wanyu/IteraTeR_v2 huggingface dataset:
Original:  This is accomplished by adopting a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE) framework . Compared to the previous population synthesis approaches based on Iterative Proportional Fitting (IPF), Markov Chain Monte Carlo (MCMC) sampling or traditional generative models  , the proposed method allows unparalleled scalability with respect to the number and types of attributes.
Revised:  The model framework adopts a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE) framework . Compared to the previous population synthesis approaches based on Iterative Proportional Fitting (IPF), Markov Chain Monte Carlo (MCMC) sampling or traditional generative models , the proposed method allows unparalleled scalability with respect to the number and types of attributes.
Edit intent: clarity
Original:  This is accomplished by adopting a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE) framework . Compared to the previous population synthesis approaches based on Iterative Proportional Fitting (IPF), Markov Chain Monte Carlo (MCMC) sampling or traditional generative models  , the proposed method allows unparalleled scalability with respect to the number and types of attributes.
Revised:  This is accomplished by adopting a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE) . Compared to the previous population synthesis approaches based on Iterative Proportional Fitting (IPF), Markov Chain Monte Carlo (MCMC) sampling or traditional generative models , the proposed method allows unparalleled scalability with respect to the number and types of attributes.
Edit intent: clarity
Original:  This is accomplished by adopting a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE) framework . Compared to the previous population synthesis approaches based on Iterative Proportional Fitting (IPF), Markov Chain Monte Carlo (MCMC) sampling or traditional generative models  , the proposed method allows unparalleled scalability with respect to the number and types of attributes.
Revised:  This is accomplished by adopting a deep generative modeling approach from machine learning based on a Variational Autoencoder (VAE) framework . Compared to the previous population synthesis approaches , including Iterative Proportional Fitting (IPF), Markov Chain Monte Carlo (MCMC) sampling or traditional generative models , the proposed method allows unparalleled scalability with respect to the number and types of attributes.
Edit intent: clarity
Original:  Moreover, VAE allows for growing agents that are virtually different from those in the original data but have similar statistical properties and correlation structure .
Revised:  Moreover, VAE allows for growing agents that are virtually different from those in the original data but have similar statistical properties .
Edit intent: clarity
Original:  The presented approach will help modelers to generate better and richer populations with a high level of detail, including smaller zones , personal details and travel preferences .
Revised:  The presented approach can support agent-based modeling at all levels by enabling richer synthetic populations with smaller zones and more detailed individual characteristics .
Edit intent: clarity
Original: As a generalization of many classic problems in combinatorial optimization , submodular optimization has found a wide range of applications in machine learning (e.g., in feature engineering and active learning).
Revised: Submodular maximization is a general optimization problem with a wide range of applications in machine learning (e.g., in feature engineering and active learning).
Edit intent: clarity
Original:  For many large-scale optimization problems, we are often concerned with the adaptivity complexity of an algorithm , which quantifies the number of sequential rounds where polynomially-many independent function evaluations can be executed in parallel.
Revised:  For many large-scale optimization , the parallel running time of an algorithm , which quantifies the number of sequential rounds where polynomially-many independent function evaluations can be executed in parallel.
Edit intent: clarity
Original:  For many large-scale optimization problems, we are often concerned with the adaptivity complexity of an algorithm , which quantifies the number of sequential rounds where polynomially-many independent function evaluations can be executed in parallel.
Revised:  For many large-scale optimization problems, we are often concerned with the adaptivity complexity of an algorithm , which quantifies the number of sequential rounds needed if the algorithm can execute polynomially-many independent function evaluations can be executed in parallel.
Edit intent: clarity
Original:  For many large-scale optimization problems, we are often concerned with the adaptivity complexity of an algorithm , which quantifies the number of sequential rounds where polynomially-many independent function evaluations can be executed in parallel.
Revised:  For many large-scale optimization problems, we are often concerned with the adaptivity complexity of an algorithm , which quantifies the number of sequential rounds where polynomially-many independent oracle queries in parallel.
Edit intent: clarity
Original:  While low adaptivity is ideal, it is not sufficient for a distributed algorithm to be efficient , since in many practical applications of submodular optimization the number of function evaluations becomes prohibitively expensive.
Revised:  While low adaptivity is ideal, it is not sufficient for an algorithm to be efficient , since in many practical applications of submodular optimization the number of function evaluations becomes prohibitively expensive.
