wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To validate the effectiveness of H2R-AT, two representative task scenarios , " serve water for a human in a kitchen" and " pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP;
Revised:  To validate the effectiveness of H2R-AT, two representative task scenarios , " serve water for a human in a kitchen" and `` pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP;
Edit intent: fluency
Original:  To validate the effectiveness of H2R-AT, two representative task scenarios , " serve water for a human in a kitchen" and " pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP;
Revised:  To validate the effectiveness of H2R-AT, two representative task scenarios , " serve water for a human in a kitchen" and " pick up a defective gear in a factory" were designed in an open-access simulation platform V-REP;
Edit intent: clarity
Original:  To validate the effectiveness of H2R-AT, two representative task scenarios , " serve water for a human in a kitchen" and " pick up a defective gear in a factory" with abnormal robot executions, were designed in an open-access simulation platform V-REP;
Revised:  To validate the effectiveness of H2R-AT, two representative task scenarios , " serve water for a human in a kitchen" and " pick up a defective gear in a factory" with abnormal robot executions, were designed in a simulation framework CRAIhri with abnormal robot manipulations; and
Edit intent: meaning-changed
Original:  252 volunteers were recruited to provide about 12000 verbal reminders to learn and test the attention transfer model H2R-AT. With an \textit{\textbf{  accuracy of 73.68\\%DIF < in transferring attention and accuracy of 66.86\\% in avoiding robot execution failures, the effectiveness of H2R-AT was validated.
Revised:  252 volunteers were recruited to provide about 12000 verbal reminders to learn and test \textit{\textbf{ accuracy of 73.68\\%DIF < in transferring attention and accuracy of 66.86\\% in avoiding robot execution failures, the effectiveness of H2R-AT was validated.
Edit intent: clarity
Original:  With an \textit{\textbf{  accuracy of 73.68\\%DIF < in transferring attention and accuracy of 66.86\\% in avoiding robot execution failures, the effectiveness of H2R-AT was validated.
Revised:  With an \textit{\textbf{H2R-AT accuracy of 73.68\\%DIF < in transferring attention and accuracy of 66.86\\% in avoiding robot execution failures, the effectiveness of H2R-AT was validated.
Edit intent: meaning-changed
Original:  With an \textit{\textbf{  accuracy of 73.68\\%DIF < in transferring attention and accuracy of 66.86\\% in avoiding robot execution failures, the effectiveness of H2R-AT was validated.
Revised:  With an \textit{\textbf{ . The method effectiveness was validated by the high accuracy of 73.68\\%DIF < in transferring attention and accuracy of 66.86\\% in avoiding robot execution failures, the effectiveness of H2R-AT was validated.
Edit intent: meaning-changed
Original:  However, the behaviour of the model is in general not tractable for large networks ; therefore, mean field theories are frequently used to approximate its statistical properties. Many variants of the classical naive and TAP (i.e., second-order) mean field approximations have been proposed , each of which makes unique assumptions about the temporal evolution of the system's correlation structure .
Revised:  However, the behaviour of the model is in general not tractable for large networks , many mean-field methods have been proposed , each of which makes unique assumptions about the temporal evolution of the system's correlation structure .
Edit intent: clarity
Original:  Many variants of the classical naive and TAP (i.e., second-order) mean field approximations have been proposed , each of which makes unique assumptions about the temporal evolution of the system's correlation structure .
Revised:  Many variants of the classical naive and TAP (i.e., second-order) mean field approximations have been proposed for their analysis, each based on unique assumptions about the temporal evolution of the system's correlation structure .
Edit intent: meaning-changed
Original:  This view not only unifies previous methods but also allows us to propose novel methods that, in contrast with traditional mean-field approaches, preserve correlations of the system, both in stationary and transient states. By comparing analytical approximations and exact numerical simulations, we show that the proposed methods provide more accurate estimates of covariance evolution in the system than classical equations, and consequently outperform previous mean field theories for solving the inverse Ising problem. In sum, our framework unifies and extends current mean-field approximations of kinetic Ising model, constituting a powerful tool for studying non-equilibrium dynamics of complex systems .
Revised:  This view not only unifies previous methods but also allows us to propose novel methods that, in contrast with traditional mean-field approaches, preserve the system's correlations. We show that these new methods can outperform previous ones in predicting and assessing network properties near maximally fluctuating regimes .
Edit intent: clarity
Original:  Although the efficient parallelization of 3D FFT has been largely investigated over the last few decades, performance and scalability of parallel 3D FFT methods on new generation hardware architecture for HPC is a major challenge.
Revised:  Performance of many scientific applications such as Molecular Dynamic simulations depends on the underlying 3D FFT has been largely investigated over the last few decades, performance and scalability of parallel 3D FFT methods on new generation hardware architecture for HPC is a major challenge.
