wanyu/IteraTeR_v2 huggingface dataset:
Original:  Additionally, we consider  fluid entrapment in "pools" delimited by contact patches and its pressurisation following a non-linear compressible constitutive law.
Revised:  Additionally, we consider the possibility of fluid entrapment in "pools" delimited by contact patches and its pressurisation following a non-linear compressible constitutive law.
Edit intent: clarity
Original:  Additionally, we consider  fluid entrapment in "pools" delimited by contact patches and its pressurisation following a non-linear compressible constitutive law.
Revised:  Additionally, we consider fluid entrapment in "pools" delimited by contact patches and its pressurisation following a non-linear compressibility constitutive law.
Edit intent: fluency
Original:  Image analysis algorithms are adopted to identify the local status of each interface element (i.e. distinguish between contact, fluid flow and trapped fluid zones) within the Newton convergence loop.
Revised:  Furthermore, image analysis algorithms were adapted to identify the local status of each interface element (i.e. distinguish between contact, fluid flow and trapped fluid zones) within the Newton convergence loop.
Edit intent: coherence
Original:  The full capability of the developed two-way coupling framework is demonstrated on a problem of a fluid flow in a contact interface between a solid with representative rough surface and a rigid flat.
Revised:  The full capability of the developed two-way coupling framework is demonstrated on a problem of a fluid flow in contact interface between a solid with representative rough surface and a rigid flat.
Edit intent: fluency
Original:  The evolution of the contact pressure, fluid flow pattern and the morphology of trapped fluid zones under increasing external load until the complete sealing of the interface is displayed.
Revised:  The evolution of the contact pressure, fluid flow pattern and the morphology of trapped fluid zones until the complete sealing of the interface is displayed.
Edit intent: clarity
Original:  Finally, effective properties of flat-on-flat rough contact interfacessuch as transmissivity and real contact area growth are calculated using the developed framework, showing qualitatively new results compared to the one-way coupling approximation .
Revised:  Finally, effective properties of contact interfaces, but also to highlight the difference between one- and two-way coupling approaches and to quantify the effect of multiple trapped fluid "pools" on the coupled problem .
Edit intent: clarity
Original:  Finally, we construct a two-layer neural network to demonstrate scalability and nonlinear decision boundaries , and discuss future directions for utilizing uncontrolled neurons in computational tasks.
Revised:  Finally, we construct a two-layer neural network to demonstrate scalability and nonlinear decision boundaries and discuss future directions for utilizing uncontrolled neurons in computational tasks.
Edit intent: fluency
Original: Today's machine learning models for computer vision are typically trained on very large (benchmark) data sets with millions of samples.
Revised: Contemporary learning models for computer vision are typically trained on very large (benchmark) data sets with millions of samples.
Edit intent: clarity
Original: Today's machine learning models for computer vision are typically trained on very large (benchmark) data sets with millions of samples.
Revised: Today's machine learning models for computer vision are typically trained on very large (benchmark) datasets with millions of samples.
Edit intent: clarity
Original:  These may, however, contain biases, artifacts, or errors that have gone unnoticed and are exploited by the model.
Revised:  These may, however, contain biases, artifacts, or errors that have gone unnoticed and are exploitable by the model.
Edit intent: clarity
Original:  In the worst case, the trained model may become a 'Clever Hans' predictor that does not learn a valid and generalizable strategy to solve the problem it was trained for, but bases its decisions on spurious correlations in the training data .
Revised:  In the worst case, the trained model does not learn a valid and generalizable strategy to solve the problem it was trained for, but bases its decisions on spurious correlations in the training data .
Edit intent: clarity
Original:  In this paper, we contribute by providing a comprehensive analysis framework based on a scalable statistical analysis of attributions from explanation methods for large data corpora , here ImageNet .
Revised:  In this paper, we contribute by providing a comprehensive analysis framework based on a scalable statistical analysis of attributions from explanation methods for large data corpora .
Edit intent: coherence
Original:  Spectral Relevance Analysis (SpRAy) -
Revised:  Spectral Relevance Analysis -
Edit intent: clarity
Original:  we propose three technical contributions and resulting findings:
Revised:  we propose the following technical contributions and resulting findings:
Edit intent: clarity
Original:  (a) novel similarity metrics based on Wasserstein for comparing attributions to allow for the first time scale, translational, and rotational invariant comparisons of attributions, (b)
Revised:  (a) 
Edit intent: coherence
Original:  a scalable quantification of artifactual and poisoned classes where the ML models under study exhibit Clever Hans behavior, ( c) a cleaning procedure that allows to relief data of artifacts and biases in a systematic manner yielding significantly reduced Clever Hans behavior, i. e . we un-Hans the ImageNet data corpus.
