wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our comprehensive analysisof a multi-study patient cohort evaluates HO for three different classifier types and over a range of different optimization settings. Consistent with previous research, we find that Bayesian optimization is more efficient than grid search or random sampling-based methods , identifying promising classifiers with fewer evaluated hyperparameter configurations.
Revised:  Our comprehensive analysisof a multi-study patient cohort with our choices of dataset partitioning and hyperparameter optimization objective as well as assessing selected classifiers in external (as well as internal) validation. We find that classifiers selected by Bayesian optimization for in-hospital mortality can outperform those selected by grid search or random sampling. However, in contrast to previous research: 1) Bayesian optimization is not more efficient in selecting classifiers in all instances compared to grid search or random sampling-based methods , identifying promising classifiers with fewer evaluated hyperparameter configurations.
Edit intent: meaning-changed
Original:  Consistent with previous research, we find that Bayesian optimization is more efficient than grid search or random sampling-based methods , identifying promising classifiers with fewer evaluated hyperparameter configurations. However, we also find evidence of a lack of correspondence between internal and external validation performance of selected classifiers that complicates model selection for deployment as well as stymies development of clear-cut, practical guidelines for HO application in healthcare.We highlight the need for additional considerations about patient heterogeneity, dataset partitioning and optimization setup when applying HO methods in the healthcare context.
Revised:  Consistent with previous research, we find that Bayesian optimization is more efficient than grid search or random sampling-based methods and 2) we note marginal gains in classifier performance in only specific circumstances when using a common variant of Bayesian optimization (i.e. automatic relevance determination). Our analysis highlights the need for additional considerations about patient heterogeneity, dataset partitioning and optimization setup when applying HO methods in the healthcare context.
Edit intent: coherence
Original:  Prioritization of promising classifiers is dependent, in part, on hyperparameter optimization (HO) for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective.
Revised:  Prioritization of promising classifiers is dependent, in part, on hyperparameter optimization for which a number of approaches including grid search, random sampling and Bayesian optimization have been shown to be effective.
Edit intent: clarity
Original:  We analyze the rate \alpha in 42 different countries, choosing as a starting point in each country the first day with 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way.
Revised:  We analyze the rate \alpha in different countries, choosing as a starting point in each country the first day with 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way.
Edit intent: clarity
Original:  {\it  {\it   Fitting with a linear behavior \alpha(T), we find evidence  for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Revised:  We analyze a{\it {\it Fitting with a linear behavior \alpha(T), we find evidence for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Edit intent: meaning-changed
Original:  {\it  {\it   Fitting with a linear behavior \alpha(T), we find evidence  for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Revised:  {\it base {\it Fitting with a linear behavior \alpha(T), we find evidence for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Edit intent: fluency
Original:  {\it  {\it   Fitting with a linear behavior \alpha(T), we find evidence  for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Revised:  {\it set of 42 countries, which developed the epidemic earlier, and an{\it Fitting with a linear behavior \alpha(T), we find evidence for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Edit intent: meaning-changed
Original:  {\it  {\it   Fitting with a linear behavior \alpha(T), we find evidence  for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Revised:  {\it {\it extended Fitting with a linear behavior \alpha(T), we find evidence for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Edit intent: fluency
Original:  {\it  {\it   Fitting with a linear behavior \alpha(T), we find evidence  for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Revised:  {\it {\it set of 88 countries, which developed the epidemic more recently. Fitting with a linear behavior \alpha(T), we find evidence for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Edit intent: meaning-changed
Original:  {\it  {\it   Fitting with a linear behavior \alpha(T), we find evidence  for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Revised:  {\it {\it Fitting with a linear behavior \alpha(T), we find evidence in both datasets for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data.
Edit intent: meaning-changed
Original:  {\it  {\it   Fitting with a linear behavior \alpha(T), we find evidence  for a decreasing growth rate as a function of T . However, a decrease in the growth rate is visible also in the very low-T data. Going beyond a linear model, a peak at about (9.5%DIFDELCMD < \pm %%% 2.6)^\circ C seems to be present in the data.
Revised:  {\it {\it Fitting with a linear behavior \alpha(T), we find evidence for a decreasing growth rate as a function of T %DIFDELCMD < \pm %%% 2.6)^\circ C seems to be present in the data.
