wanyu/IteraTeR_v2 huggingface dataset:
Original:  Specially , we propose new mean-field control laws which use the real-time density and its gradient as feedback, and prove that they are globally input-to-state stable (ISS)  to estimation errors.
Revised:  Specially , we propose new mean-field control laws which use the real-time density and its gradient as feedback, and prove that they are globally input-to-state stable (ISS) with respect to estimation errors.
Edit intent: clarity
Original:  Finally, we show that the feedback interconnection of these estimation and control algorithms is still globally ISS, which is attributed to the bilinearity of the mean-field PDE system.
Revised:  Finally, we show that the feedback interconnection of these estimation and control algorithms is still globally ISS, which is attributed to the bilinearity of the PDE system.
Edit intent: clarity
Original: In this paper, we propose Multiresolution Graph Networks (MGN) and Multiresolution Graph Variational Autoencoders (MGVAE)  to learn and generate graphs in a multiresolution and equivariant manner.
Revised: In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE) to learn and generate graphs in a multiresolution and equivariant manner.
Edit intent: clarity
Original:  At each resolution level, MGN employs higher order message passing to encode the graph while learning to partition it into mutually exclusive clusters and coarsening into a lower resolution . MGVAE constructs a hierarchical generative model based on MGN to variationally autoencode the hierarchy of coarsened graphs.
Revised:  At each resolution level, MGVAE employs higher order message passing to encode the graph while learning to partition it into mutually exclusive clusters and coarsening into a lower resolution . MGVAE constructs a hierarchical generative model based on MGN to variationally autoencode the hierarchy of coarsened graphs.
Edit intent: fluency
Original:  At each resolution level, MGN employs higher order message passing to encode the graph while learning to partition it into mutually exclusive clusters and coarsening into a lower resolution . MGVAE constructs a hierarchical generative model based on MGN to variationally autoencode the hierarchy of coarsened graphs.
Revised:  At each resolution level, MGN employs higher order message passing to encode the graph while learning to partition it into mutually exclusive clusters and coarsening into a lower resolution . MGVAE constructs a hierarchical generative model to variationally decode into a hierarchy of coarsened graphs.
Edit intent: clarity
Original:  Our proposed framework is end-to-end permutation equivariant with respect to node ordering.
Revised:  Importantly, our proposed framework is end-to-end permutation equivariant with respect to node ordering.
Edit intent: coherence
Original:  Our methods have been successful with several generative tasks including link prediction on citation graphs, unsupervised molecular representation learning to predict molecular properties, molecular generation, general graph generation and graph-based image generation.
Revised:  Our methods have been successful with several generative tasks including general graph generation, molecular generation, unsupervised molecular representation learning to predict molecular properties, molecular generation, general graph generation and graph-based image generation.
Edit intent: clarity
Original:  Our methods have been successful with several generative tasks including link prediction on citation graphs, unsupervised molecular representation learning to predict molecular properties, molecular generation, general graph generation and graph-based image generation.
Revised:  Our methods have been successful with several generative tasks including link prediction on citation graphs, unsupervised molecular representation learning to predict molecular properties, link prediction on citation graphs, and graph-based image generation.
Edit intent: clarity
Original:  However, in SAC, maximizing the entropy-augmented objective may degrade the optimality of the learning outcomes.
Revised:  However, in SAC, maximizing the entropy-augmented objective may degrade the optimality of learning outcomes.
Edit intent: fluency
Original:  HER is known as a sample-efficient replay method that enhances the performance of off-policy DRL algorithms by allowing them to learn from both failures and successes.
Revised:  HER is known as a sample-efficient replay method that enhances the performance of off-policy DRL algorithms by allowing the agent to learn from both failures and successes.
Edit intent: clarity
Original:  Due to the non-unitary nature of dynamic circuit primitives, existing techniques for these tasks are no longer applicable in an out-of-the-box fashion.
Revised:  Due to the non-unitary nature of dynamic circuit primitives, most existing techniques and tools for these tasks are no longer applicable in an out-of-the-box fashion.
