wanyu/IteraTeR_full_sent huggingface dataset:
Original: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for  characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation.
Revised: Based on the concept of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation.
Edit intent: coherence
Original: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for  characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation.
Revised: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability , we extend some recent multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation.
Edit intent: coherence
Original: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for  characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation.
Revised: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models built using multivariate subordination with the aim of capturing situations in which a sudden event in one market is propagated onto related markets after a certain stochastic time delay . Consequently, we study the properties of such processes, derive closed form expressions for characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation.
Edit intent: meaning-changed
Original: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for  characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation.
Revised: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for the characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation.
Edit intent: fluency
Original: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for  characteristic function and linear correlation coefficient and develop Monte Carlo schemes for their simulation. These results are instrumental to calibrate the models on power and gas energy European markets and to price spread options written on different underlying assets using Monte Carlo and Fourier techniques.
Revised: The aim of this paper is to build dependent stochastic processes using the notion of self-decomposability in order to model dependence across different markets and extend some recently proposed multivariate L\'evy models based on subordination . Consequently, we study the properties of such processes, derive closed form expressions for characteristic function and detail how a Monte Carlo scheme can be easily implemented. We illustrate the applicability of our approach in the context of gas and power Energy markets focusing on the calibration and on the pricing of spread options written on different underlying assets using Monte Carlo and Fourier techniques.
Edit intent: coherence
Original:  Specifically, the intensity of COVID-related social media activity from different Italian regions at the beginning of the epidemic (21-24/2/2020), predicts well the total number of deaths reached almost a month later ( 21 / 3 /2020) in each region.
Revised:  Specifically, the intensity of COVID-related social media activity from different Italian regions at the beginning of the epidemic (21-24/2/2020), predicts well the total number of deaths reached almost a month later ( 7 / 3 /2020) in each region.
Edit intent: meaning-changed
Original:  Specifically, the intensity of COVID-related social media activity from different Italian regions at the beginning of the epidemic (21-24/2/2020), predicts well the total number of deaths reached almost a month later ( 21 / 3 /2020) in each region.
Revised:  Specifically, the intensity of COVID-related social media activity from different Italian regions at the beginning of the epidemic (21-24/2/2020), predicts well the total number of deaths reached almost a month later ( 21 / 4 /2020) in each region.
Edit intent: meaning-changed
Original: In this work we report that the public reacted on social media at an early stage of the COVID-19 pandemic in a surprisingly accurate way , with activity levels reflecting the severity of the contagion figures registered almost a month later.
Revised: In this work we report that the public reacted on social media at an early stage of the SARS-CoV-2 pandemic, the virus responsible of COVID-19, in a surprisingly accurate way , with activity levels reflecting the severity of the contagion figures registered almost a month later.
Edit intent: meaning-changed
Original: In this work we report that the public reacted on social media at an early stage of the COVID-19 pandemic in a surprisingly accurate way , with activity levels reflecting the severity of the contagion figures registered almost a month later.
Revised: In this work we report that the public reacted on social media at an early stage of the COVID-19 pandemic in a surprisingly accurate way . Early social media activity levels reflected the severity of the contagion figures registered almost a month later.
Edit intent: clarity
Original: In this work we report that the public reacted on social media at an early stage of the COVID-19 pandemic in a surprisingly accurate way , with activity levels reflecting the severity of the contagion figures registered almost a month later.
Revised: In this work we report that the public reacted on social media at an early stage of the COVID-19 pandemic in a surprisingly accurate way , with activity levels reflecting the severity of the disease as registered almost a month later.
Edit intent: clarity
Original:  Specifically, the intensity of COVID-related social media activity from different Italian  regions at the beginning of the epidemic (21-24/2/2020) , predicts well the total number of deaths reached almost a month later (7/4/2020) in each region.
Revised:  Specifically, the intensity of COVID-19 related social media activity from different Italian regions at the beginning of the epidemic (21-24/2/2020) , predicts well the total number of deaths reached almost a month later (7/4/2020) in each region.
Edit intent: meaning-changed
Original:  Specifically, the intensity of COVID-related social media activity from different Italian  regions at the beginning of the epidemic (21-24/2/2020) , predicts well the total number of deaths reached almost a month later (7/4/2020) in each region.
Revised:  Specifically, the intensity of COVID-related social media activity from different Italian , Spanish and United States regions at the beginning of the epidemic (21-24/2/2020) , predicts well the total number of deaths reached almost a month later (7/4/2020) in each region.
Edit intent: meaning-changed
Original:  Specifically, the intensity of COVID-related social media activity from different Italian  regions at the beginning of the epidemic (21-24/2/2020) , predicts well the total number of deaths reached almost a month later (7/4/2020) in each region.
Revised:  Specifically, the intensity of COVID-related social media activity from different Italian regions at the beginning of the epidemic , predicts well the total number of deaths reached almost a month later (7/4/2020) in each region.
Edit intent: coherence
Original:  Specifically, the intensity of COVID-related social media activity from different Italian  regions at the beginning of the epidemic (21-24/2/2020) , predicts well the total number of deaths reached almost a month later (7/4/2020) in each region.
