wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our results show that spatio-temporal convolutional networks are a good model of how the human visual system solves action recognition and that robustness to complex transformations, such as 3D viewpoint invariance, is a specific computational goal driving the organization of visual processing in the human brain .
Revised:  Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed by human visual cortex .
Edit intent: clarity
Original:  However, while differentially private machine learning and in-RDBMS data analytics have been studied separately, little previous work has explored private learning in an in-RDBMS system.
Revised:  However, while differentially private machine learning and in-RDBMS system. While significant progress has been made separately on in-RDBMS system.
Edit intent: clarity
Original:  We then integrate it , along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our proposed approach can be easily integrated into an RDBMS , incurs virtually no overhead, and yields substantially better  test accuracy than the other private SGD algorithm implementations .
Revised:  We then integrate this algorithm , along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our proposed approach can be easily integrated into an RDBMS , incurs virtually no overhead, and yields substantially better test accuracy than the other private SGD algorithm implementations .
Edit intent: clarity
Original:  We then integrate it , along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our proposed approach can be easily integrated into an RDBMS , incurs virtually no overhead, and yields substantially better  test accuracy than the other private SGD algorithm implementations .
Revised:  We then integrate it , along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our algorithm can be easily integrated into an RDBMS , incurs virtually no overhead, and yields substantially better test accuracy than the other private SGD algorithm implementations .
Edit intent: clarity
Original:  We then integrate it , along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our proposed approach can be easily integrated into an RDBMS , incurs virtually no overhead, and yields substantially better  test accuracy than the other private SGD algorithm implementations .
Revised:  We then integrate it , along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our proposed approach can be easily integrated , incurs virtually no overhead, and yields substantially better test accuracy than the other private SGD algorithm implementations .
Edit intent: clarity
Original:  We then integrate it , along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our proposed approach can be easily integrated into an RDBMS , incurs virtually no overhead, and yields substantially better  test accuracy than the other private SGD algorithm implementations .
Revised:  We then integrate it , along with two existing versions of differentially private SGD, in the Postgres RDBMS. Experimental results demonstrate that our proposed approach can be easily integrated into an RDBMS , incurs virtually no overhead, and yields substantially better test accuracy than the state-of-the-art algorithms .
Edit intent: clarity
Original: This paper studies differential privacy for stochastic gradient descent (SGD) in an in-RDBMS system.
Edit intent: coherence
Original:  While significant progress has been made separately on in-RDBMS SGD and private SGD, none of the major in-RDBMS machine learning frameworks have incorporated differentially private SGD.
Revised:  While significant progress has been made separately on in-RDBMS SGD and private SGD, none of the major scalable analytics frameworks have incorporated differentially private SGD.
Edit intent: clarity
Original:  This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address both issues in an integrated manner.
Revised:  This paper takes a first step to remedy this disconnect and proposes a private SGD algorithm to address both issues in an integrated manner.
Edit intent: fluency
Original:  In contrast to the white-box approach adopted by previous algorithms , we revisit and use the classical technique of output perturbation.
Revised:  In contrast to the white-box approach adopted by previous work , we revisit and use the classical technique of output perturbation.
Edit intent: clarity
Original:  While using output perturbation {\em   trivially addresses (2), it gives rise to challenges in addressing (1)  . We address this challenge by providing a novel analysis of the L_2-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data.
Revised:  While using output perturbation {\em trivially addresses (2), it makes (1) . We address this challenge by providing a novel analysis of the L_2-sensitivity of SGD, which allows, under the same privacy guarantees, better convergence of SGD when only a constant number of passes can be made over the data.
Edit intent: clarity
Original:  We then integrate this algorithm, along with the state-of-the-art differentially private SGD, into Bismarck, an in-RDBMS analytics system .
Revised:  We integrate our algorithm, as well as other state-of-the-art differentially private SGD, into Bismarck, an in-RDBMS analytics system .
Edit intent: clarity
Original:  Extensive experiments demonstrate that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, for a number of datasets yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms  .
Revised:  Extensive experiments show that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, for a number of datasets yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms .
Edit intent: fluency
Original:  Extensive experiments demonstrate that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, for a number of datasets yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms  .
