wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This allows us to apply standard Fenchel-Moreau techniques to tackle duality also for systemic risk measures . The same approach can be also successfully employed to obtain an elementary proof of the dual representation of "first aggregate , then allocate" -type systemic risk measures.
Revised:  This allows us to apply standard Fenchel-Moreau techniques to tackle duality also for systemic risk measures of both "first aggregate , then allocate" -type systemic risk measures.
Edit intent: clarity
Original:  The same approach can be also successfully employed to obtain an elementary proof of the dual representation of "first aggregate , then allocate" -type systemic risk measures. As a final application, we apply our results to derive a simple proof of the dual representation of univariate utility-based risk measures  .
Revised:  The same approach can be also successfully employed to obtain an elementary proof of the dual representation of "first aggregate , then allocate" type. In both cases, we provide a detailed analysis of the corresponding systemic acceptance sets and their support functions. The same approach delivers a simple and self-contained proof of the dual representation of univariate utility-based risk measures .
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Original: The network paradigm is widely accepted as the gold standard in modelling complex systems such as epidemics or neuronal activity in the brain; however, in most cases, the exact nature of the network on which such dynamics unfold is unknown. This has motivated a significant amount of work on network inference. Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when only population-level temporal incidence data are available .
Revised: Using the continuous-time susceptible-infected-susceptible (SIS) model on networks, we investigate the problem of inferring the family of the underlying network when only population-level temporal incidence data are available .
Edit intent: meaning-changed
Original:  Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when only population-level temporal incidence data are available .
Revised:  Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the class of the underlying network when only population-level temporal incidence data are available .
Edit intent: clarity
Original:  Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when only population-level temporal incidence data are available .
Revised:  Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when epidemic data is only available at population-level temporal incidence data are available .
Edit intent: meaning-changed
Original:  Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when only population-level temporal incidence data are available .
Revised:  Whilst a large body of work is concerned with inferring network structure based on detailed node-level temporal data, in this work we tackle the more challenging scenario of inferring the family of the underlying network when only population-level (i.e. the number of infected individuals at a finite set of discrete times of a single realisation of the epidemic), the only information likely to be available in real world settings .
Edit intent: meaning-changed
Original:  To tackle this, we approximate the susceptible-infected-susceptible (SIS) model on networks by a Birth-Death process , whose rates encode the structure of the underlying network and disease dynamics.
Revised:  To tackle this, epidemics on networks are approximated by a Birth-and-Death process which keeps track of the number of infected nodes at population level. The rates of this surrogate model encode both the structure of the underlying network and disease dynamics.
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Original:  Using systematic simulations , we propose a parsimonious (three-parameter) model of these ratess-R\'enyi } and  show that different wellknown network families map onto distinct regions of the parameter space of this model.
Revised:  We use extensive simulations over Regular, Erd\H{os-R\'enyi } and show that different wellknown network families map onto distinct regions of the parameter space of this model.
Edit intent: meaning-changed
Original:  Using systematic simulations , we propose a parsimonious (three-parameter) model of these ratess-R\'enyi } and  show that different wellknown network families map onto distinct regions of the parameter space of this model.
Revised:  Using systematic simulations , we propose a parsimonious (three-parameter) model of these ratess-R\'enyi } and Barab\'asi-Albert networks to build network class-specific priors for these rates. \% show that different wellknown network families map onto distinct regions of the parameter space of this model.
Edit intent: meaning-changed
Original:  Using systematic simulations , we propose a parsimonious (three-parameter) model of these ratess-R\'enyi } and  show that different wellknown network families map onto distinct regions of the parameter space of this model.
Revised:  Using systematic simulations , we propose a parsimonious (three-parameter) model of these ratess-R\'enyi } and show that different well-known network classes map onto distinct regions of the parameter space of this model.
Edit intent: clarity
Original: Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Revised: Drawing an inspiration from behavioral studies of human decision making, we propose here a more general and flexible parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Edit intent: meaning-changed
Original: Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Revised: Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for reinforcement learning that extends standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Edit intent: clarity
Original: Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Revised: Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning to a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain.
Edit intent: clarity
Original: Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream framework of reward processing with biases biologically associated with several neurological and psychiatric conditions, including Parkinson's and Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD), addiction, and chronic pain. For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and  Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks.
