wanyu/IteraTeR_v2 huggingface dataset:
Original:  In this paper, we present a neural network based data distribution aware sorting method  NN-sort  . Compared to traditional comparison-based sorting algorithms, which need to compare all of the data points in pairwise, NN-sort leverages the neural network learning model to map disordered data points into ordered ones.
Revised:  In this paper, we present a neural network based data distribution aware sorting method NN-sort . Compared to traditional comparison-based sorting algorithms, which need to compare all of the data points in pairwise, NN-sort leverages the neural network learning model to map disordered data elements into ordered ones.
Edit intent: clarity
Original:  The time complexity of NN-sort is close to the linear complexity of O(n).
Revised:  Although the complexity of NN-sort is close to the linear complexity of O(n).
Edit intent: clarity
Original:  Over the years, researchers proposed various methods, and recently, a breakthrough came into the light as Mask R-CNN  . Based on Faster R-CNN, Mask R-CNN was able to generate a segmentation mask for each instance.
Revised:  Over the years, researchers proposed various methods, and recently, Mask R-CNN . Based on Faster R-CNN, Mask R-CNN was able to generate a segmentation mask for each instance.
Edit intent: coherence
Original:  Over the years, researchers proposed various methods, and recently, a breakthrough came into the light as Mask R-CNN  . Based on Faster R-CNN, Mask R-CNN was able to generate a segmentation mask for each instance.
Revised:  Over the years, researchers proposed various methods, and recently, a breakthrough came into the light as Mask R-CNN . Based on Faster R-CNN, Mask R-CNN has been able to generate a segmentation mask for each instance.
Edit intent: clarity
Original:  We propose an application to extract multiple persons and put them into a new backgroundimage utilizing Mask R-CNN. Mask R-CNN detects all type of object mask from images.
Revised:  We propose an application to extract multiple persons and put them into a new backgroundimage utilizing Mask R-CNN. Mask R-CNN detects all types of object masks from images.
Edit intent: fluency
Original:  Our algorithm is effective for both an image and a video irrespective of the length of it . Also, extract those persons and place them into the new background .
Revised:  Our algorithm is effective for both an image and a video irrespective of the length of it .
Edit intent: coherence
Original: Twitter is among the most used microblogging and online social networking services.
Edit intent: coherence
Original:  Twitter trends has shown their powerful ability in many public events , elections and market changes .
Revised:  Twitter trends list has a powerful ability to promote public events such as natural events, political scandals, market changes and other types of breaking news .
Edit intent: clarity
Original:  Nevertheless, there has been very few works focusing on understanding the dynamics of these trending topics.
Revised:  Nevertheless, there have been very few works focusing on understanding the dynamics of these trending topics.
Edit intent: fluency
Original:  Nevertheless, there has been very few works focusing on understanding the dynamics of these trending topics.
Revised:  Nevertheless, there has been very few works focused on the dynamics of these trending topics.
Edit intent: clarity
Original: The recent success of Deep Neural Networks (DNNs) has revealed the significant capability of neuromorphic computing in many challenging applications.
Revised: The recent success of Deep Neural Networks (DNNs) has revealed the significant capability of neural computing in many challenging applications.
Edit intent: clarity
Original:  In this paper, we propose a new computational neuromorphic model, namely shallow unorganized neural networks (SUNNs), in contrast to  DNNs.
Revised:  In this paper, we propose a new computational model, namely shallow unorganized neural networks (SUNNs), in contrast to DNNs.
Edit intent: clarity
Original:  The proposed SUNNs differ from standard ANNs or DNNs in three fundamental aspects: 1) SUNNs are based on an adaptive neuron cell model, Smart Neurons, that allows each neuron to adaptively respond to its inputs rather than carrying out a fixed weighted-sum operation like the  neuron model in ANNs/DNNs;
Revised:  The proposed SUNNs differ from standard ANNs or DNNs in three fundamental aspects: 1) SUNNs are based on an adaptive neuron cell model, Smart Neurons, that allows each artificial neuron cell to adaptively respond to its inputs rather than carrying out a fixed weighted-sum operation like the neuron model in ANNs/DNNs;
Edit intent: clarity
Original:  The proposed SUNNs differ from standard ANNs or DNNs in three fundamental aspects: 1) SUNNs are based on an adaptive neuron cell model, Smart Neurons, that allows each neuron to adaptively respond to its inputs rather than carrying out a fixed weighted-sum operation like the  neuron model in ANNs/DNNs;
Revised:  The proposed SUNNs differ from standard ANNs or DNNs in three fundamental aspects: 1) SUNNs are based on an adaptive neuron cell model, Smart Neurons, that allows each neuron to adaptively respond to its inputs rather than carrying out a fixed weighted-sum operation like the classic neuron model in ANNs/DNNs;
Edit intent: clarity
Original:  2) SUNNs  cope with computational tasks using only shallow architectures;
Revised:  2) SUNNs can cope with computational tasks using only shallow architectures;
Edit intent: clarity
Original:  The success of our new computational model makes it a working example of Turing's B-Type  machine that can achieve comparable or better performance against the state-of-the-art algorithms.
