wanyu/IteraTeR_v2 huggingface dataset:
Original:  In the future, this model can be used for more useful optimization such as binding affinity against a protein target.
Revised:  In the future, this model can be used for more useful optimization such as binding affinity for a protein target.
Edit intent: clarity
Original:  To address these  we propose a new dataset, PubMed Tables One Million (PubTables-1M), and a new class of metric, grid table similarity (GriTS).
Revised:  To address these issues we propose a new dataset, PubMed Tables One Million (PubTables-1M), and a new class of metric, grid table similarity (GriTS).
Edit intent: clarity
Original:  PubTables-1M is nearly twice as large as the previous largest comparable dataset,  can be used for models across multiple architectures and modalities , and addresses issues such as ambiguity and lack of consistency in the annotations  .
Revised:  PubTables-1M is nearly twice as large as the previous largest comparable dataset, can be used for models across multiple architectures and modalities . Further, it addresses issues such as ambiguity and lack of consistency in the annotations .
Edit intent: coherence
Original:  We describe the dataset in detail to enable others to build on our work and combine this data with other datasets for these and related tasks.
Edit intent: coherence
Original:  It is our hope that PubTables-1M and the proposed metrics can further progress in this area by creating a benchmark suitable for training and evaluating a wide variety of models for table extraction.
Revised:  It is our hope that PubTables-1M and GriTS can further progress in this area by creating a benchmark suitable for training and evaluating a wide variety of models for table extraction.
Edit intent: clarity
Original:  It is our hope that PubTables-1M and the proposed metrics can further progress in this area by creating a benchmark suitable for training and evaluating a wide variety of models for table extraction.
Revised:  It is our hope that PubTables-1M and the proposed metrics can further progress in this area by creating data and metrics suitable for training and evaluating a wide variety of models for table extraction.
Edit intent: clarity
Original: Recently, interest has grown in applying machine learning to the problem of table structure inference and extraction from unstructured documents.
Revised: Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents.
Edit intent: clarity
Original:  To address these issues we propose a newdataset, PubMed Tables One Million ( PubTables-1M ), and a new class of metric, grid table similarity (GriTS) . PubTables-1M is nearly twice as large as the previous largest comparable dataset, contains highly-detailed structure annotations, and can be used for models across multiple architectures and modalities.
Revised:  To address these issues we propose a newdataset, PubMed Tables One Million ( PubTables-1M . PubTables-1M is nearly twice as large as the previous largest comparable dataset, contains highly-detailed structure annotations, and can be used for models across multiple architectures and modalities.
Edit intent: coherence
Original:  Further, it addresses issues such as ambiguity and lack of consistency in the annotations via a novel canonicalization and quality control procedure.
Revised:  Further, it addresses issues such as ambiguity and lack of consistency in the annotations via a novel canonicalization procedure.
Edit intent: clarity
Original:  We apply DETR to table extraction for the first time and show that object detection models trained on PubTables-1M produce excellent results out-of-the-box for all three tasks of detection, structure recognition, and functional analysis .
Revised:  We apply DETR to table extraction for the first time and show that object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis .
Edit intent: clarity
Original: Self- and Semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance.
Revised: Self- and semi-supervised learning methods have been actively investigated to reduce labeled training data or enhance the model performance.
Edit intent: fluency
Original:  To address this problem, recent offline RL methods attempt to introduce conservatism bias to encourage learning on high-confidence areas.
Revised:  To address this problem, recent offline RL methods attempt to introduce conservatism bias to encourage learning in high-confidence areas.
Edit intent: fluency
Original:  However, due to limited samples in offline dataset , conservatism quantifications often suffer from overgeneralization in out-of-support regions.
Revised:  However, due to limited samples in offline datasets , conservatism quantifications often suffer from overgeneralization in out-of-support regions.
Edit intent: fluency
Original:  These reverse imaginations provide informed data augmentation for the model-free policy learning and enable conservative generalization beyond the offline dataset.
Revised:  These reverse imaginations provide informed data augmentation for model-free policy learning and enable conservative generalization beyond the offline dataset.
Edit intent: fluency
Original:  The training data is generated uniformly in the domain of interest, consisting of the state samples and corresponding affine control laws, based on which the lattice PWA approximations are constructed.
Revised:  The training data are generated uniformly in the domain of interest, consisting of the state samples and corresponding affine control laws, based on which the lattice PWA approximations are constructed.
Edit intent: fluency
Original:  Resampling of data is also proposed to guarantee that the lattice PWA approximations are identical to the explicit MPC control law in  unique order (UO) regions containing the sample points as interior points.
