wanyu/IteraTeR_v2 huggingface dataset:
Original: We show that for any two values \alpha, \beta \in ( 0 ,1) for which \alpha+\beta>1 then there exists a value N so that for all n \geq N , and any binary phylogenetic tree T on n leaves there exists a set of at most n^\alpha \lfloor \rfloor characters that capture T, and for which each character has at most n^\beta\lfloor \rfloor  states.
Revised: We show that for any two values \alpha, \beta \in ( 0 ,1) for which \alpha+\beta>1 then there exists a value N so that for all n \geq N , and any binary phylogenetic tree T on n leaves there exists a set of at most n^\alpha \lfloor \rfloor characters that capture T, and for which each character takes at most\lfloor \rfloor states.
Edit intent: fluency
Original: We show that for any two values \alpha, \beta \in ( 0 ,1) for which \alpha+\beta>1 then there exists a value N so that for all n \geq N , and any binary phylogenetic tree T on n leaves there exists a set of at most n^\alpha \lfloor \rfloor characters that capture T, and for which each character has at most n^\beta\lfloor \rfloor  states.
Revised: We show that for any two values \alpha, \beta \in ( 0 ,1) for which \alpha+\beta>1 then there exists a value N so that for all n \geq N , and any binary phylogenetic tree T on n leaves there exists a set of at most n^\alpha \lfloor \rfloor characters that capture T, and for which each character has at most n^\beta\lfloor \rfloor distinct states.
Edit intent: clarity
Original:  Here ' capture' means that T is the only phylogenetic tree that is compatible with the characters.
Revised:  Here ` capture' means that T is the only phylogenetic tree that is compatible with the characters.
Edit intent: fluency
Original:  Here ' capture' means that T is the only phylogenetic tree that is compatible with the characters.
Revised:  Here ' capture' means that T is the unique perfect phylogeny for these characters.
Edit intent: clarity
Original: Living cells use readout molecules to record the state of cell-surface receptors that detect ligands in their environment.
Revised: Living cells use readout molecules to record the state of receptors that detect ligands in their environment.
Edit intent: clarity
Original:  On the surface, these readouts appear similar to measurements made by computational devices, as extensively studied in the literature following Maxwell's demon.
Revised:  This process appears to be similar to measurements made by computational devices, as extensively studied in the literature following Maxwell's demon.
Edit intent: clarity
Original:  Here, we consider a canonical biochemical network for receptor readout and show how it maps onto a measurement protocol of the type studied in the computational literature.
Revised:  Here, we consider a canonical biochemical network that reads out the state of a receptor and show rigorously how it relates to a measurement protocol of the type studied in the computational literature.
Edit intent: clarity
Original:  A key consequence is that biochemical networks face a tradeoff between the energy dissipated for a measurement and the accuracy of the measurement that is more severe than and qualitatively distinct from the tradeoff required thermodynamically .
Revised:  A key consequence is that biochemical networks face a tradeoff between accuracy and dissipation that is more severe than and qualitatively distinct from the tradeoff required thermodynamically .
Edit intent: clarity
Original:  This leads to an experimental system that might be used to test theories on the thermodynamicsof computation .
Revised:  This leads to an experimental system for testing computational thermodynamics .
Edit intent: clarity
Original: Spatial heterogeneity plays an important role in complex ecosystem dynamics and, thus, is an important consideration in harvesting theory .
Revised: Spatial heterogeneity plays an important role in complex ecosystem dynamics , and therefore is also an important consideration in harvesting theory .
Edit intent: coherence
Original: Spatial heterogeneity plays an important role in complex ecosystem dynamics and, thus, is an important consideration in harvesting theory .
Revised: Spatial heterogeneity plays an important role in complex ecosystem dynamics and, thus, is an important consideration in sustainable resource management .
Edit intent: clarity
Original:  However, little is known about how spatial effects can influence predictions derived from a non-spatial harvest model.
Revised:  However, little is known about how spatial effects can influence management targets derived from a non-spatial harvest model.
Edit intent: clarity
Original:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is  used in resource management  to a spatially-explicit harvest model by integrating environmental heterogeneities  as well as species migrations between patches.
Revised:  Here, we extended the Schaefer model, a conventional non-spatial harvest model that is used in resource management to a spatially-explicit harvest model by integrating environmental heterogeneities as well as species migrations between patches.
Edit intent: clarity
Original:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is  used in resource management  to a spatially-explicit harvest model by integrating environmental heterogeneities  as well as species migrations between patches.
Revised:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is widely used in resource management to a spatially-explicit harvest model by integrating environmental heterogeneities as well as species migrations between patches.
Edit intent: clarity
Original:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is  used in resource management  to a spatially-explicit harvest model by integrating environmental heterogeneities  as well as species migrations between patches.