Revised:  The success of our new computational model makes it the first workable example of Turing's B-Type machine that can achieve comparable or better performance against the state-of-the-art algorithms.
Edit intent: clarity
Original: Although the computational complexity of Greedy is linear in the size of the data m and cardinality constraint (k), even this linear complexity becomes }} prohibitive for large-scale datasets.
Revised: Although the computational complexity of Greedy is linear in the size of the data m and cardinality constraint k. However, the }} prohibitive for large-scale datasets.
Edit intent: coherence
Original: Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary m-dimensional sparse vector x having k nonzero entries from n measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing . Existing greedy-based algorithms achieve optimal n = O(k\logm{k \mathcal{O}%DIFDELCMD < }%%% (mk) computational complexity is still prohibitive for large-scale datasets  .
Revised:  \mathcal{O}%DIFDELCMD < }%%% (mk) computational complexity is still prohibitive for large-scale datasets .
Edit intent: coherence
Original: Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary m-dimensional sparse vector x having k nonzero entries from n measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing . Existing greedy-based algorithms achieve optimal n = O(k\logm{k \mathcal{O}%DIFDELCMD < }%%% (mk) computational complexity is still prohibitive for large-scale datasets  .
Revised: Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary m-dimensional sparse vector x having k nonzero entries from n measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing . Existing greedy-based algorithms achieve optimal n = O(k\logm{k }%DIFDELCMD < }%%% (mk) computational complexity is still prohibitive for large-scale datasets .
Edit intent: coherence
Original: Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary m-dimensional sparse vector x having k nonzero entries from n measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing . Existing greedy-based algorithms achieve optimal n = O(k\logm{k \mathcal{O}%DIFDELCMD < }%%% (mk) computational complexity is still prohibitive for large-scale datasets  .
Revised: Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary m-dimensional sparse vector x having k nonzero entries from n measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing . Existing greedy-based algorithms achieve optimal n = O(k\logm{k \mathcal{O}%DIFDELCMD < }%%% (mk) computational complexity is still prohibitive forGreedy large-scale datasets .
Edit intent: clarity
Original: Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary m-dimensional sparse vector x having k nonzero entries from n measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing . Existing greedy-based algorithms achieve optimal n = O(k\logm{k \mathcal{O}%DIFDELCMD < }%%% (mk) computational complexity is still prohibitive for large-scale datasets  .
Revised: Sparse reconstruction and sparse support selection, i.e., the tasks of inferring an arbitrary m-dimensional sparse vector x having k nonzero entries from n measurements of linear combinations of its components, are often encountered in machine learning, computer vision, and signal processing . Existing greedy-based algorithms achieve optimal n = O(k\logm{k \mathcal{O}%DIFDELCMD < }%%% (mk) computational complexity is still prohibitive for large-scale datasetsGreedy .
Edit intent: coherence
Original:  In this paper, we present the first sparse support selection algorithm for arbitrary sparse vectors that achieves exact identification of the optimal subset from n = O(k\logm{k) measurements with }%DIFDELCMD < \mathcal{O}%%% (m) computational complexity.
Revised:  In this ) measurements with }%DIFDELCMD < \mathcal{O}%%% (m) computational complexity.
Edit intent: coherence
Original: The amount of ribosomes in a cell is considered as limiting, and gene expression is thus largely determined by their cellular concentration.
Revised: The number of ribosomes in a cell is considered as limiting, and gene expression is thus largely determined by their cellular concentration.
Edit intent: clarity
Original:  In this work we develop a toy-model to study the trade-off between the ribosomal supply and the demand of the translation machinery, dictated by the composition of the transcript pool.
Revised:  In this work we develop a toy model to study the trade-off between the ribosomal supply and the demand of the translation machinery, dictated by the composition of the transcript pool.
Edit intent: fluency
Original:  Two countermeasures, GROVER and GLTR, were found to be able to accurately detect fake review.
