wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The pension manager makes investment decisions for the benefit and on behalf of the representative pension member whose objective is to maximize his expected utility of the terminal surplus between the final fund level and the minimum guarantee . To solve the initial constrained non-self-financing optimization problem , we transform it to an unconstrained self-financing problem by replicating the future contributions and the minimum guarantee .
Revised:  The pension scheme invests in the risky assets available in the market, including the longevity bond, by using the contributions from a representative scheme member to ensure a minimum guarantee such that the member is able to purchase a lifetime annuity upon retirement. We transform this constrained optimal investment problem into an unconstrained problem by replicating the future contributions and the minimum guarantee .
Edit intent: meaning-changed
Original:  To solve the initial constrained non-self-financing optimization problem , we transform it to an unconstrained self-financing problem by replicating the future contributions and the minimum guarantee . By applying dynamic programming method, analytical solutions to the equivalent optimization problem are derived and optimal investment strategies to the original problem are obtained by simple calculations. The numerical applications reveal that the longevity risk has an important impact on the investment strategies and show evidence that mortality-linked securities could provide an efficient way to hedge the longevity risk.
Revised:  To solve the initial constrained non-self-financing optimization problem , we transform it to an unconstrained self-financing problem by replicating the future contributions and the minimum guarantee provided by the scheme. We solve the resulting optimisation problem using the dynamic programming principle and through a series of numerical studies reveal that the longevity risk has an important impact on the investment strategies and show evidence that mortality-linked securities could provide an efficient way to hedge the longevity risk.
Edit intent: clarity
Original: We establish and build theoretical and numerical connections between the theories and methods of integrated information and algorithmic complexity. We introduce a method for estimating integrated information performing a programmability test rooted in algorithmic information dynamics quantifying the change in program size of a system subject to perturbations.
Revised: The concept of information has emerged as a language in its own right, bridging several disciplines that analyze natural phenomena and man-made systems. Integrated information has been introduced as a metric to quantify the amount of information generated by a system beyond the information generated by its elements. Yet, this intriguing notion comes with the price of being prohibitively expensive to calculate, since the calculations require an exponential number of sub-divisions of a system. Here we introduce a novel framework to connect algorithmic randomness and integrated information and a numerical method for estimating integrated information performing a programmability test rooted in algorithmic information dynamics quantifying the change in program size of a system subject to perturbations.
Edit intent: meaning-changed
Original:  We introduce a method for estimating integrated information performing a programmability test rooted in algorithmic information dynamics quantifying the change in program size of a system subject to perturbations. Our method is based on the idea that simple rules of causal dynamical systems can shorten the calculation needed to estimate integrated information whereas program-size descriptions of non-integrated systems subject to perturbations, whose parts are causally disconnected (cannot be explained from each other or do not follow from each other) behave differently. On the basis of the perturbation test , we demonstrate how a system can be regarded as providing its own explanations for its own behavioursomething today highly sought in machine learning, helping us infer causal properties of a system while, if integrated, reducing the number of calculations required.
Revised:  We introduce a method for estimating integrated information performing a programmability test rooted in algorithmic information dynamics quantifying the change in program size of a system when subjected to a perturbation. The intuition behind is that if an object is random then random perturbations have little to no effect to what happens when a shorter program but when an object has the ability to move in both directions (towards or away from randomness) it will be shown to be better integrated as a measure of sophistication telling apart randomness and simplicity from structure. We show that an object with a high integrated information value is also more compressible, and is, therefore, more sensitive to perturbations. We find that such a perturbation test quantifying compression sensitivity provides a system with a means to extract explanations--causal accounts--of its own behaviour. Our technique can reduce the number of calculations required.
Edit intent: meaning-changed
Original:  On the basis of the perturbation test , we demonstrate how a system can be regarded as providing its own explanations for its own behavioursomething today highly sought in machine learning, helping us infer causal properties of a system while, if integrated, reducing the number of calculations required. We expect this approach to contribute toward a better understanding of integrated informationand of its connections to other, more established areas of science, such as dynamical systems and algorithmic information theory, and to help avoid some of the current abuses (e.g. relating \phi to lossless compression algorithms such as LZW) and limitations arising from the bottleneck principle which other approaches to \phi have been heavily subject to .
Revised:  On the basis of the perturbation test , we demonstrate how a system can be regarded as providing its own explanations for its own behavioursomething today highly sought in machine learning, helping us infer causal properties of a system while, if integrated, reducing the number of calculations to arrive at some bounds or estimations, as the algorithmic perturbation test guides an efficient search for estimating integrated information. Our work sets the stage for a systematic exploration of connections between algorithmic complexity and integrated information at the level of both theory and practice .
Edit intent: meaning-changed
Original:  The main result is a joint likelihood surface defined as a function of two parameters: (1) the population size at the time of the most recent common ancestor, and (2) the time elapsed since the existence of the most recent common ancestor.
Revised:  We obtain a formula for the probability that a finite random sample of the population is descended from a single ancestor in the initial population, and derive a confidence interval for the initial population size in terms of the final population size and the time since initiating the process. We also determine a joint likelihood surface defined as a function of two parameters: (1) the population size at the time of the most recent common ancestor, and (2) the time elapsed since the existence of the most recent common ancestor.
