wanyu/IteraTeR_v2 huggingface dataset:
Original:  Finally, we conduct several deep learning experiments (e.g., LeNet, VGG, ResNet) on real datasets in PyTorch , and the results demonstrate that PAGE not only converges much faster than SGD in training but also achieves the higher test accuracy, validating our theoretical results and confirming the practical superiority of PAGE.
Revised:  Finally, we conduct several deep learning experiments (e.g., LeNet, VGG, ResNet) on real datasets in PyTorch , and the results demonstrate that PAGE not only converges much faster than SGD in training but also achieves the higher test accuracy, validating the optimal theoretical results and confirming the practical superiority of PAGE.
Edit intent: clarity
Original:  Unfortunately, there is not a comprehensive summary of the EC-based methods.
Revised:  Unfortunately, there has not yet been a comprehensive summary of the EC-based methods.
Edit intent: clarity
Original:  Four steps of the process have been covered in this paper including  population initialization, population operators, evaluation and selection .
Revised:  Four steps of the process have been covered in this paper including population initialization, population updating and evaluation .
Edit intent: clarity
Original: Deep Neural Networks (DNNs) have achieved great success in many applications , such as image classification, natural language processing and speech recognition .
Revised: Deep Neural Networks (DNNs) have achieved great success in many applications .
Edit intent: clarity
Original:  The architectures of DNNs have been proved to play a crucial role in its performance .
Revised:  The architectures of DNNs play a crucial role in its performance .
Edit intent: clarity
Original:  However, mamually designing architectures for different tasks is a difficult and time-consuming process of trial and error . Neural Architecture Search (NAS) , which received great attention in recent years, can design the architecture automatically.
Revised:  However, such a design process is labour intensive because of the trial-and-error process, and also not easy to realize due to the rare expertise in practice . Neural Architecture Search (NAS) , which received great attention in recent years, can design the architecture automatically.
Edit intent: coherence
Original:  However, mamually designing architectures for different tasks is a difficult and time-consuming process of trial and error . Neural Architecture Search (NAS) , which received great attention in recent years, can design the architecture automatically.
Revised:  However, mamually designing architectures for different tasks is a difficult and time-consuming process of trial and error . Neural Architecture Search (NAS) is a type of technology that can design the architecture automatically.
Edit intent: clarity
Original:  However, mamually designing architectures for different tasks is a difficult and time-consuming process of trial and error . Neural Architecture Search (NAS) , which received great attention in recent years, can design the architecture automatically.
Revised:  However, mamually designing architectures for different tasks is a difficult and time-consuming process of trial and error . Neural Architecture Search (NAS) , which received great attention in recent years, can design the architectures automatically.
Edit intent: fluency
Original:  Among different kinds of NAS methods , Evolutionary Computation (EC) based NAS methods have recently gained much attention and success.
Revised:  Among different methods to realize NAS , Evolutionary Computation (EC) based NAS methods have recently gained much attention and success.
Edit intent: clarity
Original:  Among different kinds of NAS methods , Evolutionary Computation (EC) based NAS methods have recently gained much attention and success.
Revised:  Among different kinds of NAS methods , Evolutionary Computation (EC) methods have recently gained much attention and success.
Edit intent: coherence
Original:  Unfortunately, there has not yet been a comprehensive summary of the EC-based methods .
Revised:  Unfortunately, there has not yet been a comprehensive summary of the EC-based NAS algorithms .
Edit intent: clarity
Original:  Furthermore, current challenges and issues are also discussed to identify future research in this  field.
Revised:  Furthermore, current challenges and issues are also discussed to identify future research in this emerging field.
Edit intent: clarity
Original: Instability of an equilibrium point often provides a basis for an oscillatory mode of operation in certain nonlinear dynamical systems.
Edit intent: clarity
Original:  We define the instability margin as the infimum of the H_\infty norm of the stable linear perturbation that exponentially stabilizes the corresponding equilibrium point .