Revised:  Two countermeasures, GROVER and GLTR, were found to be difficult to accurately detect fake review.
Edit intent: clarity
Original: Network slicing appears as a key enabler for the future 5G networks .
Revised: With network slicing in 5G networks .
Edit intent: clarity
Original: Network slicing appears as a key enabler for the future 5G networks .
Revised: Network slicing appears as a key enabler for the future 5G networks ,
Edit intent: fluency
Original:  Mobile Network Operators  create various slices for Service Providers ( SP ) to accommodate customized services.
Revised:  Mobile Network Operators can create various slices for Service Providers ( SP ) to accommodate customized services.
Edit intent: coherence
Original:  Mobile Network Operators  create various slices for Service Providers ( SP ) to accommodate customized services.
Revised:  Mobile Network Operators create various slices for Service Providers ( SPs ) to accommodate customized services.
Edit intent: fluency
Original:  Nothing ensures that the InP will be able to allocate enough resources to cope with the increasing demands of some SP.
Revised:  Nothing ensures that the Infrastructure Provider (InP) will be able to allocate enough resources to cope with the increasing demands of some SP.
Edit intent: clarity
Original: The resource requirements of the various Service Function Chains to be deployed within a slice are aggregated within a graph of Slice Resource Demands (SRD). Coverage and rate constraints are also taken into account in the SRD.
Revised: The resource requirements of the various SFCs within a slice are aggregated within a graph of Slice Resource Demands (SRD). Coverage and rate constraints are also taken into account in the SRD.
Edit intent: clarity
Original: The resource requirements of the various Service Function Chains to be deployed within a slice are aggregated within a graph of Slice Resource Demands (SRD). Coverage and rate constraints are also taken into account in the SRD.
Revised: The resource requirements of the various Service Function Chains to be deployed within a slice are aggregated within a graph of Slice Resource Demands (SRD). 
Edit intent: coherence
Original:  Infrastructure nodes and links have then to be provisioned so as to satisfy all types of resource demands .
Revised:  Infrastructure nodes and links have then to be provisioned so as to satisfy all SRDs .
Edit intent: clarity
Original:  A two-step deployment approach is considered, with several variants, depending on whether the constraints of each slide to be deployed are taken into account sequentially or jointly.
Revised:  A two-step approach is considered, with several variants, depending on whether the constraints of each slide to be deployed are taken into account sequentially or jointly.
Edit intent: fluency
Original:  A two-step deployment approach is considered, with several variants, depending on whether the constraints of each slide to be deployed are taken into account sequentially or jointly.
Revised:  A two-step deployment approach is considered, with several variants, depending on whether the constraints of each slice to be provisioned are taken into account sequentially or jointly.
Edit intent: clarity
Original:  Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph representing the nodes and links on which resources have been provisioned.
Revised:  Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph on which resources have been provisioned.
Edit intent: clarity
Original:  Situations involving multiple decision makers are particularly complex, leading to computation intractability of principled solution methods.
Revised:  Situations involving multiple decision makers are particularly complex, leading to computational intractability of principled solution methods.
Edit intent: fluency
Original:  A body of work in AI 4, 3, 41, 45, 47, 2 has tried to mitigate this problem by trying to bring down interaction to its core : how does the policy of one agent influence another agent?
Revised:  A body of work in AI has tried to mitigate this problem by trying to bring down interaction to its core : how does the policy of one agent influence another agent?
Edit intent: coherence
Original:  A body of work in AI 4, 3, 41, 45, 47, 2 has tried to mitigate this problem by trying to bring down interaction to its core : how does the policy of one agent influence another agent?
Revised:  A body of work in AI 4, 3, 41, 45, 47, 2 has tried to mitigate this problem by trying to distill interaction to its core : how does the policy of one agent influence another agent?
Edit intent: clarity
Original:  A body of work in AI 4, 3, 41, 45, 47, 2 has tried to mitigate this problem by trying to bring down interaction to its core : how does the policy of one agent influence another agent?
Revised:  A body of work in AI 4, 3, 41, 45, 47, 2 has tried to mitigate this problem by trying to bring down interaction to its essence : how does the policy of one agent influence another agent?
Edit intent: clarity
Original:  If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than that of policies 45 . However, so far these notions of influence have been restricted in their applicability to special cases of interaction.
Revised:  If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than 45 . However, so far these notions of influence have been restricted in their applicability to special cases of interaction.
Edit intent: clarity
Original:  If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than that of policies 45 . However, so far these notions of influence have been restricted in their applicability to special cases of interaction.
