wanyu/IteraTeR_v2 huggingface dataset:
Original:  The main contributions of this paper are : 1) introduce a radio performance tuning algorithm that self-organizing networks can implement in a polynomial runtime , 2) employ deep reinforcement learning to perform fault management, and 3) show that this fault management method can improve the radio link performance in a realistic network setup.
Revised:  The main contributions of this paper are : 1) introduce a radio performance tuning algorithm that self-organizing networks can implement in a polynomial runtime and 2) employ deep reinforcement learning to perform fault management, and 3) show that this fault management method can improve the radio link performance in a realistic network setup.
Edit intent: coherence
Original:  The main contributions of this paper are : 1) introduce a radio performance tuning algorithm that self-organizing networks can implement in a polynomial runtime , 2) employ deep reinforcement learning to perform fault management, and 3) show that this fault management method can improve the radio link performance in a realistic network setup.
Revised:  The main contributions of this paper are : 1) introduce a radio performance tuning algorithm that self-organizing networks can implement in a polynomial runtime , 2) show that this fault management method can improve the radio link performance in a realistic network setup.
Edit intent: coherence
Original:  Simulation results show that an optimal action sequence to clear alarms is feasible even against the randomness of the network faults and user movements.
Revised:  Simulation results show that an optimal action sequence to clear alarms is feasible even against the randomness of the network fault occurrences and user movements.
Edit intent: clarity
Original:  This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio and spectral efficiency through exploration and exploitation of various alarm corrective actions.
Revised:  This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through exploration and exploitation of various alarm corrective actions.
Edit intent: clarity
Original:  Simulation results show that our proposed  learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.
Revised:  Simulation results show that our proposed algorithm learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.
Edit intent: fluency
Original: The evaluation of the individual fingerprint of a human functional connectome (FC) is becoming a promising avenue for neuroscientific research, due to its enormous potential inherent to drawing single subject inferences from functional connectivity profiles.
Revised: The evaluation of the individual 'fingerprint' of a human functional connectome (FC) is becoming a promising avenue for neuroscientific research, due to its enormous potential inherent to drawing single subject inferences from functional connectivity profiles.
Edit intent: fluency
Original:  The numerical implementations are carried out by the variant Finsler fast marching method, leading to very efficient interactive segmentation schemes.
Revised:  The numerical implementations are carried out by the Finsler variant of the fast marching method, leading to very efficient interactive segmentation schemes.
Edit intent: clarity
Original:  We apply the proposed Finsler fronts propagation model to  foreground and background segmentation implemented by the Voronoi index map.
Revised:  We apply the proposed Finsler fronts propagation model to the foreground and background segmentation implemented by the Voronoi index map.
Edit intent: fluency
Original:  In addition,  the vessel extraction task can be carried out by identifying the level set lines of the distance map providing a thresholding value is given.
Revised:  In addition, the level set lines of the distance map providing a thresholding value is given.
Edit intent: clarity
Original: In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distances and Eikonal partial differential equations.
Revised: In this paper, we introduce a generalized asymmetric fronts propagation model based on the geodesic distance maps and the Eikonal partial differential equations.
Edit intent: clarity
Original:  One of the key ingredients for the computation of the geodesic distances is the geodesic metrics , which can govern the action of the geodesic distance  propagation.
Revised:  One of the key ingredients for the computation of the geodesic distance map is the geodesic metrics , which can govern the action of the geodesic distance propagation.
Edit intent: clarity
Original:  One of the key ingredients for the computation of the geodesic distances is the geodesic metrics , which can govern the action of the geodesic distance  propagation.
Revised:  One of the key ingredients for the computation of the geodesic distances is the geodesic metric , which can govern the action of the geodesic distance propagation.
Edit intent: fluency
Original:  These enhancements can be derived from the image edge-dependent vector field such as the gradient vector flow field .
Revised:  These enhancements can be derived from the image edge-dependent vector field such as the gradient vector flow .
Edit intent: fluency
Original:  We apply the proposed Finsler fronts propagation model to  the foreground and background segmentation  implemented by the Voronoi index map.
Revised:  We apply the proposed Finsler fronts propagation model to image segmentation applications. Specifically, the foreground and background segmentation implemented by the Voronoi index map.
Edit intent: clarity
Original:  We apply the proposed Finsler fronts propagation model to  the foreground and background segmentation  implemented by the Voronoi index map.
Revised:  We apply the proposed Finsler fronts propagation model to the foreground and background segmentation is implemented by the Voronoi index map.
Edit intent: clarity
Original:  The main idea is that a property witnessed in a prior state can be  recalled in the current state, provided (1) state evolves according to a given preorder, and (2) the property is preserved by this preorder.