Revised:  Specifically, the intensity of COVID-related social media activity from different Italian regions at the beginning of the epidemic (21-24/2/2020) , predicts well the total number of deaths reached almost a month later in each region.
Edit intent: meaning-changed
Original:  By the 24th February 2020 only two regions reported death cases and only three reported infected subjects  .
Revised:  By the 24th February 2020 only two regions reported death cases and only three reported infected subjects . Our findings could underpin the creation of real-time novelty detection systems aimed at early reporting of SARS-like mortality and thus early activation of control measures in future pandemics .
Edit intent: meaning-changed
Original: In this work we report that the public reacted on social media at an early stage of the SARS-CoV-2 pandemic, the virus responsible of COVID-19 , in a surprisingly accurate way.
Revised: During the unfolding of a crisis, it is crucial to determine its severity, yet access to reliable data is challenging. We investigate the relation between geolocated Tweet Intensity of initial COVID-19 , in a surprisingly accurate way.
Edit intent: meaning-changed
Original: In this work we report that the public reacted on social media at an early stage of the SARS-CoV-2 pandemic, the virus responsible of COVID-19 , in a surprisingly accurate way. Early social media activity levels reflected the severity of the disease as registered almost a month later.
Revised: In this work we report that the public reacted on social media at an early stage of the SARS-CoV-2 pandemic, the virus responsible of COVID-19 related tweet at the beginning of the pandemic across Italian, Spanish and USA regions and mortality in the region a month later.
Edit intent: meaning-changed
Original:  Specifically, the intensity of COVID-19 related social media activity from different Italian, Spanish and United States regions at the beginning of the epidemic, predicts well the total number of deaths reached almost a month later in each region. More strikingly, at the time of the initial twitter reaction no tabled regional data on the epidemic was readily available in Italy. By the 24th February 2020 only two regions reported death cases and only three reported infected subjects. Our findings could underpin the creation of real-time novelty detection systems aimed at early reporting of SARS-like mortality and thus early activation of control measures in future pandemics .
Revised:  Specifically, the intensity of COVID-19 related social media activity from different Italian, Spanish and United States regions at the beginning of the epidemic, predicts well the total number of deaths reached almost a month later . Our findings suggest that "the crowds" perceived the risk correctly. This is one of the few examples where the "wisdom of crowds" can be quantified and applied in practice. This can be used to create real-time novelty detection systems aimed at early reporting of SARS-like mortality and thus early activation of control measures in future pandemics .
Edit intent: coherence
Original:  Methods: We proposed the transfer learning framework  entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject.
Revised:  We propose the transfer learning framework entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject.
Edit intent: coherence
Original:  Methods: We proposed the transfer learning framework  entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject.
Revised:  Methods: We proposed the transfer learning framework , entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject.
Edit intent: fluency
Original:  Methods: We proposed the transfer learning framework  entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject.
Revised:  Methods: We proposed the transfer learning framework entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject.
Edit intent: coherence
Original:  Methods: We proposed the transfer learning framework  entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subject. The capability of MAML was elicited for this task by allowing clinicians to label for a few samples and let the rest be handled by the system.
Revised:  Methods: We proposed the transfer learning framework entitled MetaSleepLearner, using a Model Agnostic Meta-Learning (MAML), in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects. The framework was demonstrated to require the labelling of only a few sleep epochs by the clinicians and allow the remainder to be handled by the system.
Edit intent: clarity
Original:  Results: In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 6.15 \% to 12.12 \% improvement with statistical difference in the mean of both approaches.
Revised:  In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 6.15 \% to 12.12 \% improvement with statistical difference in the mean of both approaches.
Edit intent: coherence
Original:  Results: In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 6.15 \% to 12.12 \% improvement with statistical difference in the mean of both approaches. The illustration of the model interpretation after the adaptation to each subject also confirmed that the performance was directed towards reasonable learning. Conclusion: MetaSleepLearner outperformed the conventional approach as a result from the fine-tuning using the recordings of both healthy subjects and patients. Significance: This is the first paper that investigated a non-conventional pre-training method, MAML, in this task, resulting in a framework for human-machine collaboration in sleep stage classification, easing the burden of the clinicians in labelling the sleep stages through only several epochs rather than an entire recording.
Revised:  Results: In all acquired datasets, in comparison to the conventional approach, MetaSleepLearner achieved a range of 5.4
Edit intent: meaning-changed
Original:  Using a biologically plausible model based on recurrent networks of excitatory and inhibitory neurons arranged in cluster, we classify the effects of cell-type specific perturbations on the circuit 's stimulus-processing capability. We found that perturbations differentially controlled processing speed, leading to counterintuitive effects such as improved performance with increased input variance.
Revised:  Our theory demonstrates that perturbation effects on stimulus processing can be predicted by intrinsic gain modulation, which controls the timescale of the circuit dynamics. Our theory leads to counter-intuitive effects such as improved performance with increased input variance.