Edit intent: clarity
Original:  More precisely, we propose two different schemes that eventually allow to treat the involved circuits as if they were not dynamic at all.
Revised:  More precisely, we propose two different schemes that eventually allow to treat the involved circuits as if they did not contain non-unitaries at all.
Edit intent: clarity
Original:  By this, we provide a basis for applying existing techniques for quantum circuit verification to this broader class of circuits.
Revised:  As a result, we demonstrate methodically, as well as, experimentally that existing techniques for quantum circuit verification to this broader class of circuits.
Edit intent: coherence
Original:  Overall, the grasp system is demonstrated to achieve stable equilibrium poses on a range of objects varying in shape and softness, with the system being robust to perturbations and measurement errors.
Revised:  Overall, the grasp system is demonstrated to achieve stable equilibrium poses on various objects ranging in shape and softness, with the system being robust to perturbations and measurement errors.
Edit intent: fluency
Original: This paper considers the Tikhonv regularization continuation method and the trust-region updating strategy for the linearly equality-constrained optimization problem (Trcmtr).
Revised: This paper considers the regularization continuation method and the trust-region updating strategy for the linearly equality-constrained optimization problem (Trcmtr).
Edit intent: fluency
Original:  That is to say , the new method uses the L-BFGS method as the preconditioned technique to improve its computational efficiency in the well-posed phase .
Revised:  That is to say , the new method uses the L-BFGS method as the preconditioning technique to improve its computational efficiency in the well-posed phase .
Edit intent: fluency
Original:  In this letter, we aim to quantitatively analyse why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and its cost in terms of computational complexity.
Revised:  In this letter, we aim to quantitatively analyse why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity.
Edit intent: fluency
Original: We give a finite presentation by generators and relations for the group \mathrm{O  ) of n-dimensional orthogonal matrices with entries in \mathbb{Z  .
Revised: We give a finite presentation by generators and relations for the group \mathrm{O ) of n-dimensional orthogonal matrices with entries in Z .
Edit intent: clarity
Original:  The proposed algorithm achieves fast convergence and robust steady-state performance for scenarios characterized by  non-white and non-stationary  noise signals, time-varying environment changes and additional model inaccuracies.
Revised:  The proposed algorithm achieves fast convergence and robust steady-state performance for scenarios characterized by non-white and non-stationary noise signals, abrupt environment changes and additional model inaccuracies.
Edit intent: clarity
Original:  EIP-1559 is a proposal to make several tightly coupled changes to the Ethereum blockchain 's transaction fee mechanism , including the introduction of variable-size blocks and a burned base fee that rises and falls with demand.
Revised:  This paper investigates the problem of designing a blockchain transaction fee mechanism , including the introduction of variable-size blocks and a burned base fee that rises and falls with demand.
Edit intent: clarity
Original: In this paper, we propose a matching theory based multi-user cooperative framework to minimize the overall energy consumption of all the user equipments (UEs), where the UEs can be classified into the following roles: resource demander (RD), resource provider (RP), and standalone UE (SU).
Revised: In this paper, we propose a matching theory based multi-user cooperative computing (MUCC) scheme to minimize the overall energy consumption of all the user equipments (UEs), where the UEs can be classified into the following roles: resource demander (RD), resource provider (RP), and standalone UE (SU).
Edit intent: clarity
Original: In this paper, we propose a matching theory based multi-user cooperative framework to minimize the overall energy consumption of all the user equipments (UEs), where the UEs can be classified into the following roles: resource demander (RD), resource provider (RP), and standalone UE (SU).
Revised: In this paper, we propose a matching theory based multi-user cooperative framework to minimize the overall energy consumption of a group of user equipments (UEs), where the UEs can be classified into the following roles: resource demander (RD), resource provider (RP), and standalone UE (SU).
Edit intent: clarity
Original:  We first determine the role of the UE by leveraging the roommate matching method.
Revised:  We first determine the role of each UE by leveraging the roommate matching method.
