wanyu/IteraTeR_v2 huggingface dataset:
Original:  Hence,  there is a need for a method to define regions with consistent underlying degree of social media attitudes from noisy, geographically-inconsistent, individual Tweets .
Revised:  Hence, there is a need for a method to define regions with consistent underlying degree of social media attitudes by region .
Edit intent: clarity
Original:  We find that not only are SS-SOMs robust to missing data, the exposure of a cohort of men at high risk for HIV to racism and homophobia changes by up to 42\%  as compared to using Zip code-based measures.
Revised:  We find that not only are SS-SOMs robust to missing data, the exposure of a cohort of men at high risk for HIV to racism and homophobia changes by up to 42\% using SS-SOM measures as compared to using Zip code-based measures.
Edit intent: fluency
Original:  Word-based or `alignment-free' methods for phylogeny reconstruction are much faster than traditional approaches, but they are generally less accurate.
Revised: Motivation: Word-based or `alignment-free' methods for phylogeny reconstruction are much faster than traditional approaches, but they are generally less accurate.
Edit intent: coherence
Original:  Most of these methods calculate pairwise distances for a set of input sequences, for example from word frequencies or from so-called spaced-word matches .
Revised:  Most of these methods calculate pairwise distances for a set of input sequences, for example from word frequencies , from so-called spaced-word matches .
Edit intent: fluency
Original: It is known that the evolutionary algorithm (1+1)-EA with mutation rate c/n optimises every monotone function efficiently if c<1, and needs exponential time on the some monotone functions (HotTopic functions) if c\geq 2.2.
Revised: It is known that the evolutionary algorithm (1+1)-EA with mutation rate c/n optimises every monotone function efficiently if c<1, and needs exponential time on some monotone functions (HotTopic functions) if c\geq 2.2.
Edit intent: fluency
Original:  computationally tractable sub-problems , with the consequent desire that these should be as independent as possible to reduce the inevitable side-effects of not taking a global approach . Current techniques use a multilevel approach that first coarsens the hypergraph into a smaller set of representative super nodes, partitions these, prior to uncoarsening to achieve a final set of partitions for the full hypergraph.
Revised:  computationally tractable sub-problems . Current techniques use a multilevel approach that first coarsens the hypergraph into a smaller set of representative super nodes, partitions these, prior to uncoarsening to achieve a final set of partitions for the full hypergraph.
Edit intent: clarity
Original: We introduce KiNetX, a fully automated meta-algorithm for the kinetic simulation and analysis of general (complex and noisy) chemical reaction networks with rigorous uncertainty control .
Revised: We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks with rigorous uncertainty control .
Edit intent: clarity
Original:  For a rigorous analysis of the KiNetX algorithm , we developed a random generator of artificial reaction networks , AutoNetGen, which encodes chemical logic into their underlying graph structure.
Revised:  For a rigorous analysis of the KiNetX algorithm , we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure.
Edit intent: clarity
Original:  AutoNetGen allows us to consider a vast number of distinct chemical scenarios which is necessary to investigate the reliability and efficiency of KiNetX in a statistical context.
Revised:  AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss assess the importance of rigorous uncertainty propagation in a statistical context.
Edit intent: clarity
Original: Computer-generated graphics  are images generated by computer software.
Revised: Computer-generated graphics (CGs) are images generated by computer software.
Edit intent: clarity
Original:  The  rapid development of computer graphics technologies has made it easier to generate a photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images by our naked eyes .
Revised:  The ~ rapid development of computer graphics technologies has made it easier to generate a photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images by our naked eyes .
Edit intent: fluency
Original:  The  rapid development of computer graphics technologies has made it easier to generate a photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images by our naked eyes .
Revised:  The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images by our naked eyes .
Edit intent: fluency
Original:  The  rapid development of computer graphics technologies has made it easier to generate a photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images by our naked eyes .
Revised:  The rapid development of computer graphics technologies has made it easier to generate a photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye .
Edit intent: clarity
Original:  In this paper, we propose a method based on sensor pattern noise  and deep learning to distinguish computer-generated graphics (CG) from natural images (NI) . Before being fed into our convolutional neural network (CNN)-based model, these images---including the CG and NI---are clipped into image patches.
Revised:  In this paper, we propose a method based on sensor pattern noise and deep learning to distinguish CGs from NIs . Before being fed into our convolutional neural network (CNN)-based model, these images---including the CG and NI---are clipped into image patches.
Edit intent: clarity
Original:  In this paper, we propose a method based on sensor pattern noise  and deep learning to distinguish computer-generated graphics (CG) from natural images (NI) . Before being fed into our convolutional neural network (CNN)-based model, these images---including the CG and NI---are clipped into image patches.
