wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This allows us to extend the framework of Fox and Shamma ( Games, 2013 ) to prove not just global asymptotic stability results for game dynamics, but Poincar\'e recurrence results as well.
Revised:  This allows us to extend the framework of Fox and Shamma Games, 2013 ) to prove not just global asymptotic stability results for game dynamics, but Poincar\'e recurrence results as well.
Edit intent: fluency
Original:  This allows us to extend the framework of Fox and Shamma ( Games, 2013 ) to prove not just global asymptotic stability results for game dynamics, but Poincar\'e recurrence results as well.
Revised:  This allows us to extend the framework of Fox and Shamma ( Games, 2013 to prove not just global asymptotic stability results for game dynamics, but Poincar\'e recurrence results as well.
Edit intent: fluency
Original:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 ) .
Revised:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamics, their feedback interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 ) .
Edit intent: clarity
Original:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 ) .
Revised:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 ) .
Edit intent: fluency
Original:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 ) .
Revised:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 ) .
Edit intent: fluency
Original:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 ) .
Revised:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras SODA, 2018 ) .
Edit intent: fluency
Original:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 ) .
Revised:  Intuitively, when a lossless game (e.g. graphical constant-sum game) is coupled with lossless learning dynamic, their interconnection is also lossless, which results in a pendulum-like energy-preserving recurrent behavior, generalizing the results of Piliouras and Shamma ( SODA, 2014 ) and Mertikopoulos, Papadimitriou and Piliouras ( SODA, 2018 .
Edit intent: fluency
Original:  Following from recent work on multirate exponential Runge--Kutta (MERK) methods , that construct multirate schemes by approximating the action of matrix \varphi-functions within explicit exponential Runge--Kutta methods, the proposed methods similarly build off of explicit exponential Rosenbrock (ExpRB) methods .
Revised:  The proposed methods construct multirate schemes by approximating the action of matrix \varphi-functions within explicit exponential Runge--Kutta methods, the proposed methods similarly build off of explicit exponential Rosenbrock (ExpRB) methods .
Edit intent: clarity
Original:  Following from recent work on multirate exponential Runge--Kutta (MERK) methods , that construct multirate schemes by approximating the action of matrix \varphi-functions within explicit exponential Runge--Kutta methods, the proposed methods similarly build off of explicit exponential Rosenbrock (ExpRB) methods .
Revised:  Following from recent work on multirate exponential Runge--Kutta (MERK) methods , that construct multirate schemes by approximating the action of matrix \varphi-functions within explicit exponential Rosenbrock (ExpRB) methods .
Edit intent: clarity
Original:  Following from recent work on multirate exponential Runge--Kutta (MERK) methods , that construct multirate schemes by approximating the action of matrix \varphi-functions within explicit exponential Runge--Kutta methods, the proposed methods similarly build off of explicit exponential Rosenbrock (ExpRB) methods . By leveraging the exponential Rosenbrock structure, the proposed Multirate Exponential Rosenbrock (MERB) methods  consist of the solution to a sequence of modified ``fast'' initial-value problems, that may themselves be approximated through subcycling any desired IVP solver.
Revised:  Following from recent work on multirate exponential Runge--Kutta (MERK) methods , that construct multirate schemes by approximating the action of matrix \varphi-functions within explicit exponential Runge--Kutta methods, the proposed methods similarly build off of explicit exponential Rosenbrock (ExpRB) methods , thereby called Multirate Exponential Rosenbrock (MERB) methods consist of the solution to a sequence of modified ``fast'' initial-value problems, that may themselves be approximated through subcycling any desired IVP solver.
Edit intent: clarity
Original:  By leveraging the exponential Rosenbrock structure, the proposed Multirate Exponential Rosenbrock (MERB) methods  consist of the solution to a sequence of modified ``fast'' initial-value problems, that may themselves be approximated through subcycling any desired IVP solver.
Revised:  By leveraging the exponential Rosenbrock structure, the proposed Multirate Exponential Rosenbrock (MERB) methods . They consist of the solution to a sequence of modified ``fast'' initial-value problems, that may themselves be approximated through subcycling any desired IVP solver.
Edit intent: coherence
Original:  By leveraging the exponential Rosenbrock structure, the proposed Multirate Exponential Rosenbrock (MERB) methods  consist of the solution to a sequence of modified ``fast'' initial-value problems, that may themselves be approximated through subcycling any desired IVP solver.