Revised: Drawing an inspiration from behavioral studies of human decision making, we propose here a general parametric framework for a reinforcement learning problem, which extends the standard Q-learning approach to incorporate a two-stream model for processing positive and negative rewards, and allows to incorporate a wide range of reward-processing biases -- an important component of human decision making which can help us better understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks.
Edit intent: coherence
Original:  For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and  Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks.
Revised:  For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems , as well as various neuropsychiatric conditions associated with disruptions in normal reward processing. From the computational perspective, we observe that the proposed Split-QL model and its clinically inspired variants consistently outperform standard Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks.
Edit intent: meaning-changed
Original:  For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and  Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks.
Revised:  For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and SARSA methods, as well as recently proposed Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks.
Edit intent: meaning-changed
Original:  For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and  Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks.
Revised:  For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and Double Q-Learning approaches, on simulated tasks with particular reward distributions, a real-world human decision making gambling tasks.
Edit intent: clarity
Original:  For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and  Double Q-Learning in artificial scenarios with certain reward distributionsand real-world human decision making gambling tasks. Moreover, from the behavioral modeling perspective, our parametric framework can be viewed as a first step towards a unifying computational model capturing reward processing abnormalities across multiple mental conditions and user preferences in long-term recommendation systems .
Revised:  For AI community, the development of agents that react differently to different types of rewards can enable us to understand a wide spectrum of multi-agent interactions in complex real-world socioeconomic systems . Empirically, the proposed model outperforms Q-Learning and Double Q-Learning in artificial scenarios with certain reward distributionsand real-world dataset capturing human decision-making in gambling tasks, and the Pac-Man game in a lifelong learning setting across different reward stationarities .
Edit intent: meaning-changed
Original:  We obtain these results under the same moment restrictions and conditions on the class of functions as with i.i.d. data. As a result, convergence and asymptotic normality of nonlinear estimators can be obtained under the same regularity conditions as with i.i.d. data.
Revised:  We obtain these results under the same moment restrictions and conditions on the class of functions as those typically assumed with i.i.d. data.
Edit intent: clarity
Original: A process is polynomial if its extended generator maps any polynomial to a polynomial of equal or lower degree. Then its conditional moments can be calculated in closed form, up to the computation of the exponential of the so-called generator matrix. In this article , we provide an explicit formula to the problem of computing correlators, that is, computing the expected value of moments of the process at different time points along its path.
Revised: In the setting of polynomial jump-diffusion dynamics , we provide an explicit formula to the problem of computing correlators, that is, computing the expected value of moments of the process at different time points along its path.
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Original:  The strength of our formula is that it only involves linear combinations of the exponential of the  generator matrix, as in the one-dimensional case. The framework developed allows then for easy-to-implement solutions when it comes to financial pricing, such as for path-dependent options or in a stochastic volatility models context.
Revised:  The strength of our formula is that it only involves linear combinations of the exponential of the generator matrix, extending the well-known moment formula for polynomial processes. The developed framework allows to replace costly simulations with more accurate estimates, and it may be used for increasing the accuracy in financial pricing, such as for path-dependent options or in a stochastic volatility models context.
Edit intent: meaning-changed
Original:  The developed framework allows to replace costly simulations with more accurate estimates, and it may be used for increasing the accuracy in financial pricing, such as for path-dependent options or in a stochastic volatility models context  .
Revised:  The developed framework allows to replace costly simulations with more accurate estimates, and it may be used for increasing the accuracy in financial pricing, such as for path-dependent options and in a stochastic volatility models context .
Edit intent: fluency
Original:  The developed framework allows to replace costly simulations with more accurate estimates, and it may be used for increasing the accuracy in financial pricing, such as for path-dependent options or in a stochastic volatility models context  .
Revised:  The developed framework allows to replace costly simulations with more accurate estimates, and it may be used for increasing the accuracy in financial pricing, such as for path-dependent options or in a stochastic volatility models context . In applications to options, having closed and compact formulations is attractive for sensitivity analysis and risk management, since Greeks can be derived explicitly .
Edit intent: meaning-changed
Original:  We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) acting simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as are demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy.
Revised:  We show both qualitatively and quantitatively that using our multidimensional extension of CLAHE (MCLAHE) acting simultaneously on all dimensions of the datasets allows better visualization and discernment of multidimensional image features, as demonstrated using cases from 4D photoemission spectroscopy and fluorescence microscopy.