Revised:  a scalable quantification of artifactual and poisoned classes where the machine learning models under study exhibit Clever Hans behavior, ( c) a cleaning procedure that allows to relief data of artifacts and biases in a systematic manner yielding significantly reduced Clever Hans behavior, i. e . we un-Hans the ImageNet data corpus.
Edit intent: clarity
Original:  a scalable quantification of artifactual and poisoned classes where the ML models under study exhibit Clever Hans behavior, ( c) a cleaning procedure that allows to relief data of artifacts and biases in a systematic manner yielding significantly reduced Clever Hans behavior, i. e . we un-Hans the ImageNet data corpus.
Revised:  a scalable quantification of artifactual and poisoned classes where the ML models under study exhibit CH behavior, ( c) a cleaning procedure that allows to relief data of artifacts and biases in a systematic manner yielding significantly reduced Clever Hans behavior, i. e . we un-Hans the ImageNet data corpus.
Edit intent: clarity
Original:  a scalable quantification of artifactual and poisoned classes where the ML models under study exhibit Clever Hans behavior, ( c) a cleaning procedure that allows to relief data of artifacts and biases in a systematic manner yielding significantly reduced Clever Hans behavior, i. e . we un-Hans the ImageNet data corpus.
Revised:  a scalable quantification of artifactual and poisoned classes where the ML models under study exhibit Clever Hans behavior, ( c) a cleaning procedure that allows to relief data of artifacts and biases in a systematic manner yielding significantly reduced Clever Hans behavior, i. e . we un-Hans models trained on (poisoned) datasets, such as the popular ImageNet data corpus.
Edit intent: clarity
Original:  Using this novel method set , we provide qualitative and quantitative analyses of the biases and artifacts in ImageNet and demonstrate that the usage of these insights can give rise to improved models and functionally cleaned data corpora.
Revised:  Using this novel method set , we provide qualitative and quantitative analyses of the biases and artifacts in various datasets. We demonstrate that these insights can give rise to improved models and functionally cleaned data corpora.
Edit intent: clarity
Original:  Using this novel method set , we provide qualitative and quantitative analyses of the biases and artifacts in ImageNet and demonstrate that the usage of these insights can give rise to improved models and functionally cleaned data corpora.
Revised:  Using this novel method set , we provide qualitative and quantitative analyses of the biases and artifacts in ImageNet and demonstrate that the usage of these insights can give rise to improved , more representative and fairer models operating on implicitly cleaned data corpora.
Edit intent: clarity
Original:  By  Alon--Boppana theorem, when n is sufficiently large, \Delta(X) is quite high if \mu(X)=q^{-1{2}} 2\leq i\leq n|\lambda_i| is close to 2.
Revised:  By the Alon--Boppana theorem, when n is sufficiently large, \Delta(X) is quite high if \mu(X)=q^{-1{2}} 2\leq i\leq n|\lambda_i| is close to 2.
Edit intent: fluency
Original:  In this paper we introduce a number sequence \{H_k\}_{k=1^\infty and study its connection with \mu(X).
Revised:  In this paper ^\infty and study its connection with \mu(X).
Edit intent: clarity
Original: Moving around in the world is naturally a multisensory experience, but today's embodied agents are deaf - restricted to solely their visual perception of the environment.
Revised: Moving around in the world is naturally a multisensory experience, but today's embodied agents are deaf---restricted to solely their visual perception of the environment.
Edit intent: fluency
Original:  However, most of the successful DNN models have a high computational complexity , which makes them difficult to deploy on mobile or embedded platforms.
Revised:  However, most successful DCNN models have a high computational complexity , which makes them difficult to deploy on mobile or embedded platforms.
Edit intent: clarity
Original:  However, most of the successful DNN models have a high computational complexity , which makes them difficult to deploy on mobile or embedded platforms.
Revised:  However, most of the successful DNN models have a high computational complexity making them difficult to deploy on mobile or embedded platforms.
Edit intent: coherence
Original:  This  has prompted many researchers to develop algorithms and approaches to help reduce the computational complexity of such models.
Revised:  This problem has prompted many researchers to develop algorithms and approaches to help reduce the computational complexity of such models.
Edit intent: clarity
Original:  One of them is called filter pruning  where convolution filters are eliminated to reduce the number of parameters and, consequently, the computational complexity of the given model.
Revised:  One of them is called filter pruning , where convolution filters are eliminated to reduce the number of parameters and, consequently, the computational complexity of the given model.
Edit intent: fluency
Original:  Our approach avoids the need for using any knowledge during the pruning procedure and helps decision makers by returning three pruned DNN models with different trade-offs between performance and computational complexity.
Revised:  Our approach avoids the need for using any knowledge during the pruning procedure and helps decision-makers by returning three pruned DNN models with different trade-offs between performance and computational complexity.
Edit intent: fluency
Original:  We show that DeepPruningES can significantly reduce a model's computational complexity by testing it on three DNN architectures: Convolutional Neural Networks  , Residual Neural Networks  , and Densely Connected Neural Networks  .
