wanyu/IteraTeR_v2 huggingface dataset:
Original:  While we specifically focus on variational autoencoders (VAE), we demonstrate how our approach could also be used for other popular DL models such as generative adversarial networks (GAN) and deep Bayesian networks (DBN) . Our other contributions include (a) identifying model bias and minimizing it through a rejection sampling based approach (b) An algorithm to build model ensembles for AQP for improved accuracy and (c) an analysis of VAE latent space to understand its suitability to AQP .
Revised:  While we specifically focus on variational autoencoders (VAE), we demonstrate how our approach could also be used for other popular DL models such as generative adversarial networks (GAN) and deep Bayesian networks (DBN) . Our other contributions include (a) identifying model bias and minimizing it through a rejection sampling based approach (b) An algorithm to build model ensembles for AQP for improved accuracy .
Edit intent: coherence
Original:  Our extensive experiments show that deep learning is a very promising approach for AQP .
Revised:  Our extensive experiments show that our proposed approach can provide answers with high accuracy and low latency .
Edit intent: clarity
Original:  Importantly, we discovered several new minimal problems with small degrees that might be practical in image matching and 3D reconstruction.
Revised:  Importantly, several new minimal problems with small degrees that might be practical in image matching and 3D reconstruction.
Edit intent: coherence
Original:  Importantly, we discovered several new minimal problems with small degrees that might be practical in image matching and 3D reconstruction.
Revised:  Importantly, we discovered several new minimal problems have small degrees that might be practical in image matching and 3D reconstruction.
Edit intent: fluency
Original: Summary: Non-canonical nucleic and amino acid monomers are essential to enhance the information capacity, functional capabilities, and stability of DNA, RNA, and protein biopolymers.
Revised: Although non-canonical residues, caps, crosslinks, and nicks play an important role in the function of many DNA, RNA, and protein biopolymers.
Edit intent: clarity
Original:  In this paper, we answer in the negative a question from Breen et al., Throttling for the game of Cops and Robbers on graphs,%DIFDELCMD < {\em %%% Discrete Math. , 341 (2018) 2418--2430.
Revised:  In this paper, we Breen et al., Throttling for the game of Cops and Robbers on graphs,%DIFDELCMD < {\em %%% Discrete Math. , 341 (2018) 2418--2430.
Edit intent: clarity
Original:  In this paper, we answer in the negative a question from Breen et al., Throttling for the game of Cops and Robbers on graphs,%DIFDELCMD < {\em %%% Discrete Math. , 341 (2018) 2418--2430.
Revised:  In this paper, we answer in the negative a question from %DIFDELCMD < {\em %%% Discrete Math. , 341 (2018) 2418--2430.
Edit intent: coherence
Original:  In this paper, we answer in the negative a question from Breen et al., Throttling for the game of Cops and Robbers on graphs,%DIFDELCMD < {\em %%% Discrete Math. , 341 (2018) 2418--2430.
Revised:  In this paper, we answer in the negative a question from Breen et al., Throttling for the game of Cops and Robbers on graphs,%DIFDELCMD < {\em %%% , 341 (2018) 2418--2430.
Edit intent: coherence
Original:  In this paper, we answer in the negative a question from Breen et al., Throttling for the game of Cops and Robbers on graphs,%DIFDELCMD < {\em %%% Discrete Math. , 341 (2018) 2418--2430.
Revised:  In this paper, we answer in the negative a question from Breen et al., Throttling for the game of Cops and Robbers on graphs,%DIFDELCMD < {\em %%% Discrete Math. 
Edit intent: coherence
Original: %DIFDELCMD < ] %%% about whether the cop throttling number of any graph is O(\sqrt n) by constructing a family of graphs having th_c(G)= \Omega(n^{2/3}) . We establish a sublinear upper bound on the cop throttling number  and show that the cop throttling number of chordal graphs is O(n).
Revised: %DIFDELCMD < ] %%% construct a family of graphs having th_c(G)= \Omega(n^{2/3}) . We establish a sublinear upper bound on the cop throttling number and show that the cop throttling number of chordal graphs is O(n).
Edit intent: clarity
Original: %DIFDELCMD < ] %%% about whether the cop throttling number of any graph is O(\sqrt n) by constructing a family of graphs having th_c(G)= \Omega(n^{2/3}) . We establish a sublinear upper bound on the cop throttling number  and show that the cop throttling number of chordal graphs is O(n).
Revised: %DIFDELCMD < ] %%% about whether the cop throttling number of any graph is O(\sqrt n) by constructing a family of graphs having th_c(G)= \Omega(n^{2/3}) , establish a sublinear upper bound on the cop throttling number and show that the cop throttling number of chordal graphs is O(n).
