wanyu/IteraTeR_v2 huggingface dataset:
Original:  While evaluating the model transferability for tumor segmentation under a low-annotation regime, including an extreme case of zero-shot segmentation, the proposed approach demonstrates state-of-the-art performance, substantially outperforming all existing self-supervised approaches, and opening up the usage of self-supervised learning in practical scenarios.
Revised:  While evaluating the model transferability for tumor segmentation under a low-annotation regime, the proposed approach demonstrates state-of-the-art performance, substantially outperforming all existing self-supervised approaches, and opening up the usage of self-supervised learning in practical scenarios.
Edit intent: clarity
Original:  While evaluating the model transferability for tumor segmentation under a low-annotation regime, including an extreme case of zero-shot segmentation, the proposed approach demonstrates state-of-the-art performance, substantially outperforming all existing self-supervised approaches, and opening up the usage of self-supervised learning in practical scenarios.
Revised:  While evaluating the model transferability for tumor segmentation under a low-annotation regime, including an extreme case of zero-shot segmentation, the proposed approach also outperforms all existing self-supervised approaches, and opening up the usage of self-supervised learning in practical scenarios.
Edit intent: clarity
Original:  While evaluating the model transferability for tumor segmentation under a low-annotation regime, including an extreme case of zero-shot segmentation, the proposed approach demonstrates state-of-the-art performance, substantially outperforming all existing self-supervised approaches, and opening up the usage of self-supervised learning in practical scenarios.
Revised:  While evaluating the model transferability for tumor segmentation under a low-annotation regime, including an extreme case of zero-shot segmentation, the proposed approach demonstrates state-of-the-art performance, substantially outperforming all existing self-supervised approaches, opening up the usage of self-supervised learning in practical scenarios.
Edit intent: fluency
Original: Malignant melanoma is a common skin cancer that is mostly curable before metastasis , where melanoma growths spawn in organs away from the original site.
Revised: Malignant melanoma is a common skin cancer that is mostly curable before metastasis -when growths spawn in organs away from the original site.
Edit intent: coherence
Original:  Melanoma is the most dangerous type of skin cancer if left untreated due to the high chance of metastasis.
Revised:  Melanoma is the most dangerous type of skin cancer if left untreated due to the high risk of metastasis.
Edit intent: fluency
Original: This article is a review on deep learning methods for single and multiple sound source localization.
Revised: This article is a survey on deep learning methods for single and multiple sound source localization.
Edit intent: clarity
Original:  Tables summarizing the literature review are provided at the end of the review for a quick search of methods with a given set of target characteristics.
Revised:  Tables summarizing the literature survey are provided at the end of the review for a quick search of methods with a given set of target characteristics.
Edit intent: clarity
Original:  Tables summarizing the literature review are provided at the end of the review for a quick search of methods with a given set of target characteristics.
Revised:  Tables summarizing the literature review are provided at the end of the paper for a quick search of methods with a given set of target characteristics.
Edit intent: clarity
Original:  We employed the susceptible-infected-recovered-dead (SIRD) model to investigate various vaccination strategies.
Revised:  We employ the susceptible-infected-recovered-dead (SIRD) model to investigate various vaccination strategies.
Edit intent: fluency
Original:  We also constructed a metapopulation model with empirical human contact and CFR data for SARS-CoV-2 and investigated vaccination strategies.
Revised:  We also constructed a metapopulation model constructed from empirical human contact and CFR data for SARS-CoV-2 and investigated vaccination strategies.
Edit intent: clarity
Original:  We also constructed a metapopulation model with empirical human contact and CFR data for SARS-CoV-2 and investigated vaccination strategies.
Revised:  We also constructed a metapopulation model with empirical human contact and fatality of SARS-CoV-2 and investigated vaccination strategies.
Edit intent: clarity
Original:  We identified a first-order phase transition in the vaccination strategies which is characterized by  discontinuous transition in the optimal strategy and the hysteresis.
