wanyu/IteraTeR_v2 huggingface dataset:
Original: In this paper we introduce a procedure, based on the method of equivariant moving frames, for formulating continuous Galerkin finite element schemes that preserve the Lie point symmetries of initial value  ordinary differential equations.
Revised: In this paper we introduce a procedure, based on the method of equivariant moving frames, for formulating continuous Galerkin finite element schemes that preserve the Lie point symmetries of initial value problems for ordinary differential equations.
Edit intent: clarity
Original:  Implementations will be made available in all major toolboxes (Foolbox, CleverHans and ART) . Furthermore, we will soon add additional content and experiments, including L0 and L1 versions of our attack as well as additional comparisons to other L2 and L_infinity attacks .
Revised:  Implementations will soon be available in all major toolboxes (Foolbox, CleverHans and ART) . Furthermore, we will soon add additional content and experiments, including L0 and L1 versions of our attack as well as additional comparisons to other L2 and L_infinity attacks .
Edit intent: clarity
Original:  Implementations will be made available in all major toolboxes (Foolbox, CleverHans and ART) . Furthermore, we will soon add additional content and experiments, including L0 and L1 versions of our attack as well as additional comparisons to other L2 and L_infinity attacks .
Revised:  Implementations will be made available in all major toolboxes (Foolbox, CleverHans and ART) .
Edit intent: coherence
Original: Symmetry is a key feature observed in nature (from flowers and leaves, to butterflies and birds) and in man-made objects (from paintings and sculptures, to manufactured objects and architectural design).
Revised: Symmetry is a key feature observed in nature (from flowers and leaves, to butterflies and birds) and in human-made objects (from paintings and sculptures, to manufactured objects and architectural design).
Edit intent: fluency
Original:  While the pretrained network serves as a backbone of our model, we learn how to aggregate feature representations along five stages of the network.
Revised:  While the pretrained network serves as a backbone of our model, we learn how to aggregate feature representations along five stages of the network.
Edit intent: fluency
Original:  During learning, our construction enables to modulate and screen outputs from each stage along the network as governed by the optimized objective.
Revised:  During learning, our method enables to modulate and screen outputs from each stage along the network as governed by the optimized objective.
Edit intent: clarity
Original: The Algonauts challenge requires to construct an multi-subject encoder of images to brain activity.
Revised: The Algonauts challenge requires to construct a multi-subject encoder of images to brain activity.
Edit intent: fluency
Original:  We demonstrate the practical utility of our work with two examples -- logistic regression and Gaussian process regression.
Revised:  We demonstrate the practical utility of our work with two examples - logistic regression and Gaussian process regression.
Edit intent: fluency
Original:  We demonstrate the practical utility of our work with two examples -- logistic regression and Gaussian process regression.
Revised:  We demonstrate the practical utility of our work with two examples -- logistic regression and Gaussian Process regression.
Edit intent: fluency
Original:  In this paper, we propose a new adaptive Cauchy mutation based DE variant called ACM-DE (Adaptive Cauchy Mutation Differential Evolution), which removes all of these limitations.
Revised:  In this paper, we propose a new adaptive Cauchy mutation-based DE variant called ACM-DE (Adaptive Cauchy Mutation Differential Evolution), which removes all of these limitations.
Edit intent: fluency
Original: A new Cauchy mutation for improving the convergence of differential evolution (DE) is proposed in this paper . DE is an efficient evolutionary algorithm for optimizing multidimensional real-valued functions, which has been successfully applied to various real-world problems.
Revised: A new Cauchy mutation for improving the convergence of differential evolution (DE) is proposed in this paper . DE is a simple yet powerful evolutionary algorithm that has been used successfully to optimize various real-world problems.
Edit intent: clarity
Original:  This condition shows that the interpolation operators should be constructed such that their norm is close to unity .
Revised:  This condition shows that the interpolation operators should be constructed such that their norm is close to one .
