wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Exploiting public data on crime in Los Angeles, and relying on routine activity and pattern theories of crime, this work investigates whether and how new coronavirus containment policies have an impact on crime trends in a metropolis using Bayesian structural time-series (BSTS) models.
Revised:  Exploiting public data on crime in Los Angeles, and relying on routine activity and pattern theories of crime, this work investigates whether and how new coronavirus containment policies have an impact on crime trends in Los Angeles. The analysis is conducted using Bayesian structural time-series (BSTS) models.
Edit intent: clarity
Original:  Exploiting public data on crime in Los Angeles, and relying on routine activity and pattern theories of crime, this work investigates whether and how new coronavirus containment policies have an impact on crime trends in a metropolis using Bayesian structural time-series (BSTS) models. The article specifically focuses on nine urban crime categories , daily monitored from January 1st 2017 to March 28th 2020.
Revised:  Exploiting public data on crime in Los Angeles, and relying on routine activity and pattern theories of crime, this work investigates whether and how new coronavirus containment policies have an impact on crime trends in a metropolis using Bayesian structural time-series and focuses on nine urban crime categories , daily monitored from January 1st 2017 to March 28th 2020.
Edit intent: clarity
Original:  The analyses have been updated bi-weekly (up to March 16 and up to March 28 2020 ) to dynamically assess the short-term effects of mild and hard interventions to shed light on how crime adapts to such structural modification of the environment.
Revised:  and up to March 28 2020 ) to dynamically assess the short-term effects of mild and hard interventions to shed light on how crime adapts to such structural modification of the environment.
Edit intent: coherence
Original:  The analyses have been updated bi-weekly (up to March 16 and up to March 28 2020 ) to dynamically assess the short-term effects of mild and hard interventions to shed light on how crime adapts to such structural modification of the environment.
Revised:  The analyses have been updated bi-weekly (up to March 16 We concentrate on two post-intervention time windows - from March 4th to March 16th and from March 4th to March 28th 2020 ) to dynamically assess the short-term effects of mild and hard interventions to shed light on how crime adapts to such structural modification of the environment.
Edit intent: meaning-changed
Original:  The analyses have been updated bi-weekly (up to March 16 and up to March 28 2020 ) to dynamically assess the short-term effects of mild and hard interventions to shed light on how crime adapts to such structural modification of the environment.
Revised:  The analyses have been updated bi-weekly (up to March 16 and up to March 28 2020 - to dynamically assess the short-term effects of mild and hard interventions to shed light on how crime adapts to such structural modification of the environment.
Edit intent: fluency
Original:  The analyses have been updated bi-weekly (up to March 16 and up to March 28 2020 ) to dynamically assess the short-term effects of mild and hard interventions to shed light on how crime adapts to such structural modification of the environment. The results show that overall crime in Las Angeles is significantly decreasing , as well as robbery, shoplifting, theft  and battery.
Revised:  The analyses have been updated bi-weekly (up to March 16 and up to March 28 2020 ) to dynamically assess the short-term effects of mild and strict policies. In Los Angeles, overall crime has significantly decreased , as well as robbery, shoplifting, theft and battery.
Edit intent: clarity
Original: We propose and analyze zeroth-order algorithms for optimization over Riemannian manifolds, where we observe only potentially noisy evaluations of the objective function. Our approach is based on estimating the Riemannian gradient from the objective function evaluations .
Revised: We propose and analyze zeroth-order optimization concerns problems where only noisy function evaluations are available. Such problems arises frequently in many important applications. In this paper, we consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in an Euclidean space, an important but less studied area, and propose four algorithms for solving this class of problems under different settings. Our algorithms are based on estimating the Riemannian gradient from the objective function evaluations .
Edit intent: meaning-changed
Original:  Our approach is based on estimating the Riemannian gradient from the objective function evaluations . We consider three settings for the objective function: (i) deterministic and smooth , (ii) stochastic and smooth , and (iii) composition of smooth and non-smooth parts.
Revised:  Our approach is based on estimating the Riemannian gradient from the objective function evaluations , based on a Riemannian version of the Gaussian smoothing technique. In particular, we consider the following settings for the objective function: (i) deterministic and smooth , (ii) stochastic and smooth , and (iii) composition of smooth and non-smooth parts.
Edit intent: meaning-changed
Original:  We consider three settings for the objective function: (i) deterministic and smooth , (ii) stochastic and smooth , and (iii) composition of smooth and non-smooth parts.
Revised:  We consider three settings for the objective function: (i) stochastic and gradient-Lipschitz (in both nonconvex and geodesic convex settings) , (ii) stochastic and smooth , and (iii) composition of smooth and non-smooth parts.
Edit intent: meaning-changed
Original:  We consider three settings for the objective function: (i) deterministic and smooth , (ii) stochastic and smooth , and (iii) composition of smooth and non-smooth parts.
Revised:  We consider three settings for the objective function: (i) deterministic and smooth , (ii) sum of gradient-Lipschitz and non-smooth functions , and (iii) composition of smooth and non-smooth parts.
