wanyu/IteraTeR_full_sent huggingface dataset:
Original:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 } ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Revised:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ S } ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Edit intent: meaning-changed
Original:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 } ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Revised:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 \o} ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Edit intent: meaning-changed
Original:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 } ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Revised:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 }rensen et al., Nature 532 ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Edit intent: meaning-changed
Original:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 } ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Revised:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 } ], as a consequence also algorithms like CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Edit intent: fluency
Original:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 } ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches evolve which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Revised:  While it seems clear from the presented data that human players have indeed outperformed a particular implementation of the Krotov algorithm (and, according to [ sorensen16 } ], as a consequence also algorithms like CRAB CRAB and evolutionary approaches which perform worse than Krotov), there is no reason to believe that this finding is of any particular significance.
Edit intent: clarity
Original:  In fact, as has been discussed first by D. Sels [ DSels ], a very simple approach using classical arguments can capture the BringHomeWater Quantum Moves game far better than the player approach.
Revised:  In fact, as has been discussed first by D. Sels [ D. Sels, Phys. Rev. A 97, 040302 ], a very simple approach using classical arguments can capture the BringHomeWater Quantum Moves game far better than the player approach.
Edit intent: meaning-changed
Original:  Furthermore, as also shown by [ DSels ], one of the simplest optimization algorithms available, Stochastic Ascent, can outperform all of the above.
Revised:  Furthermore, as also shown by [ D. Sels, Phys. Rev. A 97, 040302 ], one of the simplest optimization algorithms available, Stochastic Ascent, can outperform all of the above.
Edit intent: meaning-changed
Original:  While the clonal selection of the T helper (Th) cells is the simple and established mechanism for learning a better recognition of new pathogens, the question  still remains unexplored is how the Th cells can learn to eliminate them by activating or deactivating appropriate immune cells after recognition .
Revised:  While the clonal selection of T helper (Th) cells is the simple and established mechanism for learning a better recognition of new pathogens, the question still remains unexplored is how the Th cells can learn to eliminate them by activating or deactivating appropriate immune cells after recognition .
Edit intent: fluency
Original:  While the clonal selection of the T helper (Th) cells is the simple and established mechanism for learning a better recognition of new pathogens, the question  still remains unexplored is how the Th cells can learn to eliminate them by activating or deactivating appropriate immune cells after recognition .
Revised:  While the clonal selection of the T helper (Th) cells is the simple and established mechanism to better recognize new pathogens, the question still remains unexplored is how the Th cells can learn to eliminate them by activating or deactivating appropriate immune cells after recognition .
Edit intent: fluency
Original:  While the clonal selection of the T helper (Th) cells is the simple and established mechanism for learning a better recognition of new pathogens, the question  still remains unexplored is how the Th cells can learn to eliminate them by activating or deactivating appropriate immune cells after recognition .
Revised:  While the clonal selection of the T helper (Th) cells is the simple and established mechanism for learning a better recognition of new pathogens, the question that still remains unexplored is how the Th cells can learn to eliminate them by activating or deactivating appropriate immune cells after recognition .
Edit intent: fluency
Original:  While the clonal selection of the T helper (Th) cells is the simple and established mechanism for learning a better recognition of new pathogens, the question  still remains unexplored is how the Th cells can learn to eliminate them by activating or deactivating appropriate immune cells after recognition .
Revised:  While the clonal selection of the T helper (Th) cells is the simple and established mechanism for learning a better recognition of new pathogens, the question still remains unexplored is how the Th cells can acquire better ways to bias the responses of immune cells for eliminating pathogens more efficiently by translating the recognized antigen information into regulatory signals .
Edit intent: clarity
Original:   This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video belongs . Experiments were carried out using the Montalbano and GRIT datasets.
Revised:  We call this technique star RGB. This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video belongs . Experiments were carried out using the Montalbano and GRIT datasets.
Edit intent: meaning-changed
Original:   This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video belongs . Experiments were carried out using the Montalbano and GRIT datasets.
Revised:  This image is then passed to a classifier formed by two Resnet CNNs , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video belongs . Experiments were carried out using the Montalbano and GRIT datasets.
Edit intent: fluency
Original:   This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video belongs . Experiments were carried out using the Montalbano and GRIT datasets.
Revised:  This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a fully connected layer, which indicates the class of the gesture present in the input video belongs . Experiments were carried out using the Montalbano and GRIT datasets.