Edit intent: meaning-changed
Original:  Although the efficient parallelization of 3D FFT has been largely investigated over the last few decades, performance and scalability of parallel 3D FFT methods on new generation hardware architecture for HPC is a major challenge. Looking at upcoming exascale cluster architectures, the conventional parallel 3D FFT calculations on HPC needs improvement for better performance .
Revised:  Although the efficient parallelization of 3D parallel FFT library being used .
Edit intent: clarity
Original:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has a feature of overlapping compute and memory I /O with MPI communication .
Revised:  To exploit the hyperthreading capabilities of processor cores without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has a feature of overlapping compute and memory I /O with MPI communication .
Edit intent: clarity
Original:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has a feature of overlapping compute and memory I /O with MPI communication .
Revised:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use multithreading along with MPI. CROFT implementation has a feature of overlapping compute and memory I /O with MPI communication .
Edit intent: clarity
Original:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has a feature of overlapping compute and memory I /O with MPI communication .
Revised:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has an innovative feature of overlapping compute and memory I /O with MPI communication .
Edit intent: clarity
Original:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has a feature of overlapping compute and memory I /O with MPI communication .
Revised:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has a feature of overlapping compute and memory-I /O with MPI communication .
Edit intent: fluency
Original:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has a feature of overlapping compute and memory I /O with MPI communication . Depending on the number of processes used, CROFT shows performance improvement of about 51  to 42 percent as compared to FFTW3 library.
Revised:  To exploit the multithreading capabilities of hardware without affecting performance, CROFT is designed to use hybrid programming model of OpenMP and MPI. CROFT implementation has a feature of overlapping compute and memory I /O with MPI communication using multithreading. As opposed to other 3D FFT implementations, CROFT uses only two threads where one thread is dedicated for communication so that it can be effectively overlapped with computations. Thus, depending on the number of processes used, CROFT shows performance improvement of about 51 to 42 percent as compared to FFTW3 library.
Edit intent: meaning-changed
Original: Recurrent spiking neural networks (RSNN) in the  brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of time and energy consumption . Moreover, learning can happen rapidly after very few examples.
Revised: Recurrent spiking neural networks (RSNN) in the brain learn to perform a wide range of perceptual, cognitive and motor tasks very efficiently in terms of energy consumption and requires very few examples.
Edit intent: coherence
Original:  This is due to the optimality of coding and learning schemes, which have yet to be clearly understood. This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
Revised:  This motivates the search for biologically inspired learning rules for RSNNs to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed but it remains a challenge to design RSNNs that use biologically plausible mechanisms capable of solving complex temporal tasks.
Edit intent: clarity
Original:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
Revised:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of brain computation and the efficiency of artificial ones . Several spiking network models have been proposed but it remains a challenge to design RSNNs that use biologically plausible mechanisms capable of solving complex temporal tasks.
Edit intent: clarity
Original:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
Revised:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial intelligence . Several spiking network models have been proposed but it remains a challenge to design RSNNs that use biologically plausible mechanisms capable of solving complex temporal tasks.
Edit intent: clarity
Original:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
Revised:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking models and learning rules have been proposed but it remains a challenge to design RSNNs that use biologically plausible mechanisms capable of solving complex temporal tasks.
Edit intent: meaning-changed
Original:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
Revised:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed , but it remains a challenge to design RSNNs that use biologically plausible mechanisms capable of solving complex temporal tasks.
Edit intent: fluency
Original:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
Revised:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed but it remains a challenge to design RSNNs whose learning relies on biologically plausible mechanisms capable of solving complex temporal tasks.
Edit intent: clarity
Original:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed  but it remains a challenge to design RSNNs that use biologically plausible mechanisms  capable of solving complex temporal tasks.
Revised:  This naturally challenges the formulation of biologically inspired RSNNs in order to improve our understanding of biological intelligence and the efficiency of artificial ones . Several spiking network models have been proposed but it remains a challenge to design RSNNs that use biologically plausible mechanisms and are capable of solving complex temporal tasks.
Edit intent: coherence
Original:  We use a general probabilistic framework that relies on the principle of maximizing the likelihood for the network to reproduce some temporal dynamics. This principle permits to analytically work out an explicit and biologically plausible plasticity rule. Here we propose a novel target-based learning scheme in which such a rule can be used to efficiently train a RSNN to solve several temporal taskssuch as learning multidimensional trajectory and an implementation of the temporal XOR.
Revised:  We use a general probabilistic framework that relies on the principle of maximizing the likelihood for the network to solve a specific task. We propose a novel target-based learning scheme in which such a rule can be used to efficiently train a RSNN to solve several temporal taskssuch as learning multidimensional trajectory and an implementation of the temporal XOR.
