wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We show that the complex eigenstructure of directed transitions corresponds to the Discrete Fourier Transform, the eigenvalues encode displacement via the Fourier Shift Theorem, and the Fourier components are analogous to " velocity-controlled oscillators " in oscillatory interference models. The resulting model supports computationally efficient prediction with directed transitions in spatial and non-spatial tasks and provides an explanation for theta phase precession and path integration in grid cell firing. We also discuss the efficient generalisation of our approach to deal with local changes in transition structure and its contribution to behavioural policy via a " sense of direction " corresponding to prediction of the effects of fixed ratios of actions .
Revised:  We show that the complex eigenstructure of directed transitions corresponds to the Discrete Fourier Transform, the eigenvalues encode displacement via the Fourier Shift Theorem, and the Fourier components are analogous to " velocity-controlled oscillators ) or continuous attractor networks (via analysis of the update dynamics). We thus provide a unifying framework for the role of the grid system in predictive planning, sense of direction " corresponding to prediction of the effects of fixed ratios of actions .
Edit intent: meaning-changed
Original:  We also discuss the efficient generalisation of our approach to deal with local changes in transition structure and its contribution to behavioural policy via a " sense of direction " corresponding to prediction of the effects of fixed ratios of actions .
Revised:  We also discuss the efficient generalisation of our approach to deal with local changes in transition structure and its contribution to behavioural policy via a " sense of direction and path integration: supporting generalisable inference over directed actions across different tasks .
Edit intent: meaning-changed
Original:  We look at three classes of problems with an external resource: a class of problems where the renting period is budgeted and the scheduling cost needs to be minimized, a class of problems where the scheduling cost is budgeted and the renting period needs to be minimized, and a class of two-objective problems where both, the renting period and the scheduling cost, are to be minimized  .
Revised:  We look at four classes of problems with an external resource: a class of problems where the renting period is budgeted and the scheduling cost needs to be minimized, a class of problems where the scheduling cost is budgeted and the renting period needs to be minimized, and a class of two-objective problems where both, the renting period and the scheduling cost, are to be minimized .
Edit intent: meaning-changed
Original:  We look at three classes of problems with an external resource: a class of problems where the renting period is budgeted and the scheduling cost needs to be minimized, a class of problems where the scheduling cost is budgeted and the renting period needs to be minimized, and a class of two-objective problems where both, the renting period and the scheduling cost, are to be minimized  .
Revised:  We look at three classes of problems with an external resource: a class of problems where the renting period is budgeted and the scheduling cost needs to be minimized, a class of problems where the scheduling cost is budgeted and the renting period needs to be minimized, a class of two-objective problems where both, the renting period and the scheduling cost, are to be minimized .
Edit intent: coherence
Original:  We look at three classes of problems with an external resource: a class of problems where the renting period is budgeted and the scheduling cost needs to be minimized, a class of problems where the scheduling cost is budgeted and the renting period needs to be minimized, and a class of two-objective problems where both, the renting period and the scheduling cost, are to be minimized  .
Revised:  We look at three classes of problems with an external resource: a class of problems where the renting period is budgeted and the scheduling cost needs to be minimized, a class of problems where the scheduling cost is budgeted and the renting period needs to be minimized, and a class of two-objective problems where both, the renting period and the scheduling cost, are to be minimized , and a class of problems where a linear combination of the scheduling cost and the renting period is minimized .
Edit intent: meaning-changed
Original:  An oscillating electric field is generated from audio clips of music spanning various genres and played to electrodes that are immersed into a dispersion of yeast vacuolar membrane vesicles natively hosting a high concentration of active V-ATPase .
Revised:  An oscillating electric field is generated from audio clips of music spanning various genres and played to electrodes that are immersed into a dispersion of yeast vacuolar membrane vesicles natively hosting a high concentration of active V- ATPase .
Edit intent: fluency
Original:  Most prominent identified frequencies are harmonically related to each other and to the estimated rotation rate of the enzyme  .
Revised:  Most prominent identified frequencies are harmonically related to each other and to the estimated rotation rate of the enzyme . Under the given experimental conditions, the top four music clips that most enhance the specific activity of V-ATPase are in the musical key of D, in a perfect agreement with the Fourier analysis of the clips .
Edit intent: meaning-changed
Original:  By using an attention model  to leverage the contextual metadata that accompanies a video, we observe a relative improvement of about 12 \% in Word Error Rate on Named Entities (WER-NE) for videos with related metadata.
Revised:  By using an attention model and a biasing model to leverage the contextual metadata that accompanies a video, we observe a relative improvement of about 12 \% in Word Error Rate on Named Entities (WER-NE) for videos with related metadata.
Edit intent: meaning-changed
Original:  By using an attention model  to leverage the contextual metadata that accompanies a video, we observe a relative improvement of about 12 \% in Word Error Rate on Named Entities (WER-NE) for videos with related metadata.
Revised:  By using an attention model to leverage the contextual metadata that accompanies a video, we observe a relative improvement of about 16 \% in Word Error Rate on Named Entities (WER-NE) for videos with related metadata.