Edit intent: clarity
Original:  While low adaptivity is ideal, it is not sufficient for a distributed algorithm to be efficient , since in many practical applications of submodular optimization the number of function evaluations becomes prohibitively expensive.
Revised:  While low adaptivity is ideal, it is not sufficient for a distributed algorithm to be efficient in practice---there are many applications of distributed submodular optimization where the number of function evaluations becomes prohibitively expensive.
Edit intent: clarity
Original:  Motivated by such applications, we study the adaptivity and query complexity of non-monotone submodular optimization.
Revised:  Motivated by these applications, we study the adaptivity and query complexity of non-monotone submodular optimization.
Edit intent: clarity
Original:  We provide the first constant approximation algorithm for maximizing a non-monotone submodular function with cardinality constraint k that has nearly-optimal adaptivity complexity O(\log(n)) . Furthermore, our algorithm makes only O( \log(k)) calls per element to the function evaluation oracle in expectation .
Revised:  We provide the first constant approximation algorithm for maximizing a non-monotone submodular function subject to a cardinality constraint k that has nearly-optimal adaptivity complexity O(\log(n)) . Furthermore, our algorithm makes only O( \log(k)) calls per element to the function evaluation oracle in expectation .
Edit intent: clarity
Original: Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments ( typically called windows or patches), processing them separately, and then stitching them back into place to produce a unified output.
Revised: Many signal processing algorithms break the target signal into possibly overlapping segments ( typically called windows or patches), processing them separately, and then stitching them back into place to produce a unified output.
Edit intent: clarity
Original: Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments ( typically called windows or patches), processing them separately, and then stitching them back into place to produce a unified output.
Revised: Many signal processing algorithms operate by breaking the target signal into overlapping segments ( typically called windows or patches), processing them separately, and then stitching them back into place to produce a unified output.
Edit intent: clarity
Original: Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments ( typically called windows or patches), processing them separately, and then stitching them back into place to produce a unified output.
Revised: Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments ( also called windows, or patches), processing them separately, and then stitching them back into place to produce a unified output.
Edit intent: clarity
Original: Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments ( typically called windows or patches), processing them separately, and then stitching them back into place to produce a unified output.
Revised: Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments ( typically called windows or patches), process them separately, and then stitching them back into place to produce a unified output.
Edit intent: fluency
Original: Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments ( typically called windows or patches), processing them separately, and then stitching them back into place to produce a unified output.
Revised: Many signal processing algorithms operate by breaking the target signal into possibly overlapping segments ( typically called windows or patches), processing them separately, and then stitch them back into place to produce a unified output.
Edit intent: fluency
Original:  In most cases where pach overlapping occurs , the final value of those samples that are estimated by more than one patch is resolved by averaging those estimates;
Revised:  At the overlaps , the final value of those samples that are estimated by more than one patch is resolved by averaging those estimates;
Edit intent: clarity
Original:  Averaging, the simplest approach, tends to produce blurred results.
Revised:  Averaging, the simplest approach, often leads to unsatisfactory results.
Edit intent: clarity
Original:  Significant work has been devoted to this issue in recent years : several works explore the idea of a weighted average of the overlapped patches and/or pixels;
Revised:  Significant work has been devoted to this issue in recent years . Several works explore the idea of a weighted average of the overlapped patches and/or pixels;
Edit intent: fluency
Original:  a more recent approach is to promote agreement (consensus) between the patches at their intersections.
Revised:  others promote agreement (consensus) between the patches at their intersections.
Edit intent: clarity
Original:  This work investigates the case where consensus is imposed as a hard constraint on the restoration problem.
Revised:  Agreement can be either encouraged or imposed as a hard constraint on the restoration problem.
Edit intent: clarity
Original:  The framework itself consists of a general optimization problem and a simple and efficient%DIFDELCMD < \admm%%% -based algorithm for solving it.
Revised:  The framework itself consists of a %DIFDELCMD < \admm%%% -based algorithm for solving it.
Edit intent: coherence
Original:  We derive a priori {\it  error estimates for the  method on simplicial meshes in both two- and three-dimensions.
Revised:  We derive {\it error estimates for the method on simplicial meshes in both two- and three-dimensions.
Edit intent: clarity
Original:  However, at the high densities at which the system flocks , binary interactions are too weak to account for the ordering transition  .
Revised:  However, at the high densities at which the system flocks , binary interactions are not sufficient to account for the ordering transition .
