wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the  fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Revised:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo and de novo mutation are proportionally modulated by the fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Edit intent: meaning-changed
Original:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the  fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Revised:  We show that, for a range of models, the rate of increase in heteroplasmy variance , and the rate ofde novo mutation , is proportionately modulated by the (physical) fraction of unfused mitochondria, independently of the absolute fission-fusion rate.
Edit intent: clarity
Original:  Our findings imply that modulating network state, mitophagy rate and copy number to slow down heteroplasmy dynamics when mean heteroplasmy is low , could have therapeutic advantages for mitochondrial disease and healthy aging.
Revised:  Our findings imply that modulating network state, mitophagy rate and copy number to slow down heteroplasmy dynamics when mean heteroplasmy is low could have therapeutic advantages for mitochondrial disease and healthy aging.
Edit intent: fluency
Original:  Finally, these motifs are less abundant in natural native states than in simulated protein-like structures, yet they appear in 32 %DIFDELCMD < \\%%% %DIF < of proteins, which in some cases display an amazingly complex intertwining.abstract 
Revised:  Finally, these motifs are less abundant in natural native states than in simulated protein-like structures, yet they appear in 32 %DIFDELCMD < \\%%% %DIF < of proteins, which in some cases display an amazingly complex intertwining.abstract \% of proteins, which in some cases display an amazingly complex intertwining.
Edit intent: coherence
Original:  By relaxing the requirement of exact and deterministic solutions, FRESH reaches high performance and outperforms the state-of-the-art approaches. The experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over a baseline given by the best solutions developed for the ACM SIGSPATIAL 2017 challenge on the Fr\'echet distance .
Revised:  By relaxing the requirement of exact and deterministic solutions, FRESH reaches high performance : the experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over a baseline given by the best solutions developed for the ACM SIGSPATIAL 2017 challenge on the Fr\'echet distance .
Edit intent: clarity
Original:  The experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over a baseline given by the best solutions developed for the ACM SIGSPATIAL 2017 challenge on the Fr\'echet distance .
Revised:  The experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach .
Edit intent: clarity
Original: Massive datasets of curves, such as time series and trajectories, are continuously generated by mobile and sensing devices. A relevant operation on curves is similarity search : given a dataset S of curves , construct a data structure that, for any query curve q, finds the curves in S similar to q. Similarity search is a computational demanding task, in particular when a robust distance function is used, such as the continuous Fr\'echet distance.
Revised: This paper studies the r-range search problem for curves under the continuous Fr\'echet distance : given a dataset S of curves , construct a data structure that, for any query curve q, finds the curves in S similar to q. Similarity search is a computational demanding task, in particular when a robust distance function is used, such as the continuous Fr\'echet distance.
Edit intent: meaning-changed
Original:  A relevant operation on curves is similarity search : given a dataset S of curves , construct a data structure that, for any query curve q, finds the curves in S similar to q. Similarity search is a computational demanding task, in particular when a robust distance function is used, such as the continuous Fr\'echet distance.
Revised:  A relevant operation on curves is similarity search : given a dataset S of n polygonal curves and a threshold r>0 , construct a data structure that, for any query curve q, finds the curves in S similar to q. Similarity search is a computational demanding task, in particular when a robust distance function is used, such as the continuous Fr\'echet distance.
Edit intent: meaning-changed
Original:  A relevant operation on curves is similarity search : given a dataset S of curves , construct a data structure that, for any query curve q, finds the curves in S similar to q. Similarity search is a computational demanding task, in particular when a robust distance function is used, such as the continuous Fr\'echet distance. In this paper, we propose FRESH, a novel approximate solution to find similar curves under the continuous Fr\'echet distance.
Revised:  A relevant operation on curves is similarity search : given a dataset S of curves , construct a data structure that, for any query curve q, efficiently returns all entries in S with distance at most r from q. We propose FRESH, a novel approximate solution to find similar curves under the continuous Fr\'echet distance.
Edit intent: clarity
Original:  In this paper, we propose FRESH, a novel approximate solution to find similar curves under the continuous Fr\'echet distance. FRESH leverages on a locality sensitive hashing scheme for detecting candidate near neighbors of the query curve, and on a subsequent pruning step based on a cascade of curve simplifications.