Edit intent: clarity
Original:  Second, dynamic matching markets exhibit a form of externality that is not present in static markets: who is available to match in a given period depends not only on the arrivals into the economy, but also on who remains unmatched from previous periods . Dynamically stable matchings always exist.
Revised:  Second, dynamic matching markets exhibit a form of externality that is not present in static markets: an agent's payoff from remaining unmatched cannot be defined independently of what other contemporaneous agents' outcomes are . Dynamically stable matchings always exist.
Edit intent: clarity
Original: Background: Patients undergoing critical care can experience negative outcomes due to a variety of causes such as lack of sleep, prolonged pain, and anxiety. Our goal was to evaluate the feasibility and efficacy of using meditative virtual reality (VR) to improve the hospital experience of intensive care unit (ICU) patients.
Revised: Purpose: Preliminarily evaluate the feasibility and efficacy of using meditative virtual reality (VR) to improve the hospital experience of intensive care unit (ICU) patients.
Edit intent: clarity
Original:  Methods:  A Google Daydream headset was used to expose ICU patients to commercially available VR applications focused on calmness and relaxation (Google Spotlight Stories and RelaxVR).
Revised:  Methods: Effects of VR were examined in a non-randomized, single-center cohort. Fifty-nine patients admitted to the surgical or trauma ICU of the University of Florida Health Shands Hospital participated. A Google Daydream headset was used to expose ICU patients to commercially available VR applications focused on calmness and relaxation (Google Spotlight Stories and RelaxVR).
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Original:  Outcome measures including pain level, anxiety, depression, medication administration, sleep quality, heart rate, respiratory rate, blood pressure, delirium status, and patient ratings of the VR system were evaluated using paired t-tests and mixed models where appropriate.
Revised:  Outcome measures included pain level, anxiety, depression, medication administration, sleep quality, heart rate, respiratory rate, blood pressure, delirium status, and patient ratings of the VR system were evaluated using paired t-tests and mixed models where appropriate.
Edit intent: fluency
Original:  Outcome measures including pain level, anxiety, depression, medication administration, sleep quality, heart rate, respiratory rate, blood pressure, delirium status, and patient ratings of the VR system were evaluated using paired t-tests and mixed models where appropriate.
Revised:  Outcome measures including pain level, anxiety, depression, medication administration, sleep quality, heart rate, respiratory rate, blood pressure, delirium status, and patient ratings of the VR system . Comparisons were made using paired t-tests and mixed models where appropriate.
Edit intent: clarity
Original:  Results: 46 participants (M = 50 years, SD = 18) completed the study. The clinical effects of VR were minimal for pain, sleep, and physiological measures. Delirium prevalence after VR exposure was low (13\%). Most participants had strong positive reactions to the VR exposure and showed considerable improvements in affect over time.
Revised:  Results: The VR meditative intervention was found to improve patients' ICU experience with reduced levels of anxiety and depression; however, there was no evidence suggesting that VR had any significant effects on physiological measures, pain, or sleep.
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Original:  The lessons learned from deploying VR in the ICU are discussed and future avenues of research on the use of VR  in the ICU are identified. Trial registration: The trial was registered on December 29, 2017 with ClinicalTrials.gov with the identifier: NCT03385993 .
Revised:  The lessons learned from deploying VR in the ICU are discussed and future avenues of research on the use of VR in the ICU was shown to be easily implemented and well-received by patients .
Edit intent: meaning-changed
Original:  Usually, these measurements are characterized by the scaling of the contact probability as a function of the genomic distance between regions.
Revised:  These probabilities can be visualised as matrices called contact maps. Contact maps are characterized by self-similar blocks along the diagonal, corresponding to a hierarchy of tightly packed chromosomal domains. Statistical properties of these maps, and thus of chromosomal interactions, are usually summarized by the scaling of the contact probability as a function of the genomic distance between regions.
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Original:  Usually, these measurements are characterized by the scaling of the contact probability as a function of the genomic distance between regions. In this work, we introduce a multifractal analysis of chromosomal contact maps.
Revised:  Usually, these measurements are characterized by the scaling of the contact probability as a function of the genomic distance . This approach is fraught with limitations, since such scaling is usually rather noisy. Here, we introduce a multifractal analysis of chromosomal contact maps.
Edit intent: meaning-changed