wanyu/IteraTeR_full_sent huggingface dataset:
Original:  It is supported by the computation time cost of robot+edge+cloud architecture, which are 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for  processing one-minute length respiratory signals, respectively.
Revised:  It is supported by the computation time cost of robot+edge+cloud architecture, which are 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for convolution operation, similarity calculation, processing one-minute length respiratory signals, respectively.
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Original:  Furthermore, the computation time costs of using the proposed system to calculate the respiratory rate are less than that of edge+cloud architecture and cloud architecture  .
Revised:  Furthermore, the computation time costs of using the proposed system to calculate the respiratory rate are less than that of edge+cloud architecture and cloud architecture . Moreover, we use our three-tire architecture for CT image diagnosis task decomposition. The evaluation of a CT image dataset of COVID-19 proves that our three-tire architecture is useful for resolving tasks on deep learning networks by edge equipment. There are broad application scenarios in smart hospitals in the future .
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Original:  To mitigate large-scale annotations  , domain adaptation (DA) provides an alternate solution by learning a transferable model from another labeled source domain. Since the labeled data may be from multiple sources, multi-source domain adaptation (MDA) would be more practical to exploit the complementary information from different domains.
Revised:  To mitigate large-scale annotations , domain adaptation (DA) provides an alternate solution by learning a transferable model from other labeled source domains. Existing multi-source domain adaptation (MDA) would be more practical to exploit the complementary information from different domains.
Edit intent: coherence
Original:  Since the labeled data may be from multiple sources, multi-source domain adaptation (MDA) would be more practical to exploit the complementary information from different domains. Existing MDA methods might fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources as well as the distribution difference among different sub-domains even in the same source, and cannot reflect the varying optimal weighting during different training stages.
Revised:  Since the labeled data may be from multiple sources, multi-source domain adaptation (MDA) methods either fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources as well as the distribution difference among different sub-domains even in the same source, and cannot reflect the varying optimal weighting during different training stages.
Edit intent: clarity
Original:  Existing MDA methods might fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources as well as the distribution difference among different sub-domains even in the same source, and cannot reflect the varying optimal weighting during different training stages.
Revised:  Existing MDA methods might fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources and the distribution difference among different sub-domains even in the same source, and cannot reflect the varying optimal weighting during different training stages.
Edit intent: coherence
Original:  Existing MDA methods might fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources as well as the distribution difference among different sub-domains even in the same source, and cannot reflect the varying optimal weighting during different training stages.
Revised:  Existing MDA methods might fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources as well as the distribution difference among different sub-domains even in the same source, or cannot reflect the varying optimal weighting during different training stages.
Edit intent: coherence
Original:  In this paper, we propose an instance-level multi-source domain adaptation framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN)  . Specifically, C-CycleGAN consists of three components:
Revised:  In this paper, we propose a novel instance-level multi-source domain adaptation framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN) . Specifically, C-CycleGAN consists of three components:
Edit intent: clarity
Original:  In this paper, we propose an instance-level multi-source domain adaptation framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN)  . Specifically, C-CycleGAN consists of three components:
Revised:  In this paper, we propose an instance-level MDA framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN) . Specifically, C-CycleGAN consists of three components:
Edit intent: clarity
Original:  In this paper, we propose an instance-level multi-source domain adaptation framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN)  . Specifically, C-CycleGAN consists of three components:
Revised:  In this paper, we propose an instance-level multi-source domain adaptation framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN) , to address the above issues . Specifically, C-CycleGAN consists of three components:
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Original: Zero-shot execution of unseen robotic tasks is an important problem in robotics. One potential approach is through task planning: combining known skills based on their preconditions and effects to achieve a user-specified goal. In this work, we propose such a task planning approach to build a reactive system for multi-step manipulation tasks that can be trained on simulation data and applied in the real-world  .
Revised: Zero-shot execution of unseen robotic tasks is important to allowing robots to perform a wide variety of tasks in human environments, but collecting the amounts of data necessary to train end-to-end policies in the real-world .
Edit intent: clarity
Original:  In this work, we propose such a task planning approach to build a reactive system for multi-step manipulation tasks that can be trained on simulation data and applied in the real-world  .
Revised:  In this work, we propose such a task planning approach to build a reactive system for multi-step manipulation tasks that can be trained on simulation data and applied in the real-world is often infeasible. We describe an approach for sim-to-real training that can accomplish unseen robotic tasks using models learned in simulation to ground components of a simple task planner. We learn a library of parameterized skills, along with a set of predicates-based preconditions and termination conditions, entirely in simulation .
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Original:  We explore a block-stacking task because it has a clear structure, where multiple skills must be chained together : pick up a block, place it on top of another block, etc. We learn these skills, along with a set of predicate preconditions and termination conditions, entirely in simulation . All components are learned as PointNet++ models, parameterized by the masks of relevant objects. The predicates allow us to create high-level plans combining different skills. They also serve as precondition functions for the skills, which enables the system to recognize failures and accomplish long-horizon tasks from perceptual input, which is critical for real-world execution.