Edit intent: meaning-changed
Original:  We consider three settings for the objective function: (i) deterministic and smooth , (ii) stochastic and smooth , and (iii) composition of smooth and non-smooth parts. For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Revised:  We consider three settings for the objective function: (i) deterministic and smooth , (ii) stochastic and smooth , and (iii) Hessian-Lipschitz. For these settings , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Edit intent: meaning-changed
Original:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Revised:  For each of the setting , we characterize the oracle complexity of our algorithms to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Edit intent: fluency
Original:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Revised:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary point or \epsilon-approximate local minimizer . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Edit intent: meaning-changed
Original:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Revised:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Edit intent: fluency
Original:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold .
Revised:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the ambient Euclidean space and depend only on the intrinsic dimension of the manifold .
Edit intent: clarity
Original:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold . As a proof of concept, we demonstrate the applicability of our method to the problem of black-box attacks to deep neural networks, by providing simulation and real-world image data based experimental results.
Revised:  For each of the setting , we characterize the oracle complexity of our algorithm to obtain appropriately defined notions of \epsilon-stationary points . Notably, our complexities are independent of the ambient dimension of the Euclidean space in which the manifold is embedded in, and only depend on the intrinsic dimension of the manifold under consideration. We demonstrate the applicability of our method to the problem of black-box attacks to deep neural networks, by providing simulation and real-world image data based experimental results.
Edit intent: clarity
Original:  Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments by weeks. The risk reports differ substantially between industries , both in magnitude and in nature .  Based on natural language processing techniques, we can identify corona-related risk topics and their perceived relevance for different industries.
Revised:  Preliminary findings suggest that the companies' awareness towards corona-related business risks is ahead of the overall stock market developments . Based on natural language processing techniques, we can identify corona-related risk topics and their perceived relevance for different industries.
Edit intent: coherence
Original:  The risk reports differ substantially between industries , both in magnitude and in nature .  Based on natural language processing techniques, we can identify corona-related risk topics and their perceived relevance for different industries.
Revised:  The risk reports differ substantially between industries , both in magnitude and in nature . Our approach allows to distinguish the industries by their risk awareness towards COVID-19. Based on natural language processing techniques, we can identify corona-related risk topics and their perceived relevance for different industries.
Edit intent: meaning-changed
Original:  Due to their abundance as movement patterns of URLanisms, significant theoretical efforts have been devoted to identify the foraging circumstances that would make such patterns advantageous Viswanathan et al. Nature, 1999 . Despite numerous attempts , there is currently no conclusive analytical evidence that L\'evy flights are preferable strategies in higher dimensions than one.
Revised:  Due to their abundance as movement patterns of URLanisms, significant theoretical efforts have been devoted to identify the foraging circumstances that would make such patterns advantageous . Despite numerous attempts , there is currently no conclusive analytical evidence that L\'evy flights are preferable strategies in higher dimensions than one.
Edit intent: coherence
Original:  Here we show that the optimality of inverse-square L\'evy walks in two-dimensions becomes striking when targets are sparse and unpredictable in size, and when detection is weak. Specifically, we prove that under the intermittent model, in which detection is not possible while moving ballistically B\'enichou et al.
Revised:  Here we show that the optimality of inverse-square L\'evy B\'enichou et al.
Edit intent: coherence
Original:  Specifically, we prove that under the intermittent model, in which detection is not possible while moving ballistically B\'enichou et al. Reviews of Modern Physics, 2011%DIFDELCMD < ]%%% , this strategy optimally finds sparse targets of any size and shape.
Revised:  Specifically, we prove that under the intermittent model, in which detection is not possible while moving ballistically %DIFDELCMD < ]%%% , this strategy optimally finds sparse targets of any size and shape.
Edit intent: coherence
Original:  That is, in a square torus of area~n, and assuming that the detection radius is normalized to 1, the strategy finds any connected set of diameter D in%DIFDELCMD < \tilde{O}%%% (n/D) expected time, whereas \Omega(n/D) is an unconditional lower bound on the expected time, that holds even when assuming that the shape and size of the target are known.
Revised:  %DIFDELCMD < \tilde{O}%%% (n/D) expected time, whereas \Omega(n/D) is an unconditional lower bound on the expected time, that holds even when assuming that the shape and size of the target are known.
Edit intent: coherence
Original:  That is, in a square torus of area~n, and assuming that the detection radius is normalized to 1, the strategy finds any connected set of diameter D in%DIFDELCMD < \tilde{O}%%% (n/D) expected time, whereas \Omega(n/D) is an unconditional lower bound on the expected time, that holds even when assuming that the shape and size of the target are known. Furthermore, this particular L\'evy process stands in stark contrast to many other basic intermittent processes, including all other L\'evy walks, whose competitive ratio is shown to be polynomial in n, for wide ranges of target scales .