Revised:  We define the instability margin as the infimum of the H_\infty norm of the stable linear perturbation that stabilizes the nominal system .
Edit intent: clarity
Original:  There are two main theoretical results ;
Revised:  There are two main theoretical results :
Edit intent: fluency
Original:  We define the instability margin as the smallest H_\infty norm of the stable linear perturbation that stabilizes the nominal system.
Revised:  We define the robust instability radius as the smallest H_\infty norm of the stable linear perturbation that stabilizes the nominal system.
Edit intent: clarity
Original:  There are two main theoretical results: one is on a partial characterization of unperturbed nominal systems for which the robust instability radius can be calculated exactly, and the other is a numerically tractable procedure for calculating the exact instability margin for nominal systems parametrized by a perturbation parameter.
Revised:  There are two main theoretical results: one is on a partial characterization of unperturbed nominal systems for which the robust instability radius can be calculated exactly, and the other is a numerically tractable procedure for calculating the exact robust instability radius for nominal systems parametrized by a perturbation parameter.
Edit intent: clarity
Original:  The results are applied to the Repressilator in synthetic biology, where hyperbolic instability of a unique equilibrium guarantees the persistence of oscillation phenomena in the global sense, and the effectiveness of our linear robust instability analysis is confirmed by numerical simulations.
Revised:  The results are applied to the repressilator in synthetic biology, where hyperbolic instability of a unique equilibrium guarantees the persistence of oscillation phenomena in the global sense, and the effectiveness of our linear robust instability analysis is confirmed by numerical simulations.
Edit intent: fluency
Original: Prudent and meaningful algorithm performance evaluation is essential for the progression of any research field.
Revised: Prudent and meaningful performance evaluation of algorithms is essential for the progression of any research field.
Edit intent: clarity
Original:  It has long been suspected that testing on these denoised aggregates provides better evaluation results mainly due to the  fact that the signal is less complex.
Revised:  It has long been suspected that testing on these denoised aggregates provides better evaluation results mainly due to the the fact that the signal is less complex.
Edit intent: fluency
Original:  Although this is a know performance reporting problem, an investigation in the actual performance gap between real and denoised testing is still pending.
Revised:  Although this is a known performance reporting problem, an investigation in the actual performance gap between real and denoised testing is still pending.
Edit intent: fluency
Original:  Starting with an assessment of noise levels in three datasets, we find significant differences in test cases.
Revised:  Starting with an assessment of noise levels in datasets, we find significant differences in test cases.
Edit intent: clarity
Original:  A closer look at the outcome of our studies reveals that all appliance types could be subject to this phenomenon.
Revised:  A closer look into the outcome of our studies reveals that all appliance types could be subject to this phenomenon.
Edit intent: fluency
Original:  However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification , imbalanced data and open vocabulary exist .
Revised:  However, to build a real-world intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification , imbalanced data and open vocabulary exist .
Edit intent: clarity
Original:  However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification , imbalanced data and open vocabulary exist .
Revised:  However, to build an intelligent assistant that recommends commonly composed charts, it should take the challenges of efficiency , imbalanced data and open vocabulary exist .
Edit intent: clarity
Original:  However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification , imbalanced data and open vocabulary exist .
Revised:  However, to build an intelligent assistant that recommends commonly composed charts, the fundamental problems of "multi-dialect" unification , imbalanced data hungry and table context into consideration .
Edit intent: clarity
Original:  However, to build a real-world intelligent assistant that recommends commonly composed charts , it should take the challenges of efficiency, imbalanced data hungry and table context into consideration.
Revised:  However, to recommend commonly composed charts , it should take the challenges of efficiency, imbalanced data hungry and table context into consideration.
Edit intent: clarity
Original:  However, to build a real-world intelligent assistant that recommends commonly composed charts , it should take the challenges of efficiency, imbalanced data hungry and table context into consideration.
Revised:  However, to build a real-world intelligent assistant that recommends commonly composed charts in real world, one should take the challenges of efficiency, imbalanced data hungry and table context into consideration.
