wanyu/IteraTeR_full_sent huggingface dataset:
Original: We show how to efficiently provide differentially private answers to counting queries (or integer-valued low-sensitivity queries) by adding discrete Gaussian noise , with essentially the same privacy and accuracy as the continuous Gaussian. The use of a discrete distribution is necessary in practice, as finite computers cannot  represent samples from continuous distributions and numerical errors may destroy the privacy guarantee .
Revised: A key tool for building differentially private systems is adding Gaussian noise to the output of a function evaluated on a sensitive dataset. Unfortunately, using a continuous distribution presents several practical challenges. First and foremost, finite computers cannot represent samples from continuous distributions and numerical errors may destroy the privacy guarantee .
Edit intent: clarity
Original:  The use of a discrete distribution is necessary in practice, as finite computers cannot  represent samples from continuous distributions and numerical errors may destroy the privacy guarantee .
Revised:  The use of a discrete distribution is necessary in practice, as finite computers cannot represent samples from continuous distributions , and previous work has demonstrated that seemingly innocuous numerical errors can entirely destroy privacy. Moreover, when the underlying data is itself discrete (e.g., population counts), adding continuous noise makes the result less interpretable. With these shortcomings in mind, we introduce and analyze the discrete Gaussian in the context of differential privacy. Specifically, we theoretically and experimentally show that adding discrete Gaussian noise provides essentially the same privacy and accuracy guarantees as the addition of continuous Gaussian noise. We also present an simple and efficient algorithm for exact sampling from this distribution. This demonstrates its applicability for privately answering counting queries, or more generally, low-sensitivity integer-valued queries .
Edit intent: meaning-changed
Original:  The scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and consuming 1.9 mJ per inference for operating the smallest model, and on a Cortex-M7 with 44ms and 6.2 mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI.
Revised:  The scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and consuming 4.28 mJ per inference for operating the smallest model, and on a Cortex-M7 with 44ms and 6.2 mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI.
Edit intent: meaning-changed
Original:  The scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and consuming 1.9 mJ per inference for operating the smallest model, and on a Cortex-M7 with 44ms and 6.2 mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI.
Revised:  The scaled models are deployed on a commercial Cortex-M4F MCU taking 101ms and consuming 1.9 mJ per inference for operating the smallest model, and on a Cortex-M7 with 44ms and 18.1 mJ per inference for the medium-sized model, enabling a fully autonomous, wearable, and accurate low-power BCI.
Edit intent: meaning-changed
Original: We consider crowdsourced labeling under a  worker-task specialization block model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to the tasks of unmatched types.
Revised: We consider crowdsourced labeling under a d-type worker-task specialization block model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to the tasks of unmatched types.
Edit intent: meaning-changed
Original: We consider crowdsourced labeling under a  worker-task specialization block model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to the tasks of unmatched types.
Revised: We consider crowdsourced labeling under a worker-task specialization model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to the tasks of unmatched types.
Edit intent: clarity
Original: We consider crowdsourced labeling under a  worker-task specialization block model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to the tasks of unmatched types.
Revised: We consider crowdsourced labeling under a worker-task specialization block model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to tasks of unmatched types.
Edit intent: fluency
Original:  The designed inference algorithm does not require any information about worker types, task typesas well as worker reliability parameters, and achieve any targeted recovery accuracy with the best known performance (minimum number of queries per task) for any parameter regimes .
Revised:  The designed inference algorithm does not require any information about worker /task types, and achieves any targeted recovery accuracy with the best known performance (minimum number of queries per task) for any parameter regimes .
Edit intent: clarity
Original:  The designed inference algorithm does not require any information about worker types, task typesas well as worker reliability parameters, and achieve any targeted recovery accuracy with the best known performance (minimum number of queries per task) for any parameter regimes .
Revised:  The designed inference algorithm does not require any information about worker types, task typesas well as worker reliability parameters, and achieve any targeted recovery accuracy with the best known performance (minimum number of queries per task) .