Revised:  We identified a first-order phase transition in the vaccination strategies which is characterized by a discontinuous transition in the optimal strategy and the hysteresis.
Edit intent: fluency
Original:  This phase transition implies that mixing the age-based and rate-based strategy is ineffective in reducing the number of deaths.
Revised:  This phase transition implies that combining the age-based and rate-based strategy is ineffective in reducing the number of deaths.
Edit intent: fluency
Original:  \%In this way the coherence between agents' stances on the different issues is taken into account too.
Edit intent: coherence
Original:  \%The model is studied also from the numerical point of view and Several numerical tests are presented in order to highlight the main features of the model.
Revised:  Several numerical tests are presented in order to highlight the main features of the model.
Edit intent: coherence
Original:  However, recent studies found that complexity metrics may be unreliable indicators for understandability of the source code and therefore may have less impact on software quality .
Revised:  However, recent studies found that complexity metrics may be unreliable indicators for understandability of the source code .
Edit intent: clarity
Original:  To explore the relationshipbetween quality and changes , we leverage the intent of developers about what constitutes a quality improvement in their own code base.
Revised:  To explore this relationship , we leverage the intent of developers about what constitutes a quality improvement in their own code base.
Edit intent: clarity
Original:  We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for  fine-tuning  a state-of-the art deep learning model created for natural language processing.
Revised:  We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for the fine-tuning a state-of-the art deep learning model created for natural language processing.
Edit intent: fluency
Original:  We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for  fine-tuning  a state-of-the art deep learning model created for natural language processing.
Revised:  We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for fine-tuning of a state-of-the art deep learning model created for natural language processing.
Edit intent: clarity
Original:  We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for  fine-tuning  a state-of-the art deep learning model created for natural language processing.
Revised:  We distinguish between perfective and corrective maintenance via predefined guidelines and use this data as ground truth for fine-tuning a state-of-the art deep learning model for natural language processing.
Edit intent: clarity
Original:  The benchmark we provide with our ground truth indicates that the deep learning model can be confidently used for commit intent classification.
Edit intent: coherence
Original:  Perfective changes have a positive impact on static source code metrics while corrective changes do tend to add complexity.
Revised:  Perfective changes have a positive impact on static source code metrics while corrective changes add complexity.
Edit intent: clarity
Original:  Furthermore, we find that files which are the target of perfective maintenance already have a lower median complexity than other files  .
Revised:  Files which are the target of perfective maintenance already have a lower median complexity than other files .
Edit intent: coherence
Original: Background : Patients with Brachial Monoplegia (BM) , paralysis of the upper limb following various neurological disorders, require therapeutic interventions with proper assessment for rehabilitation.
Revised: Introduction : Patients with Brachial Monoplegia (BM) , paralysis of the upper limb following various neurological disorders, require therapeutic interventions with proper assessment for rehabilitation.
Edit intent: coherence
Original: Background : Patients with Brachial Monoplegia (BM) , paralysis of the upper limb following various neurological disorders, require therapeutic interventions with proper assessment for rehabilitation.
Revised: Background : Patients with Brachial Monoplegia , paralysis of the upper limb following various neurological disorders, require therapeutic interventions with proper assessment for rehabilitation.
Edit intent: clarity
Original:  Aim: To design a sensor-based assistive technology for the physiotherapists, facilitating a reliable quantitative assessment with real-time visualization of Performance Metrics (PM).
Revised:  Methods: We designed a sensor-based assistive technology for the physiotherapists, facilitating a reliable quantitative assessment with real-time visualization of Performance Metrics (PM).
Edit intent: coherence
Original:  Aim: To design a sensor-based assistive technology for the physiotherapists, facilitating a reliable quantitative assessment with real-time visualization of Performance Metrics (PM).
Revised:  Aim: To design a sensor-based therapeutic system for assisting the physiotherapists with real-time visualization of Performance Metrics (PM).
Edit intent: clarity
Original:  Conclusions and Significance : To ensure effective rehabilitation, proper assessment is obligatory.