Revised:  We show that DeepPruningES can significantly reduce a model's computational complexity by testing it on three DNN architectures: Convolutional Neural Networks , Residual Neural Networks (ResNets) , and Densely Connected Neural Networks .
Edit intent: clarity
Original:  We show that DeepPruningES can significantly reduce a model's computational complexity by testing it on three DNN architectures: Convolutional Neural Networks  , Residual Neural Networks  , and Densely Connected Neural Networks  .
Revised:  We show that DeepPruningES can significantly reduce a model's computational complexity by testing it on three DNN architectures: Convolutional Neural Networks , Residual Neural Networks , and Densely Connected Neural Networks (DenseNets) .
Edit intent: clarity
Original: This paper provides an a  priori error analysis of a localized orthogonal decomposition method (LOD) for the numerical stochastic homogenization of a model random diffusion problem.
Revised: This paper provides an a ~ priori error analysis of a localized orthogonal decomposition method (LOD) for the numerical stochastic homogenization of a model random diffusion problem.
Edit intent: fluency
Original: In this paper, we study an N server fork-join queueing network with nearly deterministic arrivals and service times.
Revised: In this paper, we study an N server fork-join queue with nearly deterministic arrivals and service times.
Edit intent: clarity
Original: In this paper, we study an N server fork-join queueing network with nearly deterministic arrivals and service times.
Revised: In this paper, we study an N server fork-join queueing network with nearly deterministic arrival and service times.
Edit intent: fluency
Original:  We present a fluid limit and a steady-state result for the maximum queue length , as N\to\infty.
Revised:  We present a fluid limit for the maximum queue length , as N\to\infty.
Edit intent: clarity
Original:  We present a fluid limit and a steady-state result for the maximum queue length , as N\to\infty.
Revised:  We present a fluid limit and a steady-state result for the maximum queue length as N\to\infty.
Edit intent: fluency
Original:  In order to prove these results, we use extreme value theory and diffusion approximations for the queue lengths.
Revised:  In order to prove these results, we develop extreme value theory and diffusion approximations for the queue lengths.
Edit intent: clarity
Original: A crucial aspect of mobile intelligent agents is their ability to integrate the evidence from multiple sensory inputs in an environment and plan a sequence of actions to achieve their goals.
Revised: A crucial ability of mobile intelligent agents is their ability to integrate the evidence from multiple sensory inputs in an environment and plan a sequence of actions to achieve their goals.
Edit intent: clarity
Original: A crucial aspect of mobile intelligent agents is their ability to integrate the evidence from multiple sensory inputs in an environment and plan a sequence of actions to achieve their goals.
Revised: A crucial aspect of mobile intelligent agents is to integrate the evidence from multiple sensory inputs in an environment and plan a sequence of actions to achieve their goals.
Edit intent: clarity
Original: A crucial aspect of mobile intelligent agents is their ability to integrate the evidence from multiple sensory inputs in an environment and plan a sequence of actions to achieve their goals.
Revised: A crucial aspect of mobile intelligent agents is their ability to integrate the evidence from multiple sensory inputs in an environment and to make a sequence of actions to achieve their goals.
Edit intent: clarity
Original: A crucial aspect of mobile intelligent agents is their ability to integrate the evidence from multiple sensory inputs in an environment and plan a sequence of actions to achieve their goals.
Revised: A crucial aspect of mobile intelligent agents is their ability to integrate the evidence from multiple sensory inputs in an environment and plan a sequence of actions to reach their goals.
Edit intent: clarity
Original:  In this paper, we attempt to address the problem of Audio-Visual Embodied Navigation, the task of planning the shortest path from a random starting location in a scene to the sound source in an indoor environment, given only raw egocentric visual and audio sensory data.
Revised:  In this paper, we attempt to approach the problem of Audio-Visual Embodied Navigation, the task of planning the shortest path from a random starting location in a scene to the sound source in an indoor environment, given only raw egocentric visual and audio sensory data.
Edit intent: clarity
Original:  Here we describe an approach to the audio-visual embodied navigation that can take advantage of both visual and audio pieces of evidence.
Revised:  Here we describe an approach to audio-visual embodied navigation that can take advantage of both visual and audio pieces of evidence.
Edit intent: fluency
Original:  Here we describe an approach to the audio-visual embodied navigation that can take advantage of both visual and audio pieces of evidence.
Revised:  Here we describe an approach to the audio-visual embodied navigation that takes advantage of both visual and audio pieces of evidence.
Edit intent: clarity
Original:  Our solution is based on three key ideas: a visual perception mapper module that can construct its spatial memory of the environment, a sound perception module that infers the relative location of the sound source from the agent, and a dynamic path planner that plans a sequence of actions based on the visual-audio observations and the spatial memory of the environment , and then navigates towards the goal.