Edit intent: clarity
Original:   Since there has already been a broad body of heterogeneous network embedding (HNE ) algorithmsbut no dedicated survey , as the first contribution of this work, we pioneer in providing a unified paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
Revised:  In this work, we aim to provide a unified framework to deeply summarize and evaluate existing research on heterogeneous network embedding (HNE), which includes but goes beyond a normal survey. Since there has already been a broad body of heterogeneous network embedding (HNE ) algorithmsbut no dedicated survey , as the first contribution of this work, we pioneer in providing a unified paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
Edit intent: meaning-changed
Original:   Since there has already been a broad body of heterogeneous network embedding (HNE ) algorithmsbut no dedicated survey , as the first contribution of this work, we pioneer in providing a unified paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
Revised:  Since there has already been a broad body of HNE algorithms , as the first contribution of this work, we pioneer in providing a unified paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
Edit intent: clarity
Original:   Since there has already been a broad body of heterogeneous network embedding (HNE ) algorithmsbut no dedicated survey , as the first contribution of this work, we pioneer in providing a unified paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
Revised:  Since there has already been a broad body of heterogeneous network embedding (HNE ) algorithmsbut no dedicated survey , as the first contribution of this work, we provide a generic paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms.
Edit intent: clarity
Original:  Understandable due to the natural application favor of HNE, such indirect comparisons largely hinder the proper attribution of improved task performance towards effective data preprocessing and novel technical design, especially considering the various ways possible to construct a heterogeneous network from real-world application data.
Revised:  Understandable due to the application favor of HNE, such indirect comparisons largely hinder the proper attribution of improved task performance towards effective data preprocessing and novel technical design, especially considering the various ways possible to construct a heterogeneous network from real-world application data.
Edit intent: clarity
Original:  Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and %DIFDELCMD < \etc%%% . ~ from different sources, towards the comprehensive evaluation of HNE algorithms.
Revised:  Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and %DIFDELCMD < \etc%%% etc. from different sources, towards the comprehensive evaluation of HNE algorithms.
Edit intent: coherence
Original:  Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and %DIFDELCMD < \etc%%% . ~ from different sources, towards the comprehensive evaluation of HNE algorithms.
Revised:  Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and %DIFDELCMD < \etc%%% . ~ from different sources, towards handy and fair evaluations of HNE algorithms.
Edit intent: clarity
Original:  As the third contribution, we carefully refactor and amend the implementations of and create friendly interfaces for ten popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.
Revised:  As the third contribution, we carefully refactor and amend the implementations of and create friendly interfaces for eleven popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.
Edit intent: meaning-changed
Original:  As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for eleven popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.
Revised:  As the third contribution, we carefully refactor and amend the implementations and create friendly interfaces for 13 popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.
Edit intent: meaning-changed
Original:  In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots,  brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs.
Revised:  In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, give a brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs.
Edit intent: fluency
Original:  In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, give a brief mathematical description of 3D CNN and  the preprocessing steps required for medical images before feeding them to 3D CNNs.
Revised:  In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and the preprocessing steps required for medical images before feeding them to 3D CNNs.
Edit intent: clarity
Original:  In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, give a brief mathematical description of 3D CNN and  the preprocessing steps required for medical images before feeding them to 3D CNNs.
Revised:  In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, give a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs.
Edit intent: clarity
Original:  We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection , and localization.
Revised:  We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization.
Edit intent: fluency
Original:  In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after March 30 , 2020, by using the Italian epidemiological data at national and regional level.
Revised:  In this article I estimate an autoregressive integrated moving average (ARIMA) model to forecast the epidemic trend over the period after April 4 , 2020, by using the Italian epidemiological data at national and regional level.
Edit intent: meaning-changed
Original:  The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to March 30 , 2020.
Revised:  The data refer to the number of daily confirmed cases officially registered by the Italian Ministry of Health (www.salute.gov.it) for the period February 20 to April 4 , 2020.
