wanyu/IteraTeR_full_sent huggingface dataset:
Original:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect the nonlinearity has on global network properties.
Revised:  It is achieved by a systematic multi-step approach that allows us to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect the nonlinearity has on global network properties.
Edit intent: fluency
Original:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect the nonlinearity has on global network properties.
Revised:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling ; correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect the nonlinearity has on global network properties.
Edit intent: fluency
Original:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect the nonlinearity has on global network properties.
Revised:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity ; potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect the nonlinearity has on global network properties.
Edit intent: fluency
Original:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect the nonlinearity has on global network properties.
Revised:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity ; and, finally, study the effect the nonlinearity has on global network properties.
Edit intent: fluency
Original:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect the nonlinearity has on global network properties.
Revised:  It is achieved by a systematic multi-step approach , that allows 1. to quantify the nonlinearity of coupling , 2. to correct its effects wherever it is caused by simple univariate non-Gaussianity , 3. to potentially localize in space and time any remaining strong sources of this nonlinearity , andfinally, 4. to study the effect nonlinearity has on global network properties.
Edit intent: fluency
Original:  By applying the presented approach to stocks included in three prominent indices (NYSE100, FTSE100 and SP500), we document that most of the apparent nonlinearity is due to univariate non-Gaussianity.
Revised:  By applying this multi-step approach to stocks included in three prominent indices (NYSE100, FTSE100 and SP500), we document that most of the apparent nonlinearity is due to univariate non-Gaussianity.
Edit intent: meaning-changed
Original:  By applying the presented approach to stocks included in three prominent indices (NYSE100, FTSE100 and SP500), we document that most of the apparent nonlinearity is due to univariate non-Gaussianity.
Revised:  By applying the presented approach to stocks included in three prominent indices (NYSE100, FTSE100 and SP500), we establish that the apparent nonlinearity is due to univariate non-Gaussianity.
Edit intent: clarity
Original:  By applying the presented approach to stocks included in three prominent indices (NYSE100, FTSE100 and SP500), we document that most of the apparent nonlinearity is due to univariate non-Gaussianity.
Revised:  By applying the presented approach to stocks included in three prominent indices (NYSE100, FTSE100 and SP500), we document that most of the apparent nonlinearity that has been observed is largely due to univariate non-Gaussianity.
Edit intent: clarity
Original:   We show that  there is no polynomial-time (\frac{3{2}-\varepsilon)-approximation algorithm for any \varepsilon~>~0 }  unless \mathsf{P}=\mathsf{NP}.
Revised:  In this paper, we substantially extend and improve the known approximation bounds for curves in dimension 2 and higher. We show that there is no polynomial-time (\frac{3{2}-\varepsilon)-approximation algorithm for any \varepsilon~>~0 } unless \mathsf{P}=\mathsf{NP}.
Edit intent: meaning-changed
Original:   We show that  there is no polynomial-time (\frac{3{2}-\varepsilon)-approximation algorithm for any \varepsilon~>~0 }  unless \mathsf{P}=\mathsf{NP}.
Revised:  We show that , if \ell is part of the input, then there is no polynomial-time (\frac{3{2}-\varepsilon)-approximation algorithm for any \varepsilon~>~0 } unless \mathsf{P}=\mathsf{NP}.
Edit intent: meaning-changed
Original:   We show that  there is no polynomial-time (\frac{3{2}-\varepsilon)-approximation algorithm for any \varepsilon~>~0 }  unless \mathsf{P}=\mathsf{NP}.
Revised:  We show that there is no polynomial-time {2}-\varepsilon)-approximation algorithm for any \varepsilon~>~0 } unless \mathsf{P}=\mathsf{NP}.
Edit intent: meaning-changed
Original:   We show that  there is no polynomial-time (\frac{3{2}-\varepsilon)-approximation algorithm for any \varepsilon~>~0 }  unless \mathsf{P}=\mathsf{NP}.
Revised:  We show that there is no polynomial-time (\frac{3{2}-\varepsilon)-approximation algorithm for any \varepsilon~>~0 } approximation scheme unless \mathsf{P}=\mathsf{NP}.
Edit intent: clarity
Original:  This bound even holds for one-dimensional curves under the continuous Fr\'echet distance , and is further improved to (3\sin(\frac{\pi{3})-\varepsilon) if the curves may lie in the plane and if the }  discrete Fr\'echet distance is used.
Revised:  Our constructions yield different bounds for one and two-dimensional curves and the discrete and continuous Fr\'echet distance , and is further improved to (3\sin(\frac{\pi{3})-\varepsilon) if the curves may lie in the plane and if the } discrete Fr\'echet distance is used.
