wanyu/IteraTeR_v2 huggingface dataset:
Original:  Finally, we provide an overview of sharing and reproducible cyberinfrastructure based on sciunits , increasingly being used in the domain of geosciences.
Revised:  Finally, we provide an overview of sharing and reproducible cyberinfrastructure based on sciunits gaining adoption in the domain of geosciences.
Edit intent: clarity
Original:  However, the mechanism through which  such single-cell gradients form is not yet fully understood.
Revised:  However, the mechanism through which such single-cell gradients is not yet fully understood.
Edit intent: clarity
Original:  We first demonstrate that one of the models for  gradient formation, based on differential diffusion, is limited to proteins with large ratios of their diffusion constants or to specific protein-large molecule interactions.
Revised:  One of the models for gradient formation, based on differential diffusion, is limited to proteins with large ratios of their diffusion constants or to specific protein-large molecule interactions.
Edit intent: coherence
Original:  We first demonstrate that one of the models for  gradient formation, based on differential diffusion, is limited to proteins with large ratios of their diffusion constants or to specific protein-large molecule interactions.
Revised:  We first demonstrate that one of the models for such gradient formation, based on differential diffusion, is limited to proteins with large ratios of their diffusion constants or to specific protein-large molecule interactions.
Edit intent: clarity
Original:  We then introduce an alternative for gradient formation via the coupling of the proteins within a single cell with a molecule  whose action is similar to that of morphogens in multi-cell assemblies ;
Revised:  We introduce a novel mechanism for gradient formation via the coupling of the proteins within a single cell with a molecule whose action is similar to that of morphogens in multi-cell assemblies ;
Edit intent: clarity
Original:  This coupling results in an effectively non-linear diffusion degradation model for the "morphogen" dynamics within the cell ;
Revised:  This coupling results in an effectively non-linear diffusion degradation model for the pronogen dynamics within the cell ;
Edit intent: clarity
Original:  it is the non-linearity that leads to a steady state gradient of the protein concentration.
Revised:  it is the non-linearity that leads to a steady-state gradient of the protein concentration.
Edit intent: fluency
Original:  In this paper , we outline a virtual reality learning environment (VRLE) over a high-speed network, which promotes educational effectiveness / efficiency via our creation of flexible content that meets established VLE standards but with better immersion.
Revised:  This paper outlines a virtual reality learning environment (VRLE) over a high-speed network, which promotes educational effectiveness / efficiency via our creation of flexible content that meets established VLE standards but with better immersion.
Edit intent: fluency
Original:  In this paper , we outline a virtual reality learning environment (VRLE) over a high-speed network, which promotes educational effectiveness / efficiency via our creation of flexible content that meets established VLE standards but with better immersion.
Revised:  In this paper , we outline a virtual reality learning environment (VRLE) over a high-speed network, which promotes educational effectiveness and efficiency via our creation of flexible content that meets established VLE standards but with better immersion.
Edit intent: fluency
Original:  We describe the implementation of multiple learning modules we developed utilizing High Fidelity, a 'Social VR' platform.
Revised:  We describe the implementation of multiple learning modules developed in High Fidelity, a 'Social VR' platform.
Edit intent: clarity
Original:  We describe the implementation of multiple learning modules we developed utilizing High Fidelity, a 'Social VR' platform.
Revised:  We describe the implementation of multiple learning modules we developed utilizing High Fidelity, a "social VR" platform.
Edit intent: fluency
Original:  Our experiment results show that the VR medium of content delivery better stimulates the generalization of lessons to the real world than non-VR lessons , and provides improved immersion when compared to an equivalent desktop version.
Revised:  Our experiment results show that the VR mode of content delivery better stimulates the generalization of lessons to the real world than non-VR lessons , and provides improved immersion when compared to an equivalent desktop version.
Edit intent: clarity
Original:  Our experiment results show that the VR medium of content delivery better stimulates the generalization of lessons to the real world than non-VR lessons , and provides improved immersion when compared to an equivalent desktop version.
Revised:  Our experiment results show that the VR medium of content delivery better stimulates the generalization of lessons to the real world than non-VR lessons and provides improved immersion when compared to an equivalent desktop version.
