wanyu/IteraTeR_v2 huggingface dataset:
Original: 1. The recent improvements in recording technology, data storage and battery life have led to an increased interest in the use of passive acoustic monitoring for a variety of research questions.
Revised:  The recent improvements in recording technology, data storage and battery life have led to an increased interest in the use of passive acoustic monitoring for a variety of research questions.
Edit intent: coherence
Original:  2. Here we describe the new, open-source R package GIBBONR which has functions for classification, detection and visualization of acoustic signals using different readily available machine learning algorithms in the R programming environment.
Revised:  2. Here we describe the new, open-source R package GIBBONR which has functions for detection, classification and visualization of acoustic signals using different readily available machine learning algorithms in the R programming environment.
Edit intent: fluency
Original: The paper presents a new method for the classification of chimera states, which characterizes the synchronization of two coupled oscillatorsmore accurately.
Revised: This paper presents a new method for the classification of chimera states, which characterizes the synchronization of two coupled oscillatorsmore accurately.
Edit intent: fluency
Original:  As an example of method application, a neural network information converter based on a network of pulsed oscillators is used, which can convert input information from digital to analogue type and perform information processing after training the network by selecting control parameters.
Revised:  As an example of method application, a neural network information converter based on a network of pulsed oscillators . In addition to transforming input information from digital to analogue type and perform information processing after training the network by selecting control parameters.
Edit intent: coherence
Original:  As an example of method application, a neural network information converter based on a network of pulsed oscillators is used, which can convert input information from digital to analogue type and perform information processing after training the network by selecting control parameters.
Revised:  As an example of method application, a neural network information converter based on a network of pulsed oscillators is used, which can convert input information from digital to analogue , the converter can perform information processing after training the network by selecting control parameters.
Edit intent: clarity
Original:  In the proposed neural network scheme, the data arrives at the input layer in the form of current levels of the oscillators and is converted into a set of non-repeating states of the chimera synchronization of the output oscillator.
Revised:  In the proposed neural network scheme, the data arrives at the input layer in the form of current levels of the oscillators and is converted into a set of non-repeating states of the chimeric synchronization of the output oscillator.
Edit intent: fluency
Original:  By modelling a thermally coupled VO2-oscillator circuit, the network setup is demonstrated through the selection of coupling strength, power supply levels  and the synchronization efficiency parameter.
Revised:  By modelling a thermally coupled VO2-oscillator circuit, the network setup is demonstrated through the selection of coupling strength, power supply levels , and the synchronization efficiency parameter.
Edit intent: fluency
Original:  The proposed method for the classification of chimera states helps significantly expand the capabilities of neuromorphic and logical devices based on synchronization effects.
Revised:  The proposed method helps to significantly expand the capabilities of neuromorphic and logical devices based on synchronization effects.
Edit intent: clarity
Original: The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process .
Revised: The lottery ticket hypothesis proposes that over-parameterization of deep neural net-works (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process .
Edit intent: clarity
Original:  Here, we evaluate whether "winning ticket" initializations exist in two different domains: reinforcement learning (RL) and in natural language processing (NLP ).
Revised:  Here, we evaluate whether "winning ticket" initializations exist in two differ-ent domains: natural language processing (NLP) and reinforcement learning (RL) and in natural language processing (NLP ).
Edit intent: clarity
Original: The lottery ticket hypothesis proposes that over-parameterization of deep neural net-works (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin, 2019).
Revised: The lottery ticket hypothesis proposes that over-parameterization of deep neural networks (DNNs) aids training by increasing the probability of a "lucky" sub-network initialization being present rather than by helping the optimization process (Frankle & Carbin, 2019).
Edit intent: clarity
Original:  Here, we evaluate whether "winning ticket" initializations exist in two differ-ent domains: natural language processing (NLP) and reinforcement learning (RL).
Revised:  Here, we evaluate whether "winning ticket" initializations exist in two different domains: natural language processing (NLP) and reinforcement learning (RL).
Edit intent: fluency
Original: A quasi-automatic semigroup is defi0ned by a finite set of generators, a rational (regular) set of representatives, such that if a is a generator or neutral, then the graph of right multiplication by a on the set of representatives is a rational relation.
Revised: A quasi-automatic semigroup is defined by a finite set of generators, a rational (regular) set of representatives, such that if a is a generator or neutral, then the graph of right multiplication by a on the set of representatives is a rational relation.
Edit intent: fluency
Original:  However, the biological foundation of human intelligence remains to be revealed .
Revised:  However, the interindividual variability of cortical thickness has not been taken into account .