Edit intent: clarity
Original:  However, to build a real-world intelligent assistant that recommends commonly composed charts , it should take the challenges of efficiency, imbalanced data hungry and table context into consideration.
Revised:  However, to build a real-world intelligent assistant that recommends commonly composed charts , it should take the challenges of efficiency, imbalanced data and table context into consideration.
Edit intent: clarity
Original:  We introduce here a new family of fixed point iterations that include the classical iterations for the numerical computation of G . We perform a detailed convergence analysis and prove that the iterations in the new class converge faster than the classical iterations.
Revised:  We introduce here a new family of fixed point iterations for the numerical computation of G . We perform a detailed convergence analysis and prove that the iterations in the new class converge faster than the classical iterations.
Edit intent: clarity
Original:  Numerical experiments show that the acceleration in terms of CPU time is substantial with a speed-up which is generally greater than 2 and, in several cases, reaches much larger values .
Revised:  Numerical experiments confirm the effectiveness of our extension .
Edit intent: clarity
Original:  But, it fails to capture the fine structures of speech signals, and limits the performance of separation .
Revised:  But, it fails to capture the fine structures of speeches .
Edit intent: clarity
Original:  Here, we first time show that it is possible to efficiently learn the derivative of speech density with universal approximators like deep neural networks  by optimizing certain proxy separation related performance indices.
Revised:  Here, we first time show that it is possible to efficiently estimate the derivative of speech density with universal approximators like deep neural networks by optimizing certain proxy separation related performance indices.
Edit intent: clarity
Original:  Specifically, we consider neural network density models for speech signals represented in the time-frequency domain, and compare them against the classic multivariate Laplace model for independent vector analysis (IVA).
Edit intent: coherence
Original:  Experimental results suggest that the  neural network density models significantly outperform multivariate Laplace one in tasks that require real time implementations, or involve the separation of a large number of speech sources .
Revised:  Experimental results suggest that the resultant neural network density models significantly outperform multivariate Laplace one in tasks that require real time implementations, or involve the separation of a large number of speech sources .
Edit intent: clarity
Original: The problem of finding a diagonal flip path between two triangulations has been studied for nearly a century in the combinatorial (topological) setting and for decades in the geometric setting .
Revised: The problem of finding a diagonal flip path between two triangulations has been studied for nearly a century in the combinatorial (topological) setting and for decades in the geometric setting .
Edit intent: fluency
Original: The problem of finding a diagonal flip path between two triangulations has been studied for nearly a century in the combinatorial (topological) setting and for decades in the geometric setting .
Revised: The problem of finding a diagonal flip path between two triangulations has been studied for nearly a century .
Edit intent: clarity
Original:  In the geometric setting, finding a diagonal flip path between two triangulations that minimizes the number of diagonal flips over all such paths is NP-complete.
Revised:  In the geometric setting, finding a flip path between two triangulations that minimizes the number of diagonal flips over all such paths is NP-complete.
Edit intent: fluency
Original:  In the geometric setting, finding a diagonal flip path between two triangulations that minimizes the number of diagonal flips over all such paths is NP-complete.
Revised:  In the geometric setting, finding a diagonal flip path between two triangulations containing the minimum number of flips is NP-complete.
Edit intent: clarity
Original: The problem of finding a diagonalflip path between two triangulations has been studied for nearly a century.
Revised: flip path between two triangulations has been studied for nearly a century.
Edit intent: clarity
Original: The problem of finding a diagonalflip path between two triangulations has been studied for nearly a century.
Revised: The problem of finding a diagonal path between two triangulations has been studied for nearly a century.
Edit intent: fluency
Original:   In the geometric setting, finding a  flip path between two triangulations containing the minimum number of flips is NP-complete.
Revised:  In the geometric setting, finding a minimum flip path between two triangulations containing the minimum number of flips is NP-complete.
Edit intent: fluency
Original:   In the geometric setting, finding a  flip path between two triangulations containing the minimum number of flips is NP-complete.