Edit intent: meaning-changed
Original:  Here we show that the evolution and strength of the recent\perc  SARS-Cov-2 pandemics, might have been modulated by the intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Revised:  Here we combine our measurements of the action spectrum of Covid-19 in response to UV light, Solar irradiation measurements on Earth during the SARS-CoV-2 pandemics, worldwide recorded Covid-19 mortality data and our 'Solar-Pump' diffusive model of epidemics to show that (a) UV-B/A photons have a powerful virucidal effect on the single-stranded RNA virus Covid-19 and that (b) the Solar radiation that reaches temperate regions of the Earth at noon during summers, is sufficient to inactivate 63\perc SARS-Cov-2 pandemics, might have been modulated by the intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Edit intent: meaning-changed
Original:  Here we show that the evolution and strength of the recent\perc  SARS-Cov-2 pandemics, might have been modulated by the intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Revised:  Here we show that the evolution and strength of the recent\perc of virions in open-space concentrations (1.5 x 103 TCID50/mL, higher than typical aerosol) in less than 2 min. We conclude that the characteristic seasonality imprint displayed world-wide by the SARS-Cov-2 pandemics, might have been modulated by the intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Edit intent: meaning-changed
Original:  Here we show that the evolution and strength of the recent\perc  SARS-Cov-2 pandemics, might have been modulated by the intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Revised:  Here we show that the evolution and strength of the recent\perc SARS-Cov-2 mortality time-series throughout the diffusion of the outbreak (with temperate regions showing clear seasonal trends and equatorial regions suffering, on average, a systematically lower mortality), might have been modulated by the intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Edit intent: meaning-changed
Original:  Here we show that the evolution and strength of the recent\perc  SARS-Cov-2 pandemics, might have been modulated by the intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Revised:  Here we show that the evolution and strength of the recent\perc SARS-Cov-2 pandemics, might have been efficiently set by the different intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Edit intent: clarity
Original:  Here we show that the evolution and strength of the recent\perc  SARS-Cov-2 pandemics, might have been modulated by the intensity of UV-B/A Solar radiation hitting different regions of Earthduring the diffusion of the outbreak .
Revised:  Here we show that the evolution and strength of the recent\perc SARS-Cov-2 pandemics, might have been modulated by the intensity of UV-B/A Solar radiation hitting different Earth's locations at different times of the year .
Edit intent: clarity
Original:  In this paper, we studied Knock Codes security asking participants to select codes on mobile devices in three settings: a control treatment, a blacklist treatment, and a treatment with a larger, 2x3 grid.
Revised:  In this paper, we studied Knock Codes security asking participants to select codes on mobile devices in three settings: a control treatment, a blocklist treatment, and a treatment with a larger, 2x3 grid.
Edit intent: fluency
Original:  Our analysis characterizes the effects of market liquidity, market participants' forecasts about inter-area price spreads, transaction fees and interaction of CTS markets with financial transmission rights . Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from the equilibrium analysis.
Revised:  Our analysis characterizes the effect of market liquidity, market participants' forecasts about inter-area price spreads, transaction fees and interaction of CTS markets with financial transmission rights . Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from the equilibrium analysis.
Edit intent: fluency
Original:  Our analysis characterizes the effects of market liquidity, market participants' forecasts about inter-area price spreads, transaction fees and interaction of CTS markets with financial transmission rights . Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from the equilibrium analysis.
Revised:  Our analysis characterizes the effects of market liquidity, market participants' forecasts about inter-area price spreads, transactions fees and coupling of CTS markets with financial transmission rights . Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from the equilibrium analysis.
Edit intent: clarity
Original:  Our analysis characterizes the effects of market liquidity, market participants' forecasts about inter-area price spreads, transaction fees and interaction of CTS markets with financial transmission rights . Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from the equilibrium analysis.
Revised:  Our analysis characterizes the effects of market liquidity, market participants' forecasts about inter-area price spreads, transaction fees and interaction of CTS markets with up-to-congestion virtual transactions . Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from the equilibrium analysis.
Edit intent: clarity
Original:  Our analysis characterizes the effects of market liquidity, market participants' forecasts about inter-area price spreads, transaction fees and interaction of CTS markets with financial transmission rights . Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from the equilibrium analysis.
Revised:  Our analysis characterizes the effects of market liquidity, market participants' forecasts about inter-area price spreads, transaction fees and interaction of CTS markets with financial transmission rights . Using real data, we empirically verify that CTS bidders can employ simple learning algorithms to discover Nash equilibria that support the conclusions drawn from equilibrium analysis.
Edit intent: fluency
Original:  We show that the proposed GAN can augment data for a specific class of images (pneumonia) using images from both classes (pneumonia and normal) in an image domain ( chest X-rays ). We demonstrate that using our proposed GAN-based data augmentation method significantly improves the performance of the state-of-the-art anomaly detection architecture, AnoGAN, in detecting pneumonia in chest X-rays, increasing AUC from 0.83 to 0.88 .
Revised:  We show that the proposed GAN can augment data for a specific class of images (pneumonia) using images from both classes (pneumonia and normal) in an image domain ( chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based data augmentation method significantly improves the performance of the state-of-the-art anomaly detection architecture, AnoGAN, in detecting pneumonia in chest X-rays, increasing AUC from 0.83 to 0.88 .