Edit intent: clarity
Original:  Then, by adopting college admission based algorithm , we divide the UEs into multiple cooperation groups, each consisting of one RP and multiple RDs. Next, we propose the rotation swap operation to further improve the performance without deteriorating the system stability.
Revised:  Then, we propose the college admission based algorithm , we divide the UEs into multiple cooperation groups, each consisting of one RP and multiple RDs. Next, we propose the rotation swap operation to further improve the performance without deteriorating the system stability.
Edit intent: coherence
Original:  Then, by adopting college admission based algorithm , we divide the UEs into multiple cooperation groups, each consisting of one RP and multiple RDs. Next, we propose the rotation swap operation to further improve the performance without deteriorating the system stability.
Revised:  Then, by adopting college admission based algorithm to divide the UEs into multiple cooperation groups, each consisting of one RP and multiple RDs. Next, we propose the rotation swap operation to further improve the performance without deteriorating the system stability.
Edit intent: fluency
Original: We present a new architecture of convolutional neural networks (CNNs) based on ResNet for audio pattern recognition tasks.
Revised: This paper presents a new convolutional neural network architecture for audio pattern recognition tasks.
Edit intent: clarity
Original:  The main modification is introducing a new hyper-parameter for decreasing temporal sizes of tensors with increased stride sizes which we call "the decreasing temporal size parameter".
Revised:  We introduce a new hyper-parameter for decreasing temporal sizes of tensors with increased stride sizes which we call "the decreasing temporal size parameter".
Edit intent: coherence
Original:  Our best system achieves the state-of-the-art performance on the AudioSet dataset with mAP of 0.450 . We also transfer a model pre-trained on the AudioSet dataset to the ESC-50 dataset and RAVDESS and obtain the state-of-the-art results with accuracies of  0.961 and 0.748, respectively.
Revised:  Our best model achieves an mAP of~ 0.450 . We also transfer a model pre-trained on the AudioSet dataset to the ESC-50 dataset and RAVDESS and obtain the state-of-the-art results with accuracies of 0.961 and 0.748, respectively.
Edit intent: clarity
Original:  Our best system achieves the state-of-the-art performance on the AudioSet dataset with mAP of 0.450 . We also transfer a model pre-trained on the AudioSet dataset to the ESC-50 dataset and RAVDESS and obtain the state-of-the-art results with accuracies of  0.961 and 0.748, respectively.
Revised:  Our best system achieves the state-of-the-art performance on the AudioSet dataset with mAP of 0.450 . We also transfer a model pre-trained on the AudioSet dataset to the ESC-50 dataset and RAVDESS , we obtain state-of-the-art results with accuracies of 0.961 and 0.748, respectively.
Edit intent: coherence
Original:  Our best system achieves the state-of-the-art performance on the AudioSet dataset with mAP of 0.450 . We also transfer a model pre-trained on the AudioSet dataset to the ESC-50 dataset and RAVDESS and obtain the state-of-the-art results with accuracies of  0.961 and 0.748, respectively.
Revised:  Our best system achieves the state-of-the-art performance on the AudioSet dataset with mAP of 0.450 . We also transfer a model pre-trained on the AudioSet dataset to the ESC-50 dataset and RAVDESS and obtain the state-of-the-art results with accuracies of ~ 0.961 and 0.748, respectively.
Edit intent: fluency
Original:   We call our system " ERANN " (Efficient Residual Audio Neural Network ).
Revised:  We call our models " ERANN " (Efficient Residual Audio Neural Network ).
Edit intent: clarity
Original:   We call our system " ERANN " (Efficient Residual Audio Neural Network ).
Revised:  We call our system " ERANN " (Efficient Residual Audio Neural Networks ).
Edit intent: fluency
Original:  Our best model achieves an mAP of ~ 0.450 on the AudioSet dataset, which is less than the performance of the state-of-the-art model, but our model is 7.1x faster and 9.7x smaller in parameter size.
Revised:  Our best model achieves an mAP of 0.450 on the AudioSet dataset, which is less than the performance of the state-of-the-art model, but our model is 7.1x faster and 9.7x smaller in parameter size.