Revised:  By leveraging the exponential Rosenbrock structure, the proposed Multirate Exponential Rosenbrock (MERB) methods consist of the solution to a sequence of modified "fast" initial-value problems, that may themselves be approximated through subcycling any desired IVP solver.
Edit intent: fluency
Original:  In addition to proving how to construct MERB methods from certain classes of ExpRB methods, we provide rigorous convergence analysis of the resulting schemes, and present candidate MERB schemes of orders two through six  .
Revised:  In addition to proving how to construct MERB methods from certain classes of ExpRB methods, we provide rigorous convergence analysis of these methods and derive efficient MERB schemes of orders two through six .
Edit intent: clarity
Original:  In addition to proving how to construct MERB methods from certain classes of ExpRB methods, we provide rigorous convergence analysis of the resulting schemes, and present candidate MERB schemes of orders two through six  .
Revised:  In addition to proving how to construct MERB methods from certain classes of ExpRB methods, we provide rigorous convergence analysis of the resulting schemes, and present candidate MERB schemes of orders two through six (the highest order ever constructed infinitesimal multirate methods) .
Edit intent: meaning-changed
Original:  In this perspective, we present major applications of AI in drug discovery and discuss the relevant AI techniques, covering most recent progress in AI-driven drug discovery  .
Revised:  In this perspective, we present major applications of AI in drug discovery and discuss related applications, which can be reduced to two major tasks, i.e., molecular property prediction and molecule generation. We then discuss common data resources, molecule representations and benchmark platforms. Furthermore, to summarize the progress in AI-driven drug discovery .
Edit intent: meaning-changed
Original:  In particular, we propose a framework, ~%DIFDELCMD < {\bf %%% SPAIR3D , to model a point cloud as a spatial mixture model and jointly learn the multiple-object representation and segmentation in 3D via Variational Autoencoders (VAE).
Revised:  In particular, we propose a framework, %DIFDELCMD < {\bf %%% SPAIR3D , to model a point cloud as a spatial mixture model and jointly learn the multiple-object representation and segmentation in 3D via Variational Autoencoders (VAE).
Edit intent: coherence
Original:  ~ Experimental results demonstrate that ~%DIFDELCMD < {\bf %%% SPAIR3D is capable of detecting and segmenting variable number of objects without appearance information across diverse scenes.
Revised:  Experimental results demonstrate that ~%DIFDELCMD < {\bf %%% SPAIR3D is capable of detecting and segmenting variable number of objects without appearance information across diverse scenes.
Edit intent: coherence
Original:  ~ Experimental results demonstrate that ~%DIFDELCMD < {\bf %%% SPAIR3D is capable of detecting and segmenting variable number of objects without appearance information across diverse scenes.
Revised:  ~ Experimental results demonstrate that %DIFDELCMD < {\bf %%% SPAIR3D is capable of detecting and segmenting variable number of objects without appearance information across diverse scenes.
Edit intent: fluency
Original:  Recent work has shown that many such problems may be viewed as special cases of the authorization policy existence problem (APEP), which returns an authorization policy if one exists and 'No' otherwise.
Revised:  Recent work has shown that many problems of satisfiability and resiliency in workflows may be viewed as special cases of the authorization policy existence problem (APEP), which returns an authorization policy if one exists and 'No' otherwise.
Edit intent: meaning-changed
Original:  However, in many practical settings it would be more useful to obtain a 'least bad' policy than just a 'No', where 'least bad' is characterized by some numerical value associated with the policy indicating the extent to which the policy violates the base authorization relation and constraints.
Revised:  However, in many practical settings it would be more useful to obtain a 'least bad' policy than just a 'No', where 'least bad' is characterized by some numerical value indicating the extent to which the policy violates the base authorization relation and constraints.
Edit intent: clarity
Original:  Accordingly, we introduce the Valued APEP, which returns an authorization policy of minimum weight, where the (non-negative) weight is determined by the constraints violated by the returned solution (and is 0 if all constraints are satisfied) .
Revised:  Accordingly, we introduce the Valued APEP, which returns an authorization policy of minimum weight, where the (non-negative) weight is determined by the constraints violated by the returned solution .
Edit intent: coherence
Original:  We introduce the notion of a user profile for a weighted constraint, which enables us to prove a powerful result, a corollary of which improves on known complexity results for APEP. Finally, we consider Valued APEP when restricted to particular sub-classes of constraints and show that instances of such problems can be reduced to the Valued WSP, enabling us to exploit known algorithms to solve these particular instances .
