wanyu/IteraTeR_full_sent huggingface dataset:
Original:  And we will end with the recent interest in neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining  data-intensive statistical AI systems with  symbolic AI systems which results in more capable AI systems that support more human-like intelligence.
Revised:  And we will end with the recent interest in neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining data-intensive statistical AI systems with symbolic AI systems , resulting in more capable AI systems that support more human-like intelligence.
Edit intent: coherence
Original:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a huge search space including approximately 10^{13} subnets and achieves 1.66 \% EER and 0.156 DCF_{0.01} with 565M  MACs.
Revised:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a vast search space including approximately 10^{13} subnets and achieves 1.66 \% EER and 0.156 DCF_{0.01} with 565M MACs.
Edit intent: fluency
Original:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a huge search space including approximately 10^{13} subnets and achieves 1.66 \% EER and 0.156 DCF_{0.01} with 565M  MACs.
Revised:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a huge search space including approximately 10^{13} subnets and achieves 1.55 \% EER and 0.156 DCF_{0.01} with 565M MACs.
Edit intent: meaning-changed
Original:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a huge search space including approximately 10^{13} subnets and achieves 1.66 \% EER and 0.156 DCF_{0.01} with 565M  MACs.
Revised:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a huge search space including approximately 10^{13} subnets and achieves 1.66 \% EER and 0.138 DCF_{0.01} with 565M MACs.
Edit intent: meaning-changed
Original:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a huge search space including approximately 10^{13} subnets and achieves 1.66 \% EER and 0.156 DCF_{0.01} with 565M  MACs.
Revised:  Results of experiments on the VoxCeleb dataset show EfficientTDNN provides a huge search space including approximately 10^{13} subnets and achieves 1.66 \% EER and 0.156 DCF_{0.01} with 565M MACs as well as 0.96\% EER and 0.108 DCF_{0.01 MACs.
Edit intent: meaning-changed
Original:  whereas, CNN recognizes high-level patterns of dominant units with respect to the sentence and thread context. The attention mechanism is applied on top of CNN to further highlight the high-level representations that capture any important units contributing to a desirable summary.
Revised:  whereas, CNN recognizes high-level patterns of dominant units with respect to the sentence and thread . Attention mechanism is applied on top of CNN to further highlight the high-level representations that capture any important units contributing to a desirable summary.
Edit intent: clarity
Original:  In order to improve this situation, image analysis-based URLanism counting systems are developed since 1980s, which consists of digital image processing, image segmentation, image classification and so on. Moreover, image analysis-based URLanism counting methods are efficient comparing with traditional plate counting methods.
Revised:  In order to improve this situation, image analysis-based URLanism counting systems are developed since 1980s, which consists of digital image processing, image segmentation, image classification and suchlike. Image analysis-based URLanism counting methods are efficient comparing with traditional plate counting methods.
Edit intent: coherence
Original: Recent trend demonstrates the effectiveness of deep neural networks (DNNs) apply on the task of environment perception in autonomous driving system. While large-scale and complete data can train out fine DNNs, collecting it is always difficult, expensive, and time-consuming.
Revised: Recent trend demonstrates the effectiveness of deep Convolutional Neural Networks(CNNs) for this task has been demonstrated. State-of-art CNNs for semantic segmentation suffer from excessive computations as well as large-scale and complete data can train out fine DNNs, collecting it is always difficult, expensive, and time-consuming.
Edit intent: meaning-changed
Original:  While large-scale and complete data can train out fine DNNs, collecting it is always difficult, expensive, and time-consuming. Also, the significance of both accuracy and efficiency cannot be over-emphasized due to the requirementof real-time recognition. To alleviate the conflicts between weak data and high computational consumption of DNNs , we propose a new training framework named Spirit Distillation(SD) . It extends the ideas of fine-tuning-based transfer learning(FTT) and feature-based knowledge distillation.
Revised:  While large-scale training data requirement. Inspired by the ideas of Fine-tuning-based Transfer Learning (FTT) and feature-based knowledge distillation , we propose a new training framework named Spirit Distillation(SD) . It extends the ideas of fine-tuning-based transfer learning(FTT) and feature-based knowledge distillation.
Edit intent: coherence
Original:  To alleviate the conflicts between weak data and high computational consumption of DNNs , we propose a new training framework named Spirit Distillation(SD) . It extends the ideas of fine-tuning-based transfer learning(FTT) and feature-based knowledge distillation.