Revised:  In the geometric setting, finding a flip path between two triangulations is NP-complete.
Edit intent: clarity
Original:  However, for minimum flip paths between lattice triangulations,  Eppstein and Caputo et al. gave algorithms running in O\left(n^2\right) time, where n is the number of points in the point-set.
Revised:  However, for two lattice triangulations, Eppstein and Caputo et al. gave algorithms running in O\left(n^2\right) time, where n is the number of points in the point-set.
Edit intent: clarity
Original:  Caputo et al. extended this result to the cases of non-convex bounding polygons and constrained flip paths that preserve a set of edges.
Revised:  Caputo et al. extended this result to the cases of non-convex bounding polygons and constrained flip paths that ensure a set of edges.
Edit intent: clarity
Original:  As a further improvement over previous results, in many natural cases, our algorithm runs in time linear in the length of the minimum flip path.
Revised:  In fact, for a large, natural class of inputs, the bound is tight, i.e., our algorithm runs in time linear in the length of the minimum flip path.
Edit intent: clarity
Original:  As a further improvement over previous results, in many natural cases, our algorithm runs in time linear in the length of the minimum flip path.
Revised:  As a further improvement over previous results, in many natural cases, our algorithm runs in time linear in the length of this output flip path.
Edit intent: clarity
Original: Notably, the above results follow from simple number-theoretic and geometric concepts\emph{  .
Revised: Notably, the above results follow from simple number-theoretic and geometric conceptsplan\emph{ .
Edit intent: fluency
Original: Deep Neural Networks (DNNs) have been shown to be prone to adversarial attacks.
Revised: Deep Neural Networks (DNNs) have been shown to be prone to adversarial attacks.
Edit intent: fluency
Original:  With a growing need to enable intelligence in embedded devices in thisInternet of Things (IoT) era, secure hardware implementation of DNNs has become imperative.
Revised:  Internet of Things (IoT) era, secure hardware implementation of DNNs has become imperative.
Edit intent: clarity
Original:  With a growing need to enable intelligence in embedded devices in thisInternet of Things (IoT) era, secure hardware implementation of DNNs has become imperative.
Revised:  With a growing need to enable intelligence in embedded devices in this (IoT) era, secure hardware implementation of DNNs has become imperative.
Edit intent: clarity
Original:  With a growing need to enable intelligence in embedded devices in thisInternet of Things (IoT) era, secure hardware implementation of DNNs has become imperative.
Revised:  With a growing need to enable intelligence in embedded devices in thisInternet of Things 
Edit intent: clarity
Original:  Memristive crossbars, being able to perform Matrix-Vector-Multiplications (MVMs) efficiently, are used to realize DNNs on hardware.
Revised:  Memristive crossbars, being able to perform Matrix-Vector-Multiplications (MVMs) efficiently, are used to realize DNNs on hardware.
Edit intent: fluency
Original:  However, crossbar non-idealities have always been devalued since they cause errors in performing MVMs, leading to degradation in the accuracy of the DNNs. Several software-based adversarial defenses have been proposed in the past to make DNNs adversarially robust.
Revised:  However, crossbar non-idealities have always been devalued since they cause errors in performing MVMs, leading to computational accuracy losses in DNNs. Several software-based adversarial defenses have been proposed in the past to make DNNs adversarially robust.
Edit intent: clarity
Original:  However, crossbar non-idealities have always been devalued since they cause errors in performing MVMs, leading to degradation in the accuracy of the DNNs. Several software-based adversarial defenses have been proposed in the past to make DNNs adversarially robust.
Revised:  However, crossbar non-idealities have always been devalued since they cause errors in performing MVMs, leading to degradation in the accuracy of the DNNs. Several software-based defenses have been proposed in the past to make DNNs adversarially robust.
Edit intent: fluency
Original:  However, crossbar non-idealities have always been devalued since they cause errors in performing MVMs, leading to degradation in the accuracy of the DNNs. Several software-based adversarial defenses have been proposed in the past to make DNNs adversarially robust.