Edit intent: coherence
Original:  We propose AutoGrow to automate depth discovery in Deep Neural Networks (DNNs) : starting from a shallow seed architecture, AutoGrow grows new layers if the growth improves the accuracy;
Revised:  We propose AutoGrow to automate depth discovery in DNNs : starting from a shallow seed architecture, AutoGrow grows new layers if the growth improves the accuracy;
Edit intent: clarity
Original:  Our experiments show that by applying the same policy to different tasks , AutoGrow can always discover network depth effectively and achieve state-of-the-art accuracy on various datasets of MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100 and ImageNet.
Revised:  Our experiments show that by applying the same policy to different network architectures , AutoGrow can always discover network depth effectively and achieve state-of-the-art accuracy on various datasets of MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100 and ImageNet.
Edit intent: clarity
Original:  Our experiments show that by applying the same policy to different tasks , AutoGrow can always discover network depth effectively and achieve state-of-the-art accuracy on various datasets of MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100 and ImageNet.
Revised:  Our experiments show that by applying the same policy to different tasks , AutoGrow can always discover near-optimal depth on various datasets of MNIST, FashionMNIST, SVHN, CIFAR10, CIFAR100 and ImageNet.
Edit intent: clarity
Original:  This can correspond to a realistic scenario given by the difficulty of gathering large amounts of manually labeled data.
Revised:  This can correspond to a realistic scenario given by the difficulty in gathering large amounts of manually labeled data.
Edit intent: fluency
Original: In quantum control, the robustness with respect to uncertainties in the system's parameters or driving field characteristics is of paramount importance and has been studied theoretically, numerically and experiementally .
Revised: In quantum control, the robustness with respect to uncertainties in the system's parameters or driving field characteristics is of paramount importance and has been studied theoretically, numerically and experimentally .
Edit intent: fluency
Original:  Namely, we discuss how, when synthesizing a set of data-paths, other data paths (including loops) may be unintentionally configured .
Revised:  Namely, we discuss how, when synthesizing a set of data paths, other not requested and undesired paths may be unintentionally configured .
Edit intent: clarity
Original: Complex networking systems have resources that are highly dynamic and moreover, programmable;
Edit intent: coherence
Original:  Software Defined Networking (SDN) is a  network management technology for such systems.
Revised:  Software Defined Networking (SDN) is a novel network management technology for such systems.
Edit intent: clarity
Original:  We propose a novel preventive approach for verifying that no misconfigurations (e.g., infinite loops), can occur given a requested set of paths.
Revised:  We propose a novel preventive approach for verifying that no misconfigurations (e.g., infinite loops), can occur given the requested set of paths.
Edit intent: fluency
Original:  Additionally , we propose a model-based technique for debugging / repairing a set of paths in such a way that the corrected set does not induce undesired paths  , if the latter is possible.
Revised:  Finally , we propose a model-based technique for debugging / repairing a set of paths in such a way that the corrected set does not induce undesired paths , if the latter is possible.
Edit intent: coherence
Original:  Additionally , we propose a model-based technique for debugging / repairing a set of paths in such a way that the corrected set does not induce undesired paths  , if the latter is possible.
Revised:  Additionally , we propose a technique for debugging / repairing a set of paths in such a way that the corrected set does not induce undesired paths , if the latter is possible.
Edit intent: clarity
Original:  Additionally , we propose a model-based technique for debugging / repairing a set of paths in such a way that the corrected set does not induce undesired paths  , if the latter is possible.
Revised:  Additionally , we propose a model-based technique for debugging and repairing a set of paths in such a way that the corrected set does not induce undesired paths , if the latter is possible.
Edit intent: fluency
Original:  These are useful in practice because many graph algorithms are amenable to sparse matrix multiplication.
Edit intent: coherence
Original:  Despite many redundant operations over nonzeros that ultimately lead to suboptimal performance , the algebraic BFS is appealing for practical implementations because it is simple and embarrassingly parallel .
Revised:  Despite many redundant operations over nonzeros ultimately lead to suboptimal performance , the algebraic BFS is appealing for practical implementations because it is simple and embarrassingly parallel .
Edit intent: fluency
Original:  Despite many redundant operations over nonzeros that ultimately lead to suboptimal performance , the algebraic BFS is appealing for practical implementations because it is simple and embarrassingly parallel .
Revised:  Despite many redundant operations over nonzeros that ultimately lead to suboptimal performance .
Edit intent: coherence
Original:  This has been an area of recent recent in GraphBLAS and other libraries.
Revised:  This has been an area of recent research in GraphBLAS and other libraries.
Edit intent: clarity
Original:  Our algebraic BFS algorithm takes O(m) algebraic operations on a sparse graph with O(m) edges as opposed to O(mn) operations of other sparse matrix approaches  .
