(O)}\big) samples .
Edit intent: fluency
Original:  We also give the first algorithm with a runtime subexponential in n, which solves population recovery in \exp\big(\tilde{O}(n^{1/3}) \cdot \ell^3\big) samples and time.
Revised:  We also give the first algorithm with a runtime subexponential in n, solving population recovery in \exp\big(\tilde{O}(n^{1/3}) \cdot \ell^3\big) samples and time.
Edit intent: coherence
Original:  Notably, our dependence on n nearly matches the known upper bound  when \ell =  1  , and we reduce the dependence on \ell from doubly to nearly singly exponential.
Revised:  Notably, our dependence on n nearly matches the known upper bound DOS17, NP17 when \ell = 1 , and we reduce the dependence on \ell from doubly to nearly singly exponential.
Edit intent: meaning-changed
Original:  Notably, our dependence on n nearly matches the known upper bound  when \ell =  1  , and we reduce the dependence on \ell from doubly to nearly singly exponential.
Revised:  Notably, our dependence on n nearly matches the known upper bound when \ell = O( 1 , and we reduce the dependence on \ell from doubly to nearly singly exponential.
Edit intent: meaning-changed
Original:  Notably, our dependence on n nearly matches the known upper bound  when \ell =  1  , and we reduce the dependence on \ell from doubly to nearly singly exponential.
Revised:  Notably, our dependence on n nearly matches the known upper bound when \ell = 1 ) , and we reduce the dependence on \ell from doubly to nearly singly exponential.
Edit intent: fluency
Original:  Therefore, we are able to learn the mixture even for much larger values of \ell. For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big)  and time.
Revised:  Therefore, we are able to learn large mixtures of strings: while Ban et al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big) and time.
Edit intent: clarity
Original:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big)  and time.
Revised:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of samples, we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big) and time.
Edit intent: clarity
Original:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big)  and time.
Revised:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of n^{o(1)} strings in subexponential queries\big(}\big) and time.
Edit intent: clarity
Original:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big)  and time.
Revised:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in \exp\big(}\big) and time.
Edit intent: clarity
Original:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big)  and time.
Revised:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(n^{1/3 + o(1)}\big) and time.
Edit intent: meaning-changed
Original:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big)  and time.
Revised:  For instance, Ban et . al.'s algorithm can only learn a mixture of O(\log n/\log \log n) strings with a subexponential number of queries, whereas we are able to learn a mixture of up to n^{o(1)} strings in subexponential queries\big(}\big) samples and time.
Edit intent: fluency
Original:  I show that these variations are strongly correlated with the levels of the ACE2 protein in the lungsbut surprisingly, despite ACE2 is the viral receptor, higher levels lead to lower fatality. This behaviour is consistent with a previous mathematical model that predicts that the speed of viral progression in URLanism has a maximum and then declines with the receptor level.
Revised:  I show that these variations are strongly correlated with the levels of the ACE2 protein in the lungsbut surprisingly, despite ACE2 is the level of the viral receptor protein ACE2 in rat lungs, which is consistent with the still limited and apparently contradictory data on human ACE2. Surprisingly, lower levels of the receptor correlate with higher fatality. However, a previous mathematical model that predicts that the speed of viral progression in URLanism has a maximum and then declines with the receptor level.
Edit intent: meaning-changed
Original:  SARS-Cov-2 degrades ACE2 and thus worsens lung injury, causes vasoconstriction, thrombotic problems, and exacerbated inflammatory response . I developed a mathematical model based on the influence of ACE2 on viral propagation and on the negative effects of its degradation .
Revised:  SARS-Cov-2 degrades ACE2 and thus worsens lung injury, causes vasoconstriction, thrombotic problems, and exacerbated inflammatory response and thrombotic problems may derive from increased Angiotensin II (Ang-II) level that results from degradation of ACE2 by the virus. I present here a mathematical model based on the influence of ACE2 on viral propagation and on the negative effects of its degradation .
Edit intent: meaning-changed
Original:  The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2>0.9) . Rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Revised:  The model fits Covid-19 fatality rate across age and gender with high accuracy (r^2>0.9) . Rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Edit intent: meaning-changed
Original:  The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2>0.9) . Rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Revised:  The model fits SARS-CoV-2 fatality rate across age and sex with high accuracy (r^2>0.9) . Rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Edit intent: clarity
Original:  The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2>0.9) . Rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Revised:  The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2>0.9) under the hypothesis that SARS-CoV-2 infections are in the dynamical regimes in which increased receptor slows down viral propagation. Moreover, rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Edit intent: meaning-changed
Original:  The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2>0.9) . Rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Revised:  The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2>0.9) . Rescaling the model parameters by the ratio of the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Edit intent: meaning-changed
Original:  The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2>0.9) . Rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and gender, in particular its higher severity for young patients, thus linking the molecular and epidemiological levels.
