wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The main performance measure is the total number of agent updates required by the algorithms to find those global maxima and the baseline performance, which establishes the worth of the cooperative algorithms, is set by the blind search in which the agents explore the problem space (binary strings) by flipping randomly chosen bits .
Revised:  The main performance measure is the total number of agent updates required by the algorithms to find those global maxima and the baseline performance, which establishes the worth of the cooperative algorithms, is set by the blind search in which the agents explore the problem space (binary strings) by flipping bits at random .
Edit intent: clarity
Original:  In fact, in the case of rugged landscapes with a not too low density of local maxima, the blind search beats the cooperative algorithms regardless of whether the task is to find the global maximum or to find the fittest state within a given runtime.
Revised:  In fact, in the case of rugged landscapes with a mild density of local maxima, the blind search beats the cooperative algorithms regardless of whether the task is to find the global maximum or to find the fittest state within a given runtime.
Edit intent: meaning-changed
Original:  In fact, in the case of rugged landscapes with a not too low density of local maxima, the blind search beats the cooperative algorithms regardless of whether the task is to find the global maximum or to find the fittest state within a given runtime.
Revised:  In fact, in the case of rugged landscapes with a not too low density of local maxima, the blind search either beats or matches the cooperative algorithms regardless of whether the task is to find the global maximum or to find the fittest state within a given runtime.
Edit intent: meaning-changed
Original:  As random coloring incurs \Omega(T^{1/2}) discrepancy, while the offline bounds are \Theta( (n\log T) ^{1/2 for vector balancing and 1 for interval balancing, a natural question is whether one can (nearly) match the offline bounds in the online setting for these problems.
Revised:  As random coloring incurs \Omega(T^{1/2}) discrepancy, while the offline bounds are \Theta( \sqrt{n \log (T/n) for vector balancing and 1 for interval balancing, a natural question is whether one can (nearly) match the offline bounds in the online setting for these problems.
Edit intent: meaning-changed
Original:  Given a distribution of phenotypes on a fitness landscape , we summarize how natural selection moves a population in the direction of a natural gradient , similar to natural gradientdescent, and show how intermediate selection is most informative of the fitness landscape.
Revised:  Optimization of a continuous objective function is analogous to searching for high fitness phenotypes on a fitness landscape , we summarize how natural selection moves a population in the direction of a natural gradient , similar to natural gradientdescent, and show how intermediate selection is most informative of the fitness landscape.
Edit intent: meaning-changed
Original:  Given a distribution of phenotypes on a fitness landscape , we summarize how natural selection moves a population in the direction of a natural gradient , similar to natural gradientdescent, and show how intermediate selection is most informative of the fitness landscape.
Revised:  Given a distribution of phenotypes on a fitness landscape . We summarize how natural selection moves a population in the direction of a natural gradient , similar to natural gradientdescent, and show how intermediate selection is most informative of the fitness landscape.
Edit intent: fluency
Original:  Given a distribution of phenotypes on a fitness landscape , we summarize how natural selection moves a population in the direction of a natural gradient , similar to natural gradientdescent, and show how intermediate selection is most informative of the fitness landscape.
Revised:  Given a distribution of phenotypes on a fitness landscape , we summarize how natural selection moves a population along the non-euclidean gradient that is induced by the population on the fitness landscape (the natural gradient). Under normal approximations common in quantitative genetics, we show how selection is related to Newton's method in optimization. We find that intermediate selection is most informative of the fitness landscape.
Edit intent: meaning-changed
Original:  Finally we combine natural selection, our recombination operator, and an adaptive method to increase selection to create a quantitative genetic algorithm (QGA). QGA is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, with similar performance, although QGA requires tuning of its single hyperparameter.
Revised:  Finally we combine natural selection, our recombination operator, and an adaptive method to increase selection . Our algorithm is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, with similar performance, although QGA requires tuning of its single hyperparameter.
Edit intent: clarity
Original:  QGA is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, with similar performance, although QGA requires tuning of its single hyperparameter. QGA is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, is trivial to parallelize, and may form the basis of more robust algorithms .
Revised:  QGA is similar to covariance matrix adaptation and natural evolutionary strategies in optimization, and has similar performance. The algorithm is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, is trivial to parallelize, and may form the basis of more robust algorithms .
Edit intent: coherence
Original:  QGA is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, is trivial to parallelize, and may form the basis of more robust algorithms .
Revised:  QGA is extremely simple in implementation with no matrix inversion or factorization, does not require storing a covariance matrix, is trivial to parallelize, and may form the basis of more general model-based optimization algorithms with natural gradient updates .