Edit intent: coherence
Original: %DIFDELCMD < ] %%% about whether the cop throttling number of any graph is O(\sqrt n) by constructing a family of graphs having th_c(G)= \Omega(n^{2/3}) . We establish a sublinear upper bound on the cop throttling number  and show that the cop throttling number of chordal graphs is O(n).
Revised: %DIFDELCMD < ] %%% about whether the cop throttling number of any graph is O(\sqrt n) by constructing a family of graphs having th_c(G)= \Omega(n^{2/3}) . We establish a sublinear upper bound on the cop throttling number , and show that the cop throttling number of chordal graphs is O(n).
Edit intent: fluency
Original:   We establish bounds on the product cop throttling number in terms of the cop throttling number, characterize graphs with low product cop throttling number, and show that for a chordal graph G, th_c^{\times} (G) =1+rad(G).
Revised:  We establish bounds on the product cop throttling number in terms of the cop throttling number, characterize graphs with low product cop throttling number, and show that for a chordal graph G, th_c^{\times} =1+rad(G).
Edit intent: fluency
Original: We give an approximate formula of the distribution of the largest eigenvalue of real Wishart matrices by the expected Euler characteristic method for the general dimension.
Revised: We give an approximate formula for the distribution of the largest eigenvalue of real Wishart matrices by the expected Euler characteristic method for the general dimension.
Edit intent: fluency
Original: We present LOGAN, a deep neural network aimed at learning generic shape transforms from unpaired domains.
Revised: We present LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains.
Edit intent: clarity
Original:  We show that our network is able to learn what shape features to preserve during shape transforms , either local or non-local, whether content or style, etc., depending solely on the input domain pairs .
Revised:  We show that our network is able to learn what shape features to preserve during shape translations , either local or non-local, whether content or style, etc., depending solely on the input domain pairs .
Edit intent: clarity
Original:  We show that our network is able to learn what shape features to preserve during shape transforms , either local or non-local, whether content or style, etc., depending solely on the input domain pairs .
Revised:  We show that our network is able to learn what shape features to preserve during shape transforms , either local or non-local, whether content or style, depending solely on the input domain pairs .
Edit intent: coherence
Original: We present LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains.
Revised: We introduce LOGAN, a deep neural network aimed at learning general-purpose shape transforms from unpaired domains.
Edit intent: clarity
Original:  The network is trained on two sets of shapes, e.g., tables and chairs, but there is neither a pairing between shapes in the two domains to supervise the shape translation nor any point-wise correspondence between any shapes.
Revised:  The network is trained on two sets of shapes, e.g., tables and chairs, while there is neither a pairing between shapes in the two domains to supervise the shape translation nor any point-wise correspondence between any shapes.
Edit intent: coherence
Original:  The network is trained on two sets of shapes, e.g., tables and chairs, but there is neither a pairing between shapes in the two domains to supervise the shape translation nor any point-wise correspondence between any shapes.
Revised:  The network is trained on two sets of shapes, e.g., tables and chairs, but there is neither a pairing between shapes from the domains as supervision nor any point-wise correspondence between any shapes.
Edit intent: clarity
Original:  Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes encode multi-scale shape features  in an overcomplete manner .
Revised:  Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes encode multi-scale shape features , resulting in an overcomplete manner .
Edit intent: clarity
Original:  Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes encode multi-scale shape features  in an overcomplete manner .
Revised:  Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes encode multi-scale shape features in an overcomplete representation .
Edit intent: clarity
Original:  We conduct various ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches.
Revised:  We conduct ablation studies to validate each of our key network designs and demonstrate superior capabilities in unpaired shape transforms on a variety of examples over baselines and state-of-the-art approaches.
Edit intent: clarity
Original:  We show that our network is able to learn what shape features to preserve during shape translations , either local or non-local, whether content or style, depending solely on the two input domains for training.
Revised:  We show that our network is able to learn what shape features to preserve during shape translation , either local or non-local, whether content or style, depending solely on the two input domains for training.
Edit intent: fluency
Original:  We show that our network is able to learn what shape features to preserve during shape translations , either local or non-local, whether content or style, depending solely on the two input domains for training.
Revised:  We show that our network is able to learn what shape features to preserve during shape translations , either local or non-local, whether content or style, depending solely on the input domains for training.
Edit intent: clarity
Original: To monitor critical infrastructure, high quality sensors sampled at a high frequency are increasingly installed .
Revised: To monitor critical infrastructure, high quality sensors sampled at a high frequency are increasingly used .
Edit intent: clarity
Original:  However, due to the big amounts of data produced , only simple aggregates are stored.
Revised:  However, as they produce huge amounts of data produced , only simple aggregates are stored.
Edit intent: clarity
Original:  However, due to the big amounts of data produced , only simple aggregates are stored.
Revised:  However, due to the big amounts of data , only simple aggregates are stored.
Edit intent: clarity
Original:  This removes outliers and hides fluctuations that could indicate problems.