Revised:  Our algebraic BFS algorithm takes O(m) algebraic operations on a sparse graph of O(m) edges as opposed to O(mn) operations of other sparse matrix approaches .
Edit intent: clarity
Original:  We give a new optimal, algebraic BFS for sparse graphs that is also a constant factor faster than theoretically optimal SpMSpV methods, closing a gap in the literature.
Revised:  We give a new optimal, algebraic BFS for sparse graphs , thus closing a gap in the literature.
Edit intent: coherence
Original:  Our method multiplies progressively smaller submatrices of the adjacency matrix at each step, taking O( m ) algebraic operations on a sparse graph of O(m) edges as opposed to O( mn) operations of other sparse matrix approaches.
Revised:  Our method multiplies progressively smaller submatrices of the adjacency matrix at each step, taking O( n ) algebraic operations on a sparse graph of O(m) edges as opposed to O( mn) operations of other sparse matrix approaches.
Edit intent: fluency
Original:  The algebraic BFS algorithm is simply a recursion of matrix-vector multiplications with the n \times n adjacency matrix, but the many redundant operations over nonzeros ultimately lead to suboptimal performance.
Revised:  The algebraic BFS algorithm is simply a recurrence of matrix-vector multiplications with the n \times n adjacency matrix, but the many redundant operations over nonzeros ultimately lead to suboptimal performance.
Edit intent: fluency
Original:  Our method multiplies progressively smaller submatrices of the adjacency matrix at each step , taking O(n ) algebraic operations on a sparse graph of O(m) edges as opposed to O(m) operations needed by theoretically optimal sparse matrix approaches.
Revised:  Our method multiplies progressively smaller submatrices of the adjacency matrix at each step , taking O(n ) algebraic operations on a sparse graph , our method takes O(n) algebraic operations as opposed to O(m) operations needed by theoretically optimal sparse matrix approaches.
Edit intent: clarity
Original:  For instance, crossbar arrays of resistive memory devices can be used to store a matrix and perform analog matrix-vector multiplications at constant O(1) time complexity without intermediate movements of data.
Revised:  For instance, crossbar arrays of resistive memory devices can be used to store a matrix and perform analog matrix-vector multiplications without intermediate movements of data.
Edit intent: clarity
Original:  This functionality is very appealing for making energy-efficient deep learning inference hardware, where the weights of the neural network layers would be encoded in such crossbar arrays.
Edit intent: coherence
Original:  However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in appreciable loss of accuracy.
Revised:  However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in significant loss of accuracy.
Edit intent: clarity
Original:  Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in almost no accuracy loss when transferring weights to analog in-memory computing hardware based on phase-change memory (PCM).
Revised:  Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to analog in-memory computing hardware based on phase-change memory (PCM).
Edit intent: clarity
Original:  Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in almost no accuracy loss when transferring weights to analog in-memory computing hardware based on phase-change memory (PCM).
Revised:  Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in almost no accuracy loss when transferring weights to in-memory computing hardware based on phase-change memory (PCM).
Edit intent: clarity
Original: Prediction problems in finance go beyond estimating the unknown parameters of a model (e.g  of expected returns).
Revised: Prediction problems in finance go beyond estimating the unknown parameters of a model (e.g . of expected returns).
Edit intent: fluency
Original:  This is because such a model would have to include knowledge about the market participants' propensity to change their opinions on the validity of that model.
Revised:  This is because such a model would have to include parameters governing the market participants' propensity to change their opinions on the validity of that model.
Edit intent: clarity
Original:  This leads to a  circular situation characteristic of  markets, where participants collectively create the target variables they wish to estimate.
Revised:  This leads to a circular situation characteristic of markets, where participants collectively create the future they wish to estimate.
Edit intent: clarity
Original:  We first define the homotopic convex (HC) transformation of a TSP as a convex combination of a well-constructed simple TSP and the original TSP.
Revised:  We first define the Homotopic Convex (HC) transformation of a TSP as a convex combination of a well-constructed simple TSP and the original TSP.
Edit intent: fluency
Original:   We then propose an iterative algorithmic framework in which the proposed HC transformation is combined with a heuristic TSP solver.
Revised:  We then propose an iterative algorithmic framework in which the proposed HC transformation is combined within a heuristic TSP solver.
Edit intent: clarity
Original:  It works as an escaping scheme from local optima for improving the global search ability of the combined heuristic.
Revised:  It works as an escaping scheme from local optima aiming to improve the global search ability of the combined heuristic.
Edit intent: clarity
Original:  A case study with the 3-Opt  local search as the heuristic solver shows that the resultant algorithm significantly outperforms iterated local search and two other smoothing-based TSP heuristic solvers on most of commonly-used test instances .
