wanyu/IteraTeR_v2 huggingface dataset:
Original:  We consider applications to [{18}F]-fluorodeoxyglucose Positron Emission Tomography ( FDG--PET ) data and analyze the two--compartment catenary model describing the standard FDG metabolization by an homogeneous tissue and the three--compartment non--catenary model representing the renal physiology.
Revised:  We consider applications to [{18}F]-fluorodeoxyglucose Positron Emission Tomography ( FDG--PET ) data and analyze the two--compartment catenary model describing the standard FDG metabolization by an homogeneous tissue and the three-compartment non-catenary model representing the renal physiology.
Edit intent: fluency
Original:  The proposed imaging method starts from the reconstructed FDG--PET images of tracer concentration and preliminarily applies image processing algorithms for noise reduction and image segmentation.
Revised:  The proposed imaging method starts from the reconstructed FDG-PET images of tracer concentration and preliminarily applies image processing algorithms for noise reduction and image segmentation.
Edit intent: fluency
Original:  The optimization procedure solves pixelwise the non--linear inverse problem of determining the kinetic parameters from dynamic concentration data through a regularized Gauss--Newton iterative algorithm.
Revised:  The optimization procedure solves pixelwise the non-linear inverse problem of determining the kinetic parameters from dynamic concentration data through a regularized Gauss--Newton iterative algorithm.
Edit intent: fluency
Original:  The optimization procedure solves pixelwise the non--linear inverse problem of determining the kinetic parameters from dynamic concentration data through a regularized Gauss--Newton iterative algorithm.
Revised:  The optimization procedure solves pixelwise the non--linear inverse problem of determining the kinetic parameters from dynamic concentration data through a regularized Gauss-Newton iterative algorithm.
Edit intent: fluency
Original:  The reliability of the method is validated against synthetic data, for the two--compartment system, and experimental real data of murine models, for the renal three--compartment system.
Revised:  The reliability of the method is validated against synthetic data, for the two-compartment system, and experimental real data of murine models, for the renal three--compartment system.
Edit intent: fluency
Original:  The reliability of the method is validated against synthetic data, for the two--compartment system, and experimental real data of murine models, for the renal three--compartment system.
Revised:  The reliability of the method is validated against synthetic data, for the two--compartment system, and experimental real data of murine models, for the renal three-compartment system.
Edit intent: fluency
Original: Boolean matrix factorisation (BooMF) infers interpretable decompositions of a binary data matrix into a pair of low-rank , binary matrices: One containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns.
Revised: Boolean matrix factorisation aims to decompose a binary data matrix into a pair of low-rank , binary matrices: One containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns.
Edit intent: clarity
Original: Boolean matrix factorisation (BooMF) infers interpretable decompositions of a binary data matrix into a pair of low-rank , binary matrices: One containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns.
Revised: Boolean matrix factorisation (BooMF) infers interpretable decompositions of a binary data matrix into a pair of low-rank , binary matrices: one containing meaningful patterns, the other quantifying how the observations can be expressed as a combination of these patterns.
Edit intent: fluency
Original:  We introduce the OrMachine, a probabilistic generative model for BooMF and derive a Metropolised Gibbs sampler that facilitates very efficient parallel posterior inference.
Revised:  We introduce the OrMachine, a probabilistic generative model for BooMF and derive a Metropolised Gibbs sampler that facilitates efficient parallel posterior inference.
Edit intent: clarity
Original:  Our method outperforms all currently existing approaches for Boolean Matrix factorization and completion, as we show on simulated and real world data .
Revised:  On real world and simulated data, our method outperforms all currently existing approaches for Boolean Matrix factorization and completion, as we show on simulated and real world data .
Edit intent: clarity
Original:  Our method outperforms all currently existing approaches for Boolean Matrix factorization and completion, as we show on simulated and real world data .
Revised:  Our method outperforms all currently existing approaches for Boolean matrix factorisation and completion .
Edit intent: coherence
Original:  This is the first method to provide full posterior inference for BooMF which is relevant in applications, e.g. for controlling false positive rates in collaborative filtering , andcrucially it improves the interpretability of the inferred patterns.