Edit intent: clarity
Original: The emerging neuromorphic computing (NC) architectures have shown compelling energy efficiency to perform machine learning tasks with spiking neural networks (SNNs).
Revised: Emerging neuromorphic computing (NC) architectures have shown compelling energy efficiency to perform machine learning tasks with spiking neural networks (SNNs).
Edit intent: coherence
Original: The emerging neuromorphic computing (NC) architectures have shown compelling energy efficiency to perform machine learning tasks with spiking neural networks (SNNs).
Revised: The emerging neuromorphic computing (NC) architectures have shown compelling energy efficiency in machine learning tasks with spiking neural networks (SNNs).
Edit intent: clarity
Original: The emerging neuromorphic computing (NC) architectures have shown compelling energy efficiency to perform machine learning tasks with spiking neural networks (SNNs).
Revised: The emerging neuromorphic computing (NC) architectures have shown compelling energy efficiency to perform machine learning tasks using spiking neural networks (SNNs).
Edit intent: fluency
Original:  However, due to the non-differentiable nature of spike generation ,
Revised:  However, due to the non-differentiable nature of spiking neuronal functions ,
Edit intent: clarity
Original:  the standard error backpropagation algorithm is not directly applicable to SNNs. In this work,
Revised:  the standard error back-propagation algorithm is not directly applicable to SNNs. In this work,
Edit intent: fluency
Original:  and an equivalent ANNis used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs  trained with the proposed learning rule have demonstrated competitive classification accuracies on the CIFAR-10 and IMAGENET- 2012 datasets with significant savings on the inference time and total synaptic operations compared to other state-of-the-art SNN implementations.
Revised:  and an equivalent ANNis used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs that are trained with the proposed learning rule have demonstrated competitive classification accuracies on the CIFAR-10 and IMAGENET- 2012 datasets with significant savings on the inference time and total synaptic operations compared to other state-of-the-art SNN implementations.
Edit intent: clarity
Original:  and an equivalent ANNis used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs  trained with the proposed learning rule have demonstrated competitive classification accuracies on the CIFAR-10 and IMAGENET- 2012 datasets with significant savings on the inference time and total synaptic operations compared to other state-of-the-art SNN implementations.
Revised:  and an equivalent ANNis used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs trained with the proposed tandem learning rule show competitive classification accuracies on the CIFAR-10 and IMAGENET- 2012 datasets with significant savings on the inference time and total synaptic operations compared to other state-of-the-art SNN implementations.
Edit intent: clarity
Original:  and an equivalent ANNis used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs  trained with the proposed learning rule have demonstrated competitive classification accuracies on the CIFAR-10 and IMAGENET- 2012 datasets with significant savings on the inference time and total synaptic operations compared to other state-of-the-art SNN implementations.
Revised:  and an equivalent ANNis used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs trained with the proposed learning rule have demonstrated competitive classification accuracies on the CIFAR-10 and ImageNet-2012 datasets with significantly reduced inference time and total synaptic operations compared to other state-of-the-art SNN implementations.
Edit intent: clarity
Original:  and an equivalent ANNis used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs  trained with the proposed learning rule have demonstrated competitive classification accuracies on the CIFAR-10 and IMAGENET- 2012 datasets with significant savings on the inference time and total synaptic operations compared to other state-of-the-art SNN implementations.
Revised:  and an equivalent ANNis used during error backpropagation to approximate the gradients for the coupled SNN. The SNNs trained with the proposed learning rule have demonstrated competitive classification accuracies on the CIFAR-10 and IMAGENET- 2012 datasets with significant savings on the inference time and total synaptic operations over other state-of-the-art SNN implementations.
Edit intent: fluency
Original:  The proposed  learning rule offers an intriguing solution to enable on-chip computing on the pervasive mobile and embedded devices with limited computational budgets .
Revised:  The proposed learning rule offers a novel solution to training efficient, low latency and high accuracy deep SNNs with low computing resources .
Edit intent: clarity
Original: Emerging neuromorphic computing (NC) architectures have shown compelling energy efficiency in machine learning tasks using spiking neural networks (SNNs) .