Revised:  Re-sampling of data is also proposed to guarantee that the lattice PWA approximations are identical to the explicit MPC control law in unique order (UO) regions containing the sample points as interior points.
Edit intent: fluency
Original:  Resampling of data is also proposed to guarantee that the lattice PWA approximations are identical to the explicit MPC control law in  unique order (UO) regions containing the sample points as interior points.
Revised:  Resampling of data is also proposed to guarantee that the lattice PWA approximations are identical to explicit MPC control law in unique order (UO) regions containing the sample points as interior points.
Edit intent: fluency
Original:  Resampling of data is also proposed to guarantee that the lattice PWA approximations are identical to the explicit MPC control law in  unique order (UO) regions containing the sample points as interior points.
Revised:  Resampling of data is also proposed to guarantee that the lattice PWA approximations are identical to the explicit MPC control law in the unique order (UO) regions containing the sample points as interior points.
Edit intent: fluency
Original:  Besides , under mild assumptions, the equivalence of the 2 lattice PWA approximations guarantees  the approximations are error-free in the domain of interest.
Revised:  Additionally , under mild assumptions, the equivalence of the 2 lattice PWA approximations guarantees the approximations are error-free in the domain of interest.
Edit intent: coherence
Original:  Besides , under mild assumptions, the equivalence of the 2 lattice PWA approximations guarantees  the approximations are error-free in the domain of interest.
Revised:  Besides , under mild assumptions, the equivalence of the two lattice PWA approximations guarantees the approximations are error-free in the domain of interest.
Edit intent: fluency
Original:  Besides , under mild assumptions, the equivalence of the 2 lattice PWA approximations guarantees  the approximations are error-free in the domain of interest.
Revised:  Besides , under mild assumptions, the equivalence of the 2 lattice PWA approximations guarantees that the approximations are error-free in the domain of interest.
Edit intent: fluency
Original:  The algorithms for deriving statistical error-free approximation to the explicit linear MPC is proposed and the complexity of the whole procedure is analyzed, which is polynomial with respect to the number of samples.
Revised:  The algorithms for deriving statistically error-free approximation to the explicit linear MPC is proposed and the complexity of the whole procedure is analyzed, which is polynomial with respect to the number of samples.
Edit intent: fluency
Original:  The algorithms for deriving statistical error-free approximation to the explicit linear MPC is proposed and the complexity of the whole procedure is analyzed, which is polynomial with respect to the number of samples.
Revised:  The algorithms for deriving statistical error-free approximation to the explicit linear MPC are proposed and the complexity of the whole procedure is analyzed, which is polynomial with respect to the number of samples.
Edit intent: fluency
Original:  The algorithms for deriving statistical error-free approximation to the explicit linear MPC is proposed and the complexity of the whole procedure is analyzed, which is polynomial with respect to the number of samples.
Revised:  The algorithms for deriving statistical error-free approximation to the explicit linear MPC is proposed and the complexity of the entire procedure is analyzed, which is polynomial with respect to the number of samples.
Edit intent: fluency
Original:  The performance of the proposed approximation strategy is tested through 2 simulation examples, and the result shows that with a moderate number of sample points, we can construct lattice PWA approximations that are equivalent to optimal control law of the explicit linear MPC.
Revised:  The performance of the proposed approximation strategy is tested through two simulation examples, and the result shows that with a moderate number of sample points, we can construct lattice PWA approximations that are equivalent to optimal control law of the explicit linear MPC.
Edit intent: fluency
Original:  In the end, our team named THU_IVG_2018 achieved the result of 0.366 for AP@0.50:0.95 on the test set, which is competitive with other top-ranking methods while only one GPU is used.
Revised:  In the end, our team achieved the result of 0.366 for AP@0.50:0.95 on the test set, which is competitive with other top-ranking methods while only one GPU is used.
Edit intent: coherence
Original:  We design a filtering layer that uses a novel hash, XHash .
Revised:  We design a filtering layer that uses a novel hash, XASH .
Edit intent: fluency
Original:  The code to fully reproduce this work is available at: URL
Revised:  This work is fully reproducible using the publicly available code at: URL
Edit intent: clarity
Original: Despite the success of a number of recent techniques for visual self-supervised deep learning, there remains limited investigation into the representations that are ultimately learned.
Revised: Despite the success of a number of recent techniques for visual self-supervised deep learning, there has been limited investigation into the representations that are ultimately learned.
Edit intent: clarity
Original:  By using recent advances in comparing neural representations, we explore in this direction by comparing a constrastive self-supervised algorithm (SimCLR) to supervision for simple image data in a common architecture.