Revised:  The main idea is that a property witnessed in a prior state can be soundly recalled in the current state, provided (1) state evolves according to a given preorder, and (2) the property is preserved by this preorder.
Edit intent: clarity
Original: We provide a practical way to ease the verification of programs whose state evolves monotonically.
Revised: We provide a way to ease the verification of programs whose state evolves monotonically.
Edit intent: clarity
Original:  In many realistic scenarios, such monotonic reasoning yields concise modular proofs, saving the need for explicit program invariants.
Revised:  In many scenarios, such monotonic reasoning yields concise modular proofs, saving the need for explicit program invariants.
Edit intent: clarity
Original:  Kleinberg studied a close variant of this network model and proved that the (decentralised) routing time is O((\log n)^2) ) when r=2 and n^{\Omega(1)} when r\neq 2. Here, we prove that the random walk also undergoes a phase transition at r=2, but in this case the phase transition is of a different form.
Revised:  Kleinberg studied a close variant of this network model and proved that the (decentralised) routing time is O((\log n)^2) when r=2 and n^{\Omega(1)} when r\neq 2. Here, we prove that the random walk also undergoes a phase transition at r=2, but in this case the phase transition is of a different form.
Edit intent: fluency
Original: As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine to finance, and language processing.
Revised: As one of the most important paradigms of recurrent neural networks, the echo state network (ESN) has been applied to a wide range of fields, from robotics to medicine , finance, and language processing.
Edit intent: fluency
Original:  A key feature of the ESN paradigm is its reservoir ---a directed and weighted network--- that represents the connections between neurons and projects the input signals into a high dimensional space  . Despite extensive studies, the impact of the reservoir network on the ESN performance remains unclear.
Revised:  A key feature of the ESN paradigm is its reservoir --- a directed and weighted network--- that represents the connections between neurons and projects the input signals into a high dimensional space . Despite extensive studies, the impact of the reservoir network on the ESN performance remains unclear.
Edit intent: fluency
Original:  A key feature of the ESN paradigm is its reservoir ---a directed and weighted network--- that represents the connections between neurons and projects the input signals into a high dimensional space  . Despite extensive studies, the impact of the reservoir network on the ESN performance remains unclear.
Revised:  A key feature of the ESN paradigm is its reservoir ---a directed and weighted network of neurons that projects the input signals into a high dimensional space . Despite extensive studies, the impact of the reservoir network on the ESN performance remains unclear.
Edit intent: clarity
Original:  A key feature of the ESN paradigm is its reservoir ---a directed and weighted network--- that represents the connections between neurons and projects the input signals into a high dimensional space  . Despite extensive studies, the impact of the reservoir network on the ESN performance remains unclear.
Revised:  A key feature of the ESN paradigm is its reservoir ---a directed and weighted network--- that represents the connections between neurons and projects the input time series into a high dimensional space . Despite extensive studies, the impact of the reservoir network on the ESN performance remains unclear.
Edit intent: clarity
Original:  Moreover, we find that adding short loops to the reservoir network can tailor ESN for specific tasks and optimal learning.
Revised:  Moreover, we find that adding short loops to the reservoir network can tailor ESN for specific tasks and optimize learning.
Edit intent: fluency
Original:  However, very little has been done to exploit these data for training audio event recognition and detection models . Moreover, to be able to successfully detect or recognize events in such data for different applications it is important to train the models themselves from such data.
Revised:  However, very little has been done to exploit these data for training audio event recognition and detection models . Moreover, to be able to successfully detect or recognize events in such data for different applications it is important to train the recognizers themselves from such data.
Edit intent: clarity
Original:  Learning event recognition models from these web data  poses several challenges, the most important being the availability of labels for these data{\em   .
Revised:  Training from these web data poses several challenges, the most important being the availability of labels for these data{\em .
Edit intent: clarity
Original:  Learning event recognition models from these web data  poses several challenges, the most important being the availability of labels for these data{\em   .
Revised:  Learning event recognition models from these web data , however, poses several challenges, the most important being the availability of labels for these data{\em .
Edit intent: coherence
Original:  Learning event recognition models from these web data  poses several challenges, the most important being the availability of labels for these data{\em   .
Revised:  Learning event recognition models from these web data poses several challenges, the most important being the availability of labels for these data{\em weak .
Edit intent: fluency
Original:  We propose that weak label learning offers an effective method to learn from web data.
Revised:  We propose that learning algorithms that can exploit weak labels offer an effective method to learn from web data.
Edit intent: clarity
Original:  We then propose a robust and efficient deep convolutional neural network (CNN) based framework to learn audio event recognition models from weakly labeled data.
Revised:  We then propose a robust and efficient deep convolutional neural network (CNN) based framework to learn audio event recognizers from weakly labeled data.