Revised: Emerging neuromorphic computing (NC) architectures .
Edit intent: clarity
Original:  However, due to the non-differentiable nature of spiking neuronal functions, the standard error back-propagation algorithm is not directly applicable to SNNs. In this work, we propose a tandem learning framework, that consists of a SNN and an Artificial Neural Network (ANN) that share weights .
Revised:  However, due to the non-differentiable nature of spiking neuronal functions, the standard error back-propagation algorithm is not directly applicable to SNNs. In this work, we propose a tandem learning framework, that consists of an SNN and an Artificial Neural Network (ANN) that share weights .
Edit intent: fluency
Original:  However, due to the non-differentiable nature of spiking neuronal functions, the standard error back-propagation algorithm is not directly applicable to SNNs. In this work, we propose a tandem learning framework, that consists of a SNN and an Artificial Neural Network (ANN) that share weights .
Revised:  However, due to the non-differentiable nature of spiking neuronal functions, the standard error back-propagation algorithm is not directly applicable to SNNs. In this work, we propose a tandem learning framework, that consists of a SNN and an Artificial Neural Network (ANN) coupled through weight sharing .
Edit intent: clarity
Original:  we consider the spike count as the discrete neural representation  and design ANN neuronal activation function that can effectively approximate the spike count of the coupled SNN. The SNNs that are trained with the proposed tandem learning rule show competitive classification accuracies on the CIFAR-10 and ImageNet-2012 datasetswith significantly reduced inference time and total synaptic operations over other state-of-the-art SNN implementations.
Revised:  we consider the spike count as the discrete neural representation in the SNN, and design ANN neuronal activation function that can effectively approximate the spike count of the coupled SNN. The SNNs that are trained with the proposed tandem learning rule show competitive classification accuracies on the CIFAR-10 and ImageNet-2012 datasetswith significantly reduced inference time and total synaptic operations over other state-of-the-art SNN implementations.
Edit intent: clarity
Original:  we consider the spike count as the discrete neural representation  and design ANN neuronal activation function that can effectively approximate the spike count of the coupled SNN. The SNNs that are trained with the proposed tandem learning rule show competitive classification accuracies on the CIFAR-10 and ImageNet-2012 datasetswith significantly reduced inference time and total synaptic operations over other state-of-the-art SNN implementations.
Revised:  we consider the spike count as the discrete neural representation and design ANN neuronal activation function that can effectively approximate the spike count of the coupled SNN. The proposed tandem learning rule show competitive classification accuracies on the CIFAR-10 and ImageNet-2012 datasetswith significantly reduced inference time and total synaptic operations over other state-of-the-art SNN implementations.
Edit intent: clarity
Original:  The proposed tandem learning rule offers a novel solution to training efficient, low latency  and high accuracy deep SNNs with low computing resources.
Revised:  Therefore, the proposed tandem learning rule offers a novel solution to training efficient, low latency and high accuracy deep SNNs with low computing resources.
Edit intent: coherence
Original:  The proposed tandem learning rule offers a novel solution to training efficient, low latency  and high accuracy deep SNNs with low computing resources.
Revised:  The proposed tandem learning rule offers a novel solution to training efficient, low latency , and high accuracy deep SNNs with low computing resources.
Edit intent: fluency
Original:  Every subfamily S' \subset S defines a hyperedge of H if and only if there exists a halfspace h that fully contains S' , and no other set of S is fully contained in h.
Revised:  Every subfamily S' of S defines a hyperedge of H if and only if there exists a halfspace h that fully contains S' , and no other set of S is fully contained in h.
Edit intent: clarity
Original:  We show that the VC-dimension for pairwise disjoint convex sets in the plane is bounded by 3, and this is tight.
Revised:  We show that the VC-dimension for pairwise disjoint convex sets in the plane is bounded by 3, and this is tight.