Revised:  Conclusions : To ensure effective rehabilitation, proper assessment is obligatory.
Edit intent: coherence
Original:  Sensor-based measurement of PM ensures reliability, reducing the chances of human error.
Revised:  Sensor-based measurement of performance metrics ensures reliability, reducing the chances of human error.
Edit intent: clarity
Original:  In this paper, we focus on the problem of finding the optimum policies that `` flatten the epidemic curve" while limiting the negative consequences for the society, and formulate it as a nonlinear control problem over a finite prediction horizon.
Revised:  In this paper, we focus on the problem of finding the optimum policies that " flatten the epidemic curve" while limiting the negative consequences for the society, and formulate it as a nonlinear control problem over a finite prediction horizon.
Edit intent: fluency
Original:  When considering an inductive scenario, i. e., when adding new documents to a corpus, a simple MLP even outperforms most graph-based models  .
Revised:  When considering an inductive scenario, i. e., when adding new documents to a corpus, a simple MLP even outperforms the recent graph-based models .
Edit intent: clarity
Original:  We further fine-tune DistilBERT for comparison and find that it outperforms all state-of-the-art models.
Revised:  We further fine-tune DistilBERT and find that it outperforms all state-of-the-art models.
Edit intent: clarity
Original:  Moreover, we analyze the drawbacks of the existing metrics for the panoptic tracking problem and propose a novel instance-centric  metric that addresses the concerns.
Revised:  Moreover, we analyze the drawbacks of the existing metrics for panoptic tracking and propose the novel instance-centric metric that addresses the concerns.
Edit intent: clarity
Original:  We present extensive experiments that demonstrate the utility of Panoptic nuScenes compared to existing datasets and make the online evaluation server available at  .
Revised:  We present exhaustive experiments that demonstrate the utility of Panoptic nuScenes compared to existing datasets and make the online evaluation server available at .
Edit intent: clarity
Original:  We observe that the existing quantum neuron designs may be quite different but complementary, such as neurons from variation quantum circuits (VQC) and Quantumflow.
Revised:  We observe that the existing quantum neuron designs may be quite different but complementary, such as neurons from variational quantum circuits (VQC) and Quantumflow.
Edit intent: clarity
Original:  This work provides a general framework for the non-asymptotic analysis of  sampling error in 2-Wasserstein distance , which also leads to a bound of mixing time.
Revised:  This work provides a general framework for the non-asymptotic analysis of its sampling error in 2-Wasserstein distance , which also leads to a bound of mixing time.
Edit intent: clarity
Original:  When applied to Langevin Monte Carlo algorithm, it establishes%DIFDELCMD < \mathcal{O}\left( %%% \frac{\sqrt{d}{\epsilon} }%DIFDELCMD < \right) %%% mixing time /\epsilon) mixing time bound for LMC} , without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures at infinity .
Revised:  %DIFDELCMD < \mathcal{O}\left( %%% \frac{\sqrt{d}{\epsilon} }%DIFDELCMD < \right) %%% mixing time /\epsilon) mixing time bound for LMC} , without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures at infinity .
Edit intent: coherence
Original:  When applied to Langevin Monte Carlo algorithm, it establishes%DIFDELCMD < \mathcal{O}\left( %%% \frac{\sqrt{d}{\epsilon} }%DIFDELCMD < \right) %%% mixing time /\epsilon) mixing time bound for LMC} , without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures at infinity .
Revised:  When applied to Langevin Monte Carlo algorithm, it establishes%DIFDELCMD < \mathcal{O}\left( %%% \frac{\sqrt{d}{\epsilon} }%DIFDELCMD < \right) %%% We establish an /\epsilon) mixing time bound for LMC} , without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures at infinity .
Edit intent: clarity
Original:  When applied to Langevin Monte Carlo algorithm, it establishes%DIFDELCMD < \mathcal{O}\left( %%% \frac{\sqrt{d}{\epsilon} }%DIFDELCMD < \right) %%% mixing time /\epsilon) mixing time bound for LMC} , without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures at infinity .
