wanyu/IteraTeR_v2 huggingface dataset:
Original:  The computational efficiencies of the methods are demonstrated by computing the expected information gain from an electrical impedance tomography experiment where the fiber orientation in composite laminate materials are inferred through Bayesian inversion . MLDLSC performs better than MLDLMC when the regularity of the underlying computational model , with respect to the additive noise and the unknown parameters, can be exploited.
Revised:  The computational efficiencies of the methods are demonstrated by estimating the expected information gain from an electrical impedance tomography experiment where the fiber orientation in composite laminate materials are inferred through Bayesian inversion . MLDLSC performs better than MLDLMC when the regularity of the underlying computational model , with respect to the additive noise and the unknown parameters, can be exploited.
Edit intent: clarity
Original:  The computational efficiencies of the methods are demonstrated by computing the expected information gain from an electrical impedance tomography experiment where the fiber orientation in composite laminate materials are inferred through Bayesian inversion . MLDLSC performs better than MLDLMC when the regularity of the underlying computational model , with respect to the additive noise and the unknown parameters, can be exploited.
Revised:  The computational efficiencies of the methods are demonstrated by computing the expected information gain for Bayesian inference of the fiber orientation in composite laminate materials are inferred through Bayesian inversion . MLDLSC performs better than MLDLMC when the regularity of the underlying computational model , with respect to the additive noise and the unknown parameters, can be exploited.
Edit intent: clarity
Original:  The computational efficiencies of the methods are demonstrated by computing the expected information gain from an electrical impedance tomography experiment where the fiber orientation in composite laminate materials are inferred through Bayesian inversion . MLDLSC performs better than MLDLMC when the regularity of the underlying computational model , with respect to the additive noise and the unknown parameters, can be exploited.
Revised:  The computational efficiencies of the methods are demonstrated by computing the expected information gain from an electrical impedance tomography experiment where the fiber orientation in composite laminate materials from an electrical impedance tomography experiment . MLDLSC performs better than MLDLMC when the regularity of the underlying computational model , with respect to the additive noise and the unknown parameters, can be exploited.
Edit intent: clarity
Original:  The computational efficiencies of the methods are demonstrated by computing the expected information gain from an electrical impedance tomography experiment where the fiber orientation in composite laminate materials are inferred through Bayesian inversion . MLDLSC performs better than MLDLMC when the regularity of the underlying computational model , with respect to the additive noise and the unknown parameters, can be exploited.
Revised:  The computational efficiencies of the methods are demonstrated by computing the expected information gain from an electrical impedance tomography experiment where the fiber orientation in composite laminate materials are inferred through Bayesian inversion . MLDLSC performs better than MLDLMC when the regularity of the quantity of interest , with respect to the additive noise and the unknown parameters, can be exploited.
Edit intent: clarity
Original:  An n-dimensional hypercube Q_n , is an undirected graph with 2^n nodes, each labeled with a distinct binary string of length n.
Revised:  An n-dimensional hypercube Q_n is an undirected graph with 2^n nodes, each labeled with a distinct binary string of length n.
Edit intent: fluency
Original:  An n-dimensional hypercube Q_n , is an undirected graph with 2^n nodes, each labeled with a distinct binary string of length n.
Revised:  An n-dimensional hypercube Q_n , is an undirected graph with 2^n nodes, each labeled with a distinct binary strings of length n.
Edit intent: fluency
Original:  We show that Q_n with n%DIFDELCMD < \ge %%% 4, is\ge4 \ge4  Hamiltonian if and only if for each dimension i, there is a healthy crossing edge of parity 0 and a healthy crossing edge of parity 1.
Revised:  We show that Q_n %DIFDELCMD < \ge %%% 4, is\ge4 \ge4 Hamiltonian if and only if for each dimension i, there is a healthy crossing edge of parity 0 and a healthy crossing edge of parity 1.
Edit intent: clarity
Original:  A common example arises in the process collecting network data: we often obtain network datasets by recording all of the interactions among a small set of core nodes, so that we end up with a measurement of the network consisting of these core nodes together with a potentially much larger set of fringe nodes that have links to the core.