Edit intent: fluency
Original: We present a proof of concept for adapting the finite-difference time-domain method (FDTD) for solving a 1+1D complex-valued, delay partial differential equation (PDE) that emerges in the study of waveguide quantum electrodynamics (QED)  .
Revised: We present a proof of concept for solving a 1+1D complex-valued, delay partial differential equation (PDE) that emerges in the study of waveguide quantum electrodynamics (QED) .
Edit intent: coherence
Original: We present a proof of concept for adapting the finite-difference time-domain method (FDTD) for solving a 1+1D complex-valued, delay partial differential equation (PDE) that emerges in the study of waveguide quantum electrodynamics (QED)  .
Revised: We present a proof of concept for adapting the finite-difference time-domain method (FDTD) for solving a 1+1D complex-valued, delay partial differential equation (PDE) that emerges in the study of waveguide quantum electrodynamics (QED) by adapting the finite-difference time-domain (FDTD) method .
Edit intent: coherence
Original:  The delay term exists in both spatial and temporal directions, rendering the conventional approaches such as the method of lines inapplicable.
Revised:  The delay term is spatially non-local, rendering conventional approaches such as the method of lines inapplicable.
Edit intent: clarity
Original:  We show that by properly designing the grid and by using the exact (partial)  solution as the boundary condition, the delay PDE can be numerically solved.
Revised:  We show that by properly designing the grid and by supplying the (partial) solution as the boundary condition, the delay PDE can be numerically solved.
Edit intent: clarity
Original:   Our code provides a numerically exact solution to the time-dependent multi-photon scattering problem in waveguide QED . The program is written in C and open-sourced on GitHub .
Revised:  Our code provides a numerically exact solution to the time-dependent multi-photon scattering problem in waveguide QED .
Edit intent: clarity
Original: Deep neural networks are being used to solve complex classification problems, in which other machine learning classifiers, such as SVM, fall short.
Revised: Deep neural networks (DNNs) are used to solve complex classification problems, in which other machine learning classifiers, such as SVM, fall short.
Edit intent: clarity
Original: Deep neural networks are being used to solve complex classification problems, in which other machine learning classifiers, such as SVM, fall short.
Revised: Deep neural networks are being used to solve complex classification problems, for which other machine learning classifiers, such as SVM, fall short.
Edit intent: clarity
Original:  Recurrent Neural Networks (RNNs) have been used for tasks that involves sequential inputs, like speech to text.
Revised:  Recurrent neural networks (RNNs) have been used for tasks that involves sequential inputs, like speech to text.
Edit intent: fluency
Original:  Recurrent Neural Networks (RNNs) have been used for tasks that involves sequential inputs, like speech to text.
Revised:  Recurrent Neural Networks (RNNs) have been used for tasks that involves sequential inputs, such as speech to text.
Edit intent: fluency
Original:  In the cyber security domain, RNNs based on API calls have been able to classify unsigned malwarebetter than other classifiers .
Revised:  In the cyber security domain, RNNs based on API calls have been used effectively to classify previously un-encountered malware .
Edit intent: clarity
Original:  In this paper  we present a black-box attack against RNNs, focusing on finding adversarial API call sequences that would be misclassified by a RNN without affecting the malware functionality.
Revised:  In this paper , we present a black-box attack against RNNs, focusing on finding adversarial API call sequences that would be misclassified by a RNN without affecting the malware functionality.
Edit intent: fluency
Original:  We also show that the this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and state-of-the-art traditional machine learning classifiers  . Finally, we introduce the transferability by transitivity principle, causing an attack against generalized classifierlike RNN variants to be transferable to less generalized classifiers like feed-forward DNNs . We conclude by discussing possible defense mechanisms  .
Revised:  We also show that this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and state-of-the-art traditional machine learning classifiers . Finally, we introduce the transferability by transitivity principle, causing an attack against generalized classifierlike RNN variants to be transferable to less generalized classifiers like feed-forward DNNs . We conclude by discussing possible defense mechanisms .
Edit intent: fluency
Original:  We also show that the this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and state-of-the-art traditional machine learning classifiers  . Finally, we introduce the transferability by transitivity principle, causing an attack against generalized classifierlike RNN variants to be transferable to less generalized classifiers like feed-forward DNNs . We conclude by discussing possible defense mechanisms  .