Revised:  Extensive experiments demonstrate that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms .
Edit intent: clarity
Original:  Extensive experiments demonstrate that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, for a number of datasets yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms  .
Revised:  Extensive experiments demonstrate that our algorithm can be easily integrated, incurs virtually no overhead, scales well, and most importantly, for a number of datasets yields substantially better (up to 4X) test accuracy than the state-of-the-art algorithms on many real datasets .
Edit intent: clarity
Original:  To finely study the collisions, we propose an approach to model random access schemes based on stochastic geometry , in which the total time-frequency resources is a two-dimensional plane , and transmissions from sensor nodes are represented by time- frequency patterns randomly drawn in the time-frequency plane.
Revised:  To finely study the collisions, we propose an approach to model random access schemes based on stochastic geometry , in which the total time-frequency resources to be a two-dimensional plane , and transmissions from sensor nodes are represented by time- frequency patterns randomly drawn in the time-frequency plane.
Edit intent: fluency
Original:  To finely study the collisions, we propose an approach to model random access schemes based on stochastic geometry , in which the total time-frequency resources is a two-dimensional plane , and transmissions from sensor nodes are represented by time- frequency patterns randomly drawn in the time-frequency plane.
Revised:  To finely study the collisions, we propose an approach to model random access schemes based on stochastic geometry , in which the total time-frequency resources is a two-dimensional plane and transmissions from sensor nodes are represented by time- frequency patterns randomly drawn in the time-frequency plane.
Edit intent: fluency
Original:  To finely study the collisions, we propose an approach to model random access schemes based on stochastic geometry , in which the total time-frequency resources is a two-dimensional plane , and transmissions from sensor nodes are represented by time- frequency patterns randomly drawn in the time-frequency plane.
Revised:  To finely study the collisions, we propose an approach to model random access schemes based on stochastic geometry , in which the total time-frequency resources is a two-dimensional plane , and transmissions from Internet of Things (IoT) devices time- frequency patterns randomly drawn in the time-frequency plane.
Edit intent: clarity
Original:  We hope our results provide valuable insights to developers and maintainers, which could help them on building and evolving systems in a competitive software market.
Revised:  We hope our results provide valuable insights to developers and maintainers, which can help them on building and evolving systems in a competitive software market.
Edit intent: fluency
Original: We study the problem of a trader who wants to maximize the expected reward from liquidating a given stock position.
Revised: We study the problem of a trader who wants to maximize the expected revenue from liquidating a given stock position.
Edit intent: clarity
Original:  This leads to a control problem for piecewise deterministic Markov processes ( in short PDMP ).
Revised:  This leads to a control problem for piecewise deterministic Markov processes ( PDMPs ).
Edit intent: clarity
Original:  In particular, we derive the optimality equation for the value function and we characterize the value function as unique viscosity solution of the associated dynamic programming equation  .
Revised:  In particular, we derive the optimality equation for the value function , we characterize the value function as unique viscosity solution of the associated dynamic programming equation .
Edit intent: coherence
Original:  In particular, we derive the optimality equation for the value function and we characterize the value function as unique viscosity solution of the associated dynamic programming equation  .
Revised:  In particular, we derive the optimality equation for the value function and we characterize the value function as viscosity solution of the associated dynamic programming equation .
Edit intent: clarity
Original:  We model the stock price dynamics as a geometric pure jump process with local characteristics driven by an unobservable finite-state Markov chain and  the liquidation rate.
Revised:  We model the stock price dynamics as a geometric pure jump process with local characteristics driven by an unobservable finite-state Markov chain and by the liquidation rate.
Edit intent: coherence
Original:  The paper concludes with a detailed analysis of specific examples .
Revised:  The paper concludes with a detailed analysis of a specific example .
Edit intent: fluency
Original:  We model the stock price dynamics as a geometric pure jump process with local characteristics driven by an unobservable finite-state Markov chain and by the liquidation rate.
Revised:  We model the stock price dynamics as a geometric pure jump process whose local characteristics are driven by an unobservable finite-state Markov chain and by the liquidation rate.
Edit intent: clarity
Original:  This reflects uncertainty about activity of other traders and feedback effects from trading .