Revised:  In this paper, we propose FRESH, an approximate and randomized approach for r-range search, that leverages on a locality sensitive hashing scheme for detecting candidate near neighbors of the query curve, and on a subsequent pruning step based on a cascade of curve simplifications.
Edit intent: clarity
Original:  By relaxing the requirement of\fresh  exact and deterministic solutions, FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach.
Revised:  We experimentally compare\fresh exact and deterministic solutions, FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach.
Edit intent: clarity
Original:  By relaxing the requirement of\fresh  exact and deterministic solutions, FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach.
Revised:  By relaxing the requirement of\fresh to exact and deterministic solutions, FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach.
Edit intent: fluency
Original:  By relaxing the requirement of\fresh  exact and deterministic solutions, FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach.
Revised:  By relaxing the requirement of\fresh exact and deterministic solutions, and we show that high performance can be reached by suitably relaxing precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach.
Edit intent: coherence
Original:  By relaxing the requirement of\fresh  exact and deterministic solutions, FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and precision 100\%, we have at least a factor 10 improvement in performance over the state-of-the-art exact approach. Furthermore, the improvement peaks up to two orders of magnitude, and even more, by relaxing the precision .
Revised:  By relaxing the requirement of\fresh exact and deterministic solutions, FRESH reaches high performance: the experiments indeed show that, with a recall larger than 80\% and precision and recall .
Edit intent: clarity
Original:  By deriving an approximate mean-field description of the global network dynamics , we show that this task admits several classes of solutions, characterized by different stability properties.
Revised:  As a starting point, we derive an approximate analytical description of global dynamics in trained networks which assumes uncorrelated connectivity weights in the feedback and in the random bulk. The resulting mean-field theory suggests that the task admits several classes of solutions, characterized by different stability properties.
Edit intent: meaning-changed
Original:  As a result, we have received the nonmonotonic behavior of the generalized Hurst exponent (which is a basic exponent herein) and hence a multi-branched left-sided spectrum of dimensions.
Revised:  As a result, we discern the non-monotonic behavior of the generalized Hurst exponent (which is a basic exponent herein) and hence a multi-branched left-sided spectrum of dimensions.
Edit intent: clarity
Original:  As a result, we have received the nonmonotonic behavior of the generalized Hurst exponent (which is a basic exponent herein) and hence a multi-branched left-sided spectrum of dimensions.
Revised:  As a result, we have received the nonmonotonic behavior of the generalized Hurst exponent - the fundamental exponent studied by us - and hence a multi-branched left-sided spectrum of dimensions.
Edit intent: meaning-changed
Original:  This kind of multifractality is a direct result of the nonmonotonic behavior of the generalized Hurst exponent and not because of its non-analytic behavior as it found before. We have examined the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases within a hierarchy of fluctuations as well as to first and the second order phase transitions between them.
Revised:  This kind of multifractality is a direct result of the nonmonotonic behavior of the generalized Hurst exponent and not because of its non-analytic behavior as has been previously suggested. We examine the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases within a hierarchy of fluctuations as well as to first and the second order phase transitions between them.
Edit intent: clarity
Original:  We have examined the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases within a hierarchy of fluctuations as well as to first and the second order phase transitions between them.
Revised:  We have examined the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases within a hierarchy of fluctuations , and also to the first and second order phase transitions between them.
Edit intent: coherence
Original:  We apply this analysis to studying time series consisting of inter-event times.
Revised:  In our variant of coarse-graining pre-processing, the local detrending of time series has been replaced by an appropriate averaging over days combined with properly-suited detrending on a daily time scale. This new approach is devoid of troublesome artifacts in the form of innumerable faults of these local trends that can deform the hierarchy of fluctuations and hence the final multifractality. Notably, our analysis is sensitive to the change of time scale as it should be. This analysis has developed, e.g., for empirical time series of inter-event times.