Revised:  In this paper, we propose a method based on sensor pattern noise and deep learning to distinguish computer-generated graphics (CG) from natural images (NI) . Before being fed into our convolutional neural network (CNN)-based model, these images---CGs and NIs---are clipped into image patches.
Edit intent: clarity
Original:  Furthermore, several high-pass filters ( HPF ) are used to remove low-frequency signal, which represents the image content.
Revised:  Furthermore, several high-pass filters ( HPFs ) are used to remove low-frequency signal, which represents the image content.
Edit intent: fluency
Original:  Furthermore, several high-pass filters ( HPF ) are used to remove low-frequency signal, which represents the image content.
Revised:  Furthermore, several high-pass filters ( HPF ) are used to remove low-frequency signals, which represent the image content.
Edit intent: fluency
Original:  These filters are also used to enhance the residual signal as well as sensor pattern noise introduced by the digital camera device.
Revised:  These filters are also used to reveal the residual signal as well as sensor pattern noise introduced by the digital camera device.
Edit intent: clarity
Original:  These filters are also used to enhance the residual signal as well as sensor pattern noise introduced by the digital camera device.
Revised:  These filters are also used to enhance the residual signal as well as SPN introduced by the digital camera device.
Edit intent: clarity
Original:  Different from the traditional methods of distinguishing CG from NI , the proposed method utilizes a five-layer CNN to classify the input image patches.
Revised:  Different from the traditional methods of distinguishing CGs from NIs , the proposed method utilizes a five-layer CNN to classify the input image patches.
Edit intent: fluency
Original:  The  experiments have demonstrated that : 1) the proposed method with three high-pass filters can achieve better results than that with only one high-pass filter or no high-pass filter.
Revised:  The ~ experiments have demonstrated that : 1) the proposed method with three high-pass filters can achieve better results than that with only one high-pass filter or no high-pass filter.
Edit intent: fluency
Original:  The  experiments have demonstrated that : 1) the proposed method with three high-pass filters can achieve better results than that with only one high-pass filter or no high-pass filter.
Revised:  The experiments have demonstrated that ( 1) the proposed method with three high-pass filters can achieve better results than that with only one high-pass filter or no high-pass filter.
Edit intent: fluency
Original:  The  experiments have demonstrated that : 1) the proposed method with three high-pass filters can achieve better results than that with only one high-pass filter or no high-pass filter.
Revised:  The experiments have demonstrated that : 1) the proposed method with three HPFs can achieve better results than that with only one high-pass filter or no high-pass filter.
Edit intent: clarity
Original:  2) the proposed method with three high-pass filters achieves 100\\%DIF < accuracy, although the natural images undergo %DIF >  a JPEG compression with a quality factor of 75.
Revised:  2) the proposed method with three HPFs achieves 100\\%DIF < accuracy, although the natural images undergo %DIF > a JPEG compression with a quality factor of 75.
Edit intent: clarity
Original:  2) the proposed method with three high-pass filters achieves 100\\%DIF < accuracy, although the natural images undergo %DIF >  a JPEG compression with a quality factor of 75.
Revised:  2) the proposed method with three high-pass filters achieves 100\\%DIF < accuracy, although the natural images undergo %DIF > accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.
Edit intent: clarity
Original:  We present a general path-based representation for learning from programs.
Revised:  We present a general path-based representation for learning from programs.
Edit intent: fluency
Original: The brain carries out demanding computations and learning processes with recurrent networks of spiking neurons (RSNNs)  .
Revised: Recurrent networks of spiking neurons (RSNNs) .
Edit intent: clarity
Original:  But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with the performance of recurrent networks of artificial neurons, such as Long Short-Term Memory (LSTM) networks.
Revised:  But computing and learning capabilities of RSNN models have remained poor, especially in comparison with the performance of recurrent networks of artificial neurons, such as Long Short-Term Memory (LSTM) networks.
Edit intent: clarity
Original:  But computing and learning capabilities of currently available RSNN models have remained poor, especially in comparison with the performance of recurrent networks of artificial neurons, such as Long Short-Term Memory (LSTM) networks.
Revised:  But computing and learning capabilities of currently available RSNN models have remained poor, at least in comparison with the performance of recurrent networks of artificial neurons, such as Long Short-Term Memory (LSTM) networks.
Edit intent: clarity
Original:  But computing and learning capabilities of RSNN models have remained poor, at least in comparison with ANNs . We address two possible reasons for that.
Revised:  But computing and learning capabilities of RSNN models have remained poor, at least in comparison with artificial neural networks (ANNs) . We address two possible reasons for that.