Revised:  A common example arises in collecting network data: we often obtain network datasets by recording all of the interactions among a small set of core nodes, so that we end up with a measurement of the network consisting of these core nodes together with a potentially much larger set of fringe nodes that have links to the core.
Edit intent: clarity
Original:  A common example arises in the process collecting network data: we often obtain network datasets by recording all of the interactions among a small set of core nodes, so that we end up with a measurement of the network consisting of these core nodes together with a potentially much larger set of fringe nodes that have links to the core.
Revised:  A common example arises in the process collecting network data: we often obtain network datasets by recording all of the interactions among a small set of core nodes, so that we end up with a measurement of the network consisting of these core nodes along with a potentially much larger set of fringe nodes that have links to the core.
Edit intent: fluency
Original:  Given the ubiquity of this process for assembling network data, it becomes crucial to understand the role of such a  core-fringe  structure.
Revised:  Given the ubiquity of this process for assembling network data, it is crucial to understand the role of such a core-fringe structure.
Edit intent: coherence
Original:  Given the ubiquity of this process for assembling network data, it becomes crucial to understand the role of such a  core-fringe  structure.
Revised:  Given the ubiquity of this process for assembling network data, it becomes crucial to understand the role of such a ` core-fringe structure.
Edit intent: fluency
Original:  Given the ubiquity of this process for assembling network data, it becomes crucial to understand the role of such a  core-fringe  structure.
Revised:  Given the ubiquity of this process for assembling network data, it becomes crucial to understand the role of such a core-fringe ' structure.
Edit intent: fluency
Original:  In some datasets, once an algorithm is selected  , including any additional data from the fringe can actually hurt prediction performance;
Revised:  Once an algorithm is selected , including any additional data from the fringe can actually hurt prediction performance;
Edit intent: clarity
Original:  In some datasets, once an algorithm is selected  , including any additional data from the fringe can actually hurt prediction performance;
Revised:  In some datasets, once an algorithm is selected , in some datasets , including any additional data from the fringe can actually hurt prediction performance;
Edit intent: coherence
Original: We develop PlatonicGAN to discover  3D structure of an object class from an unstructured collection of 2D images .
Revised: We introduce PlatonicGAN to discover 3D structure of an object class from an unstructured collection of 2D images .
Edit intent: clarity
Original: We develop PlatonicGAN to discover  3D structure of an object class from an unstructured collection of 2D images .
Revised: We develop PlatonicGAN to discover the 3D structure of an object class from an unstructured collection of 2D images .
Edit intent: fluency
Original:  The key idea is to learn a deep neural network that generates 3D shapes that are never objectionable to a discriminator looking only at its 2D projections, i.e. renderings of the generated volumes.
Revised:  The key idea is to train a deep neural network that generates 3D shapes that are never objectionable to a discriminator looking only at its 2D projections, i.e. renderings of the generated volumes.
Edit intent: clarity
Original:  The key idea is to learn a deep neural network that generates 3D shapes that are never objectionable to a discriminator looking only at its 2D projections, i.e. renderings of the generated volumes.
Revised:  The key idea is to learn a deep neural network to generate 3D shapes that are never objectionable to a discriminator looking only at its 2D projections, i.e. renderings of the generated volumes.
Edit intent: fluency
Original:  Using such a 2D instead of a 3D discriminator allows tapping into massive 2D image collections instead of relying on much smaller 3D data sets.
Revised:  Using such a 2D images instead of 3D discriminator allows tapping into massive 2D image collections instead of relying on much smaller 3D data sets.
Edit intent: fluency
Original:  Using such a 2D instead of a 3D discriminator allows tapping into massive 2D image collections instead of relying on much smaller 3D data sets.
Revised:  Using such a 2D instead of a 3D shapes allows tapping into massive 2D image collections instead of relying on much smaller 3D data sets.