Revised:  That is, in a square torus of area~n, and assuming that the detection radius is normalized to 1, the strategy finds any connected set of diameter D in%DIFDELCMD < \tilde{O}%%% Moreover, this holds even under the weak model of intermittent detection. Conversely, any other intermittent L\'evy process stands in stark contrast to many other basic intermittent processes, including all other L\'evy walks, whose competitive ratio is shown to be polynomial in n, for wide ranges of target scales .
Edit intent: meaning-changed
Original:  In this study, we proposed a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory  . Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation  as neurofeedback, resulting in strong targeting effect.
Revised:  In this study, we investigated a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory . Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation as neurofeedback, resulting in strong targeting effect.
Edit intent: clarity
Original:  In this study, we proposed a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory  . Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation  as neurofeedback, resulting in strong targeting effect.
Revised:  In this study, we proposed a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory , involving healthy adults . Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation as neurofeedback, resulting in strong targeting effect.
Edit intent: clarity
Original:  In this study, we proposed a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory  . Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation  as neurofeedback, resulting in strong targeting effect.
Revised:  In this study, we proposed a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory . Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation , as neurofeedback, resulting in strong targeting effect.
Edit intent: fluency
Original:  In this study, we proposed a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory  . Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation  as neurofeedback, resulting in strong targeting effect.
Revised:  In this study, we proposed a novel functional near-infrared spectroscopy (fNIRS)-based frontoparietal functional connectivity (FC) neurofeedback training paradigm related to working memory . Compared with conventional cognitive training studies, we chose the frontoparietal network, a key brain region for cognitive function modulation as neurofeedback, yielding a strong targeting effect.
Edit intent: clarity
Original:  In the experiment, ten participants received three 20 min cognitive training sessions with fNIRS-based frontoparietal FC as neurofeedback, and the other ten participants served as the normal control (NC) group.
Revised:  In the experiment, 10 participants (test group) received three cognitive training sessions with fNIRS-based frontoparietal FC as neurofeedback, and the other ten participants served as the normal control (NC) group.
Edit intent: meaning-changed
Original:  In the experiment, ten participants received three 20 min cognitive training sessions with fNIRS-based frontoparietal FC as neurofeedback, and the other ten participants served as the normal control (NC) group.
Revised:  In the experiment, ten participants received three 20 min cognitive training sessions of 15 min using fNIRS-based frontoparietal FC as neurofeedback, and the other ten participants served as the normal control (NC) group.
Edit intent: meaning-changed
Original:  In the experiment, ten participants received three 20 min cognitive training sessions with fNIRS-based frontoparietal FC as neurofeedback, and the other ten participants served as the normal control (NC) group.
Revised:  In the experiment, ten participants received three 20 min cognitive training sessions with fNIRS-based frontoparietal FC as neurofeedback, and another 10 participants served as the normal control (NC) group.
Edit intent: fluency
Original:  In the experiment, ten participants received three 20 min cognitive training sessions with fNIRS-based frontoparietal FC as neurofeedback, and the other ten participants served as the normal control (NC) group.
Revised:  In the experiment, ten participants received three 20 min cognitive training sessions with fNIRS-based frontoparietal FC as neurofeedback, and the other ten participants served as the control group.
Edit intent: clarity
Original:  Frontoparietal FC was significantly increased in the tested group (p = 0.005) and the cognitive functions (memory and attention) were significantly promoted compared to the NC group . Follow-up evaluations indicated that the training effect can last for over half a month. The proposed method shows great potential to be developed as a fast, effective  and widespread training approach for cognitive functions enhancementand rehabilitation applications .
Revised:  Frontoparietal FC was significantly increased in the tested group (p = 0.005) and the cognitive functions (memory and attention) were significantly promoted compared with the control group (accuracy of 3-back test: p D 0.0005, reaction time of 3-back test: p D 0.0009). After additional validations on long-term training effect and on different patient populations, the proposed method exhibited considerable potential to be developed as a fast, effective and widespread training approach for cognitive functions enhancementand rehabilitation applications .
Edit intent: meaning-changed
Original:  Based on trajectory  big data and the theory of mean-field , we establish an aggregated risk mean = field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map.
Revised:  Based on trajectory provided by the big data and the theory of mean-field , we establish an aggregated risk mean = field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map.
Edit intent: clarity
Original:  Based on trajectory  big data and the theory of mean-field , we establish an aggregated risk mean = field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map.
Revised:  Based on trajectory big data and the mean field theory , we establish an aggregated risk mean = field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map.
Edit intent: clarity
Original:  Based on trajectory  big data and the theory of mean-field , we establish an aggregated risk mean = field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map.
Revised:  Based on trajectory big data and the theory of mean-field , we establish an aggregated risk mean field that contains information of all risk-spreading particles by proposing a spatio-temporal model named HiRES risk map.
Edit intent: fluency
Original:  We then propose an objective personal epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference based method and machine learning based method for detecting suspected epidemic-infected individuals.
Revised:  We then propose an objective individual epidemic risk scoring model named HiRES-p based on HiRES risk maps, and use it to develop statistical inference based method and machine learning based method for detecting suspected epidemic-infected individuals.
Edit intent: clarity