Revised:  When applied to Langevin Monte Carlo algorithm, it establishes%DIFDELCMD < \mathcal{O}\left( %%% \frac{\sqrt{d}{\epsilon} }%DIFDELCMD < \right) %%% mixing time (\sqrt{d/\epsilon) mixing time bound for LMC} , without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures at infinity .
Edit intent: fluency
Original:  When applied to Langevin Monte Carlo algorithm, it establishes%DIFDELCMD < \mathcal{O}\left( %%% \frac{\sqrt{d}{\epsilon} }%DIFDELCMD < \right) %%% mixing time /\epsilon) mixing time bound for LMC} , without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures at infinity .
Revised:  When applied to Langevin Monte Carlo algorithm, it establishes%DIFDELCMD < \mathcal{O}\left( %%% \frac{\sqrt{d}{\epsilon} }%DIFDELCMD < \right) %%% mixing time /\epsilon) mixing time bound for LMC} , without warm start, under the common log-smooth and log-strongly-convex conditions, plus a growth condition on the 3rd-order derivative of the potential of target measures .
Edit intent: clarity
Original:  This bound improves the best previously known %DIFDELCMD < \mathcal{O}\left( %%% \frac{d{\epsilon} }%DIFDELCMD < \right) %%%  result and is optimal (in terms of order) in both dimension d and accuracy tolerance \epsilon for target measures satisfying the aforementioned assumptions.
Revised:  This bound improves the best previously known %DIFDELCMD < \mathcal{O}\left( %%% {\epsilon} }%DIFDELCMD < \right) %%% result and is optimal (in terms of order) in both dimension d and accuracy tolerance \epsilon for target measures satisfying the aforementioned assumptions.
Edit intent: clarity
Original:  Moreover, frozen stabilisation allows robust memory manifolds in small networks, and this is relevant to debates of implementing continuous attractors with a small number of neurons in light of recent experimental discoveries.
Revised:  Moreover, FS allows robust memory manifolds in small networks, and this is relevant to debates of implementing continuous attractors with a small number of neurons in light of recent experimental discoveries.
Edit intent: clarity
Original:  Finally , we compare our system's performance to two benchmarks: a proportional navigation guidance system benchmark in a simplified 3-DOF environment, which we take as an upper bound on performance attainable with separate guidance and flight control systems, and a longitudinal model of proportional navigation coupled with a three loop autopilot .
Revised:  Importantly , we compare our system's performance to two benchmarks: a proportional navigation guidance system benchmark in a simplified 3-DOF environment, which we take as an upper bound on performance attainable with separate guidance and flight control systems, and a longitudinal model of proportional navigation coupled with a three loop autopilot .
Edit intent: coherence
Original:  Finally , we compare our system's performance to two benchmarks: a proportional navigation guidance system benchmark in a simplified 3-DOF environment, which we take as an upper bound on performance attainable with separate guidance and flight control systems, and a longitudinal model of proportional navigation coupled with a three loop autopilot .
Revised:  Finally , we compare our system's performance to a longitudinal model of proportional navigation coupled with a three loop autopilot .
Edit intent: coherence
Original:  We find that our system moderately outperforms the former, and outperforms the latter by a large margin  .
Revised:  We find that our system outperforms the benchmark by a large margin .
Edit intent: clarity
Original:  Prior works relying on explicit state estimation or end-to-end learning struggle with novel objects  .
Revised:  Prior works relying on explicit state estimation or end-to-end learning struggle with novel objects or new tasks .
Edit intent: clarity
Original:  \%Analogous results for subgaussian or subexponential priors are also obtained.
Edit intent: coherence
Original: We study  iterated vector fields and investigate whether they are conservative, in the sense that they are the gradient of some scalar-valued function.
Revised: We study whether iterated vector fields and investigate whether they are conservative, in the sense that they are the gradient of some scalar-valued function.