Edit intent: meaning-changed
Original:  This bound even holds for one-dimensional curves under the continuous Fr\'echet distance , and is further improved to (3\sin(\frac{\pi{3})-\varepsilon) if the curves may lie in the plane and if the }  discrete Fr\'echet distance is used.
Revised:  This bound even holds for one-dimensional curves under the continuous Fr\'echet distance {3})-\varepsilon) if the curves may lie in the plane and if the } discrete Fr\'echet distance is used.
Edit intent: coherence
Original:  This bound even holds for one-dimensional curves under the continuous Fr\'echet distance , and is further improved to (3\sin(\frac{\pi{3})-\varepsilon) if the curves may lie in the plane and if the }  discrete Fr\'echet distance is used.
Revised:  This bound even holds for one-dimensional curves under the continuous Fr\'echet distance , and is further improved to (3\sin(\frac{\pi{3})-\varepsilon) if the curves may lie in the plane and if the } . In the case of the discrete Fr\'echet distance is used.
Edit intent: coherence
Original:  This bound even holds for one-dimensional curves under the continuous Fr\'echet distance , and is further improved to (3\sin(\frac{\pi{3})-\varepsilon) if the curves may lie in the plane and if the }  discrete Fr\'echet distance is used. These hardness results hold even when k=1, -hardness extends to the case that \ell=\infty, } i.e., for the  minimum-enclosing ball version of the problem.
Revised:  This bound even holds for one-dimensional curves under the continuous Fr\'echet distance , and is further improved to (3\sin(\frac{\pi{3})-\varepsilon) if the curves may lie in the plane and if the } discrete Fr\'echet distance on two-dimensional curves, we show hardness of approximation within a factor close to 2.598. This result also holds when k=1, -hardness extends to the case that \ell=\infty, } i.e., for the minimum-enclosing ball version of the problem.
Edit intent: meaning-changed
Original:  These hardness results hold even when k=1, -hardness extends to the case that \ell=\infty, } i.e., for the  minimum-enclosing ball version of the problem.
Revised:  These hardness results hold even when k=1, and the \mathsf{NP-hardness extends to the case that \ell=\infty, } i.e., for the minimum-enclosing ball version of the problem.
Edit intent: meaning-changed
Original:  These hardness results hold even when k=1, -hardness extends to the case that \ell=\infty, } i.e., for the  minimum-enclosing ball version of the problem.
Revised:  These hardness results hold even when k=1, -hardness extends to the case that \ell=\infty, } i.e., for the problem of computing the minimum-enclosing ball version of the problem.
Edit intent: clarity
Original:  These hardness results hold even when k=1, -hardness extends to the case that \ell=\infty, } i.e., for the  minimum-enclosing ball version of the problem. At the same time, we prove that a careful adaptation of Gonzalez' algorithm in combination with a curve simplification yields a 3-approximation in any dimension, provided that an optimal simplification can be computed exactly.
Revised:  These hardness results hold even when k=1, -hardness extends to the case that \ell=\infty, } i.e., for the minimum-enclosing ball under the Fr\'echet distance. Finally, we observe that a careful adaptation of Gonzalez' algorithm in combination with a curve simplification yields a 3-approximation in any dimension, provided that an optimal simplification can be computed exactly.
Edit intent: meaning-changed
Original:  A central question is whether modern machine learning methods can be applied to construct predictive models based on large data sets drawn from complex, natural systems like cells and brains . In machine learning, the predictive power or generalizability of a model is determined by the statistics of training data .
Revised:  A central question is whether modern machine learning methods can be applied to construct predictive models based on large data sets drawn from complex, natural systems like cells and brains based on large data sets?
Edit intent: coherence
Original:  Our analysis reveals the essential role that thermal fluctuations play in determining the efficiency of predictive inference. Thermal noise drives a system to explore a range of configurations providing `raw' information for a learning algorithm to construct a predictive model.
Revised:  Our analysis reveals the essential role that thermal fluctuations play in determining the difficulty of the underlying machine learning problem. Specifically, stochastic fluctuations drive a system to explore a range of configurations providing `raw' information for a learning algorithm to construct a predictive model.
Edit intent: meaning-changed
Original:  Conversely, thermal energy degrades information by blurring energetic differences between network states . In general, spin networks have an intrinsic optimal temperature at which inference becomes maximally efficient.
Revised:  Conversely, thermal energy degrades information by blurring energetic differences between network states and thereby degrade information. This competition leads spin networks to generically have an intrinsic optimal temperature at which inference becomes maximally efficient.
Edit intent: meaning-changed
Original:  In general, spin networks have an intrinsic optimal temperature at which inference becomes maximally efficient. Simple active learning protocols allow optimization of network temperature , without prior knowledge, to dramatically increase the efficiency of inference  .