Revised:  Our solution is based on three key ideas: a visual perception mapper module that constructs its spatial memory of the environment, a sound perception module that infers the relative location of the sound source from the agent, and a dynamic path planner that plans a sequence of actions based on the visual-audio observations and the spatial memory of the environment , and then navigates towards the goal.
Edit intent: clarity
Original:  Our solution is based on three key ideas: a visual perception mapper module that can construct its spatial memory of the environment, a sound perception module that infers the relative location of the sound source from the agent, and a dynamic path planner that plans a sequence of actions based on the visual-audio observations and the spatial memory of the environment , and then navigates towards the goal.
Revised:  Our solution is based on three key ideas: a visual perception mapper module that can construct its spatial memory of the environment, a sound perception module that infers the relative location of the sound source from the agent, and a dynamic path planner that plans a sequence of actions based on the audio-visual observations and the spatial memory of the environment , and then navigates towards the goal.
Edit intent: fluency
Original:  Our solution is based on three key ideas: a visual perception mapper module that can construct its spatial memory of the environment, a sound perception module that infers the relative location of the sound source from the agent, and a dynamic path planner that plans a sequence of actions based on the visual-audio observations and the spatial memory of the environment , and then navigates towards the goal.
Revised:  Our solution is based on three key ideas: a visual perception mapper module that can construct its spatial memory of the environment, a sound perception module that infers the relative location of the sound source from the agent, and a dynamic path planner that plans a sequence of actions based on the visual-audio observations and the spatial memory of the environment to navigate toward the goal.
Edit intent: coherence
Original: Generative models for singing voice have been mostly concerned with the task of " singing voice synthesis, " i.e., to produce singing voice waveforms given musical scores and text lyrics.
Revised: Generative models for singing voice have been mostly concerned with the task of `` singing voice synthesis, " i.e., to produce singing voice waveforms given musical scores and text lyrics.
Edit intent: fluency
Original: Generative models for singing voice have been mostly concerned with the task of " singing voice synthesis, " i.e., to produce singing voice waveforms given musical scores and text lyrics.
Revised: Generative models for singing voice have been mostly concerned with the task of " singing voice synthesis, '' i.e., to produce singing voice waveforms given musical scores and text lyrics.
Edit intent: fluency
Original:  This involves the development of source separation and transcription models for data preparation, adversarial networks for audio generation, and customized metrics for evaluation .
Revised:  Moreover, we implement such models using generative adversarial networks and evaluate them both objectively and subjectively .
Edit intent: clarity
Original: The effects of positive and negative hydrationof counterions (Na^{+ and Cs^{+} ) incorporated into the hydration shell of the DNA double helix have been studied using molecular dynamics approach .
Revised: The effects of positive and negative hydrationof counterions (Na^{+ and Cs^{+} ) incorporated into the hydration shell of the DNA double helix have been studied using molecular dynamics simulations .
Edit intent: clarity
Original:  The results show that the dynamics of the hydration shell of counterions depends on region of ion localization around the macromolecule.
Revised:  The results have shown that the dynamics of the hydration shell of counterions depends on region of ion localization around the macromolecule.
Edit intent: clarity
Original:  The counterions constrain water molecules too strong, and as the result the effect of negative hydration for K^{+} and Cs^{+} counterions was not observed in the simulations.
Revised:  The counterions constrain water molecules too strong, and , consequently, the effect of negative hydration for K^{+} and Cs^{+} counterions was not observed in the simulations.
Edit intent: coherence
Original:  The analysis show that the effects of counterion hydration may be described better by using the water models with lower dipole moments.
Revised:  The analysis has shown that the effects of counterion hydration may be described better by using the water models with lower dipole moments.
Edit intent: clarity
Original:  The analysis show that the effects of counterion hydration may be described better by using the water models with lower dipole moments.
Revised:  The analysis show that the effects of counterion hydration can be described more accurately with water models having lower dipole moments.
Edit intent: clarity
Original: Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementation have made it all the more attractive.
Revised: Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive.
Edit intent: fluency
Original:  Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert provided high-level runtimes to hide complications.
Revised:  Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert-provided high-level runtimes to hide complications.
Edit intent: fluency
Original:  Bolstered by our optimizing compiler, programmers can develop efficient general purpose FHE applications directly in EVA.
Revised:  Bolstered by our optimizing compiler, programmers can develop efficient general-purpose FHE applications directly in EVA.
Edit intent: fluency
Original:  For example, we have developed image processing applications using EVA, with  very few lines of code.
Revised:  For example, we have developed image processing applications using EVA, with a very few lines of code.
Edit intent: fluency
Original:  To demonstrate this  we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA.
Revised:  To demonstrate this , we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA.
Edit intent: fluency
Original:  We believe  EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.
Revised:  We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.
Edit intent: fluency