wanyu/IteraTeR_v2 huggingface dataset:
Original:  Network embedding represents the network in a lower dimensional representation space with the same properties which presents a compressed representation of the input network.
Revised:  Network embedding represents the network in a lower dimensional representation space with the same properties which presents a compressed representation of the network.
Edit intent: clarity
Original:  While current methods try to preserve local neighborhood information of nodes, we utilize local neighborhood and community information of network nodes to cover both local and global structure of social networks.
Revised:  Current methods try to preserve local neighborhood information of nodes, we utilize local neighborhood and community information of network nodes to cover both local and global structure of social networks.
Edit intent: coherence
Original:  CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses skip-gram model to learn representation vector of nodes.
Revised:  CARE builds customized paths, which are consisted of local and global structure of network nodes, as a basis for network embedding and uses the Skip-gram model to learn representation vector of nodes.
Edit intent: fluency
Original:  Then , stochastic gradient descent is used to optimize our objective function and learn the final representation of nodes.
Revised:  Subsequently , stochastic gradient descent is used to optimize our objective function and learn the final representation of nodes.
Edit intent: coherence
Original:  Then , stochastic gradient descent is used to optimize our objective function and learn the final representation of nodes.
Revised:  Then , stochastic gradient descent is applied to optimize our objective function and learn the final representation of nodes.
Edit intent: coherence
Original:  Experimental results on different networks indicate that the proposed method outperforms others in both Micro-f1 and Macro-f1 measures for different size of training data.
Revised:  Experimental results on various networks indicate that the proposed method outperforms others in both Micro-f1 and Macro-f1 measures for different size of training data.
Edit intent: fluency
Original:  Experimental results on different networks indicate that the proposed method outperforms others in both Micro-f1 and Macro-f1 measures for different size of training data.
Revised:  Experimental results on different networks indicate that the proposed method outperforms others in both Micro and Macro-f1 measures for different size of training data.
Edit intent: fluency
Original:  (iii) oversampling should be applied to the level that totally eliminates the imbalance, whereas undersampling can perform better when the imbalance is only removed to some extent ;
Revised:  (iii) oversampling should be applied to the level that completely eliminates the imbalance, whereas undersampling can perform better when the imbalance is only removed to some extent ;
Edit intent: fluency
Original:  (iii) oversampling should be applied to the level that totally eliminates the imbalance, whereas undersampling can perform better when the imbalance is only removed to some extent ;
Revised:  (iii) oversampling should be applied to the level that totally eliminates the imbalance, whereas the optimal undersampling ratio depends on the extent of imbalance ;
Edit intent: clarity
Original:  (iv) as opposed to some classical machine learning models, oversampling does not necessarily cause overfitting of CNNs;
Revised:  (iv) as opposed to some classical machine learning models, oversampling does not cause overfitting of CNNs;
Edit intent: clarity
Original:  Its simplest member was empirically shown to improve base models and achieve state-of-the-art performance on MNIST and CIFAR-10 benchmarks.
Revised:  Its simplest member was empirically shown to improve base models and achieve competitive performance on MNIST and CIFAR-10 benchmarks.
Edit intent: clarity
Original: This paper explains why deep learning can generalize well, despite large capacity and possible algorithmic instability, nonrobustness, and sharp minima, effectively addressing an open problem in the literature.
Revised: This paper explains why deep learning can generalize well, despite large capacity and possible algorithmic instability, nonrobustness, and sharp minima, answering to an open question in the literature.
Edit intent: clarity
Original:  Our results give insight into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, answering to an open question in the literature.
Revised:  Our results give insight into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature.
Edit intent: clarity
Original:  We also discuss limitations of our results and propose additional open problems .
Revised:  We also propose new open problems and discuss the limitations of our results and propose additional open problems .
Edit intent: clarity
Original:  We also discuss limitations of our results and propose additional open problems .
Revised:  We also discuss limitations of our results .
Edit intent: clarity
Original: This paper provides non-vacuous and numerically-tight generalization guarantees for deep learning, as well as theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature.
Revised: This paper provides theoretical insights into why and how deep learning can generalize well, despite its large capacity, complexity, possible algorithmic instability, nonrobustness, and sharp minima, responding to an open question in the literature.
Edit intent: coherence
Original: For light harvestors with a reaction center complex (LH1-RC complex) of three types, we propose an experiment to verify our analysis based upon antenna theories that automatically include the required structural information.