Edit intent: clarity
Original:  It can train from and test on recordings of variable length in an efficient manner and outperforms a network trained on web data under strong label assumption{\em   by a considerable margin.
Revised:  It can train from and analyze recordings of variable length in an efficient manner and outperforms a network trained on web data under strong label assumption{\em by a considerable margin.
Edit intent: clarity
Original:  It can train from and test on recordings of variable length in an efficient manner and outperforms a network trained on web data under strong label assumption{\em   by a considerable margin.
Revised:  It can train from and test on recordings of variable length in an efficient manner and outperforms a network trained with{\em by a considerable margin.
Edit intent: clarity
Original:  Using a natural definition of orthogonal tensors over the real field (resp.  unitary tensors over the complex field), it is shown that the obvious lower bound 1/n_1 \cdots n_{d-1} is attained if and only if a tensor is orthogonal (resp.  unitary) up to scaling.
Revised:  Using a natural definition of orthogonal tensors over the real field (resp. , unitary tensors over the complex field), it is shown that the obvious lower bound 1/n_1 \cdots n_{d-1} is attained if and only if a tensor is orthogonal (resp. unitary) up to scaling.
Edit intent: fluency
Original:  Using a natural definition of orthogonal tensors over the real field (resp.  unitary tensors over the complex field), it is shown that the obvious lower bound 1/n_1 \cdots n_{d-1} is attained if and only if a tensor is orthogonal (resp.  unitary) up to scaling.
Revised:  Using a natural definition of orthogonal tensors over the real field (resp. unitary tensors over the complex field), it is shown that the obvious lower bound 1/n_1 \cdots n_{d-1} is attained if and only if a tensor is orthogonal (resp. , unitary) up to scaling.
Edit intent: fluency
Original:  A connection between the (non)existence of real orthogonal tensors of order three and the classical Hurwitz problem on composition algebras can be established: existence of orthogonal tensors of size \ell \times m \times n is equivalent to the admissibility of the triple [\ell,m,n] to  Hurwitz problem.
Revised:  A connection between the (non)existence of real orthogonal tensors of order three and the classical Hurwitz problem on composition algebras can be established: existence of orthogonal tensors of size \ell \times m \times n is equivalent to the admissibility of the triple [\ell,m,n] to the Hurwitz problem.
Edit intent: fluency
Original: We study the assortment optimization problem under the \emph{Sequential Multinomial Logit (SML), a discrete choice model that generalizes the multinomial logit (MNL).
Revised: We study the assortment optimization problem under the \emph{Sequential Multinomial Logit (SML), a discrete choice model that generalizes the multinomial logit (MNL).
Edit intent: fluency
Original:  This model is a special case of the Perception Adjusted Luce Model (PALM) recently proposed by Echenique et al ( 2013 ).
Revised:  This model is a special case of the Perception-Adjusted Luce Model (PALM) recently proposed by Echenique et al ( 2013 ).
Edit intent: fluency
Original:  It can explain many behavioral phenomena such as the attraction, compromise, and similarity effects which cannot be explained by the MNL model or any discrete choice model based on random utility.
Revised:  It can explain many behavioural phenomena such as the attraction, compromise, and similarity effects which cannot be explained by the MNL model or any discrete choice model based on random utility.
Edit intent: fluency
Original:  In particular, the SML model allows violations to the\emph{regularity condition which states that the probability of choosing a product cannot increase if the offer set is enlarged.
Revised:  In particular, the SML model allows violations to \emph{regularity condition which states that the probability of choosing a product cannot increase if the offer set is enlarged.
Edit intent: fluency
Original:  In particular, the SML model allows violations to the\emph{regularity condition which states that the probability of choosing a product cannot increase if the offer set is enlarged.
Revised:  In particular, the SML model allows violations to the\emph{regularity which states that the probability of choosing a product cannot increase if the offer set is enlarged.
Edit intent: clarity
Original: We tackle the problem of deciding whether a pair of probabilistic programs are equivalent in the context of Probabilistic NetKAT, a formal language for  reasoning about the behavior of packet-switched networks.
Revised: We tackle the problem of deciding whether two probabilistic programs are equivalent in the context of Probabilistic NetKAT, a formal language for reasoning about the behavior of packet-switched networks.
Edit intent: fluency
Original: We tackle the problem of deciding whether a pair of probabilistic programs are equivalent in the context of Probabilistic NetKAT, a formal language for  reasoning about the behavior of packet-switched networks.
Revised: We tackle the problem of deciding whether a pair of probabilistic programs are equivalent in Probabilistic NetKAT, a formal language for reasoning about the behavior of packet-switched networks.
Edit intent: clarity
Original: Despite the growing popularity of 802.11 wireless networks in many public places, wireless users often suffer from connectivity problems  due to unstable radio conditions and dynamic user behavior among other reasons.