Edit intent: clarity
Original:  Going beyond a linear model, a peak at about (9.5%DIFDELCMD < \pm %%% 2.6)^\circ C seems to be present in the data. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general the propagation should be hopefully stopped by strong lockdown, testing and tracking policies, before the arrival of the next cold season.
Revised:  Going beyond a linear model, a peak at about (9.5%DIFDELCMD < \pm %%% , at 99.66
Edit intent: meaning-changed
Original: The recent coronavirus pandemic follows in its early stages an almost exponential expansion , with the number of cases as a function of time reasonably well fit by N(t)\propto e^{\alpha t}, in many countries.
Revised: The recent coronavirus pandemic follows in its early stages an almost exponential growth , with the number of cases as a function of time reasonably well fit by N(t)\propto e^{\alpha t}, in many countries.
Edit intent: clarity
Original:  We analyze the rate \alpha in different countries, choosing as a starting point in each countrythe first day with 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way .
Revised:  We analyze the rate \alpha for each country, starting from a threshold of 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way .
Edit intent: clarity
Original:  We analyze the rate \alpha in different countries, choosing as a starting point in each countrythe first day with 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way .
Revised:  We analyze the rate \alpha in different countries, choosing as a starting point in each countrythe first day with 30 total cases and using the next 12 days, capturing thus the early exponential growth in a rather homogeneous way .
Edit intent: clarity
Original:  We analyze the rate \alpha in different countries, choosing as a starting point in each countrythe first day with 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way .
Revised:  We analyze the rate \alpha in different countries, choosing as a starting point in each countrythe first day with 30 cases and fitting for the following 12 days, capturing thus the early growth homogeneously .
Edit intent: clarity
Original:  We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, {\it   and an {\it extended} set of 88 countries, which developed the epidemic more recently.
Revised:  We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, an{\it and an {\it extended} set of 88 countries, which developed the epidemic more recently.
Edit intent: fluency
Original:  We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, {\it   and an {\it extended} set of 88 countries, which developed the epidemic more recently.
Revised:  We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, {\it intermediate and an {\it extended} set of 88 countries, which developed the epidemic more recently.
Edit intent: meaning-changed
Original:  We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, {\it   and an {\it extended} set of 88 countries, which developed the epidemic more recently.
Revised:  We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, {\it set of 88 countries and an {\it extended} set of 88 countries, which developed the epidemic more recently.
Edit intent: fluency
Original:  We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, {\it   and an {\it extended} set of 88 countries, which developed the epidemic more recently.
Revised:  We analyze a {\it base} set of 42 countries, which developed the epidemic earlier, {\it and an {\it extended} set of 125 countries, which developed the epidemic more recently.
Edit intent: meaning-changed
Original:  Fitting with a linear behavior \alpha(T), we find evidence in both datasets for a decreasing growth rate as a function of T, at 99.66\\%DIF < C.L. and 99.86\\%C.
Revised:  Applying a linear fit \alpha(T), we find evidence in both datasets for a decreasing growth rate as a function of T, at 99.66\\%DIF < C.L. and 99.86\\%C.
Edit intent: clarity
Original:  Fitting with a linear behavior \alpha(T), we find evidence in both datasets for a decreasing growth rate as a function of T, at 99.66\\%DIF < C.L. and 99.86\\%C.
Revised:  Fitting with a linear behavior \alpha(T), we find increasing evidence for a decreasing growth rate as a function of T, at 99.66\\%DIF < C.L. and 99.86\\%C.
Edit intent: meaning-changed
Original:  Fitting with a linear behavior \alpha(T), we find evidence in both datasets for a decreasing growth rate as a function of T, at 99.66\\%DIF < C.L. and 99.86\\%C.
Revised:  Fitting with a linear behavior \alpha(T), we find evidence in both datasets for a decreasing \alpha as a function of T, at 99.66\\%DIF < C.L. and 99.86\\%C.
Edit intent: clarity
Original:  Our approach allows to cluster the industries into distinct risk groups. The findings of this study are summarised and updated in an online dashboard that tracks the industry-specific risks related to the crisis, as it spreads through the economy.
Revised:  Our approach allows to distinguish the industries by their reported risk awareness towards COVID-19. The preliminary findings are summarised an online index. The CoRisk-Index tracks the industry-specific risks related to the crisis, as it spreads through the economy.
Edit intent: meaning-changed
Original:  To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most `important' for March 2020 with respect to March 2019.