Edit intent: meaning-changed
Original:  The more general problem of covering a set of N segments (not necessarily disjoint) by min-area disjoint  polygons can also be solved in near linear time   .
Revised:  The more general problem of covering a set of N segments (not necessarily disjoint) by min-area disjoint polygons can also be solved in near linear time . A similar result is already known, see the work by Barba et al. .
Edit intent: meaning-changed
Original:  In this paper we propose  an attention-based approach that allows our model  to index into a long term memory bank constructed on a per-camera basis and aggregate contextual features from other frames to boost object detection performance on the current frame.
Revised:  In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera. Specifically, we propose an attention-based approach that allows our model to index into a long term memory bank constructed on a per-camera basis and aggregate contextual features from other frames to boost object detection performance on the current frame.
Edit intent: meaning-changed
Original:  In this paper we propose  an attention-based approach that allows our model  to index into a long term memory bank constructed on a per-camera basis and aggregate contextual features from other frames to boost object detection performance on the current frame.
Revised:  In this paper we propose an attention-based approach that allows our model , Context R-CNN, to index into a long term memory bank constructed on a per-camera basis and aggregate contextual features from other frames to boost object detection performance on the current frame.
Edit intent: meaning-changed
Original:  We apply our models to two settings: (1) species detection using camera trap data, which is sampled at a low, variable frame rate based on a motion trigger and used to study biodiversity, and (2) vehicle detection in traffic cameras, which have similarly low frame rate.
Revised:  We apply Context R-CNN to two settings: (1) species detection using camera trap data, which is sampled at a low, variable frame rate based on a motion trigger and used to study biodiversity, and (2) vehicle detection in traffic cameras, which have similarly low frame rate.
Edit intent: meaning-changed
Original:  We apply our models to two settings: (1) species detection using camera trap data, which is sampled at a low, variable frame rate based on a motion trigger and used to study biodiversity, and (2) vehicle detection in traffic cameras, which have similarly low frame rate.
Revised:  We apply our models to two settings: (1) species detection using camera traps, and (2) vehicle detection in traffic cameras, which have similarly low frame rate.
Edit intent: coherence
Original:  We apply our models to two settings: (1) species detection using camera trap data, which is sampled at a low, variable frame rate based on a motion trigger and used to study biodiversity, and (2) vehicle detection in traffic cameras, which have similarly low frame rate. We show that our model leads to performance gains over strong baselines in all settings .
Revised:  We apply our models to two settings: (1) species detection using camera trap data, which is sampled at a low, variable frame rate based on a motion trigger and used to study biodiversity, and (2) vehicle detection in traffic cameras, showing in both settings that Context R-CNN leads to performance gains over strong baselines in all settings .
Edit intent: meaning-changed
Original:  When applied to camera trap data from the Snapshot Serengeti dataset, our best model which leverages context from up to a month of images outperforms the single-frame baseline by 17.9\% mAP at 0.5 IOU , and outperforms S3D (a 3d convolution based baseline) by 11.2\% mAP.
Revised:  When applied to camera trap data from the Snapshot Serengeti dataset, Context R-CNN with context from up to a month of images outperforms the single-frame baseline by 17.9\% mAP at 0.5 IOU , and outperforms S3D (a 3d convolution based baseline) by 11.2\% mAP.
Edit intent: meaning-changed
Original:  When applied to camera trap data from the Snapshot Serengeti dataset, our best model which leverages context from up to a month of images outperforms the single-frame baseline by 17.9\% mAP at 0.5 IOU , and outperforms S3D (a 3d convolution based baseline) by 11.2\% mAP.
Revised:  When applied to camera trap data from the Snapshot Serengeti dataset, our best model which leverages context from up to a month of images outperforms a single-frame baseline by 17.9\% mAP at 0.5 IOU , and outperforms S3D (a 3d convolution based baseline) by 11.2\% mAP.
Edit intent: fluency
Original:  When applied to camera trap data from the Snapshot Serengeti dataset, our best model which leverages context from up to a month of images outperforms the single-frame baseline by 17.9\% mAP at 0.5 IOU , and outperforms S3D (a 3d convolution based baseline) by 11.2\% mAP.
Revised:  When applied to camera trap data from the Snapshot Serengeti dataset, our best model which leverages context from up to a month of images outperforms the single-frame baseline by 17.9\% mAP , and outperforms S3D (a 3d convolution based baseline) by 11.2\% mAP.