Edit intent: meaning-changed
Original:  We demonstrate that using our proposed GAN-based data augmentation method significantly improves the performance of the state-of-the-art anomaly detection architecture, AnoGAN, in detecting pneumonia in chest X-rays, increasing AUC from 0.83 to 0.88 .
Revised:  We demonstrate that using our proposed GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images .
Edit intent: clarity
Original:  We show that these structures are dependent on the ordering of the inputs for the parameter regions where the network presents synchronization malleability and we suggest that this is due to a balance between local and global effects .
Revised:  We show that these structures are dependent on the ordering of the inputs for the parameter regions where the network presents synchronization malleability and we suggest that this is due to a complex interplay between coupling, connection architecture, and individual neural inputs .
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Original:  An exhaustive review of stance detection techniques on social media ispresented ,including the task definition, the different types of targets in stance detection, the features set used, and the variousmachine learning approaches applied.
Revised:  An exhaustive review of stance detection techniques on social media is presented ,including the task definition, the different types of targets in stance detection, the features set used, and the variousmachine learning approaches applied.
Edit intent: fluency
Original:  In this paper we propose a new model architecture DeBERTa(Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques.
Revised:  In this paper wepropose a new model architecture DeBERTa(Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques.
Edit intent: fluency
Original:  In this paper we propose a new model architecture DeBERTa(Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques.
Revised:  In this paper we propose a new model architecture DeBERTa(Decoding-enhanced BERT with dis-entangled attention) that improves the BERT and RoBERTa models using two novel techniques.
Edit intent: fluency
Original:  The DeBERTa code and pre-trained models will be made publicly available at URL
Revised:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time, surpasses the human performance (89.9 vs. 89.8).
Edit intent: meaning-changed
Original:  In this paper wepropose a new model architecture DeBERTa (Decoding-enhanced BERT with dis-entangled attention) that improves the BERT and RoBERTa models using two novel techniques.
Revised:  In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with dis-entangled attention) that improves the BERT and RoBERTa models using two novel techniques.
Edit intent: fluency
Original:  In this paper wepropose a new model architecture DeBERTa (Decoding-enhanced BERT with dis-entangled attention) that improves the BERT and RoBERTa models using two novel techniques.
Revised:  In this paper wepropose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques.
Edit intent: fluency
Original:  The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions  .
Revised:  The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions , respectively .
Edit intent: fluency
Original:   We show that these two techniques significantly improve the efficiency of model pre-training and the performance of both natural languageunderstand (NLU) and natural langauge generation (NLG)  tasks.
Revised:  In addition, a new virtual adversarial training method is used for fine-tuning to improve models' generalization. We show that these two techniques significantly improve the efficiency of model pre-training and the performance of both natural languageunderstand (NLU) and natural langauge generation (NLG) tasks.
Edit intent: meaning-changed
Original:   We show that these two techniques significantly improve the efficiency of model pre-training and the performance of both natural languageunderstand (NLU) and natural langauge generation (NLG)  tasks.
Revised:  We show that these two techniques significantly improve the efficiency of model pre-training and the performance of both natural languageunderstand (NLU) and natural langauge generation (NLG) downstream tasks.
Edit intent: meaning-changed
Original:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time , surpasses the human performance ( 89.9 vs. 89.8).
Revised:  Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time , surpasses the human performance ( 89.9 vs. 89.8).
Edit intent: meaning-changed
Original:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time , surpasses the human performance ( 89.9 vs. 89.8).
Revised:  Notably, we scale up DeBERTa to 1.5 billion parameters . The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time , surpasses the human performance ( 89.9 vs. 89.8).
Edit intent: meaning-changed
Original:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time , surpasses the human performance ( 89.9 vs. 89.8).
Revised:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) for the first time , surpasses the human performance ( 89.9 vs. 89.8).
Edit intent: fluency
Original:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time , surpasses the human performance ( 89.9 vs. 89.8).
Revised:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time in terms of macro-average score ( 89.9 vs. 89.8).
Edit intent: meaning-changed
Original:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time , surpasses the human performance ( 89.9 vs. 89.8).
Revised:  Notably, we scale up DeBERTa to 1.5 billion parameters and it substantially outperforms Google's T5 with 11 billionparameters on the SuperGLUE benchmark (Wang et al., 2019a) and, for the first time , surpasses the human performance ( 89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, out performing the human baseline by a decent margin (90.3 versus 89.8).
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Original: We present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations , a self-supervised method for learning universal sentence embeddings that transfer to a wide variety of natural language processing (NLP) tasks.
Revised: Sentence embeddings are an important component of many natural language processing (NLP) systems. Like word embeddings, sentence embeddings are typically learned on large text corpora and then transferred to various downstream tasks, such as clustering and retrieval. Unlike word embeddings, the highest performing solutions for learning sentence embeddings require labelled data, limiting their usefulness to languages and domains where labelled data is abundant. In this paper, we present DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations , a self-supervised method for learning universal sentence embeddings that transfer to a wide variety of natural language processing (NLP) tasks.
Edit intent: meaning-changed