Revised:  By leveraging recent advances in comparing neural representations, we explore in this direction by comparing a constrastive self-supervised algorithm (SimCLR) to supervision for simple image data in a common architecture.
Edit intent: fluency
Original:  By using recent advances in comparing neural representations, we explore in this direction by comparing a constrastive self-supervised algorithm (SimCLR) to supervision for simple image data in a common architecture.
Revised:  By using recent advances in the comparison of neural representations, we explore in this direction by comparing a constrastive self-supervised algorithm (SimCLR) to supervision for simple image data in a common architecture.
Edit intent: fluency
Original:  By using recent advances in comparing neural representations, we explore in this direction by comparing a constrastive self-supervised algorithm (SimCLR) to supervision for simple image data in a common architecture.
Revised:  By using recent advances in comparing neural representations, we explore in this direction by comparing a contrastive self-supervised algorithm (SimCLR) to supervision for simple image data in a common architecture.
Edit intent: fluency
Original:  By using recent advances in comparing neural representations, we explore in this direction by comparing a constrastive self-supervised algorithm (SimCLR) to supervision for simple image data in a common architecture.
Revised:  By using recent advances in comparing neural representations, we explore in this direction by comparing a constrastive self-supervised algorithm to supervision for simple image data in a common architecture.
Edit intent: clarity
Original:  We investigate this divergence, finding that it is caused by these layers strongly fitting to the distinct learning objectives.
Revised:  We investigate this divergence, finding that these layers strongly fitting to the distinct learning objectives.
Edit intent: clarity
Original:  We investigate this divergence, finding that it is caused by these layers strongly fitting to the distinct learning objectives.
Revised:  We investigate this divergence, finding that it is caused by these layers strongly fit to their distinct learning objectives.
Edit intent: clarity
Original:  Our work particularly highlights the importance of the learned intermediate representations, and raises important questions for auxiliary task design.
Revised:  Our work particularly highlights the importance of the learned intermediate representations, and raises critical questions for auxiliary task design.
Edit intent: clarity
Original: This paper presents a light-weight frontend LiDAR odometry solution with consistent and accurate localization for computationally-limited robotic platforms.
Revised: This paper presents a lightweight frontend LiDAR odometry solution with consistent and accurate localization for computationally-limited robotic platforms.
Edit intent: fluency
Original:  For example, we obtain a tight linear-in-\Delta lower bound for computing a maximal independent set in \Delta-regular trees.
Revised:  For example, we obtain a tight lower bound for computing a maximal independent set in \Delta-regular trees.
Edit intent: clarity
Original:  Our lower bounds even apply ] to a much more general family of problems , such as variants of ruling sets where nodes in the set do not need to satisfy the independence requirement, but they only need to satisfy the requirements of some arbdefective coloring .
Revised:  Our lower bounds even apply ]. Our lower bound even applies to a much more general family of problems , such as variants of ruling sets where nodes in the set do not need to satisfy the independence requirement, but they only need to satisfy the requirements of some arbdefective coloring .
Edit intent: clarity
Original: The inflammable growth of misinformation on social media and other platforms during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people.
Revised: Misinformation during pandemic situations like COVID-19 can cause significant damage to the physical and mental stability of the people.
Edit intent: clarity
Original:  To detect such misinformation, researchers have been applying various machine learning (ML) and deep learning (DL) techniques.
Revised:  To detect such misinformation, researchers have been applying various machine learning (ML) , Deep Learning (DL) techniques.
Edit intent: fluency
Original:  In this study, we perform a systematic analysis of the effect of model calibration on its performance on two medical image modalities, namely, chest X-rays (CXRs) and fundus images, using various deep learning classifier backbones.
Revised:  In this study, we perform a systematic analysis of the effect of model calibration on its performance on two medical image modalities, namely, chest X-rays and fundus images, using various deep learning classifier backbones.
Edit intent: clarity
Original:  and , (iii) two classification thresholds, namely, default decision threshold of 0.5, and optimal threshold from precision-recall (PR) curves.
Revised:  and (iii) two classification thresholds, namely, default decision threshold of 0.5, and optimal threshold from precision-recall (PR) curves.
Edit intent: fluency
Original:  and , (iii) two classification thresholds, namely, default decision threshold of 0.5, and optimal threshold from precision-recall (PR) curves.
Revised:  and , (iii) two classification thresholds, namely, default decision threshold of 0.5, and optimal threshold from precision-recall curves.
Edit intent: clarity
Original:  Our results indicate that at the default operating threshold of 0.5, the performance achieved through calibration is significantly superior (p < 0.05) to an uncalibrated model .