Edit intent: meaning-changed
Original: Traditional methods to infer compartmental epidemic models with time-varying dynamics can only capture continuous changes in the dynamic.However, many changes are discontinuous due to sudden interventions, such as city lockdown and opening of field hospitals .
Revised: Compartmental epidemic models are among the most popular ones in epidemiology. For the parameters (e.g., the transmission rate) characterizing these models, the majority of researchers simplify them as constants, while some others manage to detect their continuous variations. In this paper, we aim at capturing, on the other hand, discontinuous variations, which better describe the impact of many noteworthy events, such as city lockdown and opening of field hospitals .
Edit intent: meaning-changed
Original: However, many changes are discontinuous due to sudden interventions, such as city lockdown and opening of field hospitals . To model the discontinuities, this study introduces the tool of total variationregularization , which regulates the temporal changes of the dynamic parameters, such as the transmission rate.
Revised: However, many changes are discontinuous due to sudden interventions, such as city lockdown and opening of field hospitals , and the mutation of the virus, whose effect should be instant. To achieve this, we balance the model's likelihood by total variation , which regulates the temporal changes of the dynamic parameters, such as the transmission rate.
Edit intent: meaning-changed
Original:  To model the discontinuities, this study introduces the tool of total variationregularization , which regulates the temporal changes of the dynamic parameters, such as the transmission rate. To recover the ground truth dynamic, this study designs a novel yet straightforward optimization algorithm, dubbed iterative Nelder-Mead , which repeatedly applies the Nelder-Mead algorithm.
Revised:  To model the discontinuities, this study introduces the tool of total variationregularization , which regulates the temporal variations of the model parameters. To infer these parameters, instead of using Monte Carlo methods, we design a novel yet straightforward optimization algorithm, dubbed iterative Nelder-Mead , which repeatedly applies the Nelder-Mead algorithm.
Edit intent: meaning-changed
Original:  Experiments  on the simulated data show that the proposed approach can qualitatively reproduce the discontinuities of the underlying dynamics. To extend this research to real data as well as to help researchers worldwide to fight against COVID-19, the author releases his research platform as an open-source package .
Revised:  Experiments on the simulated data demonstrate that our approach can reproduce these discontinuities and precisely depict the epidemics .
Edit intent: meaning-changed
Original: The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities in China in 2019 has become a global pandemic as declared by the World Health Organization (WHO) in the first quarter of 2020.
Revised: The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities in China in 2019 has become a global pandemic as declared by World Health Organization (WHO) in the first quarter of 2020.
Edit intent: fluency
Original: The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities in China in 2019 has become a global pandemic as declared by the World Health Organization (WHO) in the first quarter of 2020.
Revised: The outbreak of COVID-19 caused by SARS-CoV-2 in Wuhan and other cities in China in 2019 has become a global pandemic as declared by the World Health Organization (WHO) in the first quarter of 2020 .
Edit intent: fluency
Original:  In this article, we consider a dynamical model and analyze the impact of some control measures that can lead to the reduction of exposed and infectious individuals of the transmission of COVID-19 . We investigate four control strategies diagnosis and spraying the environment to curb the transmission dynamics of the novel virus .
Revised:  In this article, we consider an optimal control COVID-19 transmission model and assess the impact of some control measures that can lead to the reduction of exposed and infectious individuals of the transmission of COVID-19 . We investigate four control strategies diagnosis and spraying the environment to curb the transmission dynamics of the novel virus .
Edit intent: meaning-changed
Original:  In this article, we consider a dynamical model and analyze the impact of some control measures that can lead to the reduction of exposed and infectious individuals of the transmission of COVID-19 . We investigate four control strategies diagnosis and spraying the environment to curb the transmission dynamics of the novel virus .
Revised:  In this article, we consider a dynamical model and analyze the impact of some control measures that can lead to the reduction of exposed and infectious individuals in the population . We investigate four control strategies diagnosis and spraying the environment to curb the transmission dynamics of the novel virus .