Revised:  The model fits SARS-CoV-2 fatality rate across age and gender with high accuracy (r^2>0.9) . Rescaling the model parameters with the binding rates of the spike proteins of SARS-CoV and SARS-CoV-2 allows predicting the fatality rate of SARS-CoV across age and sex, thus linking the molecular and epidemiological levels.
Edit intent: clarity
Original:  These results support the suggestion that drugs that enhance the expression of ACE2, such as ACE inhibitors and angiotensin receptor blockers , constitute a promising therapy against the most adverse effects of CoViD-19 . Furthermore, ACE2 is a candidate prognostic factor for detecting population that needs stronger protection.
Revised:  The presented model opposes the fear that angiotensin receptor blockers , constitute a promising therapy against the most adverse effects of CoViD-19 . Furthermore, ACE2 is a candidate prognostic factor for detecting population that needs stronger protection.
Edit intent: clarity
Original:  These results support the suggestion that drugs that enhance the expression of ACE2, such as ACE inhibitors and angiotensin receptor blockers , constitute a promising therapy against the most adverse effects of CoViD-19 . Furthermore, ACE2 is a candidate prognostic factor for detecting population that needs stronger protection.
Revised:  These results support the suggestion that drugs that enhance the expression of ACE2, such as ACE inhibitors and angiotensin receptor blockers (ARB), suggested as a therapy against the most adverse effects of CoViD-19 . Furthermore, ACE2 is a candidate prognostic factor for detecting population that needs stronger protection.
Edit intent: clarity
Original:  These results support the suggestion that drugs that enhance the expression of ACE2, such as ACE inhibitors and angiotensin receptor blockers , constitute a promising therapy against the most adverse effects of CoViD-19 . Furthermore, ACE2 is a candidate prognostic factor for detecting population that needs stronger protection.
Revised:  These results support the suggestion that drugs that enhance the expression of ACE2, such as ACE inhibitors and angiotensin receptor blockers , constitute a promising therapy against the most adverse effects of CoViD-19 , may favour viral propagation, and suggests that Ang-II and ACE2 is a candidate prognostic factor for detecting population that needs stronger protection.
Edit intent: meaning-changed
Original:  These results support the suggestion that drugs that enhance the expression of ACE2, such as ACE inhibitors and angiotensin receptor blockers , constitute a promising therapy against the most adverse effects of CoViD-19 . Furthermore, ACE2 is a candidate prognostic factor for detecting population that needs stronger protection.
Revised:  These results support the suggestion that drugs that enhance the expression of ACE2, such as ACE inhibitors and angiotensin receptor blockers , constitute a promising therapy against the most adverse effects of CoViD-19 . Furthermore, ACE2 are candidate prognostic factors for detecting population that needs stronger protection.
Edit intent: fluency
Original:  In this paper, we revisit the problem in the presence of curves under the  Fr\'echet distance \Reals . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real  \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k,  report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Revised:  In this paper, we revisit the problem and propose the first data structure for curves under the Fr\'echet distance \Reals . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k, report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Edit intent: meaning-changed
Original:  In this paper, we revisit the problem in the presence of curves under the  Fr\'echet distance \Reals . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real  \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k,  report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Revised:  In this paper, we revisit the problem in the presence of curves under the (continuous) Fr\'echet distance \Reals . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k, report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Edit intent: meaning-changed
Original:  In this paper, we revisit the problem in the presence of curves under the  Fr\'echet distance \Reals . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real  \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k,  report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Revised:  In this paper, we revisit the problem in the presence of curves under the Fr\'echet distance in\Reals . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k, report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Edit intent: fluency
Original:  In this paper, we revisit the problem in the presence of curves under the  Fr\'echet distance \Reals . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real  \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k,  report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Revised:  In this paper, we revisit the problem in the presence of curves under the Fr\'echet distance \Reals^d . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k, report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Edit intent: clarity
Original:  In this paper, we revisit the problem in the presence of curves under the  Fr\'echet distance \Reals . Given a set %DIFDELCMD < {\cal %%% P of n curves of size at most m each in \mathbb{R \Reals and a real  \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k,  report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Revised:  In this paper, we revisit the problem in the presence of curves under the Fr\'echet distance \Reals . Given a set %DIFDELCMD < {\cal %%% \P of n curves of size at most m each in \mathbb{R \Reals and a real \delta>0, we aim to preprocess %DIFDELCMD < {\cal %%% P into a data structure so that for any given query curve Q of size k, report all curves in %DIFDELCMD < {\cal %%% P whose Fr\'echet distances to Q are at most \delta.
Edit intent: clarity