Revised:  This is the first method to provide full posterior inference for BooMF which is relevant in applications, e.g. for controlling false positive rates in collaborative filtering and, crucially, improves the interpretability of the inferred patterns.
Edit intent: coherence
Original:  The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.3 million mouse brain cells across 11 ,000 genes on commodity hardware.
Revised:  The proposed algorithm scales to large datasets as we demonstrate by analysing single cell gene expression data in 1.3 million mouse brain cells across 11 thousand genes on commodity hardware.
Edit intent: fluency
Original: Airborne laser scanning ( lidar ) point clouds can be process to extract tree-level information over large forested landscapes.
Revised: Airborne laser scanning ( LiDAR ) point clouds can be process to extract tree-level information over large forested landscapes.
Edit intent: clarity
Original:  Existing procedures typically detect more than 90\% of overstory trees, yet they barely detect 60\% of understory trees because of reduced number of lidar points penetrating the top canopy layer .
Revised:  Existing procedures typically detect more than 90\% of overstory trees, yet they barely detect 60\% of understory trees because of the occlusion effect of higher canopy layers .
Edit intent: clarity
Original:  Although understory trees provide limited financial value, they offer habitat for numerous wildlife species and are important for stand development.
Revised:  Although understory trees provide limited financial value, they are an essential component of ecosystem functioning by offering habitat for numerous wildlife species and are important for stand development.
Edit intent: clarity
Original:  Although understory trees provide limited financial value, they offer habitat for numerous wildlife species and are important for stand development.
Revised:  Although understory trees provide limited financial value, they offer habitat for numerous wildlife species and influencing stand development.
Edit intent: clarity
Original:  We show  at a density of about 170 pt/ m2 understory tree identification accuracy likely plateaus, which we regard as the required point density for reasonable identification of understory trees.
Revised:  We show that at a density of about 170 pt/ m2 understory tree identification accuracy likely plateaus, which we regard as the required point density for reasonable identification of understory trees.
Edit intent: fluency
Original:  We show  at a density of about 170 pt/ m2 understory tree identification accuracy likely plateaus, which we regard as the required point density for reasonable identification of understory trees.
Revised:  We show at a density of ~ 170 pt/ m2 understory tree identification accuracy likely plateaus, which we regard as the required point density for reasonable identification of understory trees.
Edit intent: clarity
Original:  Given the advancements of lidar sensor technology, point clouds can feasibly reach the required densityto enable effective identification of individual understory trees, ultimately making remote quantification of forest resources more accurate.
Revised:  Given the advancements of LiDAR sensor technology, point clouds can feasibly reach the required densityto enable effective identification of individual understory trees, ultimately making remote quantification of forest resources more accurate.
Edit intent: fluency
Original:  Given the advancements of lidar sensor technology, point clouds can feasibly reach the required densityto enable effective identification of individual understory trees, ultimately making remote quantification of forest resources more accurate.
Revised:  Given the advancements of lidar sensor technology, point clouds will affordably reach this required density. Using modern computational approaches for big data, the denser point clouds can efficiently be processed to ultimately allow accurate remote quantification of forest resources more accurate.
Edit intent: clarity
Original:  The main technical challenge is to show that a small number of randomly chosen monomials are enough to construct a degree-3 polynomial with a precisely planted orthogonal global optima over the sphere and that this fact can be certified within the sum-of-squares proof system.
Revised:  The main technical challenge is to show that a small number of randomly chosen monomials are enough to construct a degree-3 polynomial with precisely planted orthogonal global optima over the sphere and that this fact can be certified within the sum-of-squares proof system.
Edit intent: fluency
Original:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Revised:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Edit intent: fluency
Original:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Revised:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S %DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Edit intent: fluency
Original:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Revised:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Edit intent: fluency
Original:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Revised:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A %DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Edit intent: clarity
Original:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Revised:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Edit intent: fluency
Original:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R ecommendation systems(SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Revised:  To this end, this paper proposes a %DIFDELCMD < {\bf %%% S emantic%DIFDELCMD < {\bf %DIFDELCMD < %%% A nalysis approach for%DIFDELCMD < {\bf %%% R (SAR) , which applies a two-level hierarchical generative process that assigns semantic properties and categories for user and item.