Edit intent: clarity
Original:  Here we propose a novel target-based learning scheme in which such a rule can be used to efficiently train a RSNN to solve several temporal taskssuch as learning multidimensional trajectory and an implementation of the temporal XOR. We finally show that an online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output.
Revised:  Here we propose a novel target-based learning scheme in which the learning rule derived from likelihood maximization is used to mimic a specific spiking pattern that encodes the solution to complex temporal tasks. This method makes the learning extremely rapid and precise, outperforming state of the art algorithms for RSNNs. We demonstrate the capacity of our model to tackle several problems like learning multidimensional trajectories and solving the classical temporal XOR benchmark. Finally, we show that an online approximation of the gradient ascent, in addition to guaranteeing complete locality in time and space, allows learning after very few presentations of the target output.
Edit intent: meaning-changed
Original: An \alpha-additive spanner of an undirected graph G =(V, E)  is a subgraph H such that the distance between any two vertices in G is stretched by no more than an additive factor of \alpha.
Revised: An \alpha-additive spanner of an undirected graph G =(V, E)spanner is a subgraph H such that the distance between any two vertices in G is stretched by no more than an additive factor of \alpha.
Edit intent: meaning-changed
Original: An \alpha-additive spanner of an undirected graph G =(V, E)  is a subgraph H such that the distance between any two vertices in G is stretched by no more than an additive factor of \alpha.
Revised: An \alpha-additive spanner of an undirected graph G =(V, E) of a graph G is a subgraph H such that the distance between any two vertices in G is stretched by no more than an additive factor of \alpha.
Edit intent: meaning-changed
Original: An \alpha-additive spanner of an undirected graph G =(V, E)  is a subgraph H such that the distance between any two vertices in G is stretched by no more than an additive factor of \alpha. It is previously known that unweighted graphshave 2-, 4-, and 6-additive spanners containing (n^{3/2), }%DIFDELCMD < O%%% (n^{7/5), and O(n^{4/3}) edges, respectively.
Revised: An \alpha-additive spanner of an undirected graph G =(V, E) is a subgraph H (n^{3/2), }%DIFDELCMD < O%%% (n^{7/5), and O(n^{4/3}) edges, respectively.
Edit intent: coherence
Original:  It is previously known that unweighted graphshave 2-, 4-, and 6-additive spanners containing (n^{3/2), }%DIFDELCMD < O%%% (n^{7/5), and O(n^{4/3}) edges, respectively.
Revised:  It is previously known that unweighted graphshave 2-, 4-, and 6-additive spanners containing ), }%DIFDELCMD < O%%% (n^{7/5), and O(n^{4/3}) edges, respectively.
Edit intent: clarity
Original:  It is previously known that unweighted graphshave 2-, 4-, and 6-additive spanners containing (n^{3/2), }%DIFDELCMD < O%%% (n^{7/5), and O(n^{4/3}) edges, respectively.
Revised:  It is previously known that unweighted graphshave 2-, 4-, and 6-additive spanners containing (n^{3/2), }%DIFDELCMD < O%%% ), and O(n^{4/3}) edges, respectively.
Edit intent: meaning-changed
Original:  We consider \alpha=}   2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Revised:  We consider \alpha=} that approximately preserves its pairwise distances. Spanners are commonly applied to compress computation on metric spaces corresponding to weighted input graphs. Classic spanner constructions can seamlessly handle edge weights, so long as error is measured 2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Edit intent: meaning-changed
Original:  We consider \alpha=}   2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Revised:  We consider \alpha=} multiplicatively 2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Edit intent: meaning-changed
Original:  We consider \alpha=}   2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Revised:  We consider \alpha=} . In this work, we investigate whether one can similarly extend constructions of spanners with 2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Edit intent: meaning-changed
Original:  We consider \alpha=}   2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Revised:  We consider \alpha=} additive 2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Edit intent: fluency
Original:  We consider \alpha=}   2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Revised:  We consider \alpha=} error. These extensions are not immediate, due to a key lemma about the size of shortest path neighborhoods that fails for weighted graphs. Despite this, we recover a suitable amortized version, which lets us prove direct extensions of classic +2 and +4 unweighted spanners (both all-pairs and pairwise) to + 2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Edit intent: meaning-changed
Original:  We consider \alpha=}   2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Revised:  We consider \alpha=} 2W and + 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Edit intent: meaning-changed
Original:  We consider \alpha=}   2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Revised:  We consider \alpha=} 2W , 4W weighted spanners, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Edit intent: meaning-changed
Original:  We consider \alpha=}   2W , 4W , 6W, where W is the maximum edge weight in G. We first show that every n-node graph has a subsetwise 2W-spanner on O(n |S|^{1/2) edges where S \subseteq V and W is a constant.
Revised:  We consider \alpha=} 2W , 4W , 6W, where W is the maximum edge weight ) edges where S \subseteq V and W is a constant.
Edit intent: coherence