Revised:  This model is consistent with stylized facts of high frequency data such as the discrete nature of tick data and the clustering in the order flow. We include both temporary and permanent effects into our analysis .
Edit intent: coherence
Original:  We use stochastic filtering to reduce the optimization problem under partial information to an equivalent one under complete information.
Revised:  We use stochastic filtering to reduce the optimization problem under partial information to an equivalent optimization problem under complete information.
Edit intent: clarity
Original:  We apply control theory for PDMPs to our problem.
Revised:  We carry out a detailed mathematical analysis of this problem.
Edit intent: clarity
Original: Good OCR results on historical documents rely on diplomatic transcriptions of printed material as ground truth  which is both a scarce resource and time-consuming to generate.
Revised: Good OCR results for historical printings rely on the availability of recognition models trained on diplomatic transcriptions as ground truth which is both a scarce resource and time-consuming to generate.
Edit intent: clarity
Original: Good OCR results on historical documents rely on diplomatic transcriptions of printed material as ground truth  which is both a scarce resource and time-consuming to generate.
Revised: Good OCR results on historical documents rely on diplomatic transcriptions of printed material as ground truth , which is both a scarce resource and time-consuming to generate.
Edit intent: fluency
Original:  A strategy is proposed which starts from a mixed model trained on already available transcriptions from different centuries giving accuracies  over 90 \% on a test set from the same period of time, overcoming the typography barrier of having to train individual models separately for each historical typeface.
Revised:  Instead of having to train a separate model for each historical typeface, we propose a strategy to start from models trained on a combined set of available transcriptions in a variety of fonts. These over 90 \% on a test set from the same period of time, overcoming the typography barrier of having to train individual models separately for each historical typeface.
Edit intent: clarity
Original:  It is shown that both mean character confidence (as output \\%DIF > on a test set of printings from the same period of time, but without \emph{  by the OCR engine OCRopus) and lexicality (a measure of correctness of OCR tokens compared to a lexicon of \emph{  modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine) correlate with true accuracy determined from a comparison of OCR results with ground truth.
Revised:  It is shown that both mean character confidence (as output \\%DIF > on a test set of printings from the same period of time, but without \emph{mean character confidence C by the OCR engine OCRopus) and lexicality (a measure of correctness of OCR tokens compared to a lexicon of \emph{ modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine) correlate with true accuracy determined from a comparison of OCR results with ground truth.
Edit intent: clarity
Original:  It is shown that both mean character confidence (as output \\%DIF > on a test set of printings from the same period of time, but without \emph{  by the OCR engine OCRopus) and lexicality (a measure of correctness of OCR tokens compared to a lexicon of \emph{  modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine) correlate with true accuracy determined from a comparison of OCR results with ground truth.
Revised:  It is shown that both mean character confidence (as output \\%DIF > on a test set of printings from the same period of time, but without \emph{ (as given by the OCR engine OCRopus) and lexicality (a measure of correctness of OCR tokens compared to a lexicon of \emph{ modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine) correlate with true accuracy determined from a comparison of OCR results with ground truth.
Edit intent: clarity
Original:  It is shown that both mean character confidence (as output \\%DIF > on a test set of printings from the same period of time, but without \emph{  by the OCR engine OCRopus) and lexicality (a measure of correctness of OCR tokens compared to a lexicon of \emph{  modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine) correlate with true accuracy determined from a comparison of OCR results with ground truth.
Revised:  It is shown that both mean character confidence (as output \\%DIF > on a test set of printings from the same period of time, but without \emph{ by the OCR engine OCRopus) and \emph{ modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine) correlate with true accuracy determined from a comparison of OCR results with ground truth.
Edit intent: clarity
Original: Stance detection is the task of classifying the attitude expressed in a text towards a target such as "Climate Change is a Real Concern" to be "positive", " negative" or "neutral".
Revised: Stance detection is the task of classifying the attitude expressed in a text towards a target such as "Climate Change is a Real Concern" to be "positive", negative" or "neutral".
Edit intent: fluency
Original:  The method is evaluated on the SemEval 2016 Task 6 Twitter Stance Detection corpus and achieves performance second best only to a system trained on semi-automatically labelled tweets for the test target.