Revised:  To alleviate the conflicts between weak data and high computational consumption of DNNs , we propose a new knowledge distillation method for cross-domain knowledge transference and efficient data-insufficient network training, named Spirit Distillation(SD) . It extends the ideas of fine-tuning-based transfer learning(FTT) and feature-based knowledge distillation.
Edit intent: meaning-changed
Original:  To alleviate the conflicts between weak data and high computational consumption of DNNs , we propose a new training framework named Spirit Distillation(SD) . It extends the ideas of fine-tuning-based transfer learning(FTT) and feature-based knowledge distillation. By allowing the student to mimic its teacher in feature extraction, the gap of general features between the teacher-student networks is bridged. The Image Party distillation enhancement method(IP) is also proposed, which shuffling images from various domains, and randomly selecting a few as mini-batch.
Revised:  To alleviate the conflicts between weak data and high computational consumption of DNNs , we propose a new training framework named Spirit Distillation(SD) , which allow the student network to mimic the teacher network to extract general features, so that a compact and accurate student network can be trained for real-time semantic segmentation of road scenes. Then, in order to further alleviate the trouble of insufficient data and improve the robustness of the student, an Enhanced Spirit Distillation (ESD) method is proposed, which shuffling images from various domains, and randomly selecting a few as mini-batch.
Edit intent: coherence
Original:  The Image Party distillation enhancement method(IP) is also proposed, which shuffling images from various domains, and randomly selecting a few as mini-batch. With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance)
Revised:  The Image Party distillation enhancement method(IP) is also proposed, which commits to exploit a more comprehensive general features extraction capability by considering images from both the target and the proximity domains as input. To our knowledge, this paper is a pioneering work on the application of knowledge distillation to few-shot learning . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance)
Edit intent: meaning-changed
Original:  With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance)
Revised:  With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments conducted on Cityscapes semantic segmentation with the prior knowledge transferred from COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance)
Edit intent: clarity
Original:  With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance)
Revised:  With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI demonstrate that our methods can train a better student network (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance)
Edit intent: clarity
Original:  With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% output variance) , and can gain a precise compact network with only 41.8 %DIFDELCMD < \\%%% %DIF < FLOPs(see Fig. 1).
Revised:  With this approach, the overfitting that the student network to the general features of the teacher network can be easily avoided . Persuasive experiments and discussions are conducted on CityScapes with the prompt of COCO2017 and KITTI . Results demonstrate the boosting performance in segmentation (mIOU and high-precision accuracy boost by 1.4\% and 8.2\% respectively, with 78.2\% segmentation variance) with only 41.8 %DIFDELCMD < \\%%% %DIF < FLOPs(see Fig. 1).
Edit intent: coherence
Original:   We design a new practically efficient algorithm for solving one block quantifier elimination problems when the input semi-algebraic formula is a system of polynomial equations satisfying some mild assumptionssuch as transversality .
Revised:  This paper studies a variant of the one block quantifier elimination in which we compute an almost equivalent formula of the input. We design a new practically efficient algorithm for solving one block quantifier elimination problems when the input semi-algebraic formula is a system of polynomial equations satisfying some mild assumptionssuch as transversality .
Edit intent: meaning-changed
Original:   We design a new practically efficient algorithm for solving one block quantifier elimination problems when the input semi-algebraic formula is a system of polynomial equations satisfying some mild assumptionssuch as transversality .
Revised:  We design a new probabilistic efficient algorithm for solving one block quantifier elimination problems when the input semi-algebraic formula is a system of polynomial equations satisfying some mild assumptionssuch as transversality .
Edit intent: meaning-changed
Original:  When the input is generic, involves s polynomials of degree bounded by D with n quantified variables and t unquantified ones, we prove that this algorithm outputs semi-algebraic formulas of degree bounded by D using O\ {~}\left ( n \ 8^{t}\ D^{3t+2} \ \binom{t+\mathcal{D}}{t} \right ) arithmetic operations in the ground field where \mathcal{D} = n \ D^s(D-1)^{n-s+1}\ \binom{n}{s}.
Revised:  When the input is generic, involves s polynomials of degree bounded by D with n quantified variables and t unquantified ones, we prove that this algorithm outputs semi-algebraic formulas of degree bounded by D using O\ {~}\left ( (n-s+1) \ 8^{t}\ D^{3t+2} \ \binom{t+\mathcal{D}}{t} \right ) arithmetic operations in the ground field where \mathcal{D} = n \ D^s(D-1)^{n-s+1}\ \binom{n}{s}.