Edit intent: meaning-changed
Original:  We apply this analysis to studying time series consisting of inter-event times. As a result, we discern the non-monotonic behavior of the generalized Hurst exponent - the fundamental exponent studied by us - and hence a multi-branched left-sided spectrum of dimensions .
Revised:  We apply this analysis to studying time series consisting of inter-event or waiting times, which are an essential element of the popular continuous-time random walk formalism. The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent - the fundamental exponent studied by us - and hence a multi-branched left-sided spectrum of dimensions .
Edit intent: meaning-changed
Original:  As a result, we discern the non-monotonic behavior of the generalized Hurst exponent - the fundamental exponent studied by us - and hence a multi-branched left-sided spectrum of dimensions .
Revised:  As a result, we discern the non-monotonic behavior of the generalized Hurst exponent -- the fundamental exponent studied by us - and hence a multi-branched left-sided spectrum of dimensions .
Edit intent: fluency
Original:  As a result, we discern the non-monotonic behavior of the generalized Hurst exponent - the fundamental exponent studied by us - and hence a multi-branched left-sided spectrum of dimensions .
Revised:  As a result, we discern the non-monotonic behavior of the generalized Hurst exponent - the fundamental exponent of the study -- and hence a multi-branched left-sided spectrum of dimensions .
Edit intent: clarity
Original:  As a result, we discern the non-monotonic behavior of the generalized Hurst exponent - the fundamental exponent studied by us - and hence a multi-branched left-sided spectrum of dimensions . This kind of multifractality is a direct result of the non-monotonic behavior of the generalized Hurst exponent and is not caused by non-analytic behavior as has been previously suggested .
Revised:  As a result, we discern the non-monotonic behavior of the generalized Hurst exponent - the fundamental exponent studied by us - and hence a multi-branched left-sided spectrum of dimensions , which for our case is additionally of the left-sided one .
Edit intent: coherence
Original:  We examine the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases within a hierarchy of fluctuations, and also to the first and second order phase transitions between them  .
Revised:  We examine the main thermodynamic consequences of the existence of this type of multifractality . They can be expressed directly in the language of thermally stable, metastable, and unstable phases within a hierarchy of fluctuations, and also to the first and second order phase transitions between them .
Edit intent: meaning-changed
Original:  We examine the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases within a hierarchy of fluctuations, and also to the first and second order phase transitions between them  .
Revised:  We examine the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases , and phase transitions between them .
Edit intent: clarity
Original:  We examine the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases within a hierarchy of fluctuations, and also to the first and second order phase transitions between them  .
Revised:  We examine the main thermodynamic consequences of the existence of this type of multifractality related to the thermal stable, metastable, and unstable phases within a hierarchy of fluctuations, and also to the first and second order phase transitions between them as well. These phase transitions are of the first and second orders according to the modified Ehrenfest classification, sometimes called the Mandelbrot one .
Edit intent: meaning-changed
Original: We develop an extended multifractal analysis based on the Legendre-Fenchel transform (sometimes referred to as Legendre multi-branched one) rather than the routinely used canonical Legendre transform. In our variant of coarse-graining pre-processing, the local detrending of time series has been replaced by an appropriate averaging over days combined with properly-suited detrending on a daily time scale. This new approach is devoid of troublesome artifacts in the form of innumerable faults of these local trends that can deform the hierarchy of fluctuations and hence the final multifractality. Notably, our analysis is sensitive to the change of time scale as it should be. This analysis has developed, e.g., for empirical time series of inter-event or waiting times , which are an essential element of the popular continuous-time random walk formalism .
Revised: Empirical time series of inter-event or waiting times , which are an essential element of the popular continuous-time random walk formalism .
Edit intent: coherence
Original:  This analysis has developed, e.g., for empirical time series of inter-event or waiting times , which are an essential element of the popular continuous-time random walk formalism .
Revised:  This analysis has developed, e.g., for empirical time series of inter-event or waiting times are investigated using a modified Multifractal Detrended Fluctuation Analysis operating on fluctuations of mean detrended dynamics .
Edit intent: meaning-changed
Original:  The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent  -- the fundamental exponent of the study -- and hence a multi-branched spectrum of dimensions , which for our case is additionally of the left-sided one.