Revised:  We evaluate our approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus and achieves performance second best only to a system trained on semi-automatically labelled tweets for the test target.
Edit intent: clarity
Original:  The method is evaluated on the SemEval 2016 Task 6 Twitter Stance Detection corpus and achieves performance second best only to a system trained on semi-automatically labelled tweets for the test target.
Revised:  The method is evaluated on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving performance second best only to a system trained on semi-automatically labelled tweets for the test target.
Edit intent: coherence
Original: Convolutional Neural Networks (CNNs) have revolutionized the world of image classification over the last few years, pushing the computer vision close beyond human accuracy.
Revised: Convolutional neural networks (CNNs) have revolutionized the world of image classification over the last few years, pushing the computer vision close beyond human accuracy.
Edit intent: fluency
Original: Convolutional Neural Networks (CNNs) have revolutionized the world of image classification over the last few years, pushing the computer vision close beyond human accuracy.
Revised: Convolutional Neural Networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing the computer vision close beyond human accuracy.
Edit intent: clarity
Original: Convolutional Neural Networks (CNNs) have revolutionized the world of image classification over the last few years, pushing the computer vision close beyond human accuracy.
Revised: Convolutional Neural Networks (CNNs) have revolutionized the world of image classification over the last few years, pushing image classification beyond human accuracy.
Edit intent: clarity
Original:  The required computational effort of CNNs today requires power-hungry parallel processors and GP-GPUs.
Revised:  The computational effort of CNNs today requires power-hungry parallel processors and GP-GPUs.
Edit intent: clarity
Original:  The required computational effort of CNNs today requires power-hungry parallel processors and GP-GPUs.
Revised:  The required computational effort of today's CNNs requires power-hungry parallel processors and GP-GPUs.
Edit intent: fluency
Original:  The required computational effort of CNNs today requires power-hungry parallel processors and GP-GPUs.
Revised:  The required computational effort of CNNs today requires power-hungry parallel processors or GP-GPUs.
Edit intent: fluency
Original:  Recent efforts in designing CNN Application-Specific Integrated Circuits (ASICs) and accelerators for System-On-Chip (SoC) integration have achieved very promising results .
Revised:  Recent developments in CNN accelerators for system-on-chip integration have achieved very promising results .
Edit intent: clarity
Original:  Unfortunately, even these highly optimized engines are still above the power envelope imposed by mobile and deeply embedded applications and face hard limitations caused by CNN weight I/O and storage.
Revised:  Unfortunately, even these highly optimized devices are above the power envelope imposed by mobile and deeply embedded applications and face hard limitations caused by CNN weight I/O and storage.
Edit intent: clarity
Original:  This novel algorithm approach brings major optimization opportunities in the arithmetic core by removing the need for the expensive multiplications as well as in the weight storage and I/O  costs.
Revised:  This novel algorithm approach brings major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as in the weight storage and I/O costs.
Edit intent: fluency
Original:  This novel algorithm approach brings major optimization opportunities in the arithmetic core by removing the need for the expensive multiplications as well as in the weight storage and I/O  costs.
Revised:  This novel algorithm approach brings major optimization opportunities in the arithmetic core by removing the need for the expensive multiplications as well as in the I/O costs.
Edit intent: clarity
Original:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s  on a core area of only 1.33 MGE  and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W  and 1135 GOp/s/MGE  , respectively.
Revised:  In this work, we present an accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s on a core area of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W and 1135 GOp/s/MGE , respectively.
Edit intent: clarity
Original:  In this work, we present a HW accelerator optimized for BinaryConnect CNNs that achieves 1510 GOp/s  on a core area of only 1.33 MGE  and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W  and 1135 GOp/s/MGE  , respectively.
Revised:  In this work, we present a HW accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s on a core area of only 1.33 MGE and with a power dissipation of 153 mW in UMC 65 nm technology at 1.2 V. Our accelerator outperforms state-of-the-art performance in terms of ASIC energy efficiency as well as area efficiency with 61.2 TOp/s/W and 1135 GOp/s/MGE , respectively.
Edit intent: clarity