Revised:  This removes outliers and fluctuations that could indicate problems.
Edit intent: clarity
Original:  As a solution we propose compressing time series with dimensions using a model-based method we name Multi-model Group Compression (MMGC).
Revised:  As a remedy, we present a model-based approach for managing time series with dimensions using a model-based method we name Multi-model Group Compression (MMGC).
Edit intent: clarity
Original:  MMGC adaptively compresses groups of correlated time series with dimensions using an extensible set of models within a user-defined error bound (possibly zero).
Revised:  MMGC adaptively compresses groups of correlated time series using an extensible set of models within a user-defined error bound (possibly zero).
Edit intent: clarity
Original:  MMGC adaptively compresses groups of correlated time series with dimensions using an extensible set of models within a user-defined error bound (possibly zero).
Revised:  MMGC adaptively compresses groups of correlated time series with dimensions using an extensible set of model types within a user-defined error bound (possibly zero).
Edit intent: clarity
Original:  We also propose efficient query processing algorithms for executing  multi-dimensional aggregate queries on models instead of data points . Last, we provide an open-source implementation of our methods as extensions to the model-based Time Series Management System (TSMS) ModelarDB .
Revised:  We also propose efficient query processing algorithms for executing multi-dimensional aggregate queries on models . Last, we provide an open-source implementation of our methods as extensions to the model-based Time Series Management System (TSMS) ModelarDB .
Edit intent: clarity
Original:  We also propose efficient query processing algorithms for executing  multi-dimensional aggregate queries on models instead of data points . Last, we provide an open-source implementation of our methods as extensions to the model-based Time Series Management System (TSMS) ModelarDB .
Revised:  We also propose efficient query processing algorithms for executing multi-dimensional aggregate queries on models instead of data points . Last, we implement our methods in the Time Series Management System (TSMS) ModelarDB .
Edit intent: clarity
Original:  Through an evaluation we show that , compared to widely used systems, our extended ModelarDB provides up to 11 times faster ingestion due to high compression, 65 times better compression due to the adaptivity of MMGC, 92 times faster aggregate queries as they are executed on models, and close to linear scalability while also being extensible and supporting online query processing.
Revised:  Through an evaluation we show that , compared to widely used formats, ModelarDB+ provides up to 11 times faster ingestion due to high compression, 65 times better compression due to the adaptivity of MMGC, 92 times faster aggregate queries as they are executed on models, and close to linear scalability while also being extensible and supporting online query processing.
Edit intent: clarity
Original:  Through an evaluation we show that , compared to widely used systems, our extended ModelarDB provides up to 11 times faster ingestion due to high compression, 65 times better compression due to the adaptivity of MMGC, 92 times faster aggregate queries as they are executed on models, and close to linear scalability while also being extensible and supporting online query processing.
Revised:  Through an evaluation we show that , compared to widely used systems, our extended ModelarDB provides up to 11 times faster ingestion due to high compression, 65 times better compression due to the adaptivity of MMGC, 92 times faster aggregate queries as they are executed on models, and close to linear scalability . It is also extensible and supports online query processing.
Edit intent: coherence
Original:  Each RSU and OBU is equipped with two transceivers operating at different frequencies.
Revised:  Each RSU and OBU were equipped with two transceivers operating at different frequencies.
Edit intent: clarity
Original:  The dataset refers to eight experimental sessions of the length of \sim%DIFDELCMD < 2{\hour} %%% \sim2  each that have taken place over four days -- allocating two experimental sessions per-day.
Revised:  The dataset refers to eight experimental sessions of the length of %DIFDELCMD < 2{\hour} %%% \sim2 each that have taken place over four days -- allocating two experimental sessions per-day.
Edit intent: clarity
Original:  During the same day, both transceivers onboard any of the RSUs and OBUs was operated on two different frequencies.
Revised:  On each day, the two transceivers onboard each device were operated on two different frequencies.
Edit intent: clarity
Original:  For each of the received CAMs, we also recorded its Received Signal Strength Indicator (RSSI) and the location of the receiving transceiver  ] .
Revised:  For each of the received CAMs, we also recorded its Received Signal Strength Indicator (RSSI) and the location of the receiving transceiver Online] .
Edit intent: fluency
Original: A large-scale dataset of measurements of ETSI ITS-G5 Dedicated Short Range Communications (DSRC) is presented.
Revised: A dataset of measurements of ETSI ITS-G5 Dedicated Short Range Communications (DSRC) is presented.
Edit intent: clarity
Original:  The two vehicles have been driven across the Innovate UK-funded FLOURISH Test Track encompassing key roads in the center of Bristol, UK.
Revised:  The two vehicles have been driven across the FLOURISH Test Track encompassing key roads in the center of Bristol, UK.
Edit intent: clarity
Original:  The two vehicles have been driven across the Innovate UK-funded FLOURISH Test Track encompassing key roads in the center of Bristol, UK.