Revised:  The core of the extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent -- the fundamental exponent of the study -- and hence a multi-branched spectrum of dimensions , which for our case is additionally of the left-sided one.
Edit intent: fluency
Original:  The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent  -- the fundamental exponent of the study -- and hence a multi-branched spectrum of dimensions , which for our case is additionally of the left-sided one.
Revised:  The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent h(q) -- the fundamental exponent of the study -- and hence a multi-branched spectrum of dimensions , which for our case is additionally of the left-sided one.
Edit intent: meaning-changed
Original:  The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent  -- the fundamental exponent of the study -- and hence a multi-branched spectrum of dimensions , which for our case is additionally of the left-sided one.
Revised:  The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent -- the fundamental exponent in the study of multifractals. The consequence of this behavior is the non-monotonic behavior of the coarse H\"older exponent \alpha (q) leading to multi-branchedness of the spectrum of dimensions , which for our case is additionally of the left-sided one.
Edit intent: meaning-changed
Original:  The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent  -- the fundamental exponent of the study -- and hence a multi-branched spectrum of dimensions , which for our case is additionally of the left-sided one. We examine the main thermodynamic consequences of the existence of this type of multifractality .
Revised:  The core of this extended multifractal analysis is the non-monotonic behavior of the generalized Hurst exponent -- the fundamental exponent of the study -- and hence a multi-branched spectrum of dimensions . The Legendre-Fenchel transform is used instead of the routinely used canonical Legendre (single-branched) contact transform. Thermodynamic consequences of the existence of this type of multifractality .
Edit intent: meaning-changed
Original:  We examine the main thermodynamic consequences of the existence of this type of multifractality . They can be expressed directly in the language of  thermally stable, metastable, and unstable phases , and phase transitions between them as well .
Revised:  We examine the main thermodynamic consequences of the multi-branched multifractality are revealed. These are directly expressed in the language of thermally stable, metastable, and unstable phases , and phase transitions between them as well .
Edit intent: meaning-changed
Original:  Here, we deploy an unsupervised machine learning algorithm to define brainstates as time point level activity patterns from functional magnetic resonance imaging data acquired dur- ing passive visual fixation (rest) and an n-back working memory task.
Revised:  Here, we analyze the brain's trajectories through a high-dimensional activity space at the level of single time point activity patterns from functional magnetic resonance imaging data acquired dur- ing passive visual fixation (rest) and an n-back working memory task.
Edit intent: clarity
Original:  Here, we deploy an unsupervised machine learning algorithm to define brainstates as time point level activity patterns from functional magnetic resonance imaging data acquired dur- ing passive visual fixation (rest) and an n-back working memory task.
Revised:  Here, we deploy an unsupervised machine learning algorithm to define brainstates as time point level activity patterns from functional magnetic resonance imaging data acquired during passive visual fixation (rest) and an n-back working memory task.
Edit intent: fluency
Original:  We find that brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics . Using diffusion-weighted imaging acquired from the same subjects, we show that structural connectivity constrains the temporal progression of brainstates.
Revised:  We find that specific state space trajectories, which represent temporal sequences of brain activity, are modulated by cognitive load and related to task performance . Using diffusion-weighted imaging acquired from the same subjects, we show that structural connectivity constrains the temporal progression of brainstates.
Edit intent: clarity
Original:  We find that brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics . Using diffusion-weighted imaging acquired from the same subjects, we show that structural connectivity constrains the temporal progression of brainstates. We also combine tools from network control theory with geometrically conservative null models to demonstrate that brains are wired to sup- port states of high activity in default mode areas, while requiring relatively low energy .
Revised:  We find that brain states are composed of interdigitated functional networks and exhibit context-dependent dynamics . Using diffusion-weighted imaging acquired from the same subjects, we use tools from network control theory to show that linear spread of activity along white matter connections constrains the brain's state space trajectories at rest. Additionally, accounting for stimulus-driven visual inputs explains the different trajectories taken during the n-back task. We also used models of network rewiring to show that these findings are the result of non-trivial geometric and topological properties of white matter architecture .
Edit intent: clarity