Revised: For light harvesters with a reaction center complex (LH1-RC complex) of three types, we propose an experiment to verify our analysis based upon antenna theories that automatically include the required structural information.
Edit intent: fluency
Original:  Our analysis conforms to  current understanding of light-harvesting antennae in that we can explain known properties of the complex. a functional role of the notch at the light harvestor,
Revised:  Our analysis conforms to the current understanding of light-harvesting antennae in that we can explain known properties of the complex. a functional role of the notch at the light harvestor,
Edit intent: fluency
Original:  Our analysis conforms to  current understanding of light-harvesting antennae in that we can explain known properties of the complex. a functional role of the notch at the light harvestor,
Revised:  Our analysis conforms to current understanding of light-harvesting antennae in that we can explain known properties of these complexes. We provide an explanation for the functional roles of the notch at the light harvestor,
Edit intent: clarity
Original:  a reason  for the use of dielectric chlorophylls instead of a conductor to make the light harvestor ,
Revised:  a reason for the use of dielectric chlorophylls instead of a conductor to make the light harvester ,
Edit intent: fluency
Original:  The S-shape complexes should not be  the third type of light harvester but simply as a composite form.
Revised:  The S-shape complexes should not be considered as the third type of light harvester but simply as a composite form.
Edit intent: clarity
Original:  By considering such UAV heterogeneity to cover the whole target area, this paper studies two emergency UAV deployment problems: one is to minimize the maximum deployment delay among all UAVs (min-max) for fairness consideration, and the other is to minimize the total deployment delay (min-sum) for efficiency consideration.
Revised:  By considering such UAV heterogeneity to cover the whole target area, this paper studies two fast UAV deployment problems: one is to minimize the maximum deployment delay among all UAVs (min-max) for fairness consideration, and the other is to minimize the total deployment delay (min-sum) for efficiency consideration.
Edit intent: clarity
Original:  However, it was recently reported that polarized collective motion do emerge in an alignment based asynchronous lattice model with mutual anticipation.
Revised:  However, it was recently reported that polarized collective motion does emerge in an alignment based asynchronous lattice model with mutual anticipation.
Edit intent: fluency
Original:  Here we show that including anticipation promotes polarized collective motion in a synchronous, off lattice, attraction based model.
Revised:  Here we show that including anticipation induces polarized collective motion in a synchronous, off lattice, attraction based model.
Edit intent: clarity
Original: The duration of the infectious period is crucial in determining the ability of an infectious disease to spread.
Revised: The duration of the infectious period is a crucial determinant of the ability of an infectious disease to spread.
Edit intent: clarity
Original:  We consider an epidemic model that is network-based and non-Markovian, containing classic Kermack-McKendrick, pairwise, message passing  and spatial models as special cases.
Revised:  We consider an epidemic model that is network based and non-Markovian, containing classic Kermack-McKendrick, pairwise, message passing and spatial models as special cases.
Edit intent: fluency
Original:  We consider an epidemic model that is network-based and non-Markovian, containing classic Kermack-McKendrick, pairwise, message passing  and spatial models as special cases.
Revised:  We consider an epidemic model that is network-based and non-Markovian, containing classic Kermack-McKendrick, pairwise, message passing , and spatial models as special cases.
Edit intent: fluency
Original:  For this model, we prove a monotonic relationship between the variability of the infectious period (with fixed mean) and the probability that the infection will reach an arbitrary subset of the population by time t.
Revised:  For this model, we prove a monotonic relationship between the variability of the infectious period (with fixed mean) and the probability that the infection will reach any given subset of the population by time t.
Edit intent: clarity
Original: We propose an approach based on neural networks and the AC power flow equations to identify single- and double- line outages in a power grid using the information from phasor measurement unit sensors (PMUs)
Revised: We propose an approach based on neural networks and the AC power flow equations to identify single- and double-line outages in a power grid using the information from phasor measurement unit sensors (PMUs)
Edit intent: fluency
Original:  We further consider the problem of classifying outages from sensors placed at only a subset of the buses, describing a principled way to choose sensor locations and showing that highly accurate classification can be achieved from a restricted set of measurements  .
Revised:  We describe a principled way to choose sensor locations and showing that highly accurate classification can be achieved from a restricted set of measurements .
Edit intent: coherence
Original:  We further consider the problem of classifying outages from sensors placed at only a subset of the buses, describing a principled way to choose sensor locations and showing that highly accurate classification can be achieved from a restricted set of measurements  .