Edit intent: meaning-changed
Original: Identifying risk factors from longitudinal data requires statistical tools that are not restricted to linear models, yet provide interpretable associations between different types of covariates and a response variable.
Revised: Longitudinal study designs are indispensable for studying disease progression. Inferring covariate effects from longitudinal data requires statistical tools that are not restricted to linear models, yet provide interpretable associations between different types of covariates and a response variable.
Edit intent: clarity
Original: Identifying risk factors from longitudinal data requires statistical tools that are not restricted to linear models, yet provide interpretable associations between different types of covariates and a response variable. Here, we present a widely applicable and interpretable probabilistic machine learning method for nonparametric longitudinal data analysis using additive Gaussian process regression .
Revised: Identifying risk factors from longitudinal data , however, requires interpretable methods that can model complicated covariance structures and detect nonlinear effects of both categorical and continuous covariates, as well as their interactions. Detecting disease effects is hindered by the fact that they often occur rapidly near the disease initiation time, and this time point cannot be exactly observed. An additional challenge is that the effect magnitude can be heterogeneous over the subjects. We present lgpr, a widely applicable and interpretable probabilistic machine learning method for nonparametric longitudinal data analysis using additive Gaussian process regression .
Edit intent: meaning-changed
Original:  We demonstrate that it outperforms previous longitudinal modeling approaches and provides useful novel features, including the ability to account for uncertainty in disease effect times as well as heterogeneity in their effects .
Revised:  We demonstrate that it outperforms previous approaches in identifying the relevant categorical and continuous covariates in various settings. Furthermore, it implements important novel features, including the ability to account for uncertainty in disease effect times as well as heterogeneity in their effects .
Edit intent: meaning-changed
Original:  We demonstrate that it outperforms previous longitudinal modeling approaches and provides useful novel features, including the ability to account for uncertainty in disease effect times as well as heterogeneity in their effects .
Revised:  We demonstrate that it outperforms previous longitudinal modeling approaches and provides useful novel features, including the ability to account for the heterogeneity of covariate effects, their temporal uncertainty, and appropriate observation models for different types of biomedical data. The lgpr tool is implemented as a comprehensive and user-friendly R-package. lgpr is available at jtimonen.github.io/lgpr-usage with documentation, tutorials, test data, and code for reproducing the experiments of this paper .
Edit intent: meaning-changed
Original:  In the equilibrium, the interim expected payoff of the collusion initiator Pareto dominates the counterpart in any robust equilibria of the single-option model (Es\"{o} and Schummer (2004)) and any other separating equilibria in our model.
Revised:  In the equilibrium, for each type of initiator the expected payoff is generally higher than the counterpart in any robust equilibria of the single-option model (Es\"{o} and Schummer (2004)) and any other separating equilibria in our model.
Edit intent: clarity
Original:  Several illustrative examples of the new concept are given , among them a novel result on the Margrabe option to exchange one defaultable asset for another.
Revised:  The calculus is also intuitive as it allows the user to pretend all jumps are of compound Poisson type. The new calculus is very effective when it comes to computing drifts and expected values that possibly involve a change of measure. Such drift calculations yield, for example, partial integro-differential equations, Hamilton-Jacobi-Bellman equations, Feynman-Kac formulae, or exponential moments needed in numerous applications. We provide several illustrations of the new technique , among them a novel result on the Margrabe option to exchange one defaultable asset for another.
Edit intent: meaning-changed
Original:  The key technique is the interplay between the local well-posedness of fully coupled path-dependent forward backward stochastic differential equations  and backward iterations of the superquadratic BSDE. The superquadratic BSDE  in this article includes quadratic BSDEs appear in stochastic differential game and price impact model.
Revised:  The key technique is the interplay between the local well-posedness of fully coupled path-dependent forward backward stochastic differential equations (FBSDEs) and backward iterations of the superquadratic BSDE. The superquadratic BSDE in this article includes quadratic BSDEs appear in stochastic differential game and price impact model.
Edit intent: clarity
Original:  The key technique is the interplay between the local well-posedness of fully coupled path-dependent forward backward stochastic differential equations  and backward iterations of the superquadratic BSDE. The superquadratic BSDE  in this article includes quadratic BSDEs appear in stochastic differential game and price impact model.
Revised:  The key technique is the interplay between the local well-posedness of fully coupled path-dependent forward backward stochastic differential equations and backward iterations of the superquadratic BSDE. The superquadratic BSDE studied in this article includes quadratic BSDEs appear in stochastic differential game and price impact model.