Revised:  In general, spin networks have an intrinsic optimal temperature at which stochastic spin fluctuations provide maximal information for discriminating among competing models, maximizing inference efficiency. We demonstrate a simple active learning protocol that optimizes network temperature to boost inference efficiency and dramatically increases the efficiency of inference .
Edit intent: meaning-changed
Original:  We studied four groups with nine different compositions and 18 individual transfers to study 1. ) the effect of group composition on individual social centralities and 2. ) the effect of group composition changes on these centralities.
Revised:  Four groups with nine different compositions and 18 individual transfers to study 1. ) the effect of group composition on individual social centralities and 2. ) the effect of group composition changes on these centralities.
Edit intent: clarity
Original:  We studied four groups with nine different compositions and 18 individual transfers to study 1. ) the effect of group composition on individual social centralities and 2. ) the effect of group composition changes on these centralities.
Revised:  We studied four groups with nine different compositions and 18 individual transfers were studied to evaluate 1 ) the effect of group composition on individual social centralities and 2. ) the effect of group composition changes on these centralities.
Edit intent: clarity
Original:  We studied four groups with nine different compositions and 18 individual transfers to study 1. ) the effect of group composition on individual social centralities and 2. ) the effect of group composition changes on these centralities.
Revised:  We studied four groups with nine different compositions and 18 individual transfers to study 1. ) the effect of group composition on individual social centralities and 2 ) the effect of group composition changes on these centralities.
Edit intent: fluency
Original:  To answer this question and understand post-acquisition behavior and its related individual differences, we explore analytic models of Pavlovian conditioning extending the basic associative model of Hull (i.e., the Rescorla-Wagner model with just one cue) and propose three new models valid for individual data, which we argue to be quite natural.
Revised:  To answer this question and understand post-acquisition behavior and its related individual differences, we propose a psychological principle that naturally extends the basic associative model of Hull (i.e., the Rescorla-Wagner model with just one cue) and propose three new models valid for individual data, which we argue to be quite natural.
Edit intent: meaning-changed
Original:  To answer this question and understand post-acquisition behavior and its related individual differences, we explore analytic models of Pavlovian conditioning extending the basic associative model of Hull (i.e., the Rescorla-Wagner model with just one cue) and propose three new models valid for individual data, which we argue to be quite natural.
Revised:  To answer this question and understand post-acquisition behavior and its related individual differences, we explore analytic models of Pavlovian conditioning extending the basic associative single-cue Rescorla-Wagner model with just one cue) and propose three new models valid for individual data, which we argue to be quite natural.
Edit intent: meaning-changed
Original:  To answer this question and understand post-acquisition behavior and its related individual differences, we explore analytic models of Pavlovian conditioning extending the basic associative model of Hull (i.e., the Rescorla-Wagner model with just one cue) and propose three new models valid for individual data, which we argue to be quite natural. The first two are descriptive settings: (  i) a framework of dynamical models inspired by the classical mechanics of a particle in a given potential, which comprises Hull model and also a specific model encoding resistance to learning in the first few sessions followed by an over-optimal response peak ;
Revised:  To answer this question and understand post-acquisition behavior and its related individual differences, we explore analytic models of Pavlovian conditioning extending the basic associative model of Hull (i.e., the Rescorla-Wagner model ( i) a framework of dynamical models inspired by the classical mechanics of a particle in a given potential, which comprises Hull model and also a specific model encoding resistance to learning in the first few sessions followed by an over-optimal response peak ;
Edit intent: coherence
Original:  The first two are descriptive settings: (  i) a framework of dynamical models inspired by the classical mechanics of a particle in a given potential, which comprises Hull model and also a specific model encoding resistance to learning in the first few sessions followed by an over-optimal response peak ;
Revised:  The first two are descriptive settings: ( which may also be called Hull model) of Pavlovian conditioning to ( i) a framework of dynamical models inspired by the classical mechanics of a particle in a given potential, which comprises Hull model and also a specific model encoding resistance to learning in the first few sessions followed by an over-optimal response peak ;
Edit intent: meaning-changed
Original:  The first two are descriptive settings: (  i) a framework of dynamical models inspired by the classical mechanics of a particle in a given potential, which comprises Hull model and also a specific model encoding resistance to learning in the first few sessions followed by an over-optimal response peak ;
Revised:  The first two are descriptive settings: ( i) a framework of dynamical models predicting resistance to learning in the first few sessions followed by an over-optimal response peak ;
Edit intent: clarity
Original:  The first two are descriptive settings: (  i) a framework of dynamical models inspired by the classical mechanics of a particle in a given potential, which comprises Hull model and also a specific model encoding resistance to learning in the first few sessions followed by an over-optimal response peak ;
Revised:  The first two are descriptive settings: ( i) a framework of dynamical models inspired by the classical mechanics of a particle in a given potential, which comprises Hull model and also a specific model encoding resistance to learning in the first few sessions followed by an over-optimal response peak . In turn,
Edit intent: coherence
Original:  (ii) the most direct stochastic extension of Hull model, characterized by the presence of stochastic noise; (iii) a theory where response fluctuations are described by quantum mechanics and based on the general framework of dynamical models (i) explaining the noise met in (ii) and giving characteristic predictions .