Edit intent: clarity
Original:  On the ESC-50 dataset and RAVDESS, we obtain state-of-the-art results with accuracies of ~ 0.961 and 0.748, respectively.
Revised:  On the ESC-50 dataset and RAVDESS, we obtain state-of-the-art results with accuracies of 0.961 and 0.748, respectively.
Edit intent: coherence
Original:  We introduce a new hyper-parameter for reducing the computational complexity of models  . Using optimal values of this parameter, we also can save or even increase the performance of models.
Revised:  We propose a novel technique for reducing the computational complexity of models . Using optimal values of this parameter, we also can save or even increase the performance of models.
Edit intent: clarity
Original:  We call our models "ERANNs" (Efficient Residual Audio Neural Networks).
Revised:  We call our models ''ERANNs'' (Efficient Residual Audio Neural Networks).
Edit intent: coherence
Original: We establish the equivalence between the Multiscale Hybrid-Mixed (MHM) and the Multiscale Hybrid High-Order (MsHHO) methods for a variable diffusion problem with piecewise polynomial source terms . Under the idealized assumption that the local problems defining the multiscale basis functions are exactly solved, we prove that the equivalence holds for general polytopal (coarse) meshes and arbitrary approximation orders.
Revised: We establish the equivalence between the Multiscale Hybrid-Mixed (MHM) and the Multiscale Hybrid High-Order (MsHHO) methods for a variable diffusion problem with piecewise polynomial source term . Under the idealized assumption that the local problems defining the multiscale basis functions are exactly solved, we prove that the equivalence holds for general polytopal (coarse) meshes and arbitrary approximation orders.
Edit intent: fluency
Original:  Also, we leverage the interchange of properties to perform a unified convergence analysis, as well as to improve on both methods.
Revised:  We also leverage the interchange of properties to perform a unified convergence analysis, as well as to improve on both methods.
Edit intent: coherence
Original:  The results show that our method can find a feasible path, which can consider the localization uncertainty while simultaneously avoiding the crowded area. 
Revised:  The results show that our method can find a feasible path, which can consider the localization uncertainty while simultaneously avoiding the crowded area. Curiosity-based Robot Navigation under Uncertainty in Crowded Environments
Edit intent: clarity
Original:  The Zariski closure was previously shown to be computable by Derksen, Jeandel  and Koiran, but the termination argument for their algorithm appears not to yield any complexity bound.
Revised:  The Zariski closure was previously shown to be computable by Derksen, Jeandel , and Koiran, but the termination argument for their algorithm appears not to yield any complexity bound.
Edit intent: fluency
Original:  We describe several applications of this result, e.g., concerning quantum automata and quantum universal gates.
Revised:  We describe several applications of 
Edit intent: clarity
Original:  We also obtain an upper bound on the length of a strictly increasing chain of linear algebraic groups, all of which are generated over a fixed number field.
Revised:  We also obtain upper bounds on the length of a strictly increasing chain of linear algebraic groups, all of which are generated over a fixed number field.
Edit intent: fluency
Original:  We also obtain an upper bound on the length of a strictly increasing chain of linear algebraic groups, all of which are generated over a fixed number field.
Revised:  We also obtain an upper bound on the length of chains of linear algebraic groups, all of which are generated over a fixed number field.
Edit intent: clarity
Original:  We also obtain an upper bound on the length of a strictly increasing chain of linear algebraic groups, all of which are generated over a fixed number field.
Revised:  We also obtain an upper bound on the length of a strictly increasing chain of linear algebraic groups, where all the groups are generated over a fixed number field.
Edit intent: clarity
Original:  A major challenge for neuromorphic computing is that learning algorithms for traditional artificial neural networks (ANNs) do not transfer directly to spiking neural networks (SNNs) due to the discrete spikes and more complex neuronal dynamics.
Revised:  A major challenge for neuromorphic computing is that learning algorithms for traditional artificial neural networks (ANNs) to spiking neural networks (SNNs) due to the discrete spikes and more complex neuronal dynamics.