Revised:  We also introduce a new type of resiliency for workflow satisfiability problem, show how it can be addressed using Valued APEP and use this to build a set of benchmark instances for Valued APEP. Following a set of computational experiments with two mixed integer programming (MIP) formulations, we demonstrate that the Valued APEP formulation based on the user profile concept has FPT-like running time and usually significantly outperforms a naive formulation .
Edit intent: meaning-changed
Original: DNNs are known to be vulnerable to so-called adversarial attacks , in which inputs are carefully manipulated to induce misclassification.
Revised: Deep Neural Networks (DNNs) are employed in an increasing number of applications, some of which are safety critical. Unfortunately, DNNs are known to be vulnerable to so-called adversarial attacks , in which inputs are carefully manipulated to induce misclassification.
Edit intent: coherence
Original: DNNs are known to be vulnerable to so-called adversarial attacks , in which inputs are carefully manipulated to induce misclassification. Existing defenses are mostly software-based and come with high overheads or other limitations .
Revised: DNNs are known to be vulnerable to so-called adversarial attacks that manipulate inputs to cause incorrect results that can be beneficial to an attacker or damaging to the victim. Multiple defenses have been proposed to increase the robustness of DNNs. In general, these defenses have high overhead, some require attack-specific re-training of the model or careful tuning to adapt to different attacks .
Edit intent: clarity
Original:   We show an adversarial detection rate of average 87\% which exceeds the detection rate of the state-of-the-art approaches, with a much lower overhead.
Revised:  HASI uses the output distribution characteristics of noisy inference compared to a non-noisy reference to detect adversarial inputs. We show an adversarial detection rate of average 87\% which exceeds the detection rate of the state-of-the-art approaches, with a much lower overhead.
Edit intent: meaning-changed
Original:   We show an adversarial detection rate of average 87\% which exceeds the detection rate of the state-of-the-art approaches, with a much lower overhead.
Revised:  We show an adversarial detection rate of 86\% when applied to VGG16 and 93\% when applied to ResNet50, which exceeds the detection rate of the state-of-the-art approaches, with a much lower overhead.
Edit intent: meaning-changed
Original:  We demonstrate a software/hardware-accelerated co-design , which reduces the performance impact of stochastic inference to 1.58X-2X relative to the unprotected baseline, compared to 14X-20X overhead for a software-only GPU implementation.
Revised:  We demonstrate two software/hardware-accelerated co-design , which reduces the performance impact of stochastic inference to 1.58X-2X relative to the unprotected baseline, compared to 14X-20X overhead for a software-only GPU implementation.
Edit intent: meaning-changed
Original:  We demonstrate a software/hardware-accelerated co-design , which reduces the performance impact of stochastic inference to 1.58X-2X relative to the unprotected baseline, compared to 14X-20X overhead for a software-only GPU implementation.
Revised:  We demonstrate a software/hardware-accelerated co-designs , which reduces the performance impact of stochastic inference to 1.58X-2X relative to the unprotected baseline, compared to 14X-20X overhead for a software-only GPU implementation.
Edit intent: fluency
Original:  We demonstrate a software/hardware-accelerated co-design , which reduces the performance impact of stochastic inference to 1.58X-2X relative to the unprotected baseline, compared to 14X-20X overhead for a software-only GPU implementation.
Revised:  We demonstrate a software/hardware-accelerated co-design , which reduces the performance impact of stochastic inference to 1.58X-2X relative to the unprotected baseline, compared to 15X-20X overhead for a software-only GPU implementation.
Edit intent: meaning-changed
Original: We present a new higher-order accurate finite difference explicit jump Immersed Interface Method ( EJIIM ) for solving two-dimensional elliptic problems with singular source and discontinuous coefficients in the irregular region on a compact Cartesian mesh.
Revised: We present a new higher-order accurate finite difference explicit jump Immersed Interface Method ( HEJIIM ) for solving two-dimensional elliptic problems with singular source and discontinuous coefficients in the irregular region on a compact Cartesian mesh.
Edit intent: meaning-changed
Original:   In the process, we show the superiority of the proposed strategy over the EJIIM and other existing IIM methods .
Revised:  We also simulate a plethora of fluid flow problems past bluff bodies in complex flow situations, which are governed by the Navier-Stokes equations; they include problems involving multiple bodies immersed in the flow as well. In the process, we show the superiority of the proposed strategy over the EJIIM and other existing IIM methods .