Revised: Despite the growing popularity of 802.11 wireless networks , users often suffer from connectivity problems due to unstable radio conditions and dynamic user behavior among other reasons.
Edit intent: clarity
Original:  Detecting the anomalous cases and planning to prevent such problems in advance are in the thick of major challenges that network managers encounter.
Revised:  Anomaly detection and distinction are in the thick of major challenges that network managers encounter.
Edit intent: clarity
Original:  Complication of monitoring such complex networks , that often requires heavy instrumentation of the user devices, makes the anomaly detection analysis even harder.
Revised:  Complication of monitoring the broaden and complex WLANs , that often requires heavy instrumentation of the user devices, makes the anomaly detection analysis even harder.
Edit intent: clarity
Original:  We subsequently validate the proposed model by generating a number of network anomalous scenarios in OMNeT++/INET network simulator and evaluate the detection method against baseline approach (Raw_Data and PCA) results .
Revised:  We then generate a number of network anomalous scenarios in OMNeT++/INET network simulator and evaluate the detection method against baseline approach (Raw_Data and PCA) results .
Edit intent: clarity
Original:  We subsequently validate the proposed model by generating a number of network anomalous scenarios in OMNeT++/INET network simulator and evaluate the detection method against baseline approach (Raw_Data and PCA) results .
Revised:  We subsequently validate the proposed model by generating a number of network anomalous scenarios in OMNeT++/INET network simulator and compare the detection outcomes with those in baseline approaches (RawData and PCA) results .
Edit intent: clarity
Original:  We subsequently validate the proposed model by generating a number of network anomalous scenarios in OMNeT++/INET network simulator and evaluate the detection method against baseline approach (Raw_Data and PCA) results .
Revised:  We subsequently validate the proposed model by generating a number of network anomalous scenarios in OMNeT++/INET network simulator and evaluate the detection method against baseline approach (Raw_Data and PCA) .
Edit intent: clarity
Original:  The experimental outcomes show the superiority of the proposed HMM model in detection precision and sensitivity.
Revised:  The experimental results show the superiority of the proposed HMM model in detection precision and sensitivity.
Edit intent: clarity
Original:  This saves 50%DIFDELCMD < \\%%% %DIF < of the weight lookups needed by a standard online implementation of backpropagation.
Revised:  %DIFDELCMD < \\%%% %DIF < of the weight lookups needed by a standard online implementation of backpropagation.
Edit intent: coherence
Original:  This saves 50%DIFDELCMD < \\%%% %DIF < of the weight lookups needed by a standard online implementation of backpropagation.
Revised:  This saves 50%DIFDELCMD < \\%%% %DIF < 
Edit intent: clarity
Original:  Examples include nutrient transport in the slime mold Physarum polycephalum as well as mammalian and plant venation.
Revised:  Examples include nutrient transport in the slime mold Physarum polycephalum as well as mammalian and plant venation.
Edit intent: fluency
Original:  We show how hierarchically organized reticulation can be generated and maintained through spatially collective load fluctuations on a particular length scale.
Revised:  We show how hierarchically organized reticulation can be constructed and maintained through spatially collective load fluctuations on a particular length scale.
Edit intent: clarity
Original:  We show how hierarchically organized reticulation can be generated and maintained through spatially collective load fluctuations on a particular length scale.
Revised:  We show how hierarchically organized reticulation can be generated and maintained through spatially correlated load fluctuations on a particular length scale.
Edit intent: clarity
Original:  We demonstrate that the resulting network topologies represent a trade-off between optimizing power dissipation , construction cost, and damage robustness and identify the Pareto-efficient front that evolution is expected to favor and select for.
Revised:  We demonstrate that the network topologies generated represent a trade-off between optimizing power dissipation , construction cost, and damage robustness and identify the Pareto-efficient front that evolution is expected to favor and select for.
Edit intent: clarity
Original: We provide a combinatorial characterization of all testable properties of k-graphs (i.e. k-uniform hypergraphs  ).
Revised: We provide a combinatorial characterization of all testable properties of k-uniform hypergraphs ).
Edit intent: coherence
Original:  Here, a k-graph property \mathbf{P is testable if there is a randomized algorithm which makes a bounded number of edge queries and distinguishes with probability 2/3 between k-graphs that satisfy \mathbf{P and those that are far from satisfying \mathbf{P . For the 2-graph case, such a combinatorial characterization was obtained by Alon, Fischer, Newman and Shapira.
Revised:  Here, a k-graph property P is testable if there is a randomized algorithm which makes a bounded number of edge queries and distinguishes with probability 2/3 between k-graphs that satisfy \mathbf{P and those that are far from satisfying \mathbf{P . For the 2-graph case, such a combinatorial characterization was obtained by Alon, Fischer, Newman and Shapira.
Edit intent: fluency