Revised:  To in part help on the social media side, we curate a set of 2000 day-scale time series of 1-grams across 24 languages on Twitter that are most `important' for March 2020 with respect to March 2019.
Edit intent: meaning-changed
Original:  To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most `important' for March 2020 with respect to March 2019.
Revised:  To in part help on the social media side, we curate a set of 1000 day-scale time series of 1- and 2-grams across 24 languages on Twitter that are most `important' for March 2020 with respect to March 2019.
Edit intent: meaning-changed
Original:  To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most `important' for March 2020 with respect to March 2019.
Revised:  To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most `important' for April 2020 with respect to March 2019.
Edit intent: meaning-changed
Original:  To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most `important' for March 2020 with respect to March 2019.
Revised:  To in part help on the social media side, we curate a set of 1000 day-scale time series of 1-grams across 24 languages on Twitter that are most `important' for March 2020 with respect to April 2019.
Edit intent: meaning-changed
Original:  To in part help on the social media side, we curate a set of 2000 day-scale time series of 1- and 2-grams across 24 languages on Twitter that are most ` important' for April 2020 with respect to April 2019.
Revised:  To in part help on the social media side, we curate a set of 2000 day-scale time series of 1- and 2-grams across 24 languages on Twitter that are most ' important' for April 2020 with respect to April 2019.
Edit intent: fluency
Original:  In the CDI model, the state of the system is more easily altered by direct control protocols that introduce a foreign population composed of many species (e.g. a steady state composition of the system) rather than protocols that are made up of a single microbial species. Then, to better understand how to navigate the dynamical landscape of a gLV system, the geometry of the basins of attraction of point attractors is compressed into an %DIFDELCMD < {\em %%% attractor network , which decomposes a multistable high-dimensional landscape into web of bistable subsystems.
Revised:  Then, the geometry of the basins of attraction of point attractors is compressed into an %DIFDELCMD < {\em %%% attractor network , which decomposes a multistable high-dimensional landscape into web of bistable subsystems.
Edit intent: coherence
Original:  Then, to better understand how to navigate the dynamical landscape of a gLV system, the geometry of the basins of attraction of point attractors is compressed into an %DIFDELCMD < {\em %%% attractor network , which decomposes a multistable high-dimensional landscape into web of bistable subsystems.
Revised:  Then, to better understand how to navigate the dynamical landscape of a gLV system, the geometry of the basins of attraction of point attractors is compressed into an %DIFDELCMD < {\em %%% attractor network , which decomposes a multistable high-dimensional landscape into web of bistable subsystems.
Edit intent: fluency
Original:  When trying to drive a system towards a target state, sometimes the most efficient control protocol is circuitous and will take the system through intermediate states . Clinically, the efficient control of the microbiome has pertinent applications for bacteriotherapies, which seek to remedy microbiome-affiliated diseases by directly altering the composition of the gut microbiome.
Revised:  In some cases, the most efficient control protocol is circuitous and will take the system through intermediate states . Clinically, the efficient control of the microbiome has pertinent applications for bacteriotherapies, which seek to remedy microbiome-affiliated diseases by directly altering the composition of the gut microbiome.
Edit intent: clarity
Original:  When trying to drive a system towards a target state, sometimes the most efficient control protocol is circuitous and will take the system through intermediate states . Clinically, the efficient control of the microbiome has pertinent applications for bacteriotherapies, which seek to remedy microbiome-affiliated diseases by directly altering the composition of the gut microbiome.
Revised:  When trying to drive a system towards a target state, sometimes the most efficient control protocol is circuitous and will take the system through intermediate steady states with sequential interventions . Clinically, the efficient control of the microbiome has pertinent applications for bacteriotherapies, which seek to remedy microbiome-affiliated diseases by directly altering the composition of the gut microbiome.
Edit intent: meaning-changed
Original:  As a point set, this space comprises the previously developed Billera-Holmes-Vogtmann (BHV) tree space while its geometry is motivated from the edge-product space. As the latter, our new wald space also involves disconnected forests, it does not contain certain singularities of the latter, though.
Revised:  As a point set, this space comprises the previously developed Billera-Holmes-Vogtmann (BHV) tree space ; it also contains disconnected forests, it does not contain certain singularities of the latter, though.