Revised:  We also show that the this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers . Finally, we introduce the transferability by transitivity principle, causing an attack against generalized classifierlike RNN variants to be transferable to less generalized classifiers like feed-forward DNNs . We conclude by discussing possible defense mechanisms .
Edit intent: clarity
Original:  We also show that the this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and state-of-the-art traditional machine learning classifiers  . Finally, we introduce the transferability by transitivity principle, causing an attack against generalized classifierlike RNN variants to be transferable to less generalized classifiers like feed-forward DNNs . We conclude by discussing possible defense mechanisms  .
Revised:  We also show that the this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and state-of-the-art traditional machine learning classifiers such as SVM . Finally, we introduce the transferability by transitivity principle, causing an attack against generalized classifierlike RNN variants to be transferable to less generalized classifiers like feed-forward DNNs . We conclude by discussing possible defense mechanisms .
Edit intent: clarity
Original:  We also show that the this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and state-of-the-art traditional machine learning classifiers  . Finally, we introduce the transferability by transitivity principle, causing an attack against generalized classifierlike RNN variants to be transferable to less generalized classifiers like feed-forward DNNs . We conclude by discussing possible defense mechanisms  .
Revised:  We also show that the this attack is effective against many classifiers, due-to the transferability principle between RNN variants, feed-forward DNNs and state-of-the-art traditional machine learning classifiers . Finally, we introduce the transferability by transitivity principle, causing an attack against generalized classifierlike RNN variants to be transferable to less generalized classifiers like feed-forward DNNs . We conclude by discussing possible defense mechanisms and countermeasures against the attack .
Edit intent: clarity
Original:  In this paper, we present a black-box attack against RNNs , focusing on finding adversarial API call sequences that would be misclassified by a RNN without affecting the malware functionality.
Revised:  In this paper, we present a black-box attack against RNNs , focusing on generating adversarial API call sequences that would be misclassified by a RNN without affecting the malware functionality.
Edit intent: clarity
Original:  In this paper, we present a black-box attack against RNNs , focusing on finding adversarial API call sequences that would be misclassified by a RNN without affecting the malware functionality.
Revised:  In this paper, we present a black-box attack against RNNs , focusing on finding adversarial API call sequences that would be misclassified by the classifier without affecting the malware functionality.
Edit intent: clarity
Original:  We also show that this attack is effective against many classifiers , due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM.  Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier , using the proposed attack, without access to the malware source code.
Revised:  We show that this attack is effective against many classifiers , due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM. Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier , using the proposed attack, without access to the malware source code.
Edit intent: coherence
Original:  We also show that this attack is effective against many classifiers , due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM.  Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier , using the proposed attack, without access to the malware source code.
Revised:  We also show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM. Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier , using the proposed attack, without access to the malware source code.
Edit intent: fluency
Original:  We also show that this attack is effective against many classifiers , due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM.  Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier , using the proposed attack, without access to the malware source code.
Revised:  We also show that this attack is effective against many classifiers , due-to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such as SVM. Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier , using the proposed attack, without access to the malware source code.
Edit intent: fluency
Original:  We also show that this attack is effective against many classifiers , due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM.  Finally, we implemented GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier , using the proposed attack, without access to the malware source code.
Revised:  We also show that this attack is effective against many classifiers , due-to the transferability principle between RNN variants, feed-forward DNNs and traditional machine learning classifiers such as SVM. Finally, we implement GADGET, a software framework to convert any malware binary to a binary undetected by an API calls based malware classifier , using the proposed attack, without access to the malware source code.
Edit intent: fluency
Original:  We conclude by discussing possible defense mechanisms and countermeasures against the attack.
Revised:  We conclude by discussing possible defense mechanisms against the attack.
Edit intent: clarity
Original:  We conclude by discussing possible defense mechanisms against the attack .
Revised:  Finally, we discuss the robustness of this attack to existing defense mechanisms .
Edit intent: clarity
Original:  We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such as SVM and gradient boosted decision tree .
Revised:  We show that this attack is effective against many classifiers due to the transferability principle between RNN variants, feed forward DNNs, and traditional machine learning classifiers such as SVM .