Edit intent: clarity
Original: Active systems are characterized by a discontinuous flocking transition from a disordered isotropic state to a polar ordered state with increasing density and decreasing noise.
Edit intent: coherence
Original:  A large class of mesoscopic or macroscopic theories for flocking are coarse grained from microscopic models that feature binary interactions as the chief aligning mechanism.
Revised:  A large class of mesoscopic or macroscopic flocking theories are coarse-grained from microscopic models that feature binary interactions as the chief aligning mechanism.
Edit intent: fluency
Original:  Here we introduce a solvable one-dimensional model of flocking , and derive a series of approximations for the stochastic hydrodynamics.
Revised:  Here we introduce a solvable one-dimensional flocking model and derive its stochastic hydrodynamics.
Edit intent: clarity
Original:  It is the goal of the present manuscript to generalize this classic result as follows: We prove that if an alignment consists of all characters that require exactly k=2 substitutions on a particular tree, this tree will always be the unique maximum parsimony tree (and we also show that this can be generalized to characters which require at most k=2 substitutions).
Revised:  It is the goal of the present manuscript to extend this classic result as follows: We prove that if an alignment consists of all characters that require exactly k=2 substitutions on a particular tree, this tree will always be the unique maximum parsimony tree (and we also show that this can be generalized to characters which require at most k=2 substitutions).
Edit intent: clarity
Original:  However, detailed information of the full unbinding pathway is commonly lacking due, in part, to the static nature of atomic structures and ensemble averaging inherent to bulk biophysics approaches.
Revised:  However, detailed information of the full unbinding pathway is often lacking due, in part, to the static nature of atomic structures and ensemble averaging inherent to bulk biophysics approaches.
Edit intent: clarity
Original:  An effective two-dimensional compressibility can arise, for example, from the convergence and divergence of water masses at the depth at which passively traveling photosynthetic organisms are restricted to live.
Revised:  An effective 2D compressibility can arise, for example, from the convergence and divergence of water masses at the depth at which passively traveling photosynthetic organisms are restricted to live.
Edit intent: fluency
Original:  To this end, we couple an off-lattice agent-based simulation of two populations in one dimension to a weakly compressible velocity field-- first a sine wave , and then a shell model of turbulence.
Revised:  To this end, we couple an off-lattice agent-based simulation of two populations in 1D to a weakly compressible velocity field-- first a sine wave , and then a shell model of turbulence.
Edit intent: clarity
Original:  To this end, we couple an off-lattice agent-based simulation of two populations in one dimension to a weakly compressible velocity field-- first a sine wave , and then a shell model of turbulence.
Revised:  To this end, we couple an off-lattice agent-based simulation of two populations in one dimension to a weakly compressible velocity field--first a sine wave , and then a shell model of turbulence.
Edit intent: fluency
Original:  To this end, we couple an off-lattice agent-based simulation of two populations in one dimension to a weakly compressible velocity field-- first a sine wave , and then a shell model of turbulence.
Revised:  To this end, we couple an off-lattice agent-based simulation of two populations in one dimension to a weakly compressible velocity field-- first a sine wave and then a shell model of turbulence.
Edit intent: fluency
Original: Alignments of large numbers of protein sequences have revealed "sectors" of collectively coevolving amino acids in several protein families.
Revised: Statistical analysis of alignments of large numbers of protein sequences have revealed "sectors" of collectively coevolving amino acids in several protein families.
Edit intent: clarity
Original: Alignments of large numbers of protein sequences have revealed "sectors" of collectively coevolving amino acids in several protein families.
Revised: Alignments of large numbers of protein sequences has revealed "sectors" of collectively coevolving amino acids in several protein families.
Edit intent: fluency
Original:  We demonstrate that the main signature of these physical sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences.
Revised:  We demonstrate that the main signature of functional sectors lies in the small-eigenvalue modes of the covariance matrix of the selected sequences.
Edit intent: clarity
Original: Food is  essential for human life and it is fundamental to the human experience.
Revised: Food is very essential for human life and it is fundamental to the human experience.
Edit intent: coherence
Original:  With the fast development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries  leading to large-scale food data.
Revised:  With the rapid development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries leading to large-scale food data.
Edit intent: coherence
Original:  With the fast development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries  leading to large-scale food data.
Revised:  With the fast development of social networks, mobile networks, and Internet of Things (IoT), people commonly upload, share, and record food images, recipes, cooking videos, and food diaries , leading to large-scale food data.