Edit intent: coherence
Original:  As the latter, our new wald space also involves disconnected forests, it does not contain certain singularities of the latter, though. The geometry of wald spaceis that of the Fisher information metric of character distributions , either from a discrete Bernoulli or from a continuous Gaussian model.
Revised:  As the latter, our new wald space also involves disconnected forests, it does not contain certain singularities of the EP space. We investigate two related geometries on wald space. The first is the geometry of the Fisher information metric of character distributions , either from a discrete Bernoulli or from a continuous Gaussian model.
Edit intent: meaning-changed
Original:  The geometry of wald spaceis that of the Fisher information metric of character distributions , either from a discrete Bernoulli or from a continuous Gaussian model. The latter can be viewed as the trace metric of the affine-invariant metric for covariance matrices, the former is that of the Hellinger divergence, or  , as we show, equivalent to any metric obtained from an f -divergence , such as the Jensen-Shannon metric .
Revised:  The geometry of wald spaceis that of the Fisher information metric of character distributions induced by the two-state symmetric Markov substitution process on each tree. Infinitesimally, the metric is proportional to the Kullback-Leibler divergence, or , as we show, equivalent to any metric obtained from an f -divergence , such as the Jensen-Shannon metric .
Edit intent: meaning-changed
Original:  The latter can be viewed as the trace metric of the affine-invariant metric for covariance matrices, the former is that of the Hellinger divergence, or  , as we show, equivalent to any metric obtained from an f -divergence , such as the Jensen-Shannon metric .
Revised:  The latter can be viewed as the trace metric of the affine-invariant metric for covariance matrices, the former is that of the Hellinger divergence, or equivalently , as we show, equivalent to any metric obtained from an f -divergence , such as the Jensen-Shannon metric .
Edit intent: meaning-changed
Original:  The latter can be viewed as the trace metric of the affine-invariant metric for covariance matrices, the former is that of the Hellinger divergence, or  , as we show, equivalent to any metric obtained from an f -divergence , such as the Jensen-Shannon metric .
Revised:  The latter can be viewed as the trace metric of the affine-invariant metric for covariance matrices, the former is that of the Hellinger divergence, or , as we show, any to f -divergence , such as the Jensen-Shannon metric .
Edit intent: clarity
Original:  The latter can be viewed as the trace metric of the affine-invariant metric for covariance matrices, the former is that of the Hellinger divergence, or  , as we show, equivalent to any metric obtained from an f -divergence , such as the Jensen-Shannon metric . For the latter (continuous) we derive a gradient descent algorithm to project from the ambient space of covariance matrices to wald space and for both we derive computational methods to compute geodesics in polynomial time and show numerically that the two information geometries (discrete and continuous) are very similar.
Revised:  The latter can be viewed as the trace metric of the affine-invariant metric for covariance matrices, the former is that of the Hellinger divergence, or , as we show, equivalent to any metric obtained from an f -divergence . The second geometry is obtained analogously but using a related continuous-valued Gaussian process on each tree, and it can be viewed as the trace metric of the affine-invariant metric for covariance matrices. We derive a gradient descent algorithm to project from the ambient space of covariance matrices to wald space and for both we derive computational methods to compute geodesics in polynomial time and show numerically that the two information geometries (discrete and continuous) are very similar.
Edit intent: meaning-changed
Original:  For the latter (continuous) we derive a gradient descent algorithm to project from the ambient space of covariance matrices to wald space and for both we derive computational methods to compute geodesics in polynomial time and show numerically that the two information geometries (discrete and continuous) are very similar.
Revised:  For the latter (continuous) we derive a gradient descent algorithm to project from the ambient space of covariance matrices to wald space . For both geometries we derive computational methods to compute geodesics in polynomial time and show numerically that the two information geometries (discrete and continuous) are very similar.
Edit intent: clarity
Original: We address the general question of what is the best strategy to employ when searching for targets of unpredictable sizes. Motivated by biological scenarios, we focus on generalized random walk strategies, in which step-lengths follow a given distribution, while their direction is randomly uniform. To capture common biological scenarios in which motion degrades detection, we further restrict attention to intermittent settings, which means that the searcher can detect a target only while being "immobile", that is , in-between moving steps. We prove that in a finite two-dimensional torus, an intermittent L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Revised:  L\'evy walk whose step-lengths are distributed according to an ] inverse-square power-law is a near-optimal strategy for finding ] sparse targets of any size and shape.
Edit intent: meaning-changed