Edit intent: clarity
Original:   This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video belongs . Experiments were carried out using the Montalbano and GRIT datasets.
Revised:  This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video . Experiments were carried out using the Montalbano and GRIT datasets.
Edit intent: clarity
Original:   This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video belongs . Experiments were carried out using the Montalbano and GRIT datasets.
Revised:  This image is then passed to a classifier formed by two Resnet CNN's , a soft-attention ensemble, and a multilayer perceptron, which returns the predicted class label that indicates to which type of gesture the input video belongs . Experiments were carried out using both Montalbano and GRIT datasets.
Edit intent: clarity
Original:  On the GRIT dataset, our proposal achieves more than 98\% of accuracy, recall, precision, and F1-score, outperforming the reference approach in more than 6\%.
Revised:  Regarding the GRIT dataset, our proposal achieves more than 98\% of accuracy, recall, precision, and F1-score, outperforming the reference approach in more than 6\%.
Edit intent: fluency
Original:  On the GRIT dataset, our proposal achieves more than 98\% of accuracy, recall, precision, and F1-score, outperforming the reference approach in more than 6\%.
Revised:  On the GRIT dataset, our proposal achieves more than 98\% of accuracy, recall, precision, and F1-score, outperforming the reference approach by more than 6\%.
Edit intent: clarity
Original: We introduce DeepMoD, a deep learning based model discovery algorithmwhich seeks the partial differential equation underlying a spatio-temporal data set . DeepMoD employs sparse regression on a library of basis functions and their corresponding spatial derivatives.
Revised: We introduce DeepMoD, a Deep learning based Model Discovery algorithm. DeepMoD discovers the partial differential equation underlying a spatio-temporal data set . DeepMoD employs sparse regression on a library of basis functions and their corresponding spatial derivatives.
Edit intent: clarity
Original: We introduce DeepMoD, a deep learning based model discovery algorithmwhich seeks the partial differential equation underlying a spatio-temporal data set . DeepMoD employs sparse regression on a library of basis functions and their corresponding spatial derivatives.
Revised: We introduce DeepMoD, a deep learning based model discovery algorithmwhich seeks the partial differential equation underlying a spatio-temporal data set using sparse regression on a library of basis functions and their corresponding spatial derivatives.
Edit intent: coherence
Original: We introduce DeepMoD, a deep learning based model discovery algorithmwhich seeks the partial differential equation underlying a spatio-temporal data set . DeepMoD employs sparse regression on a library of basis functions and their corresponding spatial derivatives.
Revised: We introduce DeepMoD, a deep learning based model discovery algorithmwhich seeks the partial differential equation underlying a spatio-temporal data set . DeepMoD employs sparse regression on a library of possible functions and their corresponding spatial derivatives.
Edit intent: clarity
Original: We introduce DeepMoD, a deep learning based model discovery algorithmwhich seeks the partial differential equation underlying a spatio-temporal data set . DeepMoD employs sparse regression on a library of basis functions and their corresponding spatial derivatives.
Revised: We introduce DeepMoD, a deep learning based model discovery algorithmwhich seeks the partial differential equation underlying a spatio-temporal data set . DeepMoD employs sparse regression on a library of basis functions and their derivatives.
Edit intent: clarity
Original:  This construction makes it extremely robust to noise and applicable to small data sets  and, contrary to other deep learning methods, does not require a training set and is impervious to overfitting. We illustrate this approach on several physical problems , such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 \%.
Revised:  This construction makes it extremely robust to noise and applicable to small data sets and, contrary to other deep learning methods, does not require a training set . We benchmark our approach on several physical problems , such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 \%.
Edit intent: clarity
Original:  We illustrate this approach on several physical problems , such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 \%.
Revised:  We illustrate this approach on several physical problems such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 \%.
Edit intent: fluency
Original:  We illustrate this approach on several physical problems , such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 \%.
Revised:  We illustrate this approach on several physical problems , such as the Burgers', Korteweg-de Vries and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 \%.
Edit intent: coherence
Original:  We illustrate this approach on several physical problems , such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 \%.
Revised:  We illustrate this approach on several physical problems , such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as \mathcal{O (10^2) samples and works at noise levels up to 75 \%.