Revised:  Case studies using the 3-Opt local search as the heuristic solver shows that the resultant algorithm significantly outperforms iterated local search and two other smoothing-based TSP heuristic solvers on most of commonly-used test instances .
Edit intent: clarity
Original:  A case study with the 3-Opt  local search as the heuristic solver shows that the resultant algorithm significantly outperforms iterated local search and two other smoothing-based TSP heuristic solvers on most of commonly-used test instances .
Revised:  A case study with the 3-Opt local search as the heuristic solver show that the resultant algorithm significantly outperforms iterated local search and two other smoothing-based TSP heuristic solvers on most of commonly-used test instances .
Edit intent: fluency
Original:  A case study with the 3-Opt  local search as the heuristic solver shows that the resultant algorithm significantly outperforms iterated local search and two other smoothing-based TSP heuristic solvers on most of commonly-used test instances .
Revised:  A case study with the 3-Opt local search as the heuristic solver shows that the resultant algorithms significantly outperform their counterparts and two other smoothing-based TSP heuristic solvers on most of commonly-used test instances .
Edit intent: clarity
Original:  This article discussed inexpensive approaches for exploiting their dependencies by using ARCH-like context dependent models for parameters of parametric distribution for residue, also evolving in time for adaptive case.
Revised:  This article discusses inexpensive approaches for exploiting their dependencies by using ARCH-like context dependent models for parameters of parametric distribution for residue, also evolving in time for adaptive case.
Edit intent: fluency
Original:  For example tested such 4 or 11 parameter models turned out providing similar performance as 365 parameter LOCO-I model for 48 tested images.
Revised:  For example tested such 4 or 11 parameter models turned out to provide similar performance as 365 parameter LOCO-I model for 48 tested images.
Edit intent: fluency
Original:  Beside smaller headers, such reduction of number of parameters can lead to better generalization , and allows to practically exploit higher dimensional contexts, for example using information from all 3 color channels, further pixels, or of some additional region classifiers .
Revised:  Beside smaller headers, such reduction of number of parameters can lead to better generalization , and allows to practically exploit higher dimensional contexts, for example using information from all 3 color channels, further pixels, some additional region classifiers .
Edit intent: coherence
Original: Data compression often subtracts predictor and encodes the difference (residue)  assuming Laplace distribution, for example for images, videos, audio, or numerical data.
Revised: Data compression often subtracts prediction and encodes the difference (residue) assuming Laplace distribution, for example for images, videos, audio, or numerical data.
Edit intent: fluency
Original: Data compression often subtracts predictor and encodes the difference (residue)  assuming Laplace distribution, for example for images, videos, audio, or numerical data.
Revised: Data compression often subtracts predictor and encodes the difference (residue) e.g. assuming Laplace distribution, for example for images, videos, audio, or numerical data.
Edit intent: coherence
Original:  Its performance is strongly dependent on  proper choice of width (scale parameter) of this parametric distribution, can be improved if optimizing it based on local situation like context.
Revised:  Its performance is strongly dependent on the proper choice of width (scale parameter) of this parametric distribution, can be improved if optimizing it based on local situation like context.
Edit intent: fluency
Original:  For example in popular LOCO-I ~ loco (JPEG-LS) lossless image compressor there is used 3 dimensional context quantized into 365 discrete possibilities treated independently.
Revised:  For example in popular LOCO-I loco (JPEG-LS) lossless image compressor there is used 3 dimensional context quantized into 365 discrete possibilities treated independently.
Edit intent: fluency
Original: The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remains elusive.
Revised: The power of neural networks lies in their ability to generalize to unseen data, yet the underlying reasons for this phenomenon remain elusive.
Edit intent: fluency
Original: Adversarial examples have received a great deal of recent attentionbecause of their potential to uncover security flaws in machine learning systems .
Revised: Adversarially robust machine learning has received much recent attention .
Edit intent: clarity
Original:  However, most prior work on adversarial examples has been on parametric classifiers, for which generic attack and defense methods are known;
Revised:  However, prior attacks and defenses for
Edit intent: clarity
Original:  non-parametric methods have been only considered on an ad-hoc or classifier-specific basis.
Revised:  non-parametric classifiers have been developed in an ad-hoc or classifier-specific basis.
Edit intent: clarity
Original:  Motivated by the close connection between non-parametric methods and the Bayes Optimal classifier, we next exhibit a robust analogue to the Bayes Optimal , and we use it to motivate a novel and generic defense that we call adversarial pruning .
Revised:  Motivated by the close connection between non-parametric classifiers. Theoretically, we derive an optimally robust classifier, which is analogous to the Bayes Optimal , and we use it to motivate a novel and generic defense that we call adversarial pruning .
Edit intent: clarity