Edit intent: fluency
Original:  Therefore, a vast amount of research work has been done in the food area, targeting different food-oriented tasks and applications.
Revised:  Therefore, vast amounts of work has been done in the food area, targeting different food-oriented tasks and applications.
Edit intent: clarity
Original:  Therefore, a vast amount of research work has been done in the food area, targeting different food-oriented tasks and applications.
Revised:  Therefore, a vast amount of research work has been conducted in the food area, targeting different food-oriented tasks and applications.
Edit intent: fluency
Original:  With this contribution, we aim to move from the near-real time counts to investigate the semantic proximities and evolution of the papers published in the online journal Cybergeo since its creation in 1996.
Revised:  With this contribution, we aim to investigate the semantic proximities and evolution of the papers published in the online journal Cybergeo since its creation in 1996.
Edit intent: clarity
Original: This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing  we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves.
Revised: This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing , we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves.
Edit intent: fluency
Original:  We pose this problem as a per-frame image-to-image translation with spatio-temporal smoothing.
Revised:  We approach this problem as a per-frame image-to-image translation with spatio-temporal smoothing.
Edit intent: clarity
Original:  We adapt this setup for temporally coherent video generation including realistic face synthesis.
Revised:  We adapt this setup for temporally coherent video results and introduce a separate pipeline for realistic face synthesis.
Edit intent: clarity
Original:  this is what we call the multistationarity structure of the network.
Revised:  this is what we call the multistationarity structure of the network.
Edit intent: fluency
Original:  By choosing a cooperative game without the usual opposition between individual and common benefit, we recreate an experimental environment which forces the players to rely primarily on their instinct (intuition) when making cooperative decisions.
Revised:  Our experimental environment is set up to make it complicated for players to use rational calculation for making the cooperative decisions.
Edit intent: coherence
Original:  This environment is directly translated into a computer simulation, from which we extract the collaboration strategy that leads to the maximal attainable score for any group size or problem complexity .
Revised:  This environment is directly translated into a computer simulation, from which we extract the collaboration strategy that leads to the maximal attainable score .
Edit intent: clarity
Original:  Based on this  we measure the error that players make when instinctively estimating the benefits of collaboration  and find that humans massively underestimate these benefits when facing easy problems or working alone or in small groups.
Revised:  Based on this , we measure the error that players make when instinctively estimating the benefits of collaboration and find that humans massively underestimate these benefits when facing easy problems or working alone or in small groups.
Edit intent: fluency
Original:  Based on this  we measure the error that players make when instinctively estimating the benefits of collaboration  and find that humans massively underestimate these benefits when facing easy problems or working alone or in small groups.
Revised:  Based on this we measure the error that players make when estimating the benefits of collaboration and find that humans massively underestimate these benefits when facing easy problems or working alone or in small groups.
Edit intent: clarity
Original:  Based on this  we measure the error that players make when instinctively estimating the benefits of collaboration  and find that humans massively underestimate these benefits when facing easy problems or working alone or in small groups.
Revised:  Based on this we measure the error that players make when instinctively estimating the benefits of collaboration , and find that humans massively underestimate these benefits when facing easy problems or working alone or in small groups.
Edit intent: fluency
Original:  These findings are independent on groups' composition and players' personal traits  .
Revised:  Our findings are independent on groups' composition and players' personal traits .
Edit intent: fluency
Original:  We prove that no Regularity is needed: the problem remains undecidable even for finite unions of Path Queries .
Revised:  We prove that no regularity is needed: the problem remains undecidable even for finite unions of Path Queries .
Edit intent: fluency
Original:  We prove that no Regularity is needed: the problem remains undecidable even for finite unions of Path Queries .
Revised:  We prove that no Regularity is needed: determinacy remains undecidable even for finite unions of Path Queries .
Edit intent: clarity
Original:  The probability distribution of gene copies shows even-odd number oscillations.
Revised:  The probability distribution of gene copies contains even-odd number oscillations.
Edit intent: clarity
Original:  The reason the behaviour occurs is an underlying interference of Poisson and Gaussian processes.
Edit intent: coherence
Original:  A master equation previously derived in the adiabatic limit of fast switching by Kepler and Elston 1 is suggested for stochastic gene expression to take into account inter-state switching processes.
Revised:  A master equation previously derived in the adiabatic limit of fast switching by Kepler and Elston is suggested for stochastic gene expression to take into account inter-state switching processes.
Edit intent: fluency