Edit intent: fluency
Original:  When the input is generic, involves s polynomials of degree bounded by D with n quantified variables and t unquantified ones, we prove that this algorithm outputs semi-algebraic formulas of degree bounded by D using O\ {~}\left ( n \ 8^{t}\ D^{3t+2} \ \binom{t+\mathcal{D}}{t} \right ) arithmetic operations in the ground field where \mathcal{D} = n \ D^s(D-1)^{n-s+1}\ \binom{n}{s}.
Revised:  When the input is generic, involves s polynomials of degree bounded by D with n quantified variables and t unquantified ones, we prove that this algorithm outputs semi-algebraic formulas of degree bounded by D using O\ {~}\left ( n \ 8^{t}\ D^{3t+2} \binom{t+\mathcal{D}}{t} \right ) arithmetic operations in the ground field where \mathcal{D} = n \ D^s(D-1)^{n-s+1}\ \binom{n}{s}.
Edit intent: fluency
Original:  When the input is generic, involves s polynomials of degree bounded by D with n quantified variables and t unquantified ones, we prove that this algorithm outputs semi-algebraic formulas of degree bounded by D using O\ {~}\left ( n \ 8^{t}\ D^{3t+2} \ \binom{t+\mathcal{D}}{t} \right ) arithmetic operations in the ground field where \mathcal{D} = n \ D^s(D-1)^{n-s+1}\ \binom{n}{s}.
Revised:  When the input is generic, involves s polynomials of degree bounded by D with n quantified variables and t unquantified ones, we prove that this algorithm outputs semi-algebraic formulas of degree bounded by D using O\ {~}\left ( n \ 8^{t}\ D^{3t+2} \ \binom{t+\mathcal{D}}{t} \right ) arithmetic operations in the ground field where \mathcal{D} = 2(n+s) \ D^s(D-1)^{n-s+1}\ \binom{n}{s}.
Edit intent: meaning-changed
Original: Communication compression techniques are of growing interests in studying the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over networked agents using only local computation and peer-to-peer communication.
Revised: Communication compression techniques are of growing interests for solving the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over networked agents using only local computation and peer-to-peer communication.
Edit intent: clarity
Original: Communication compression techniques are of growing interests in studying the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over networked agents using only local computation and peer-to-peer communication.
Revised: Communication compression techniques are of growing interests in studying the decentralized optimization problem under limited communication, where the global objective is to minimize the average of local cost functions over a multi-agent network using only local computation and peer-to-peer communication.
Edit intent: clarity
Original:   We show that C-GT inherits the advantages of gradient tracking-based algorithms , and in particular, achieves linear convergence rate for strongly convex and smooth objective functions.
Revised:  In particular, C-GT is compatible with a general class of compression operators that unifies both unbiased and biased compressors. We show that C-GT inherits the advantages of gradient tracking-based algorithms , and in particular, achieves linear convergence rate for strongly convex and smooth objective functions.
Edit intent: meaning-changed
Original:  We adapt this framework to the set-up and optimization of atmospheric plasma spraying processes. We propose and validate a Gaussian process modeling structure to predict coatings properties. We introduce a parallel acquisition procedure tailored on the process characteristics and propose an algorithm that adapts to real-time process measurements to improve reproducibility.
Revised:  In complex manufacturing processes, the possibility to run extensive sequences of experiments with the goal of finding good process parameters is severely limited by the time required for quality evaluation of the produced parts. To accelerate the process parameter optimization, we introduce a parallel acquisition procedure tailored on the process characteristics and propose an algorithm that adapts to real-time process measurements to improve reproducibility.
Edit intent: coherence
Original:  We find that the median value of tenure is 4517 reais for federal government employees, 2560 reais for state government employees, and 672 reais for municipal government employees .
Revised:  I find that in the 2005-2019 period the monthly value of tenure is 4517 reais for federal government employees, 2560 reais for state government employees, and 672 reais for municipal government employees .
Edit intent: meaning-changed
Original:  We find that the median value of tenure is 4517 reais for federal government employees, 2560 reais for state government employees, and 672 reais for municipal government employees .
Revised:  We find that the median value of tenure was 3980 reais to the median federal government employee, 1971 reais to the median state government employee, and 500 reais to the median municipal government employee .
Edit intent: meaning-changed
Original: Recently, GAN-based neural vocoders, such as Parallel WaveGAN and MelGAN have attracted great interest due to their lightweight and parallel structures, enabling them to generate high fidelity waveform in a real-time manner.