Revised:  We further consider the problem of classifying outages from sensors placed at only a subset of the buses, describing a principled way to choose sensor locations and show that accurate classification of line outages can be achieved from a restricted set of measurements .
Edit intent: clarity
Original:  For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9 \% for Mobile NASNet-A and 0.6\% for \\%DIF > for Mobile NASNet-A and 0.6\\% for Inception-ResNet-v2.
Revised:  For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9 \\%DIF > for Mobile NASNet-A and 0.6\\% for Inception-ResNet-v2.
Edit intent: fluency
Original:  This operation was introduced by Csuhaj-Varj\'{u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends "overlap".
Revised:  This operation was introduced by Csuhaj-Varj\'u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends "overlap".
Edit intent: fluency
Original:  This operation was introduced by Csuhaj-Varj\'u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends "overlap" .
Revised:  This operation was introduced by Csuhaj-Varj\'{u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends "overlap" .
Edit intent: fluency
Original:  This operation was introduced by Csuhaj-Varj\'u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends "overlap" .
Revised:  This operation was introduced by Csuhaj-Varj\'u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends `overlap' .
Edit intent: fluency
Original:  This operation was introduced by Csuhaj-Varj\'{u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends `overlap' . We prove that the state complexity of the overlap assembly of languages L_m and L_n, where m\ge 2 and n\ge1, is at most 2 (m-1) 3^{n-1} + 2^n.
Revised:  This operation was introduced by Csuhaj-Varj\'u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends `overlap' . We prove that the state complexity of the overlap assembly of languages L_m and L_n, where m\ge 2 and n\ge1, is at most 2 (m-1) 3^{n-1} + 2^n.
Edit intent: fluency
Original:  This operation was introduced by Csuhaj-Varj\'{u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends `overlap' . We prove that the state complexity of the overlap assembly of languages L_m and L_n, where m\ge 2 and n\ge1, is at most 2 (m-1) 3^{n-1} + 2^n.
Revised:  This operation was introduced by Csuhaj-Varj\'{u , Petre, and Vaszil to model the process of self-assembly of two linear DNA strands into a longer DNA strand, provided that their ends "overlap" . We prove that the state complexity of the overlap assembly of languages L_m and L_n, where m\ge 2 and n\ge1, is at most 2 (m-1) 3^{n-1} + 2^n.
Edit intent: fluency
Original:  The analysis is a continuous version of Chv\'atal 's analysis of the greedy algorithm for the weighted set cover problem.
Revised:  The analysis is a continuous version of Chvatal 's analysis of the greedy algorithm for the weighted set cover problem.
Edit intent: fluency
Original: Low-level programming languages  such as C and C++  are vulnerable to errors related to the misuse of memory .
Revised: Low-level programming languages such as C and C++ , are vulnerable to errors related to the misuse of memory .
Edit intent: fluency
Original: Low-level programming languages  such as C and C++  are vulnerable to errors related to the misuse of memory .
Revised: Low-level programming languages such as C and C++ are vulnerable to errors relating to the misuse of memory .
Edit intent: fluency
Original:  Such errors include bounds overflows, sub-object bounds overflows,  use-after-free, "reuse"-after-free and type confusion.
Revised:  Such errors include bounds overflows, sub-object bounds overflows, (re) use-after-free, "reuse"-after-free and type confusion.
Edit intent: coherence
Original:  Such errors include bounds overflows, sub-object bounds overflows,  use-after-free, "reuse"-after-free and type confusion.
Revised:  Such errors include bounds overflows, sub-object bounds overflows, use-after-free, and type confusion.
Edit intent: coherence
Original:  These errors account for many of the exploits in programs implemented in such unsafe languages.
Revised:  Such errors account for many of the exploits in programs implemented in such unsafe languages.
Edit intent: fluency
Original:  In this paper, we present a new type confusion and memory error sanitizer based on dynamically checking the "effective type" (a. k.a. the dynamic type) of C/C++ objects at runtime.
Revised:  In this paper, we introduce the notion of dynamically typed C/C++, which aims to detect such errors by dynamically checking the "effective type" (a. k.a. the dynamic type) of C/C++ objects at runtime.
Edit intent: clarity
Original:  The algorithm uses the vertex ordering characterization in our previous paper that a graph is a simple-triangle graph if and only if there is a linear ordering of the vertices containing both an alternating orientation of the graph and a transitive orientation of the complement of the graph  .
Revised:  The algorithm uses the vertex ordering characterization that a graph is a simple-triangle graph if and only if there is a linear ordering of the vertices containing both an alternating orientation of the graph and a transitive orientation of the complement of the graph .