Edit intent: coherence
Original: We develop a mesoscopic modeling framework for diffusion in a crowded environment, particularly with applications in the description of living cells.
Revised: We develop a mesoscopic modeling framework for diffusion in a crowded environment, particularly targeting applications in the description of living cells.
Edit intent: clarity
Original: We develop a mesoscopic modeling framework for diffusion in a crowded environment, particularly with applications in the description of living cells.
Revised: We develop a mesoscopic modeling framework for diffusion in a crowded environment, particularly with applications in the modeling of living cells.
Edit intent: clarity
Original:  Analytical solutions and numerical experiments illustrate the modeling framework.
Revised:  Analytical solutions and numerical experiments illustrate the framework.
Edit intent: clarity
Original:  A learning rule based on a back-propagation (BP) algorithm is proposed to implement a hardware-based  neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics.
Revised:  A learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics.
Edit intent: clarity
Original:  Then, using a 3-layer perceptron network, we evaluate the learning performance according to the various conductance responses of the electronic synapse devices and the weight update methods.
Revised:  Then, using a 3-layer perceptron network, we evaluate the learning performance according to various conductance responses of the electronic synapse devices and the weight update methods.
Edit intent: fluency
Original:  Then, using a 3-layer perceptron network, we evaluate the learning performance according to the various conductance responses of the electronic synapse devices and the weight update methods.
Revised:  Then, using a 3-layer perceptron network, we evaluate the learning performance according to the various conductance responses of electronic synapse devices and the weight update methods.
Edit intent: fluency
Original:  Then, using a 3-layer perceptron network, we evaluate the learning performance according to the various conductance responses of the electronic synapse devices and the weight update methods.
Revised:  Then, using a 3-layer perceptron network, we evaluate the learning performance according to the various conductance responses of the electronic synapse devices and weight-updating methods.
Edit intent: fluency
Original:  It is shown that the learning accuracy can be comparable to that obtained by using software-based BP algorithm when the electronic synapse device has the linear and symmetric conductance response .
Revised:  It is shown that the learning accuracy is comparable to that obtained by using software-based BP algorithm when the electronic synapse device has the linear and symmetric conductance response .
Edit intent: clarity
Original:  It is shown that the learning accuracy can be comparable to that obtained by using software-based BP algorithm when the electronic synapse device has the linear and symmetric conductance response .
Revised:  It is shown that the learning accuracy can be comparable to that obtained when using a software-based BP algorithm when the electronic synapse device has the linear and symmetric conductance response .
Edit intent: fluency
Original:  Furthermore, the proposed unidirectional weight update method is suitable for electronic synapse devices which have nonlinear and asymmetric conductance response.
Revised:  Furthermore, the proposed unidirectional weight-updating method is suitable for electronic synapse devices which have nonlinear and asymmetric conductance response.
Edit intent: fluency
Original: This work studies the convergence properties of continuous-time fictitious play in potential games .
Revised: The paper studies the convergence properties of continuous-time fictitious play in potential games .
Edit intent: clarity
Original:  As byproducts of the proof of our main result we show that (i) a regular mixed-strategy equilibrium of a potential game can only be reached by a fictitious play process from a set of initial conditions with Lebesgue measure zero, and (ii) in regular potential games, solutions of fictitious play are unique for almost all initial conditions .
Revised:  As byproducts of the proof of our main result we show that (i) have a unique solution, (ii) in regular potential games, solutions of fictitious play are unique for almost all initial conditions .
Edit intent: clarity
Original:  Such equilibria are highly robust and relatively simple to analyze.
Revised:  Such equilibria are isolated, highly robust, and relatively simple to analyze.
Edit intent: clarity
Original:  That is, except for a closed subset of potential games with Lebesgue measure zero, all potential games are regular.
Revised:  That is, except for a closed subset with Lebesgue measure zero, all potential games are regular.
Edit intent: clarity
Original:  As an immediate consequence of this, the paper also proves an oddness result for potential games: In almost all potential games, the number of Nash equilibrium strategies is finite and odd  .
Revised:  As an immediate consequence of this, the paper also proves an oddness result for potential games: in almost all potential games, the number of Nash equilibrium strategies is finite and odd .
Edit intent: fluency