Edit intent: fluency
Original: We propose a new core calculus for programming languages with effects, interpreted using a hypergraph-rewriting abstract machine inspired by the Geometry of Interaction.
Revised: We propose a new approach to defining programming languages with effects, interpreted using a hypergraph-rewriting abstract machine inspired by the Geometry of Interaction.
Edit intent: clarity
Original:  The intrinsic calculus syntax and semantics only deal with the basic structural aspects of programming languages : variable binding, name binding, and thunking.
Revised:  It interprets a graph calculus of only three intrinsic constructs : variable binding, name binding, and thunking.
Edit intent: clarity
Original:  The graph representation yields natural concepts of locality and robustness for equational properties and reduction rules, which  enable a novel flexible and powerful reasoning methodology about (type-free) languages with effects.
Revised:  The graph representation yields natural concepts of locality and robustness for equational properties and reduction rules, which and enable a novel flexible and powerful reasoning methodology about (type-free) languages with effects.
Edit intent: fluency
Original:  The graph representation yields natural concepts of locality and robustness for equational properties and reduction rules, which  enable a novel flexible and powerful reasoning methodology about (type-free) languages with effects.
Revised:  The graph representation yields natural concepts of locality and robustness for equational properties and reduction rules, which . These concepts enable a novel flexible and powerful reasoning methodology about (type-free) languages with effects.
Edit intent: coherence
Original: Controversial social and political issues of the day spur people to express their opinion on social networks, often sharing links to online media articles and reposting statements from prominent members of the platforms.
Edit intent: coherence
Original:  Discovering the stances of people and entire media on current, debatable topics is important for social statisticians and policy makers.
Revised:  Discovering the stances of media outlets and influential people on current, debatable topics is important for social statisticians and policy makers.
Edit intent: clarity
Original:  In this paper, we propose a  method that uses unsupervised learning and is able to characterize both the general political leaning of online media and  popular Twitter users, as well as their stances with respect to controversial topics, by leveraging on the retweet behavior of users .
Revised:  In this paper, we propose a method that uses unsupervised learning and is able to characterize both the general political leaning of online media and of popular Twitter users, as well as their stances with respect to controversial topics, by leveraging on the retweet behavior of users .
Edit intent: fluency
Original:  In this paper, we propose a  method that uses unsupervised learning and is able to characterize both the general political leaning of online media and  popular Twitter users, as well as their stances with respect to controversial topics, by leveraging on the retweet behavior of users .
Revised:  In this paper, we propose a method that uses unsupervised learning and is able to characterize both the general political leaning of online media and popular Twitter users, as well as their stance with respect to controversial topics, by leveraging on the retweet behavior of users .
Edit intent: fluency
Original:  In this paper, we propose a  method that uses unsupervised learning and is able to characterize both the general political leaning of online media and  popular Twitter users, as well as their stances with respect to controversial topics, by leveraging on the retweet behavior of users .
Revised:  In this paper, we propose a method that uses unsupervised learning and is able to characterize both the general political leaning of online media and popular Twitter users, as well as their stances with respect to the target polarizing topic .
Edit intent: clarity
Original:  We evaluate the model by comparing its bias predictions to gold labels from the Media Bias/Fact Check website, and we further perform manual analysis .
Revised:  We evaluate the model by comparing its predictions to gold labels from the Media Bias/Fact Check website, and we further perform manual analysis .
Edit intent: clarity
Original:  Applying these structural results to systemic risk measures, we construct oriented selective identification functions, which induce a family of strictly consistent exhaustive elementary scoring functions.
Revised:  Applying these structural results to systemic risk measures, we construct oriented selective identification functions, which induce a mixture representation of (strictly) consistent scoring functions.
Edit intent: clarity
Original: Given any numeration system, we call carry propagation at a number N the number of digits that are changed when going from the representation of N to the one of N+1, and amortized carry propagation the limit of the mean of the carry propagations at the first N integers, when N tends to infinity, and if this limit exists.