Edit intent: fluency
Original:  We analyze the conservatism of various iterated vector fields , including gradient vector fields associated to loss functions of generalized linear models.
Revised:  We analyze the conservatism of various iterated vector fields , including gradient vector fields of loss functions associated with some generalized linear models.
Edit intent: clarity
Original: This manuscript presents a mathematical relationship between the coefficient of variation (CV) and security investment risk, defined herein as the probability of occurrence of negative returns.
Revised: This manuscript presents the mathematical relationship between the coefficient of variation (CV) and security investment risk, defined herein as the probability of occurrence of negative returns.
Edit intent: fluency
Original: This manuscript presents a mathematical relationship between the coefficient of variation (CV) and security investment risk, defined herein as the probability of occurrence of negative returns.
Revised: This manuscript presents a mathematical relationship between coefficient of variation (CV) and security investment risk, defined herein as the probability of occurrence of negative returns.
Edit intent: fluency
Original:  It was found that at least for stocks, there is a strong correlation between CV and stock performance when the CV is derived from annual returns calculated for each month (as opposed to using, for example, only annual returns based on end-of-the-year closing prices).
Revised:  We also found that at least for stocks, there is a strong correlation between CV and stock performance when the CV is derived from annual returns calculated for each month (as opposed to using, for example, only annual returns based on end-of-the-year closing prices).
Edit intent: clarity
Original:  It was found that at least for stocks, there is a strong correlation between CV and stock performance when the CV is derived from annual returns calculated for each month (as opposed to using, for example, only annual returns based on end-of-the-year closing prices).
Revised:  It was found that at least for stocks, there is a strong correlation between CV and stock performance when CV is derived from annual returns calculated for each month (as opposed to using, for example, only annual returns based on end-of-the-year closing prices).
Edit intent: clarity
Original:  It was found that a low nonnegative CV of up to ~ 1.0 (~ 15\% risk) correlates well with strong and consistent stock performance.
Revised:  We found that a low nonnegative CV of up to ~ 1.0 (~ 15\% risk) correlates well with strong and consistent stock performance.
Edit intent: coherence
Original:  At low negative to positive correlation, the efficient frontier hypothesis breaks down , and risk analysis based on CV becomes an important consideration.
Revised:  At low negative to positive correlation, the efficient frontier hypothesis breaks down and risk analysis based on CV becomes an important consideration.
Edit intent: fluency
Original: This manuscript presents the mathematical relationship between coefficient of variation (CV) and security investment risk, defined herein as the probability of occurrence of negative returns.
Revised: We presented here a direct calculation of risk from the coefficient of variation (CV) and security investment risk, defined herein as the probability of occurrence of negative returns.
Edit intent: clarity
Original:  We show that, in these cases, the K-class MARL problem can be approximated by MFC with errors given as e_1=O( \frac{\sqrt{|\mathcal{X|}+|\mathcal{U|}}{N_{pop}}} kN_k), e_2=O( \sqrt{|\mathcal{X \left[|}+|\mathcal{U|}}\right]
Revised:  We show that, in these cases, the K-class MARL problem can be approximated by MFC with errors given as e_1=O( \frac{\sqrt{|\mathcal{X|}+|\mathcal{U|}}{N_{pop}}} kN_k), e_2=O( \sqrt{|\mathcal{X \left[|}+|\mathcal{U|}}\right]
Edit intent: fluency
Original:  We show that, in these cases, the K-class MARL problem can be approximated by MFC with errors given as e_1=O( \frac{\sqrt{|\mathcal{X|}+|\mathcal{U|}}{N_{pop}}} kN_k), e_2=O( \sqrt{|\mathcal{X \left[|}+|\mathcal{U|}}\right]
Revised:  We show that, in these cases, the K-class MARL problem can be approximated by MFC with errors given as e_1=O( \frac{\sqrt{|\mathcal{X|}+|\mathcal{U|}}{N_{pop}}} kN_k), e_2=O( \left[|}+|\mathcal{U|}}\right]
Edit intent: fluency