Revised:  If we can find more compact representations of such influence, this can help us deal with the complexity, for instance by searching the space of influences rather than that of policies the space of policies . However, so far these notions of influence have been restricted in their applicability to special cases of interaction.
Edit intent: clarity
Original:  In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs) 33 . This generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex settings .
Revised:  In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs) . This generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex settings .
Edit intent: coherence
Original:  In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs) 33 . This generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex settings .
Revised:  In this paper we formalize influence-based abstraction (IBA), which facilitates the elimination of latent state factors without any loss in value, for a very general class of problems described as factored partially observable stochastic games (fPOSGs) 33 . On the one hand, this generalizes existing descriptions of influence, and thus can serve as the foundation for improvements in scalability and other insights in decision making in complex settings .
Edit intent: coherence
Original:  In this report, we propose a new algorithm to overcome some of these challenges.
Revised:  In this report, we propose an algorithm to overcome some of these challenges.
Edit intent: clarity
Original:  We show that organisms born at sources in a 2D time-independent flow experience an increase in fixation probability.
Revised:  We show that organisms born at sources in a two-dimensional time-independent flow experience an increase in fixation probability.
Edit intent: fluency
Original: The two-dimensional rotational shallow water equations (SWEs) in the non-canonical Hamiltonian/Poisson form are integrated in time by the fully implicit average vector field (AVF) method , and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity by the linearly implicit Kahan's method.
Revised: The two-dimensional rotational shallow water equations (SWEs) in the non-canonical Hamiltonian/Poisson form are integrated in time by the fully implicit average vector field (AVF) method and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity by the linearly implicit Kahan's method.
Edit intent: fluency
Original:  The accuracy and computational efficiency of the ROMs are shown in numerical test problem.
Revised:  The accuracy and computational efficiency of the ROMs are shown by numerical test problem.
Edit intent: clarity
Original:  method and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity  by the linearly implicit Kahan's method . Reduced order models (ROMs ) with proper orthogonal decomposition/discrete empirical interpolation method (POD/DEIM) preserve the Hamiltonian structure, and the tensorial POD the  linear-quadratic structure of the SWE in the f-plane .
Revised:  method and in the f-plane as a partial differential equation with quadratic nonlinearity by the linearly implicit Kahan's method . Reduced order models (ROMs ) with proper orthogonal decomposition/discrete empirical interpolation method (POD/DEIM) preserve the Hamiltonian structure, and the tensorial POD the linear-quadratic structure of the SWE in the f-plane .
Edit intent: clarity
Original:  method and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity  by the linearly implicit Kahan's method . Reduced order models (ROMs ) with proper orthogonal decomposition/discrete empirical interpolation method (POD/DEIM) preserve the Hamiltonian structure, and the tensorial POD the  linear-quadratic structure of the SWE in the f-plane .
Revised:  method and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity by the linearly implicit Kahan's method and ROMs are constructed with the linear-quadratic structure of the SWE in the f-plane .
Edit intent: coherence
Original:  method and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity  by the linearly implicit Kahan's method . Reduced order models (ROMs ) with proper orthogonal decomposition/discrete empirical interpolation method (POD/DEIM) preserve the Hamiltonian structure, and the tensorial POD the  linear-quadratic structure of the SWE in the f-plane .
Revised:  method and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity by the linearly implicit Kahan's method . Reduced order models (ROMs ) with proper orthogonal decomposition/discrete empirical interpolation method (POD/DEIM) preserve the Hamiltonian structure, and the tensorial POD the tensorial POD that preserves the linear-quadratic structure of the SWE in the f-plane .
Edit intent: coherence
Original:  method and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity  by the linearly implicit Kahan's method . Reduced order models (ROMs ) with proper orthogonal decomposition/discrete empirical interpolation method (POD/DEIM) preserve the Hamiltonian structure, and the tensorial POD the  linear-quadratic structure of the SWE in the f-plane .
Revised:  method and in the f-plane as a partial differential equation (PDE) with quadratic nonlinearity by the linearly implicit Kahan's method . Reduced order models (ROMs ) with proper orthogonal decomposition/discrete empirical interpolation method (POD/DEIM) preserve the Hamiltonian structure, and the tensorial POD the linear-quadratic structure of the SWE .
Edit intent: coherence
Original:  We show that both methods preserve numerically the invariants like energy, the Casimirs like the enstrophy, mass, and circulation  over a long time.
Revised:  We show that in both approaches, the invariants of the SWE such as the energy, enstrophy, mass, and circulation over a long time.
Edit intent: clarity