Edit intent: meaning-changed
Original:  We illustrate this approach on several physical problems , such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 \%. This resilience to noise and high performance at very few samples highlights the potential of this method to be applied on experimental data. Code and examples available at URL%DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > . Motivated by these results, we apply DeepMoD directly on noisy experimental time-series data from a gel electrophoresis experiment and find that it discovers the advection-diffusion equation describing this system.
Revised:  We illustrate this approach on several physical problems , such as the Burgers', Korteweg-de Vries , advection-diffusion and Keller-Segel equations, and find that it requires as few as O (10^2) samples and works at noise levels up to 75 %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > . Motivated by these results, we apply DeepMoD directly on noisy experimental time-series data from a gel electrophoresis experiment and find that it discovers the advection-diffusion equation describing this system.
Edit intent: coherence
Original:  We propose to estimate f nonparametrically using the method of sieves. We provide conditions for consistency of this estimator when either n or T or both divergeto infinity, and when the answers of each subject are correlated and this correlation differs across subjects.
Revised:  We propose to estimate the unknown function f linking stimuli to responses through a nonparametric sieve estimator. We give conditions for consistency of this estimator when either n or T or both divergeto infinity, and when the answers of each subject are correlated and this correlation differs across subjects.
Edit intent: clarity
Original:  We provide conditions for consistency of this estimator when either n or T or both divergeto infinity, and when the answers of each subject are correlated and this correlation differs across subjects. We further demonstrate that the rate of convergence depends upon the covariance structure of the error term taken across individuals.
Revised:  We provide conditions for consistency when either nor Tor both diverge. The rate of convergence depends upon the covariance structure of the error term taken across individuals.
Edit intent: clarity
Original:  We further demonstrate that the rate of convergence depends upon the covariance structure of the error term taken across individuals. A convergence rate is also obtained for derivatives. These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment .
Revised:  We further demonstrate that the rate of convergence depends upon the error covariance structure, that is allowed to differ across subjects. With these results we derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment .
Edit intent: clarity
Original:  These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment .
Revised:  These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T . We provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment .
Edit intent: coherence
Original:  These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment .
Revised:  These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and questions in a laboratory experiment .
Edit intent: fluency
Original:  These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment . We argue that in general a large value of n is better than a large value of T. Conditions for asymptotic normality of linear and nonlinear functionals of the estimated function of interest are derived.
Revised:  These results allow us to derive the optimal divergence rate of the dimension of the sieve basis with both n and T and thus provide guidance about the optimal balance between the number of subjects and the number of questions in a laboratory experiment and argue that a large nis often better than a large value of T. Conditions for asymptotic normality of linear and nonlinear functionals of the estimated function of interest are derived.
Edit intent: clarity
Original:  We argue that in general a large value of n is better than a large value of T. Conditions for asymptotic normality of linear and nonlinear functionals of the estimated function of interest are derived.
Revised:  We argue that in general a large value of n is better than a large T. We derive conditions for asymptotic normality of linear and nonlinear functionals of the estimated function of interest are derived.
Edit intent: coherence
Original:  We argue that in general a large value of n is better than a large value of T. Conditions for asymptotic normality of linear and nonlinear functionals of the estimated function of interest are derived. These results are further applied to obtain the asymptotic distribution of the Wald test when the number of constraints under the null is finite and when it diverges to infinity along with other asymptotic parameters.
Revised:  We argue that in general a large value of n is better than a large value of T. Conditions for asymptotic normality of linear and nonlinear functionals of the estimator of Tand apply them to obtain the asymptotic distribution of the Wald test when the number of constraints under the null is finite and when it diverges to infinity along with other asymptotic parameters.
Edit intent: clarity
Original:  These motors rely on  ATP, supplied at the limits of diffusion from the sarcoplasm to the interior of the lattice of actin and myosin . Classic sliding filament theory suggests that lattice spacing is constant .
Revised:  These motors rely on energy derived from ATP, supplied at the limits of diffusion from the sarcoplasm to the interior of the lattice of actin and myosin . Classic sliding filament theory suggests that lattice spacing is constant .
Edit intent: meaning-changed
Original:  These motors rely on  ATP, supplied at the limits of diffusion from the sarcoplasm to the interior of the lattice of actin and myosin . Classic sliding filament theory suggests that lattice spacing is constant .
Revised:  These motors rely on ATP, supplied , in part, by diffusion from the sarcoplasm to the interior of the lattice of actin and myosin . Classic sliding filament theory suggests that lattice spacing is constant .
Edit intent: clarity