Revised: Given any numeration system, we call carry propagation at a number N the number of digits that are changed when going from the representation of N to the one of N+1, and amortized carry propagation the limit of the mean of the carry propagations at the first N integers, when N tends to infinity, if this limit exists.
Edit intent: coherence
Original:  We tackle the problem by means of techniques of three different types  : combinatorial, algebraic, and ergodic.
Revised:  We tackle the problem by three different types : combinatorial, algebraic, and ergodic.
Edit intent: clarity
Original:  For each kind of numeration systems that we consider, the relevant method allows for establishing sufficient conditions for the existence of the carry propagation and examples show that these conditions are close to be necessary conditions.
Revised:  For each kind of numeration systems that we consider, the relevant method allows for establishing sufficient conditions for the existence of the carry propagation and examples show that these conditions are close to being necessary conditions.
Edit intent: fluency
Original:  We utilize properties of the skew field of quaternions, free rotation groups, representations of tuples of rationals as a linear sum of radicals and a reduction of the mixed modification  Post's correspondence problem  .
Revised:  We utilize properties of the skew field of quaternions, free rotation groups, representations of tuples of rationals as linear sums of radicals and a reduction of the mixed modification Post's correspondence problem .
Edit intent: fluency
Original:  We utilize properties of the skew field of quaternions, free rotation groups, representations of tuples of rationals as a linear sum of radicals and a reduction of the mixed modification  Post's correspondence problem  .
Revised:  We utilize properties of the skew field of quaternions, free rotation groups, representations of tuples of rationals as a linear sum of radicals and a reduction of the mixed modification of Post's correspondence problem .
Edit intent: fluency
Original:  A graph G is an S_d-graph when it is the intersection graph of connected subgraphs of a subdivision of a fixed star S_d . It is useful to mention that,
Revised:  A graph G is an S_d-graph iff it is the intersection graph of connected subgraphs of a subdivision of a fixed star S_d . It is useful to mention that,
Edit intent: clarity
Original:  A graph G is an S_d-graph when it is the intersection graph of connected subgraphs of a subdivision of a fixed star S_d . It is useful to mention that,
Revised:  A graph G is an S_d-graph when it is the intersection graph of connected subgraphs of a subdivision of a star S_d . It is useful to mention that,
Edit intent: clarity
Original:  Finally, we show that the graph isomorphism problem for S_d-graphs can be solved in FPT-time with parameter d, even when the clique size is unbounded .
Revised:  Finally, we show that the isomorphism problem of S_d-graphs can be solved in FPT-time with parameter d, even when the clique size is unbounded .
Edit intent: clarity
Original:  We give an FPT%DIFDELCMD < } %%% algorithm to solve the isomorphism problem for S_d-graphs with the parameter d . This solves an open problem of Chaplick, Toepfer, Voborn\'{ipfer, Voborn\'{\i}} k and Zeman (2016) .
Revised:  We give an %DIFDELCMD < } %%% algorithm to solve the isomorphism problem for S_d-graphs with the parameter d . This solves an open problem of Chaplick, Toepfer, Voborn\'{ipfer, Voborn\'{\i}} k and Zeman (2016) .
Edit intent: clarity
Original:  We give an FPT%DIFDELCMD < } %%% algorithm to solve the isomorphism problem for S_d-graphs with the parameter d . This solves an open problem of Chaplick, Toepfer, Voborn\'{ipfer, Voborn\'{\i}} k and Zeman (2016) .
Revised:  We give an FPT%DIFDELCMD < } %%% algorithm to solve the isomorphism problem for S_d-graphs with the parameter d . This solves an open problem of Chaplick, T\"{opfer, Voborn\'{\i}} k and Zeman (2016) .
Edit intent: fluency
Original:  In the course of our proof, we also show that the isomorphism problem of S_d-graphs is computationally at least as hard as the isomorphism problem of posets of bounded width  .
Revised:  In the course of our proof, we also show that the isomorphism problem of S_d-graphs is at least as hard as the isomorphism problem of posets of bounded width .
Edit intent: clarity