Edit intent: clarity
Original:  In this article, we consider a dynamical model and analyze the impact of some control measures that can lead to the reduction of exposed and infectious individuals of the transmission of COVID-19 . We investigate four control strategies diagnosis and spraying the environment to curb the transmission dynamics of the novel virus .
Revised:  In this article, we consider a dynamical model and analyze the impact of some control measures that can lead to the reduction of exposed and infectious individuals of the transmission of COVID-19 . We investigate three control strategies for this deadly infectious disease using personal protection, treatment with early diagnosis, treatment with delay diagnosis and spraying of virus in the environment as time-dependent control functions in our dynamical model to curb the transmission dynamics of the novel virus .
Edit intent: meaning-changed
Original:  In this article, we consider a dynamical model and analyze the impact of some control measures that can lead to the reduction of exposed and infectious individuals of the transmission of COVID-19 . We investigate four control strategies diagnosis and spraying the environment to curb the transmission dynamics of the novel virus .
Revised:  In this article, we consider a dynamical model and analyze the impact of some control measures that can lead to the reduction of exposed and infectious individuals of the transmission of COVID-19 . We investigate four control strategies diagnosis and spraying the environment to curb the disease spread .
Edit intent: clarity
Original: In response to the rapid spread of the Coronavirus (COVID-19) , with ten thousands of deaths and intensive-care hospitalizations, a large number of regions and countries have been put under lockdown by their respective governments. Policy makers are confronted in this situation with the problem of balancing public health considerations , with the economic costs of a persistent lockdown.
Revised: In response to the rapid spread of the Coronavirus (COVID-19) confronts policy makers with the problem of balancing public health considerations , with the economic costs of a persistent lockdown.
Edit intent: coherence
Original:  Social distancing measures are triggered by the number of infections, providing a dynamic feedback-loop which slows the spread of the virus. We estimate the total cost of several distinct containment policies incurring over the entire path of the endemic .
Revised:  An analytic solution is presented. The model reproduces official COVID-19 cases counts of a large number of regions and countries that surpassed the peak of the outbreak. A single unbiased feedback parameter is extracted from field data and used to formulate an index that measures the efficiency of containment policies (the CEI index). CEI values for a range of countries are given. For two variants of the controlled-SIR model, detailed estimates of the total medical and socio-economic costs are evaluated over the entire path of the endemic .
Edit intent: meaning-changed
Original:  Very strict measures fare best, with a hands-off policy coming second. Our key findings are independent of the specific parameter estimates, which are to be adjusted with the COVID-19 research status. In addition to numerical simulations, an explicit analytical solution for the controlled continuous-time SIR model is presented. For an uncontrolled outbreak and a reproduction factor of three, an additional 28\% of the population is infected beyond the herd immunity point, reached at an infection level of 66\%, which adds up to a total of 94\% .
Revised:  Very strict measures fare best, with a hands-off policy . Strategies based on actual case numbers lead to substantially higher total costs than strategies based on the overall history of the epidemic .
Edit intent: clarity
Original: The rapid spread of the Coronavirus (COVID-19) confronts policy makers with the problem of measuring the effectiveness of containment strategies and the need to balance public health considerations with the economic costs of a persistent lockdown .
Revised: The rapid spread of the Coronavirus (COVID-19) confronts policy makers with the problem of measuring the effectiveness of containment strategies and the need to balance public health considerations with the economic costs of social distancing measures .
Edit intent: clarity
Original: Conventional relation extraction methods can only identify limited relation classes and not recognize the unseen relation types that have no pre-labeled training data. In this paper, we explore the zero-shot relation extraction to overcome the challenge. The only requisite information about unseen types is the name of their labels.
Revised: Linguistics holds unique characteristics of generality, stability, and nationality, which will affect the formulation of extraction strategies and should be incorporated into the relation extraction. Chinese open relation extraction is not well-established, because of the complexity of Chinese linguistics makes it harder to operate, and the name of their labels.
Edit intent: meaning-changed