Edit intent: meaning-changed
Original:  Our theory explains the effects of all perturbations in terms of gain modulation, which controls the timescale of the circuit dynamics. We tested our model using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where  a decrease in single-cell  gain during locomotion explained the observed acceleration of visual processing speed .
Revised:  We tested the model predictions using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where a decrease in single-cell gain during locomotion explained the observed acceleration of visual processing speed .
Edit intent: clarity
Original:  We tested our model using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where  a decrease in single-cell  gain during locomotion explained the observed acceleration of visual processing speed .
Revised:  We tested our model using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where we found that a decrease in single-cell gain during locomotion explained the observed acceleration of visual processing speed .
Edit intent: coherence
Original:  We tested our model using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where  a decrease in single-cell  gain during locomotion explained the observed acceleration of visual processing speed .
Revised:  We tested our model using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where a decrease in single-cell intrinsic gain during locomotion explained the observed acceleration of visual processing speed .
Edit intent: meaning-changed
Original:  We tested our model using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where  a decrease in single-cell  gain during locomotion explained the observed acceleration of visual processing speed .
Revised:  We tested our model using large-scale electrophysiological recordings from the visual hierarchy in freely running mice, where a decrease in single-cell gain during locomotion led to an acceleration of visual processing speed .
Edit intent: clarity
Original:  In this context , we connect coherent risk measures with the Sharpe ratio from modern portfolio theory and extract the Z-spread - a widely accepted quantity in economics to hedge risk . By involving the Z-spread we demonstrate that risk-averse problems are conceptually equivalent to the risk-neutral problem .
Revised:  In this context , we connect coherent risk measures with the Sharpe ratio from modern portfolio theory and extract the Z-spread - a widely accepted quantity in economics to hedge risk .
Edit intent: coherence
Original:  The CovidSens concept is motivated by three observations: 1) people actively share their experience of COVID-19 via online social media, 2) official warning channels and news agencies are relatively slower than people reporting on social media, and 3) online users are frequently equipped with substantially capable mobile devices that can perform data processing and analytics.
Revised:  The CovidSens concept is motivated by three observations: 1) people actively share their experience of COVID-19 via online social media, 2) official warning channels and news agencies are relatively slower than people reporting on social media, and 3) online users are frequently equipped with powerful mobile devices that can perform data processing and analytics.
Edit intent: clarity
Original:  We show here that the time evolution of the pandemic doubling time Td(t) is a reliable measure of the efficiency of the Lockdown, Case Finding, Mobile Tracing (LFT) policy using modern technologies versus the traditional Lockdown Stop and Go policy.
Revised:  We show here that the time evolution of the pandemic doubling time Td(t) is a reliable measure of the efficiency of the Lockdown, case Finding, mobile Tracing (LFT) policy using modern technologies versus the traditional Lockdown Stop and Go policy.
Edit intent: fluency
Original:  We show here that the time evolution of the pandemic doubling time Td(t) is a reliable measure of the efficiency of the Lockdown, Case Finding, Mobile Tracing (LFT) policy using modern technologies versus the traditional Lockdown Stop and Go policy. We have been able to verify theoretical predictions of the time evolution of the doubling time Td(t) and the particular features of each policy.
Revised:  We show here that the time evolution of the pandemic doubling time Td(t) is a reliable measure of the efficiency of the Lockdown, Case Finding, Mobile Tracing (LFT) policy using modern technologies versus the traditional Lockdown Stop and Go (LSG) policy. This goal has been achieved to verify by extracting the time evolution of the doubling time Td(t) and the particular features of each policy.
Edit intent: coherence
Original:  We have been able to verify theoretical predictions of the time evolution of the doubling time Td(t) and the particular features of each policy. The most successful containment policyreaches the lowest s factor, the characteristic time of the exponential growing doubling time in the arrested regime . South Korea, China, Italy and USA have reacheds= 5 , 6, 10, 18 respectively.
Revised:  We have been able to verify theoretical predictions of the time evolution of the doubling time Td(t) and the lowest s factor, the characteristic time of the exponential growing doubling time in the arrested regime . South Korea, China, Italy and USA have reacheds= 5 , 6, 10, 18 respectively.
Edit intent: clarity
Original:  The most successful containment policyreaches the lowest s factor, the characteristic time of the exponential growing doubling time in the arrested regime . South Korea, China, Italy and USA have reacheds= 5 , 6, 10, 18 respectively.
Revised:  The most successful containment policyreaches the lowest s factor, the characteristic time of the exponential growing Td(t) in the arrested regime . South Korea, China, Italy and USA have reacheds= 5 , 6, 10, 18 respectively.
Edit intent: meaning-changed
Original:  The most successful containment policyreaches the lowest s factor, the characteristic time of the exponential growing doubling time in the arrested regime . South Korea, China, Italy and USA have reacheds= 5 , 6, 10, 18 respectively.
Revised:  The most successful containment policyreaches the lowest s factor, the characteristic time of the exponential growing doubling time in the arrested regime , 6, 10, 18 respectively.
Edit intent: coherence