Revised:  We explore a block-stacking task because it has a clear structure, where multiple skills must be chained together , but our methods are applicable to a wide range of other problems and domains, and can transfer from simulation to the real-world with no fine tuning. The system is able to recognize failures and accomplish long-horizon tasks from perceptual input, which is critical for real-world execution.
Edit intent: coherence
Original:  We evaluate our proposed approach in both simulation and in the real-world, showing an increase in success rate from 91.6\% to 98\% in simulation and from 10\% to 80\% success rate in the real-world as compared with naive baselines. 
Revised:  We evaluate our proposed approach in both simulation and in the real-world, showing an increase in success rate from 91.6\% to 98\% in simulation and from 10\% to 80\% success rate in the real-world as compared with naive baselines. For experiment videos including both real-world and simulation, see: URL
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Original:  While prior research has explored identification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation.
Revised:  While prior research has explored classification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation.
Edit intent: clarity
Original:  While prior research has explored identification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation.
Revised:  While prior research has explored identification of medication changes from clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation.
Edit intent: clarity
Original:  While prior research has explored identification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation.
Revised:  While prior research has explored identification of medication changes in clinical notes, studies to date have not considered the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation.
Edit intent: clarity
Original:  While prior research has explored identification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context is insufficient for use in real-world applications, such as medication timeline generation and medication reconciliation.
Revised:  While prior research has explored identification of medication changes in clinical notes, due to the longitudinal and narrative nature of clinical documentation, extraction of medication change alone without the necessary clinical context needed for their use in real-world applications, such as medication timeline generation and medication reconciliation.
Edit intent: clarity
Original:  In this process, we define specific contextual aspects pertinent to medication change events (i.e. Action, Negation, Temporality, Certainty, and Actor), describe the annotation process and challenges encountered ,  and report the results of preliminary experiments.
Revised:  In this process, we define specific contextual aspects pertinent to medication change events (i.e. Action, Negation, Temporality, Certainty, and Actor), describe the annotation process and challenges encountered , characterize the dataset, and report the results of preliminary experiments.
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Original:  The resulting dataset, CMED , consists of 9,013 medication mentions annotated over 500 clinical notes . To encourage development of methods for improved understanding of medications in clinical narratives, CMED will be released to the community as a shared task in 2021.
Revised:  The resulting dataset, CMED , consists of 9,013 medication mentions annotated over 500 clinical notes , and will be released to the community as a shared task in 2021.
Edit intent: coherence
Original: In our interviews with people who work with speech impaired persons, we learned that speech impaired people have difficulties in communicating with other people around them who do not know the sign language, and this situation may cause them to isolate themselves from society and lose their sense of independence.
Revised: According to interviews with people who work with speech impaired persons, we learned that speech impaired people have difficulties in communicating with other people around them who do not know the sign language, and this situation may cause them to isolate themselves from society and lose their sense of independence.
Edit intent: coherence
Original: In our interviews with people who work with speech impaired persons, we learned that speech impaired people have difficulties in communicating with other people around them who do not know the sign language, and this situation may cause them to isolate themselves from society and lose their sense of independence.
Revised: In our interviews with people who work with speech impaired persons, speech impaired people have difficulties in communicating with other people around them who do not know the sign language, and this situation may cause them to isolate themselves from society and lose their sense of independence.
Edit intent: coherence
Original:  digits dataset that can help to create machine learning algorithms which need to large and varied data to be successful , we published this dataset as Sign Language Digits Dataset on Kaggle Datasets web page, we present a proposal Convolutional Neural Network (CNN) architecture that can get 98\% test accuracy on our dataset , and we compared it with popular CNN models.
Revised:  digits dataset that can help to create machine learning algorithms which need to large and varied data to be successful created and published as Sign Language Digits Dataset on Kaggle Datasets web page, we present a proposal Convolutional Neural Network (CNN) architecture that can get 98\% test accuracy on our dataset , and we compared it with popular CNN models.
Edit intent: clarity
Original:  digits dataset that can help to create machine learning algorithms which need to large and varied data to be successful , we published this dataset as Sign Language Digits Dataset on Kaggle Datasets web page, we present a proposal Convolutional Neural Network (CNN) architecture that can get 98\% test accuracy on our dataset , and we compared it with popular CNN models.
Revised:  digits dataset that can help to create machine learning algorithms which need to large and varied data to be successful , we published this dataset as Sign Language Digits Dataset on Kaggle Datasets web page, a proposal Convolutional Neural Network (CNN) architecture that can get 98\% test accuracy on our dataset , and we compared it with popular CNN models.
Edit intent: clarity
Original:  digits dataset that can help to create machine learning algorithms which need to large and varied data to be successful , we published this dataset as Sign Language Digits Dataset on Kaggle Datasets web page, we present a proposal Convolutional Neural Network (CNN) architecture that can get 98\% test accuracy on our dataset , and we compared it with popular CNN models.