Edit intent: clarity
Original:  Using such a 2D instead of a 3D discriminator allows tapping into massive 2D image collections instead of relying on much smaller 3D data sets.
Revised:  Using such a 2D instead of a 3D discriminator allows tapping into unstructured 2D image collections instead of relying on much smaller 3D data sets.
Edit intent: clarity
Original:  Using such a 2D instead of a 3D discriminator allows tapping into massive 2D image collections instead of relying on much smaller 3D data sets.
Revised:  Using such a 2D instead of a 3D discriminator allows tapping into massive 2D photo collections instead of relying on much smaller 3D data sets.
Edit intent: clarity
Original:  To establish constraints between 2D image observation and their 3D interpretation  we suggest a family of rendering layers that are effectively back-propagatable .
Revised:  To establish constraints between 2D image observation and their 3D interpretation , we suggest a family of rendering layers that are effectively back-propagatable .
Edit intent: fluency
Original:  To establish constraints between 2D image observation and their 3D interpretation  we suggest a family of rendering layers that are effectively back-propagatable .
Revised:  To establish constraints between 2D image observation and their 3D interpretation we suggest a family of rendering layers that are effectively differentiable .
Edit intent: clarity
Original:  This family includes visual hull, absorption-only (akin to x-ray), and emission-absorption (that can resolve occlusion if multiple 3D points project to the same 2D pixel).
Revised:  This family includes visual hull, absorption-only (akin to x-ray), and emission-absorption . We can successfully reconstruct 3D points project to the same 2D pixel).
Edit intent: coherence
Original:  This family includes visual hull, absorption-only (akin to x-ray), and emission-absorption (that can resolve occlusion if multiple 3D points project to the same 2D pixel).
Revised:  This family includes visual hull, absorption-only (akin to x-ray), and emission-absorption (that can resolve occlusion if multiple 3D shapes from unstructured 2D pixel).
Edit intent: clarity
Original:  The key idea is to train a deep neural network to generate 3D shapes which  rendered to images  are indistinguishable from ground truth images (for a discriminator) under various camera models (i.e. rendering layers) and camera poses.
Revised:  The key idea is to train a deep neural network to generate 3D shapes which , when rendered to images are indistinguishable from ground truth images (for a discriminator) under various camera models (i.e. rendering layers) and camera poses.
Edit intent: clarity
Original:  The key idea is to train a deep neural network to generate 3D shapes which  rendered to images  are indistinguishable from ground truth images (for a discriminator) under various camera models (i.e. rendering layers) and camera poses.
Revised:  The key idea is to train a deep neural network to generate 3D shapes which rendered to images , are indistinguishable from ground truth images (for a discriminator) under various camera models (i.e. rendering layers) and camera poses.
Edit intent: fluency
Original:  The key idea is to train a deep neural network to generate 3D shapes which  rendered to images  are indistinguishable from ground truth images (for a discriminator) under various camera models (i.e. rendering layers) and camera poses.
Revised:  The key idea is to train a deep neural network to generate 3D shapes which rendered to images are indistinguishable from ground truth images (for a discriminator) under various camera poses.
Edit intent: clarity
Original:  We can also show that our method with additional 3D supervision further improves result quality and even surpasses the performance of 3D supervised methods.
Revised:  We can also show that our method with additional 3D supervision to improve on and in some cases even surpass the quality of 3D-supervised methods.
Edit intent: clarity
Original:  At no stage of the procedure an eigenvalue problem has to be solved and no auxiliary functions are introduced .
Revised:  No auxiliary functions have to be introduced for linearization .
Edit intent: clarity
Original: We propose an algorithm for general nonlinear eigenvalue problems to compute  eigenvalues within a chosen contour and to compute the corresponding eigenvectors . Eigenvalue information is explored by contour integration incorporating different weight functions.
Revised: We propose an algorithm for general nonlinear eigenvalue problems to compute eigenvalues within a chosen contour . Eigenvalue information is explored by contour integration incorporating different weight functions.