Revised:  (ii) the theory can be further extended to describe response fluctuations by the laws of quantum mechanics. (iii) We also introduce an independent model characterized by the presence of a stochastic noise of cognitive origin .
Edit intent: clarity
Original:  The best nonlinear regression to averaged data and 60\% of individual data is Hull's model, while for the remaining 40\% of the subjects the dynamical model (i) provides a better fit .
Revised:  There is weak to positive evidence that Hull model is the best nonlinear regression to averaged data and 60\% of individual data is Hull's model, while for the remaining 40\% of the subjects the dynamical model (i) provides a better fit .
Edit intent: meaning-changed
Original:  The best nonlinear regression to averaged data and 60\% of individual data is Hull's model, while for the remaining 40\% of the subjects the dynamical model (i) provides a better fit .
Revised:  The best nonlinear regression to averaged data and 60\% of individual data is Hull's model, while for the remaining 40\% of the subjects , while its dynamical extension (i) provides a better fit .
Edit intent: clarity
Original:  The best nonlinear regression to averaged data and 60\% of individual data is Hull's model, while for the remaining 40\% of the subjects the dynamical model (i) provides a better fit .
Revised:  The best nonlinear regression to averaged data and 60\% of individual data is Hull's model, while for the remaining 40\% of the subjects the dynamical model (i) can explain the totality of data .
Edit intent: clarity
Original: Objective: Higher-order tensor decompositions have hardly been used in muscle activity analysis despite the fact that multichannel electromyography (EMG) datasets used in muscle synergy studies naturally present multi-way structure .
Revised:  Higher-order tensor decompositions have hardly been used in muscle activity analysis despite the fact that multichannel electromyography (EMG) datasets used in muscle synergy studies naturally present multi-way structure .
Edit intent: coherence
Original: Objective: Higher-order tensor decompositions have hardly been used in muscle activity analysis despite the fact that multichannel electromyography (EMG) datasets used in muscle synergy studies naturally present multi-way structure .
Revised: Objective: Higher-order tensor decompositions have hardly been used in muscle activity analysis despite multichannel electromyography (EMG) datasets used in muscle synergy studies naturally present multi-way structure .
Edit intent: coherence
Original: Objective: Higher-order tensor decompositions have hardly been used in muscle activity analysis despite the fact that multichannel electromyography (EMG) datasets used in muscle synergy studies naturally present multi-way structure .
Revised: Objective: Higher-order tensor decompositions have hardly been used in muscle activity analysis despite the fact that multichannel electromyography (EMG) datasets used in muscle synergy studies naturally present multi-way structures .
Edit intent: fluency
Original:  Here, we seek to discuss and demonstrate the potential of tensor decomposition as a framework to estimate muscle synergies from 3^{rd}-order EMG tensors constructed by stacking repetitions of multi-channel EMG for several tasks.
Revised:  Here, we seek to demonstrate and discuss the potential of tensor decomposition as a framework to estimate muscle synergies from 3^{rd}-order EMG tensors constructed by stacking repetitions of multi-channel EMG for several tasks.
Edit intent: fluency
Original:  Here, we seek to discuss and demonstrate the potential of tensor decomposition as a framework to estimate muscle synergies from 3^{rd}-order EMG tensors constructed by stacking repetitions of multi-channel EMG for several tasks.
Revised:  Here, we seek to discuss and demonstrate the potential of tensor decompositions as a framework to estimate muscle synergies from 3^{rd}-order EMG tensors constructed by stacking repetitions of multi-channel EMG for several tasks.
Edit intent: fluency
Original:  Here, we seek to discuss and demonstrate the potential of tensor decomposition as a framework to estimate muscle synergies from 3^{rd}-order EMG tensors constructed by stacking repetitions of multi-channel EMG for several tasks.
Revised:  Here, we seek to discuss and demonstrate the potential of tensor decomposition as a framework to estimate muscle synergies from 3^{rd}-order EMG tensors built by stacking repetitions of multi-channel EMG for several tasks.
Edit intent: clarity