Revised:  GAN-based neural vocoders, such as Parallel WaveGAN and MelGAN have attracted great interest due to their lightweight and parallel structures, enabling them to generate high fidelity waveform in a real-time manner.
Edit intent: coherence
Original:  In this paper, inspired by Relativistic GAN \mbox{%DIFAUXCMD jolicoeur2018relativistic , we introduce a novel variant of the LSGAN framework under the context of waveform synthesis, named Pointwise Relativistic LSGAN (PRLSGAN).
Revised:  In this paper, inspired by Relativistic GAN , we introduce a novel variant of the LSGAN framework under the context of waveform synthesis, named Pointwise Relativistic LSGAN (PRLSGAN).
Edit intent: coherence
Original:  We compare against a range of diverse and previously proposed methods for generating token-level labels, and present a simple yet effective modified attention layer that significantly advances the current state of the art .
Revised:  Existing approaches to zero-shot sequence labeling do not perform well when applied on transformer-based architectures. As transformers contain multiple layers of multi-head self-attention, information in the sentence gets distributed between many tokens, negatively affecting zero-shot token-level labels, and present a simple yet effective modified attention layer that significantly advances the current state of the art .
Edit intent: meaning-changed
Original: We  identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these label errors to affect benchmark results.
Revised: We algorithmically identify label errors in the test sets of 10 of the most commonly-used computer vision, natural language, and audio datasets, and subsequently study the potential for these label errors to affect benchmark results.
Edit intent: meaning-changed
Original:  On CIFAR-10 with corrected labels: VGG-11 outperforms VGG-19 if the prevalence of originally mislabeled test examples increases by just 5\%  .
Revised:  On CIFAR-10 with corrected labels: VGG-11 outperforms VGG-19 if the prevalence of originally mislabeled test examples increases by 5\% .
Edit intent: clarity
Original:  On CIFAR-10 with corrected labels: VGG-11 outperforms VGG-19 if the prevalence of originally mislabeled test examples increases by just 5\%  .
Revised:  On CIFAR-10 with corrected labels: VGG-11 outperforms VGG-19 if the prevalence of originally mislabeled test examples increases by just 5\% . Traditionally, ML practitioners choose which model to deploy based on test accuracy -- our findings advise caution here, proposing that judging models over correctly labeled test sets may be more useful, especially for noisy real-world datasets .
Edit intent: meaning-changed
Original: Viral fusion proteins are attached to the membrane of enveloped viruses (a group that includes Coronaviruses, Dengue, HIV and Influenza) and catalyze fusion between the viral envelope and host membrane , enabling the virus to insert its genetic material into the host cell.
Revised: Viral fusion proteins are attached to the membrane of enveloped viruses (a group that includes Coronaviruses, Dengue, HIV and Influenza) and catalyze fusion between the viral and host membranes , enabling the virus to insert its genetic material into the host cell.
Edit intent: clarity
Original:  this work presents a centralized database containing the most relevant information on viral fusion proteins, available through a free-to-use web server accessible accessible by the URL URL This server also contains several bioinformatic tools, such as Clustal sequence alignment and Weblogo, and includes a machine learning-based tool capable of predicting the location of fusion peptides (the component of fusion proteins that inserts into the host's cell membrane)
Revised:  this work presents a centralized database containing the most relevant information on viral fusion proteins, available through a free-to-use web server accessible through the URL URL This server also contains several bioinformatic tools, such as Clustal sequence alignment and Weblogo, and includes a machine learning-based tool capable of predicting the location of fusion peptides (the component of fusion proteins that inserts into the host's cell membrane)
Edit intent: clarity
Original:  this work presents a centralized database containing the most relevant information on viral fusion proteins, available through a free-to-use web server accessible accessible by the URL URL This server also contains several bioinformatic tools, such as Clustal sequence alignment and Weblogo, and includes a machine learning-based tool capable of predicting the location of fusion peptides (the component of fusion proteins that inserts into the host's cell membrane)
Revised:  this work presents a centralized database containing the most relevant information on viral fusion proteins, available through a free-to-use web server accessible accessible by the URL URL This web application contains several bioinformatic tools, such as Clustal sequence alignment and Weblogo, and includes a machine learning-based tool capable of predicting the location of fusion peptides (the component of fusion proteins that inserts into the host's cell membrane)
Edit intent: clarity