Edit intent: fluency
Original:  During this campaigns, users were about distributing the avatars between each other.
Revised:  During these campaigns, users were about distributing the avatars between each other.
Edit intent: fluency
Original:  During this campaigns, users were about distributing the avatars between each other.
Revised:  During this campaigns, users were distributing the avatars between each other.
Edit intent: clarity
Original:  During these campaigns, users were distributing the avatars between each other  .
Revised:  Users distributed avatars between each other .
Edit intent: clarity
Original:  The campaign data is accompanied by the eventsthat can be used to build a multilayer network in order to be able to place these campaigns in a wider context.
Revised:  The campaign data is accompanied by events. The data can be used to build a multilayer network in order to be able to place these campaigns in a wider context.
Edit intent: fluency
Original:  The campaign data is accompanied by the eventsthat can be used to build a multilayer network in order to be able to place these campaigns in a wider context.
Revised:  The campaign data is accompanied by the eventsthat can be used to build a multilayer network to place the campaigns in a wider context.
Edit intent: coherence
Original:  To the best of our knowledge, it is the first publicly available dataset containing  complete real multilayer social network together  with five complete spreading processes in it.
Revised:  To the best of the authors knowledge, the study is the first publicly available dataset containing complete real multilayer social network together with five complete spreading processes in it.
Edit intent: clarity
Original:  To the best of our knowledge, it is the first publicly available dataset containing  complete real multilayer social network together  with five complete spreading processes in it.
Revised:  To the best of our knowledge, it is the first publicly available dataset containing a complete real multilayer social network together with five complete spreading processes in it.
Edit intent: fluency
Original:  To the best of our knowledge, it is the first publicly available dataset containing  complete real multilayer social network together  with five complete spreading processes in it.
Revised:  To the best of our knowledge, it is the first publicly available dataset containing complete real multilayer social network together , along with five complete spreading processes in it.
Edit intent: clarity
Original: Comparing images in order to recommend items from an image-inventory is a subject of continued interest.
Revised: Comparing images to recommend items from an image-inventory is a subject of continued interest.
Edit intent: coherence
Original:  Specifically, after modelling individual images using approximations of mixture models or sparse covariance estimators  we resort to their information-theoretic and Riemann geometric comparisons.
Revised:  Specifically, after modelling individual images using approximations of mixture models or sparse covariance estimators , we resort to their information-theoretic and Riemann geometric comparisons.
Edit intent: fluency
Original:  We show that using approximations of mixture models enable us to to compute a distance measure based on the Wasserstein metric that requires less effort than  computationally intensive optimal transport plans;
Revised:  We show that using approximations of mixture models enable us to compute a distance measure based on the Wasserstein metric that requires less effort than computationally intensive optimal transport plans;
Edit intent: fluency
Original:  We show that using approximations of mixture models enable us to to compute a distance measure based on the Wasserstein metric that requires less effort than  computationally intensive optimal transport plans;
Revised:  We show that using approximations of mixture models enable us to to compute a distance measure based on the Wasserstein metric that requires less effort than other computationally intensive optimal transport plans;
Edit intent: clarity
Original:  finally, an affine invariant metric is used to compare the optimal transport metric to its Riemann geometric counterpart -- we conclude that although expensive, retrieval metric based on Wasserstein geometry are more suitable than information theoretic comparison of images.
Revised:  finally, an affine invariant metric is used to compare the optimal transport metric to its Riemann geometric counterpart -- we conclude that although expensive, retrieval metric based on Wasserstein geometry is more suitable than information theoretic comparison of images.
Edit intent: fluency
Original:  In short, we combine GPU scalability in learning deep feature vectors with computationally efficient metrics that we foresee being utilized in a commercial setting.