Edit intent: clarity
Original:  In this work, we propose an alternative method for solving inverse problems using denoising algorithms, that requires less parameter tuning.
Revised:  In this work, we propose an alternative method for solving inverse problems using denoising algorithms, which requires less parameter tuning.
Edit intent: fluency
Original:  We provide  theoretical analysis of the method, and empirically demonstrate that it is competitive with task-specific techniques and the P&P approach for image inpainting and deblurring.
Revised:  We provide a theoretical analysis of the method, and empirically demonstrate that it is competitive with task-specific techniques and the P&P approach for image inpainting and deblurring.
Edit intent: fluency
Original: Inverse problems appear in many applications  such as image deblurring and inpainting.
Revised: Inverse problems appear in many applications , such as image deblurring and inpainting.
Edit intent: fluency
Original:  In this work, we propose an alternative method for solving inverse problems using denoising algorithms , which requires less parameter tuning.
Revised:  In this work, we propose an alternative method for solving inverse problems using off-the-shelf denoisers , which requires less parameter tuning.
Edit intent: clarity
Original:   We provide a theoretical analysis of the method, and empirically demonstrate that it is competitive with task-specific techniques and the P&P approach for image inpainting and deblurring.
Revised:  We provide a theoretical analysis of the method, and empirically demonstrate its competitiveness with task-specific techniques and the P&P approach for image inpainting and deblurring.
Edit intent: fluency
Original:  Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the `capacity') achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single modulator controls N targets.
Revised:  Here we use methods of statistical field theory to calculate the statistics of the maximal mutual information (the `capacity') achievable by tuning the input variable only in an ensemble of regulatory motifs, such that a single controller regulates N targets.
Edit intent: clarity
Original: In the context of industrial engineering  it is important to integrate efficient computational optimization methods in the product development process.
Revised: In the context of industrial engineering , it is important to integrate efficient computational optimization methods in the product development process.
Edit intent: fluency
Original:  Some of the most challenging simulation based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradient information , highly non-linear objectives and a limited function evaluation budget.
Revised:  Some of the most challenging simulation-based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradient information , highly non-linear objectives and a limited function evaluation budget.
Edit intent: fluency
Original:  Some of the most challenging simulation based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradient information , highly non-linear objectives and a limited function evaluation budget.
Revised:  Some of the most challenging simulation based engineering design optimization problems are characterized by: a large number of design variables, the absence of analytical gradients , highly non-linear objectives and a limited function evaluation budget.
Edit intent: clarity
Original:  In this communication  a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics.
Revised:  In this communication , a hybrid variant of Differential Evolution (DE) is introduced which combines aspects of Stochastic Quasi-Gradient (SQG) methods within the framework of DE, in order to improve optimization efficiency on problems with the previously mentioned characteristics.
Edit intent: fluency
Original:  The performance of the resulting method is compared with other state-of-the-art DE variants on 25 commonly used test functions, under tight function evaluation budget constraints of 1000 evaluations.
Revised:  The performance of the resulting derivative-free algorithm is compared with other state-of-the-art DE variants on 25 commonly used test functions, under tight function evaluation budget constraints of 1000 evaluations.
Edit intent: clarity
Original:  The performance of the resulting method is compared with other state-of-the-art DE variants on 25 commonly used test functions, under tight function evaluation budget constraints of 1000 evaluations.
Revised:  The performance of the resulting method is compared with other state-of-the-art DE variants on 25 commonly used benchmark functions, under tight function evaluation budget constraints of 1000 evaluations.
Edit intent: clarity
Original:  The experimental results indicate that the proposed method performs particularly good on the "difficult" (high dimensional, multi-modal, inseparable) test functions.
Revised:  The experimental results indicate that the new algorithm performs excellent on the 'difficult' (high dimensional, multi-modal, inseparable) test functions.
Edit intent: clarity
Original: Grinberg's theorem is a necessary condition for the planar Hamilton graphs.
Revised: Grinberg's theorem is a necessary condition only for planar Hamilton graphs.
Edit intent: clarity
Original:  We use the new constraint to improve the edge pruning technique and obtain a polynomial time algorithm for finding a Hamiltonian cycle in the connected and simple graphs .
Revised:  We use the new constraint to improve the edge pruning technique and obtain a polynomial time algorithm for finding a Hamiltonian cycle in the connected and simple graphs( .
Edit intent: fluency