Edit intent: clarity
Original:   Our result also provides the well-posedness of a system of path-dependent quasilinear PDE that generalizes Ladyzhenskaia and Uraltseva (1968) .
Revised:  We also study the well-posedness of superquadratic FBSDE using the corresponding BSDE results. Our result also provides the well-posedness of a system of path-dependent quasilinear PDE that generalizes Ladyzhenskaia and Uraltseva (1968) .
Edit intent: meaning-changed
Original:  In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlation between text representation and cluster representation . Relying on this, the representation learning and clustering for short text are seamlessly integrated into a unified framework .
Revised:  In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlations between text representations and cluster representations . Relying on this, the representation learning and clustering for short text are seamlessly integrated into a unified framework .
Edit intent: fluency
Original:  In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlation between text representation and cluster representation . Relying on this, the representation learning and clustering for short text are seamlessly integrated into a unified framework .
Revised:  In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlation between text representation and cluster representation . Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified framework .
Edit intent: fluency
Original:  In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlation between text representation and cluster representation . Relying on this, the representation learning and clustering for short text are seamlessly integrated into a unified framework .
Revised:  In this paper, we present a novel attentive representation learning model for shot text clustering, wherein cluster-level attention is proposed to capture the correlation between text representation and cluster representation . Relying on this, the representation learning and clustering for short text are seamlessly integrated into a unified model .
Edit intent: clarity
Original:  One measure of tax evasion is the so-called tax gap: the difference between the income that should be reported to the tax authorities and the amount actually reported. However, economists lack a robust method for estimating the tax gap through a bottom-up approach  based on fiscal audits.
Revised:  One measure of tax evasion is the so-called tax gap: the difference between the total amounts of tax theoretically collectable and the total amounts of tax actually collected in a given period. This paper presents an original contribution to bottom-up approach based on fiscal audits.
Edit intent: clarity
Original:  However, economists lack a robust method for estimating the tax gap through a bottom-up approach  based on fiscal audits.
Revised:  However, economists lack a robust method for estimating the tax gap through a bottom-up approach , based on results from fiscal audits, through the use of Machine Learning. The major disadvantage of bottom-up approaches is represented by selection bias when audited taxpayers are not randomly selected, as in the case of audits performed by the Italian Revenue Agency. Our proposal, based on fiscal audits.
Edit intent: meaning-changed
Original:  However, economists lack a robust method for estimating the tax gap through a bottom-up approach  based on fiscal audits. This is difficult because the declared tax base is available on the whole population but the income reported to the tax authorities is generally available only on a small, non-random sample of audited units. This induces a selection bias which invalidates standard statistical methods. Here, we use machine learning based on a 2-steps Gradient Boosting model,  to correct for the selection bias without requiring any strong assumption on the distribution.
Revised:  However, economists lack a robust method for estimating the tax gap through a bottom-up approach based on a 2-steps Gradient Boosting model, to correct for the selection bias without requiring any strong assumption on the distribution.
Edit intent: coherence
Original:  Here, we use machine learning based on a 2-steps Gradient Boosting model,  to correct for the selection bias without requiring any strong assumption on the distribution. We use our method to estimate the Italian VAT Gap related to individual firms based on information gathered from administrative sources.
Revised:  Here, we use machine learning based on a 2-steps Gradient Boosting model, to correct for the selection bias which do not require any assumptions on the underlying data distribution. The 2-steps Gradient Boosting approach is used to estimate the Italian VAT Gap related to individual firms based on information gathered from administrative sources.
Edit intent: clarity
Original:  We use our method to estimate the Italian VAT Gap related to individual firms based on information gathered from administrative sources. Our algorithm estimates the potential VAT turnover of Italian individual firms for the fiscal year 2011 and suggests that the tax gap is about 30\% of the total potential tax base.
Revised:  We use our method to estimate the Italian Value-added tax (VAT) gap on individual firms on the basis of fiscal and administrative data income tax returns gathered from Tax Administration Data Base, for the fiscal year 2011 and suggests that the tax gap is about 30\% of the total potential tax base.
Edit intent: meaning-changed
Original:  Our algorithm estimates the potential VAT turnover of Italian individual firms for the fiscal year 2011 and suggests that the tax gap is about 30\% of the total potential tax base. Comparisons with other methods show our technique offers a significant improvement in predictive performance .
Revised:  Our algorithm estimates the potential VAT turnover of Italian individual firms for the fiscal year 2011. The proposed method significantly boost the performance in predicting with respect to the classical parametric approaches .
Edit intent: coherence