Revised:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is used in resource management , to a spatially-explicit harvest model by integrating environmental heterogeneities as well as species migrations between patches.
Edit intent: fluency
Original:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is  used in resource management  to a spatially-explicit harvest model by integrating environmental heterogeneities  as well as species migrations between patches.
Revised:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is used in resource management to a spatially-explicit harvest model by integrating environmental heterogeneities , as well as species migrations between patches.
Edit intent: fluency
Original:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is  used in resource management  to a spatially-explicit harvest model by integrating environmental heterogeneities  as well as species migrations between patches.
Revised:  Here, we extended the Schaefer model, a classical non-spatial harvest model that is used in resource management to a spatially-explicit harvest model by integrating environmental heterogeneities as well as species exchange between patches.
Edit intent: clarity
Original:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population  , we showed  that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Revised:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population , we showed that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Edit intent: clarity
Original:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population  , we showed  that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Revised:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity exists , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population , we showed that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Edit intent: fluency
Original:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population  , we showed  that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Revised:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tends to overestimate the MSY, implying potential for causing overharvesting. In addition, by assuming a well-mixed population , we showed that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Edit intent: coherence
Original:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population  , we showed  that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Revised:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population in the heterogeneous environment , we showed that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Edit intent: clarity
Original:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population  , we showed  that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Revised:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population , we showed analytically that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Edit intent: clarity
Original:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population  , we showed  that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Revised:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population , we showed that the Schaefer model always overestimate the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Edit intent: fluency
Original:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population  , we showed  that the Schaefer model always overestimated the MSY, regardless of the number of patches , and that the degree of overestimation becomes significant when spatial heterogeneity is marked.
Revised:  By comparing the outputs of both models, we aimed to examine the effects of spatial heterogeneity on maximum sustainable yields (MSY) . When spatial heterogeneity existed , we found that the Schaefer model tended to overestimate MSY. By assuming a well-mixed population , we showed that the Schaefer model always overestimated the MSY, regardless of the number of patches existing. The degree of overestimation becomes significant when spatial heterogeneity is marked.
Edit intent: coherence
Original:  Collectively, these results highlight the importance of explicitly considering differences in spatial structure to prevent overharvesting resources .
Revised:  Collectively, these results highlight the importance of integrating the spatial structure to prevent overharvesting resources .
Edit intent: clarity
Original:  Collectively, these results highlight the importance of explicitly considering differences in spatial structure to prevent overharvesting resources .
Revised:  Collectively, these results highlight the importance of explicitly considering differences in spatial structure to conduct sustainable resource management .
Edit intent: clarity
Original: While machine learning is currently very successful in several application domains, we are still very far from a real Artificial Intelligence .
Revised: While machine learning is currently very successful in several application domains, we are still very far from achieving a real artificial intelligence .
Edit intent: clarity
Original:  We show that simple algorithms can be learnt from sequential data with a recurrent network associated with trainable stacks.
Revised:  We show that simple algorithms can be learned from sequential data with a recurrent network associated with trainable stacks.
Edit intent: fluency
Original:  We focus our study on algorithmically generated sequences such as a^n b^{n}, that can only be learnt by models which have the capacity to count.
Revised:  We focus our study on algorithmically generated sequences such as a^n b^{n}, that can only be learned by models which have the capacity to count.
Edit intent: fluency
Original: While machine learningis currently very successful in several application domains , we are still very far from achieving a real artificial intelligence.
Revised: Despite the recent achievements in machine learning , we are still very far from achieving a real artificial intelligence.
Edit intent: clarity
Original: While machine learningis currently very successful in several application domains , we are still very far from achieving a real artificial intelligence.
Revised: While machine learningis currently very successful in several application domains , we are still very far from achieving real artificial intelligence.
Edit intent: fluency
Original:  We focus our study on algorithmically generated sequences such as a^n b^{n can only be learned by models which have the capacity to count .
Revised:  We focus our study on algorithmically generated sequences which can only be learned by models which have the capacity to count .
Edit intent: clarity
Original:  Here, measuring protein content in individual bacteria continuously over ~70 generations, we show that single-cell trajectories fluctuate around their average with the same distribution shape as the population, i.e. their relative fluctuations are ergodic.
Revised:  Here, measuring protein content in individual bacteria over multiple generations, and compare them to those found in the previously measured population snapshots. We find that temporal fluctuations in individual traces exhibit the same universal features as those observed in populations. Scaled fluctuations around the mean of each trace exhibit the same universal distribution shape as the population, i.e. their relative fluctuations are ergodic.
Edit intent: clarity
Original:  To address this issue, we investigate here the statistical properties of temporal protein traces measured in individual bacteria over multiple generations , and compare them to those found in the previously measured population snapshots.