Revised:  In short, we combine GPU scalability in learning deep feature vectors with statistically efficient metrics that we foresee being utilized in a commercial setting.
Edit intent: clarity
Original:  In short, we combine GPU scalability in learning deep feature vectors with computationally efficient metrics that we foresee being utilized in a commercial setting.
Revised:  In short, we combine GPU scalability in learning deep feature vectors with computationally efficient metrics that we foresee being utilised in a commercial setting.
Edit intent: fluency
Original:   Experiment results show that the proposed architecture is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests ( in small batch size) .
Revised:  Experiment results show that the proposed architecture is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests in small batch size) .
Edit intent: coherence
Original:   Experiment results show that the proposed architecture is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests ( in small batch size) .
Revised:  Experiment results show that the proposed architecture is 8.3x faster and 75x more energy-efficient than a Titan X GPU for processing online individual requests ( in small batch sizes .
Edit intent: coherence
Original:  For processing static data ( in large batch size) , the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.
Revised:  For processing static data in large batch size) , the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.
Edit intent: fluency
Original:  For processing static data ( in large batch size) , the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.
Revised:  For processing static data ( in large batch sizes , the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9.5x higher energy efficiency.
Edit intent: fluency
Original: Unsupervised learning allows algorithms to adapt to different data thanks to the autonomous discovery of discriminating features during the training.
Revised: Unsupervised learning permits the development of algorithms that are able to adapt to different data thanks to the autonomous discovery of discriminating features during the training.
Edit intent: clarity
Original:  Recently, new Hebbian-like plasticity, bio-inspired, local and unsupervised learning rules for neural networks , have been shown to minimise a cost-function while performing online sparse representation learning.
Revised:  Recently, new Hebbian-like and local unsupervised learning rules for neural networks , have been shown to minimise a cost-function while performing online sparse representation learning.
Edit intent: clarity
Original:  Recently, new Hebbian-like plasticity, bio-inspired, local and unsupervised learning rules for neural networks , have been shown to minimise a cost-function while performing online sparse representation learning.
Revised:  Recently, new Hebbian-like plasticity, bio-inspired, local and unsupervised learning rules for neural networks , have been shown to minimise a cost-function . These have been shown to perform sparse representation learning.
Edit intent: coherence
Original:  However, it is unclear to what degree such rules are effective to learn features from images.
Revised:  This study tests the effectiveness of one such learning rule for learning features from images.
Edit intent: clarity
Original:  To investigate this point, this study introduces a novel multi-layer Hebbian network trained by a rule derived from a non-negative classical multidimensional scaling cost-function .
Revised:  The rule implemented is derived from a non-negative classical multidimensional scaling cost-function .
Edit intent: clarity
Original:  To investigate this point, this study introduces a novel multi-layer Hebbian network trained by a rule derived from a non-negative classical multidimensional scaling cost-function .
Revised:  To investigate this point, this study introduces a novel multi-layer Hebbian network trained by a rule derived from a nonnegative classical multidimensional scaling cost-function .
Edit intent: fluency
Original: Brains need to predict how our muscles and body react to motor commands.
Revised: Brains need to predict how the body reacts to motor commands.
Edit intent: clarity
Original:  How networks of spiking neurons can learn to reproduce these non-linear dynamics , using local, online and stable learning rules , is an important, open question .
Revised:  It is an open question how networks of spiking neurons can learn to reproduce these non-linear dynamics , using local, online and stable learning rules , is an important, open question .
Edit intent: coherence
Original:  How networks of spiking neurons can learn to reproduce these non-linear dynamics , using local, online and stable learning rules , is an important, open question .
Revised:  How networks of spiking neurons can learn to reproduce the non-linear dynamics , using local, online and stable learning rules , is an important, open question .
Edit intent: fluency
Original:  How networks of spiking neurons can learn to reproduce these non-linear dynamics , using local, online and stable learning rules , is an important, open question .
Revised:  How networks of spiking neurons can learn to reproduce these non-linear body dynamics caused by motor commands , using local, online and stable learning rules , is an important, open question .
Edit intent: clarity