Revised:  The two vehicles have been driven across the Innovate UK-funded FLOURISH Test Track in Bristol, UK.
Edit intent: clarity
Original:  As for the RSUs, they were located at fixed locations around the track.
Edit intent: coherence
Original:  Each RSU and OBU were equipped with two transceivers operating at different frequencies.
Revised:  Each RSU and OBU was equipped with two transceivers operating at different frequencies.
Edit intent: fluency
Original:  During our experiments, each transceiver broadcast Cooperative Awareness Messages (CAMs) every 10 ms to the neighboring RSUs and OBUs.
Revised:  During our experiments, each transceiver broadcasts Cooperative Awareness Messages (CAMs) every 10 ms to the neighboring RSUs and OBUs.
Edit intent: fluency
Original:  During each experimental session, for each transceiver, all the transmitted and received CAMs were recorded.
Revised:  Each transmitted and received CAMs were recorded.
Edit intent: clarity
Original:  The dataset can be   found at URL  and it has been analyzed in the following paper: I. Mavromatis, A. Tassi, and R. J. Piechocki, "Operating ITS-G5 DSRC over Unlicensed Bands: A City-Scale Performance Evaluation," Submitted to IEEE VTC-Spring 2019.
Revised:  The dataset can be found at URL , and it has been analyzed in the following paper: I. Mavromatis, A. Tassi, and R. J. Piechocki, "Operating ITS-G5 DSRC over Unlicensed Bands: A City-Scale Performance Evaluation," Submitted to IEEE VTC-Spring 2019.
Edit intent: fluency
Original:  The spatial optimisation exercise was tested along the coast of Central Chile, a highly productive area dominated by the Humboldt Current.
Revised:  The spatial optimization exercise was tested along the coast of Central Chile, a highly productive area dominated by the Humboldt Current.
Edit intent: fluency
Original:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase as well as dynamic adaptive mesh refinement (AMR) overhead.
Revised:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase and adaptive mesh refinement (AMR) overhead.
Edit intent: clarity
Original:  We therefore propose a new additive variant of the fast adaptive composite (FAC) method which can be combined with Full Approximation Storage (FAS) plus BoxMG inter-grid transfer operators on spacetrees .
Revised:  We propose a new additive variant of the fast adaptive composite (FAC) method which can be combined with Full Approximation Storage (FAS) plus BoxMG inter-grid transfer operators on spacetrees .
Edit intent: coherence
Original:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase and adaptive mesh refinement (AMR) overhead.
Revised:  Two fundamental ones read as follows: Multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase and adaptive mesh refinement (AMR) overhead.
Edit intent: clarity
Original:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase and adaptive mesh refinement (AMR) overhead.
Revised:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and many multigrid implementations suffer from an expensive coarse grid identification phase and adaptive mesh refinement (AMR) overhead.
Edit intent: fluency
Original:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase and adaptive mesh refinement (AMR) overhead.
Revised:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase plus adaptive mesh refinement (AMR) overhead.
Edit intent: clarity
Original:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase and adaptive mesh refinement (AMR) overhead.
Revised:  Two fundamental ones are that multiplicative solvers issue coarse grid solves which exhibit low concurrency and that many multigrid implementations suffer from an expensive coarse grid identification phase and adaptive mesh refinement overhead.
Edit intent: clarity
Original:  We propose a new additive variant of the fast adaptive composite (FAC) method which can be combined with Full Approximation Storage (FAS) plus BoxMG inter-grid transfer operators on spacetrees: Additivity implies a high concurrency level, FAS implies that the major solver ingredients such as smoothers can work on logically regular grids, and BoxMG implies that the solver benefits from algebraic multigrid's accuracy even though it relies on a geometric grid hierarchy .
Revised:  We propose a new additive variant of the fast adaptive composite (FAC) method which can be combined with Full Approximation Storage (FAS) plus BoxMG inter-grid transfer operators on spacetrees: Additivity implies a high concurrency level, while the transfer operators between the mesh levels can still be constructed algebraically .
Edit intent: clarity
Original:  The novel flavour of the additive scheme is an augmentation of the solver with an additive, auxiliary damping  per grid level  that is in turn constructed through the next coarser level---an idea which utilises smoothed aggregation principles or the motivation behind AFACx .
Revised:  The novel flavour of the additive scheme is an augmentation of the solver with an additive, auxiliary damping parameter per grid level that is in turn constructed through the next coarser level---an idea which utilises smoothed aggregation principles or the motivation behind AFACx .
Edit intent: clarity
Original:  We  then report numerical results comparing our upper bound on the k-RT with heuristic approximation methods.
Revised:  We then report numerical results comparing our upper bounds on the k-RT with heuristic approximation methods.
Edit intent: fluency