Revised:  To address this issue, we investigate here the statistical properties of temporal protein traces measured in individual bacteria over multiple generations and investigate their statistical properties, comparing them to previously measured population snapshots.
Edit intent: clarity
Original:  We find that temporal fluctuations in individual traces exhibit the same universal features as those  observed in populations.
Revised:  We find that temporal fluctuations in individual traces exhibit the same universal features as those previously observed in populations.
Edit intent: clarity
Original:  Scaled fluctuations around the mean of each trace exhibit the same universal distribution shape as found in populations , and the first and second moments of the traces  obey the same quadratic relationship . Analyzing the temporal features of the protein traces  reveals that within a cell cycle , protein content increases as an exponential function with a rate that varies from cycle to cycle.
Revised:  Scaled fluctuations around the mean of each trace exhibit the same universal distribution shape as found in populations , and the first and second moments of the traces over time obey the same quadratic relationship . Analyzing the temporal features of the protein traces reveals that within a cell cycle , protein content increases as an exponential function with a rate that varies from cycle to cycle.
Edit intent: clarity
Original:  Scaled fluctuations around the mean of each trace exhibit the same universal distribution shape as found in populations , and the first and second moments of the traces  obey the same quadratic relationship . Analyzing the temporal features of the protein traces  reveals that within a cell cycle , protein content increases as an exponential function with a rate that varies from cycle to cycle.
Revised:  Scaled fluctuations around the mean of each trace exhibit the same universal distribution shape as found in populations , and the first and second moments of the traces obey the same quadratic relation . Analyzing the temporal features of the protein traces reveals that within a cell cycle , protein content increases as an exponential function with a rate that varies from cycle to cycle.
Edit intent: fluency
Original:  Scaled fluctuations around the mean of each trace exhibit the same universal distribution shape as found in populations , and the first and second moments of the traces  obey the same quadratic relationship . Analyzing the temporal features of the protein traces  reveals that within a cell cycle , protein content increases as an exponential function with a rate that varies from cycle to cycle.
Revised:  Scaled fluctuations around the mean of each trace exhibit the same universal distribution shape as found in populations , and the first and second moments of the traces obey the same quadratic relationship . Analyzing the temporal features of the protein traces reveals that within a cell cycle protein content increases as an exponential function with a rate that varies from cycle to cycle.
Edit intent: fluency
Original:  This result is used to show that downward closures are computable for (i) every language class with effectively semilinear Parikh images that are closed under rational transductions, (ii) matrix languages, and (iii) indexed languages (equivalently, languages accepted by higher-order pushdown automata of order ~ 2).
Revised:  This result is used to prove that downward closures are computable for (i) every language class with effectively semilinear Parikh images that are closed under rational transductions, (ii) matrix languages, and (iii) indexed languages (equivalently, languages accepted by higher-order pushdown automata of order ~ 2).
Edit intent: coherence
Original:  This result is used to show that downward closures are computable for (i) every language class with effectively semilinear Parikh images that are closed under rational transductions, (ii) matrix languages, and (iii) indexed languages (equivalently, languages accepted by higher-order pushdown automata of order ~ 2).
Revised:  This result is used to show that downward closures are computable for (i) every language class with effectively semilinear Parikh images that are closed under rational transductions, (ii) matrix languages, and (iii) indexed languages (equivalently, languages accepted by higher-order pushdown automata of order 2).
Edit intent: clarity
Original:  We introduce a joint model of transcriptional activation and mRNA accumulation which can be used for inference of transcription rate, RNA processing delay and degradation rate given genome-wide data from high-throughput sequencing time course experiments.
Revised:  We introduce a joint model of transcriptional activation and mRNA accumulation which can be used for inference of transcription rate, RNA production delay and degradation rate given genome-wide data from high-throughput sequencing time course experiments.
Edit intent: clarity
Original:  We combine a mechanistic differential equation model with a non-parametric statistical modelling approach which allows us to capture a broad range of activation kinetics, and use Bayesian parameter estimation to quantify the uncertainty in the estimates of the kinetic parameters.
Revised:  We combine a mechanistic differential equation model with a non-parametric statistical modelling approach allowing us to capture a broad range of activation kinetics, and use Bayesian parameter estimation to quantify the uncertainty in the estimates of the kinetic parameters.
Edit intent: coherence
Original:  We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated processing delays in many genes.
Revised:  We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.
Edit intent: clarity
Original: We use a popular fictional disease, zombies, in order to introduce techniques used in modern epidemiology modelling, and ideas and techniques used in the numerical study of critical phenomenon .
Revised: We use a popular fictional disease, zombies, in order to introduce techniques used in modern epidemiology modelling, and ideas and techniques used in the numerical study of critical phenomena .
Edit intent: fluency