Revised:  However, crossbar non-idealities have always been devalued since they cause errors in performing MVMs, leading to degradation in the accuracy of the DNNs. Several software-based adversarial defenses have been proposed to make DNNs adversarially robust.
Edit intent: coherence
Original:  However, no previous work has demonstrated the advantage conferred by the  non-idealities present in analog crossbars in terms of adversarial robustness.
Revised:  However, no previous work has demonstrated the advantage conferred by the non-idealities in unleashing adversarial robustness.
Edit intent: clarity
Original:  In this work, we show that the intrinsic hardware variations manifested through crossbar non-idealities yield adversarial robustness to the mapped DNNs without any additional optimization.
Revised:  We show that the intrinsic hardware variations manifested through crossbar non-idealities yield adversarial robustness to the mapped DNNs without any additional optimization.
Edit intent: clarity
Original:  In this work, we show that the intrinsic hardware variations manifested through crossbar non-idealities yield adversarial robustness to the mapped DNNs without any additional optimization.
Revised:  In this work, we show that the intrinsic hardware non-idealities yield adversarial robustness to the mapped DNNs without any additional optimization.
Edit intent: clarity
Original:  We evaluate  resilience of state-of-the-art DNNs (VGG8 \& VGG16 networks) using benchmark datasets (CIFAR-10 \& CIFAR-100  ) across various crossbar sizes towards both hardware and software adversarial attacks .
Revised:  We evaluate the adversarial resilience of state-of-the-art DNNs (VGG8 \& VGG16 networks) using benchmark datasets (CIFAR-10 \& CIFAR-100 ) across various crossbar sizes towards both hardware and software adversarial attacks .
Edit intent: fluency
Original:  We evaluate  resilience of state-of-the-art DNNs (VGG8 \& VGG16 networks) using benchmark datasets (CIFAR-10 \& CIFAR-100  ) across various crossbar sizes towards both hardware and software adversarial attacks .
Revised:  We evaluate resilience of state-of-the-art DNNs (VGG8 VGG16 networks) using benchmark datasets (CIFAR-10 \& CIFAR-100 ) across various crossbar sizes towards both hardware and software adversarial attacks .
Edit intent: fluency
Original:  We evaluate  resilience of state-of-the-art DNNs (VGG8 \& VGG16 networks) using benchmark datasets (CIFAR-10 \& CIFAR-100  ) across various crossbar sizes towards both hardware and software adversarial attacks .
Revised:  We evaluate resilience of state-of-the-art DNNs (VGG8 \& VGG16 networks) using benchmark datasets (CIFAR-10 , CIFAR-100 ) across various crossbar sizes towards both hardware and software adversarial attacks .
Edit intent: fluency
Original:  We evaluate  resilience of state-of-the-art DNNs (VGG8 \& VGG16 networks) using benchmark datasets (CIFAR-10 \& CIFAR-100  ) across various crossbar sizes towards both hardware and software adversarial attacks .
Revised:  We evaluate resilience of state-of-the-art DNNs (VGG8 \& VGG16 networks) using benchmark datasets (CIFAR-10 \& CIFAR-100 ) across various crossbar sizes .
Edit intent: clarity
Original:  We find that crossbar non-idealities unleash  greater adversarial robustness (>10-20 %DIFDELCMD < \\%%% %DIF < ) in DNNs than baseline software DNNs.
Revised:  We find that crossbar non-idealities unleash significantly greater adversarial robustness (>10-20 %DIFDELCMD < \\%%% %DIF < ) in DNNs than baseline software DNNs.
Edit intent: clarity
Original:  We further assess the  performance of our approach with other state-of-the-art efficiency-driven adversarial defenses and find that our approach performs significantly well in terms of reducing adversarial losses .
Revised:  We further assess the performance of our approach with other state-of-the-art efficiency-driven adversarial defenses and find that our approach performs significantly well in terms of reducing adversarial loss .
Edit intent: fluency