Edit intent: clarity
Original:  While the objective in our model is difficult to compute , we show it is well-estimated by a surrogate objective which is representable by an LP.
Revised:  Although computing the objective in our model is difficult to compute , we show it is well-estimated by a surrogate objective which is representable by an LP.
Edit intent: clarity
Original: Many neurodegenerative diseases are recognized to include a disconnection syndrome initiated by the neuronal atrophy process.
Edit intent: coherence
Original:  In Alzheimer's disease (AD), the progressive destruction of axonal pathways leads to aberrant network reconfigurations both at the structural and functional level .
Revised:  In Alzheimer's disease (AD), the progressive atrophy leads to aberrant network reconfigurations both at the structural and functional level .
Edit intent: clarity
Original:  In Alzheimer's disease (AD), the progressive destruction of axonal pathways leads to aberrant network reconfigurations both at the structural and functional level .
Revised:  In Alzheimer's disease (AD), the progressive destruction of axonal pathways leads to aberrant network reconfigurations both at structural and functional level .
Edit intent: fluency
Original:  In Alzheimer's disease (AD), the progressive destruction of axonal pathways leads to aberrant network reconfigurations both at the structural and functional level .
Revised:  In Alzheimer's disease (AD), the progressive destruction of axonal pathways leads to aberrant network reconfigurations both at the structural and functional levels .
Edit intent: fluency
Original:  Finally, these multiplex network changes significantly predicted the cognitive and memory impairment of patients as measured respectively by the MMSE and FCSRT clinical scores .
Revised:  Furthermore, these network changes significantly predicted the cognitive and memory impairment of patients as measured respectively by the MMSE and FCSRT clinical scores .
Edit intent: clarity
Original:  Finally, these multiplex network changes significantly predicted the cognitive and memory impairment of patients as measured respectively by the MMSE and FCSRT clinical scores .
Revised:  Finally, these multiplex network changes significantly predicted the cognitive and memory impairment of patients .
Edit intent: clarity
Original: Learning vector representation of each node a. k.a . Network Embedding ( NE) for such an attributed network by considering both structure and attribute information has recently attracted considerable attention, since each node embedding is simply a unified low-dimension vector representation that makes downstream tasks e.g. link prediction more efficient and much easier to realize.
Revised: Learning vector representation of each node a. k.a . Network Embedding ( ANE) has recently attracted considerable attention, since each node embedding is simply a unified low-dimension vector representation that makes downstream tasks e.g. link prediction more efficient and much easier to realize.
Edit intent: clarity
Original: Learning vector representation of each node a. k.a . Network Embedding ( NE) for such an attributed network by considering both structure and attribute information has recently attracted considerable attention, since each node embedding is simply a unified low-dimension vector representation that makes downstream tasks e.g. link prediction more efficient and much easier to realize.
Revised: Learning vector representation of each node a. k.a . Network Embedding ( NE) for such an attributed network by considering both structure and attribute information has recently attracted considerable attention, which aims to learn unified low dimensional node embeddings while preserving both structural and attribute information. The resulting node embeddings can then facilitate various network downstream tasks e.g. link prediction more efficient and much easier to realize.
Edit intent: clarity
Original:  The experiments of link prediction and node classificationtasks on real-world datasets confirm the robustness and effectiveness of our method to the different levels of the incomplete structure information .
Revised:  The experiments of link prediction and node classificationtasks on real-world datasets validate the effectiveness of our method to the different levels of the incomplete structure information .
Edit intent: clarity
Original:  The experiments of link prediction and node classificationtasks on real-world datasets confirm the robustness and effectiveness of our method to the different levels of the incomplete structure information .
Revised:  The experiments of link prediction and node classificationtasks on real-world datasets confirm the robustness and effectiveness of our method to incomplete attributed networks .
Edit intent: clarity
Original:  By modeling DNA rearrangement processes using DOWs, it was discovered that over 95%DIFDELCMD < \\%%% %DIF < of the scrambled genome of certain ciliates could be described using the so called repeat pattern (\alpha\alpha) and the return pattern (\alpha\alpha^R), with gaps allowed between the \alpha's.