Edit intent: clarity
Original:  we  focus on the self-supervised learning problem of optical flow estimation from event-based camera inputs ,
Revised:  we focus on the complex task of learning to estimate optical flow from event-based camera inputs ,
Edit intent: clarity
Original:  and investigate the changes that are necessary to the state-of-the-art ANN training pipeline in order to successfully tackle it with SNNs. More specifically, we first modify the input event representation to encode a much smaller time slice with minimal explicit temporal information . Consequently, we make the network's neuronal dynamics and recurrent connections responsible for integrating information over time .
Revised:  and investigate the changes that are necessary to the state-of-the-art ANN training pipeline to encode minimal temporal information in its inputs .
Edit intent: clarity
Original: Automated Market Makers (AMMs) are decentralized applications that allow users to exchange crypto-tokens without the need to find a matching exchange order.
Revised: Automated Market Makers (AMMs) are decentralized applications that allow users to exchange crypto-tokens without the need for a matching exchange order.
Edit intent: clarity
Original:  AMMs are one of the most successful DeFi use case so far, as the main AMM platforms UniSwap and Balancer process a daily volume of transactions worth billions of dollars.
Revised:  AMMs are one of the most successful DeFi use cases: indeed, major AMM platforms process a daily volume of transactions worth billions of dollars.
Edit intent: clarity
Original: In the crowded environment of bio-inspired population-based meta-heuristics , the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.
Revised: In the crowded environment of bio-inspired population-based metaheuristics , the Salp Swarm Optimization (SSO) algorithm recently appeared and immediately gained a lot of momentum.
Edit intent: fluency
Original:  Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide interesting optimization performances .
Revised:  Inspired by the peculiar spatial arrangement of salp colonies, which are displaced in long chains following a leader, this algorithm seems to provide an interesting optimization performance .
Edit intent: fluency
Original:  In this manuscript, we perform a critical review of SSO, highlighting all the issues present in the literature and their negative effects on the optimization process carried out by the algorithm.
Revised:  In this manuscript, we perform a critical review of SSO, highlighting all the issues present in the literature and their negative effects on the optimization process carried out by this algorithm.
Edit intent: clarity
Original:  Finally, we benchmark the performance of ASSO on a set of tailored experiments, showing it achieves better results than the original  SSO.
Revised:  We benchmarked the performance of ASSO on a set of tailored experiments, showing it achieves better results than the original SSO.
Edit intent: coherence
Original:  Finally, we benchmark the performance of ASSO on a set of tailored experiments, showing it achieves better results than the original  SSO.
Revised:  Finally, we benchmark the performance of ASSO on a set of tailored experiments, showing that it is able to achieve better results than the original SSO.
Edit intent: clarity
Original:  The paper then presents the merits that can be achieved by integrating various cloud services within the HAPS , and the corresponding cloud-type applications that would utilize the HAPS for enhancing the quality, range, and types of offered services.
Revised:  The paper then describes various cloud services within the HAPS , and the corresponding cloud-type applications that would utilize the HAPS for enhancing the quality, range, and types of offered services.
Edit intent: clarity
Original:  The paper then presents the merits that can be achieved by integrating various cloud services within the HAPS , and the corresponding cloud-type applications that would utilize the HAPS for enhancing the quality, range, and types of offered services.
Revised:  The paper then presents the merits that can be achieved by integrating various cloud services that can be offered from the HAPS and the merits that can be achieved by this integration, such as enhancing the quality, range, and types of offered services.
Edit intent: clarity
Original:  The paper then presents the merits that can be achieved by integrating various cloud services within the HAPS , and the corresponding cloud-type applications that would utilize the HAPS for enhancing the quality, range, and types of offered services.
Revised:  The paper then presents the merits that can be achieved by integrating various cloud services within the HAPS , and the corresponding cloud-type applications that would utilize the HAPS for enhancing the quality, speed, and range of the offered services.
Edit intent: clarity