Revised:  digits dataset that can help to create machine learning algorithms which need to large and varied data to be successful , we published this dataset as Sign Language Digits Dataset on Kaggle Datasets web page, we present a proposal Convolutional Neural Network (CNN) architecture that can get 98\% test accuracy on our dataset presented, and compared with the existing popular CNN models.
Edit intent: clarity
Original:  Our analysis unveils that males and females interact differently depending on the interaction types, e.g., likes or comments on Instagram. Inspired by the observations of gender disparity, we propose a novel seeding framework, namely Disparity seeding, which aims to maximize information spread while reaching a target user group, e.g., a certain percentage of females -- promoting the influence of under-represented groups. An extensive simulation comparison with target-agnostic algorithms shows that the proposed Disparity can disseminate information according to the disparity requirement while effectively maximizing the information spread.
Revised:  OAn extensive simulation comparison with target-agnostic algorithms shows that the proposed Disparity can disseminate information according to the disparity requirement while effectively maximizing the information spread.
Edit intent: coherence
Original: The identification of a first case (e. g. by a disease-related death or hospitalization event) raises the question of the actual size of a local outbreak. Quick estimates of the outbreak size are required to assess the necessary testing, contact tracing and potential containment effort. Using general branching processes and assuming that epidemic parameters (including the basic reproductive number) are constant over time, we characterize the distribution of the first hospitalization time and of the epidemic size at this random time.
Revised: Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We cover the probability of establishment of an epidemic as a function of the distribution of secondary infections, the expectation of epidemic size as a function of time, and the convergence to a deterministic exponential regime. Stochasticity systematically accelerates the initial growth of an epidemic, because epidemics that do not get extinct have faster initial growth on average. This also affects the long-term epidemic growth by increasing the cumulative size by a constant factor. Applying these results, we compute the probability distribution of the first hospitalization time and of the epidemic size at this random time.
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Original:  Using general branching processes and assuming that epidemic parameters (including the basic reproductive number) are constant over time, we characterize the distribution of the first hospitalization time and of the epidemic size at this random time. We find that previous estimates either overestimate or largely underestimate the actual epidemic size. In addition, we provide upper and lower bounds for the number of infectious individuals of the local outbreak over time. The upper bound is the cumulative epidemic size, and the lower bound is a constant fraction of it. Lastly, we compute the number of detectable cases if one were to test the whole local outbreak at a single point in time.
Revised:  Using general branching processes and assuming that epidemic parameters (including the basic reproductive number) are constant over time, we characterize the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort (assumed to be constant in time), and estimate that the SARS-CoV-2 variant of concern B.1.1.7, detected in September 2020, first appeared in the United Kingdom early August 2020. We estimate a minimal testing frequency to detect clusters before they exceed a certain threshold size, and we compute the number of detectable cases if one were to test the whole local outbreak at a single point in time.
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Original:  Lastly, we compute the number of detectable cases if one were to test the whole local outbreak at a single point in time. In a growing epidemic, most individuals have been infected recently, which can strongly limit the detection of infected individuals when there is a delay between an infection and its potential detection. Overall, our analysis provides new analytical estimates about the epidemic size at identification of a first disease-related case. This piece of information is important to inform policy makers during the early stages of an epidemic outbreak .
Revised:  Lastly, we compute the detection rate of infected individuals during a single mass testing effort. For example, in a COVID-19-parameterized model with an effective reproduction number R=1.1, a fraction 1.3\% of a population needs to be randomly tested each day for cluster size to not exceed 30 infected individuals. These results improve our theoretical understanding of early epidemics and are useful for the study and control of local infectious disease clusters .
Edit intent: clarity
Original:  In this paper, we investigate whether it is still helpful to add the specific task's loss in pre-training step .
Revised:  In this paper, we investigate whether it is still helpful to add the specific task's loss in pre-training step and the fine-tuning step. Towards this goal, we propose a learning framework that making best use of the unlabel data on the low-resource and high-resource labeled dataset.
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Original:  Then we use the pseudo-label for the task-specific loss and masked language modelloss to pre-train. The experiment shows that using the fine-tuned model's predictions for pseudo-labeled pre-training offers further gains in the downstream task.
Revised:  Then we use the pseudo-label for the task-specific training on the large amounts of user-generated data. We consider this task-specific training step on pseudo labels as a pre-training offers further gains in the downstream task.
Edit intent: clarity
Original:  The experiment shows that using the fine-tuned model's predictions for pseudo-labeled pre-training offers further gains in the downstream task. The improvement of our method is stable and remarkable .
Revised:  The experiment shows that using the fine-tuned model's predictions for pseudo-labeled pre-training step for the next fine-tuning step. At last, we fine-tune on the manually labeled dataset upon the pre-trained model. In this work, we first empirically show that our method is able to solidly improve the performance by 3.6\%, when the manually labeled fine-tuning dataset is relatively small. Then we also show that our method still is able to improve the performance further by 0.2\%, when the manually labeled fine-tuning dataset is relatively large enough. We argue that our method make the best use of the unlabel data, which is superior to either pre-training or self-training alone .
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