Edit intent: clarity
Original:  Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or Fossil , as well as more simple methods based, e.g., on nearest neighbor schemes.
Revised:  Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or FOSSIL , as well as more simple methods based, e.g., on nearest neighbor schemes.
Edit intent: fluency
Original:  Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or Fossil , as well as more simple methods based, e.g., on nearest neighbor schemes.
Revised:  Our comparison includes the most recent approaches based on recurrent neural networks like GRU4REC, factorized Markov model approaches such as FISM or Fossil , as well as simpler methods based, e.g., on nearest neighbor schemes.
Edit intent: clarity
Original: Classical results in sparse recovery guarantee the exact reconstruction of a sparse signal under assumptions on the dictionary that are either too strong or NP hard to check.
Revised: Classical results in sparse recovery guarantee the exact reconstruction of a sparse signal under assumptions on the dictionary that are either too strong or NP-hard to check.
Edit intent: fluency
Original:  Moreover, such results may be too pessimistic in practice since they are based on a worst-case analysis.
Revised:  Moreover, such results may be pessimistic in practice since they are based on a worst-case analysis.
Edit intent: clarity
Original:  We show that in this case necessary and sufficient conditions can be derived in terms of properties of the cycles of the graph , which can be checked in polynomial time.
Revised:  We derive necessary and sufficient conditions can be derived in terms of properties of the cycles of the graph , which can be checked in polynomial time.
Edit intent: coherence
Original:  We show that in this case necessary and sufficient conditions can be derived in terms of properties of the cycles of the graph , which can be checked in polynomial time.
Revised:  We show that in this case necessary and sufficient conditions can be derived in terms of properties of the cycles of the graph that can be checked in polynomial time.
Edit intent: coherence
Original:  Our analysis further allows us to derive location dependent conditions for recovery that only depend on the cycles of the graph that intersect this support .
Revised:  Our analysis further allows us to derive location dependent conditions for recovery that only depend on the cycles of the graph with the support of the signal .
Edit intent: clarity
Original:  Finally, we exploit sparsity properties on the measurements to a specialized sub-graph-based recovery algorithm that outperforms the standard \ell_1-minimization  .
Revised:  Finally, we exploit sparsity properties on the measurements to a specialized sub-graph-based recovery algorithm that outperforms the standard \ell_1-minimization approach .
Edit intent: clarity
Original:  The dual of this problem is called multipack- ing : a multipacking is a set M of vertices such that for any vertex v and any positive integer r, the ball of radius r around v contains at most r vertices of M . The maximum size of a multipacking in a graph G is de- noted mp(G).
Revised:  The dual of this problem is called multipacking : a multipacking is a set M of vertices such that for any vertex v and any positive integer r, the ball of radius r around v contains at most r vertices of M . The maximum size of a multipacking in a graph G is de- noted mp(G).
Edit intent: clarity
Original:  The dual of this problem is called multipack- ing : a multipacking is a set M of vertices such that for any vertex v and any positive integer r, the ball of radius r around v contains at most r vertices of M . The maximum size of a multipacking in a graph G is de- noted mp(G).
Revised:  The dual of this problem is called multipack- ing : a multipacking is a set M of vertices such that for any vertex v and any positive integer r, the ball of radius r around v contains at most r vertices of M . The maximum size of a multipacking in a graph G is denoted mp(G).
Edit intent: fluency
Original:  Our method is inherited from Lemma 2.2 in J. Denef and L. Lipshitz,\textit{Power series solutions of algebraic differential equations , Mathematische Annalen,%DIFDELCMD < 267(1984)%%% , 213-238 for expressing high order derivatives of a differential polynomial via their lower order ones.
Revised:  Our method is inherited from Lemma 2.2 in \textit{Power series solutions of algebraic differential equations , Mathematische Annalen,%DIFDELCMD < 267(1984)%%% , 213-238 for expressing high order derivatives of a differential polynomial via their lower order ones.
Edit intent: coherence
Original:  Our method is inherited from Lemma 2.2 in J. Denef and L. Lipshitz,\textit{Power series solutions of algebraic differential equations , Mathematische Annalen,%DIFDELCMD < 267(1984)%%% , 213-238 for expressing high order derivatives of a differential polynomial via their lower order ones.
Revised:  Our method is inherited from Lemma 2.2 in J. Denef and L. Lipshitz,\textit{ , Mathematische Annalen,%DIFDELCMD < 267(1984)%%% , 213-238 for expressing high order derivatives of a differential polynomial via their lower order ones.