Revised:  %DIFDELCMD < \\%%% %DIF < of the scrambled genome of certain ciliates could be described using the so called repeat pattern (\alpha\alpha) and the return pattern (\alpha\alpha^R), with gaps allowed between the \alpha's.
Edit intent: coherence
Original:  By modeling DNA rearrangement processes using DOWs, it was discovered that over 95%DIFDELCMD < \\%%% %DIF < of the scrambled genome of certain ciliates could be described using the so called repeat pattern (\alpha\alpha) and the return pattern (\alpha\alpha^R), with gaps allowed between the \alpha's.
Revised:  By modeling DNA rearrangement processes using DOWs, it was discovered that over 95%DIFDELCMD < \\%%% %DIF < of the scrambled genome of certain ciliates could We consider the so called repeat pattern (\alpha\alpha) and the return pattern (\alpha\alpha^R), with gaps allowed between the \alpha's.
Edit intent: clarity
Original:  We introduce a notion of inserting repeat/return words into DOWs and study how two distinct insertions in the same word can produce equivalent DOWs.
Revised:  We introduce a notion of inserting repeat/return words into DOWs and study how two distinct insertions into the same word can produce equivalent DOWs.
Edit intent: fluency
Original:  In this case, we show that w belongs to one of three classes, and the class to which w belongs is only dependent on the locations of the insertions and on the length of the inserted repeat/return words .
Revised:  This characterization depends on the locations of the insertions and on the length of the inserted repeat/return words .
Edit intent: clarity
Original:  Our model produces higher accuracy output than our baselines, and is several orders of magnitude faster than those baselines with competitive accuracy.
Revised:  Our model produces higher accuracy output than our baselines, and is significantly faster than baselines with competitive accuracy.
Edit intent: clarity
Original:  These techniques rely on the concept of a grace period: nodes that should be freed are placed on adeferred free list , and all threads obey a protocol to ensure that the deallocating thread can detect when all possible readers have completed their use of the object.
Revised:  These techniques rely on the concept of a grace period: nodes that should be freed are deferred free list , and all threads obey a protocol to ensure that the deallocating thread can detect when all possible readers have completed their use of the object.
Edit intent: clarity
Original:  These techniques rely on the concept of a grace period: nodes that should be freed are placed on adeferred free list , and all threads obey a protocol to ensure that the deallocating thread can detect when all possible readers have completed their use of the object.
Revised:  These techniques rely on the concept of a grace period: nodes that should be freed are placed on a free list , and all threads obey a protocol to ensure that the deallocating thread can detect when all possible readers have completed their use of the object.
Edit intent: fluency
Original:  We present a static type system to ensure  correct use of RCU memory management: that nodes removed from a data structure are always scheduled for subsequent deallocation, and that nodes are scheduled for deallocation at most once.
Revised:  We present a static type system to ensure the correct use of RCU memory management: that nodes removed from a data structure are always scheduled for subsequent deallocation, and that nodes are scheduled for deallocation at most once.
Edit intent: fluency
Original:  A deterministic mathematical method is developed for  dynamic analysis of nonlinear compartmental systems based on the proposed theory.
Revised:  A deterministic mathematical method is developed for the dynamic analysis of nonlinear compartmental systems based on the proposed theory.
Edit intent: fluency
Original:  Various system flows and associated storages transmitted from one compartment directly or indirectly to any other or along a given flow path are then analytically characterized, systematically classified, and mathematically formulated.
Revised:  Multiple system flows and associated storages transmitted from one compartment directly or indirectly to any other or along a given flow path are then analytically characterized, systematically classified, and mathematically formulated.
Edit intent: fluency
Original:  Thus, the dynamic influence of one compartment, in terms of flow and storage transfer, directly or indirectly on any other  is ascertained.
Revised:  Thus, the dynamic influence of one compartment, in terms of flow and storage transfer, directly or indirectly on any other compartment is ascertained.
Edit intent: clarity