Edit intent: meaning-changed
Original:   In the process, we show the superiority of the proposed strategy over the EJIIM and other existing IIM methods . We also simulate the steady-state flow past a circular cylinder governed by the Navier-Stokes equations .
Revised:  In the process, we show the superiority of the proposed strategy over the EJIIM and other existing IIM methods by establishing the rate of convergence and grid independence of the computed solutions .
Edit intent: meaning-changed
Original:  Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg) challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
Revised:  Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg) challenge, we present a large-scale multi-centre dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms for the fetal environment with a focus on creating drift-free mosaics from long duration fetoscopy videos.
Edit intent: fluency
Original: Inspired by BatchNorm, there has been an explosion of normalization layers in deep learning. Recent works have identified a multitude of beneficial properties in BatchNorm to explain its success. However, given the pursuit of alternative normalization techniques, these properties need to be generalized so that any given layer's success/failure can be accurately predicted. In this work ,  we take a first step towards this goal by extending known properties of BatchNorm in randomly initialized deep neural networks (DNNs) to nine recently proposed normalization layers.
Revised: Inspired by BatchNorm, there has been an explosion of normalization layers for deep neural networks (DNNs). However , we take a first step towards this goal by extending known properties of BatchNorm in randomly initialized deep neural networks (DNNs) to nine recently proposed normalization layers.
Edit intent: clarity
Original:  In this work ,  we take a first step towards this goal by extending known properties of BatchNorm in randomly initialized deep neural networks (DNNs) to nine recently proposed normalization layers.
Revised:  In this work , these alternative normalization layers have seen minimal use, partially due to a lack of guiding principles that can help identify when these layers can serve as a replacement for BatchNorm. To address this problem, we take a first step towards this goal by extending known properties of BatchNorm in randomly initialized deep neural networks (DNNs) to nine recently proposed normalization layers.
Edit intent: meaning-changed
Original:  In this work ,  we take a first step towards this goal by extending known properties of BatchNorm in randomly initialized deep neural networks (DNNs) to nine recently proposed normalization layers. Our primary findings follow : (i) Similar to BatchNorm, activations-based normalization layers can avoid exploding activations in ResNets  ;
Revised:  In this work , we take a first step towards this goal by extending known properties of BatchNorm in randomly initialized deep neural networks (DNNs) to nine recently proposed normalization techniques. Our generalized theory leads to the following set of principles : (i) Similar to BatchNorm, activations-based normalization layers can avoid exploding activations in ResNets ;
Edit intent: clarity
Original:  (ii) Use of GroupNorm ensures rank of activationsis at least \Omega(\sqrt{\frac{\text{width ;
Revised:  (ii) use of GroupNorm can ensure informative forward propagation, with different samples being assigned dissimilar activations, but increasing group size results in increasingly indistinguishable activations for different samples, explaining slow convergence speed in models with LayerNorm ;
Edit intent: meaning-changed
Original:  For example, the experimental results show an overall improvement of 8.8 %DIFDELCMD < \\%%% %DIF < on average compared to SOTA baselines on the regression tasks, demonstrating the superiority of the proposed method.abstract 
Revised:  For example, the experimental results show an overall improvement of 8.8 %DIFDELCMD < \\%%% %DIF < on average compared to SOTA baselines on the regression tasks, demonstrating the superiority of the proposed method.abstract \% on average compared to SOTA baselines on the regression tasks, demonstrating the superiority of the proposed method.
Edit intent: meaning-changed
Original:  To reach these objectives, designers , programmers of IoMT nodes, have to face challenging optimization tasks, in order to execute fairly complex computing tasks on low-power wearable and portable processing systems, with tight power and battery lifetime budgets.
Revised:  To reach these objectives, designers and programmers of IoMT nodes, have to face challenging optimization tasks, in order to execute fairly complex computing tasks on low-power wearable and portable processing systems, with tight power and battery lifetime budgets.
Edit intent: fluency
Original:  Our experimental results show that adapting the node setup to the workload at runtime can save up to 50\% power consumption and a quantized neural network reaches an accuracy value higher than 98 \% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset.
Revised:  Our experimental results show that adapting the node setup to the workload at runtime can save up to 50\% power consumption . Our optimized and quantized neural network reaches an accuracy value higher than 98 \% for arrhythmia disorders detection on MIT-BIH Arrhythmia dataset.
Edit intent: meaning-changed