Edit intent: clarity
Original:  Our method is inherited from Lemma 2.2 in J. Denef and L. Lipshitz,\textit{Power series solutions of algebraic differential equations , Mathematische Annalen,%DIFDELCMD < 267(1984)%%% , 213-238 for expressing high order derivatives of a differential polynomial via their lower order ones.
Revised:  Our method is inherited from Lemma 2.2 in J. Denef and L. Lipshitz,\textit{Power series solutions of algebraic differential equations %DIFDELCMD < 267(1984)%%% , 213-238 for expressing high order derivatives of a differential polynomial via their lower order ones.
Edit intent: coherence
Original:  Our method is inherited from Lemma 2.2 in J. Denef and L. Lipshitz,\textit{Power series solutions of algebraic differential equations , Mathematische Annalen,%DIFDELCMD < 267(1984)%%% , 213-238 for expressing high order derivatives of a differential polynomial via their lower order ones.
Revised:  Our method is inherited from Lemma 2.2 in J. Denef and L. Lipshitz,\textit{Power series solutions of algebraic differential equations , Mathematische Annalen,%DIFDELCMD < 267(1984)%%% for expressing high order derivatives of a differential polynomial via their lower order ones.
Edit intent: coherence
Original:  We define a class of certain differential polynomials in which our method works properly, which is callednon-vanishing .
Revised:  We define a class of certain differential polynomials in which our method works properly, which is called .
Edit intent: clarity
Original:  The existence and the uniqueness conditions for the initial value problems for AODEs at singular points are included .
Revised:  If a certain regularity condition on the given AODE or on the initial values is fulfilled, we compute all of the solutions .
Edit intent: clarity
Original:  Moreover, when the existence is confirmed, we present the algebraic structure of the set of all formal power series solutions satisfying the initial value conditions .
Revised:  Moreover, when the existence is confirmed, we present the algebraic structure of the set of all formal power series solutions .
Edit intent: clarity
Original:  The lack of  solution of this problem had been one of the major issues prohibiting the efficient realization of Shor  algorithm by Instantaneous Noise-Based  , which runs on a classical Turing computer with a true random number generator.
Revised:  The lack of a solution of this problem had been one of the major issues prohibiting the efficient realization of Shor algorithm by Instantaneous Noise-Based , which runs on a classical Turing computer with a true random number generator.
Edit intent: fluency
Original:  The lack of  solution of this problem had been one of the major issues prohibiting the efficient realization of Shor  algorithm by Instantaneous Noise-Based  , which runs on a classical Turing computer with a true random number generator.
Revised:  The lack of solution of this problem had been one of the major issues prohibiting the efficient realization of Shor 's algorithm by Instantaneous Noise-Based , which runs on a classical Turing computer with a true random number generator.
Edit intent: fluency
Original: This paper discusses a machine-learning (ML)-based future received signal strength (RSS) prediction scheme using depth camera images for millimeter-wave (mmWave) networks.
Revised: This study demonstrates the feasibility of the proactive received power prediction by leveraging spatiotemporal visual sensing information toward the reliable millimeter-wave (mmWave) networks.
Edit intent: clarity
Original:  Moreover, the results confirm that the scheme is feasible for real-time prediction systems and show that the scheme predicts RSS values in 0.5 s future with RMS errors of less than 2.1 dB in the simulations and 3.5 dB in the experiments .
Revised:  Moreover, the results confirm that the scheme is feasible for real-time prediction systems and show that the scheme predicts RSS values in 0.5 s future with RMS errors of less than 2.1 dB in the simulations and 3.5 dB .
Edit intent: clarity
Original:  However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors  fail, resulting in the withdrawal of robots from the collective task, and , subsequently, the inability of the remaining active robots to coordinate with each other.
Revised:  However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors may fail, resulting in the withdrawal of robots from the collective task, and , subsequently, the inability of the remaining active robots to coordinate with each other.
Edit intent: clarity
Original:  However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors  fail, resulting in the withdrawal of robots from the collective task, and , subsequently, the inability of the remaining active robots to coordinate with each other.
Revised:  However, in failure-prone or adversarial environments, robots get attacked, their communication channels get jammed, and their sensors fail, resulting in the withdrawal of robots from the collective task, and consequently the inability of the remaining active robots to coordinate with each other.
Edit intent: coherence
Original:  In general, resilient design problems are hard, and even though they often involve objective functions that are monotone and (possibly) submodular, scalable approximation algorithms for their solution have been hitherto unknown.
Revised:  In general, resilient design problems are hard, and even though they often involve objective functions that are monotone or submodular, scalable approximation algorithms for their solution have been hitherto unknown.
Edit intent: coherence