Revised:  Our results indicate that at the default operating threshold of 0.5, the performance achieved through calibration is significantly superior (p < 0.05) to using uncalibrated probabilities .
Edit intent: clarity
Original:  However, at the PR-guided threshold, these gains were not significantly different (p > 0.05).
Revised:  However, at the PR-guided threshold, these gains are not significantly different (p > 0.05).
Edit intent: coherence
Original: We introduce Learn then Test (LTT) , a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees regardless of the underlying model and (unknown) data-generating distribution.
Revised: We introduce Learn then Test , a framework for calibrating machine learning models so that their predictions satisfy explicit, finite-sample statistical guarantees regardless of the underlying model and (unknown) data-generating distribution.
Edit intent: clarity
Original:  This also gives a new limiting procedure to apply to certain Trouchet tilings.
Revised:  This also gives a new limiting procedure to apply to certain Truchet tilings.
Edit intent: fluency
Original: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled internet of things (IoT) networks , where secondary/cognitive IoT devices simultaneously upload their data to the UAV  following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Revised: This paper investigates a cognitive unmanned aerial vehicle (UAV) enabled internet of things (IoT) networks , where secondary/cognitive IoT devices simultaneously upload their data to the UAV following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Edit intent: clarity
Original: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled internet of things (IoT) networks , where secondary/cognitive IoT devices simultaneously upload their data to the UAV  following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Revised: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled Internet of Things (IoT) networks , where secondary/cognitive IoT devices simultaneously upload their data to the UAV following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Edit intent: fluency
Original: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled internet of things (IoT) networks , where secondary/cognitive IoT devices simultaneously upload their data to the UAV  following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Revised: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled internet of things (IoT) network , where secondary/cognitive IoT devices simultaneously upload their data to the UAV following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Edit intent: fluency
Original: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled internet of things (IoT) networks , where secondary/cognitive IoT devices simultaneously upload their data to the UAV  following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Revised: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled internet of things (IoT) networks , where secondary/cognitive IoT devices upload their data to the UAV following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Edit intent: clarity
Original: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled internet of things (IoT) networks , where secondary/cognitive IoT devices simultaneously upload their data to the UAV  following a non-orthogonal multiple access (NOMA) protocol in the pre-allocated spectrum to the primary network.
Revised: This paper investigates spectrum sharing communications in unmanned aerial vehicle (UAV) enabled internet of things (IoT) networks , where secondary/cognitive IoT devices simultaneously upload their data to the UAV following a non-orthogonal multiple access (NOMA) protocol in the spectrum of the primary network.
Edit intent: clarity
Original:  We aim to maximize the minimum lifetime of IoT devices by jointly optimizing the UAV location, decoding order, and transmit powersubject to probabilistic interference-power constraints at the primary base station (BS) while considering the imperfect channel state information (CSI).
Revised:  We aim to maximize the minimum lifetime of IoT devices by jointly optimizing the UAV location, transmit power, and decoding order subject to interference-power constraints at the primary base station (BS) while considering the imperfect channel state information (CSI).
Edit intent: clarity
Original:  We aim to maximize the minimum lifetime of IoT devices by jointly optimizing the UAV location, decoding order, and transmit powersubject to probabilistic interference-power constraints at the primary base station (BS) while considering the imperfect channel state information (CSI).
Revised:  We aim to maximize the minimum lifetime of IoT devices by jointly optimizing the UAV location, decoding order, and transmit powersubject to probabilistic interference-power constraints in presence of the imperfect channel state information (CSI).
Edit intent: clarity
Original:  To solve the formulated non-convex mixed-integer programming problem, we first jointly optimize the UAV location and transmit power for  given decoding order and obtain the globally optimal solution with the assistance of Lagrange duality .
Revised:  To solve the formulated non-convex mixed-integer programming problem, we first jointly optimize the UAV location and transmit power for a given decoding order and obtain the globally optimal solution with the assistance of Lagrange duality .
Edit intent: fluency
Original:  We thoroughly investigate the tradeoffs between sparstiy and its subsequent effects on synthetic speech.
Revised:  We thoroughly investigate the tradeoffs between sparsity and its subsequent effects on synthetic speech.
Edit intent: fluency
Original:  Given the half-duplex nature of wireless links, Interest packets frequently contend for the channel with Data packets, leading to throughput degradation over wireless links .
Revised:  Given the half-duplex nature of wireless links, Interest packets frequently contend for the channel with Data packets, leading to throughput degradation .
Edit intent: clarity