Edit intent: meaning-changed
Original:  The main result is a joint likelihood surface defined as a function of two parameters: (1) the population size at the time of the most recent common ancestor, and (2) the time elapsed since the existence of the most recent common ancestor.
Revised:  The main result is a joint likelihood surface from which confidence regions can be determined for simultaneously estimating two parameters, (1) the population size at the time of the most recent common ancestor, and (2) the time elapsed since the existence of the most recent common ancestor.
Edit intent: meaning-changed
Original: The benefit of chemokinesis in motile cells, i.e., the regulation of speed and directional persistence as function of spatial position by non-directional cues, is not well understood. We develop a theory of chemokinetic search agents that can detect the proximity of hidden targets and adapt their motility in a position-dependent manner.
Revised: We present a theory of chemokinetic search agents that can detect the proximity of hidden targets and adapt their motility in a position-dependent manner.
Edit intent: clarity
Original:  We develop a theory of chemokinetic search agents that can detect the proximity of hidden targets and adapt their motility in a position-dependent manner. We show that residence probabilities are spatially homogeneous (after trivial renormalization), independent of strategy. Yet, a dynamic reflection effect allows search agents to substantially increase their target encounter rate if searchagents posses memory .
Revised:  We develop a theory of chemokinetic search agents that regulate directional fluctuations according to distance from a target. A dynamic scattering effect reduces the probability to penetrate regions with high fluctuations and thus search success for agents that respond instantaneously to positional cues. In contrast, agents with internal states that initially suppress chemokinesis can exploit scattering to increase their probability to find the target. Using matched asymptotics between the case of diffusive and ballistic search, we obtain analytic results beyond Fox' colored noise approximation .
Edit intent: meaning-changed
Original:  This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou , Peng and Heyde (2013), and also data-based versions of loss-based risk measures introduced by Cont et al. (2013) and Sun et al. (2018)\hspace{0pt}%DIFAUXCMD } .
Revised:  This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou et al. \mbox{%DIFAUXCMD 11 also data-based versions of loss-based risk measures introduced by Cont et al. (2013) and Sun et al. (2018)\hspace{0pt}%DIFAUXCMD } .
Edit intent: meaning-changed
Original:  This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou , Peng and Heyde (2013), and also data-based versions of loss-based risk measures introduced by Cont et al. (2013) and Sun et al. (2018)\hspace{0pt}%DIFAUXCMD } .
Revised:  This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou , Peng and Heyde (2013), and also data-based versions of loss-based risk measures introduced by Cont et al. \mbox{%DIFAUXCMD 5 and Sun et al. (2018)\hspace{0pt}%DIFAUXCMD } .
Edit intent: meaning-changed
Original:  This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou , Peng and Heyde (2013), and also data-based versions of loss-based risk measures introduced by Cont et al. (2013) and Sun et al. (2018)\hspace{0pt}%DIFAUXCMD } .
Revised:  This new class of risk statistics can be considered as a kind of risk extension of risk statistics introduced by Kou , Peng and Heyde (2013), and also data-based versions of loss-based risk measures introduced by Cont et al. (2013) and Sun et al. \mbox{%DIFAUXCMD 12\hspace{0pt}%DIFAUXCMD } .
Edit intent: meaning-changed
Original:  The results hold under large-network asymptotics, enabling inference in the typical setting where the sample consists of a small set of large networks. We first establish a general central limit theorem under high-level `stabilization' conditions that provide a useful formulation of weak dependence , particularly in models with strategic interactions.
Revised:  The results hold under large-network asymptotics, enabling inference in the typical setting where the sample consists of a single large network. In the presence of strategic interactions, network moments are generally complex functions of network components, where a node's component consists of all alters to which it is directly or indirectly connected. We find that a modification of "exponential stabilization" conditions from the stochastic geometry literature provides a useful formulation of weak dependence , particularly in models with strategic interactions.
Edit intent: meaning-changed
Original:  We first establish a general central limit theorem under high-level `stabilization' conditions that provide a useful formulation of weak dependence , particularly in models with strategic interactions. The result delivers a square root n rate of convergence and a closed-form expression for the asymptotic variance. Then using techniques in branching process theory, we derive primitive conditions for stabilization in the following applications: static and dynamic models of strategic network formation , network regressions, and treatment effects with network spillovers.
Revised:  We first establish a general central limit theorem under high-level `stabilization' conditions that provide a useful formulation of weak dependence for moments of this type. Our first contribution is to prove a CLT for a large class of network moments satisfying stabilization and a moment condition. Our second contribution is a methodology for deriving primitive sufficient conditions for stabilization in the following applications: static and dynamic models of strategic network formation , network regressions, and treatment effects with network spillovers.
Edit intent: clarity
Original:  Then using techniques in branching process theory, we derive primitive conditions for stabilization in the following applications: static and dynamic models of strategic network formation , network regressions, and treatment effects with network spillovers.
Revised:  Then using techniques in branching process theory, we derive primitive conditions for stabilization using results in branching process theory. We apply the methodology to static and dynamic models of strategic network formation , network regressions, and treatment effects with network spillovers.
Edit intent: clarity
Original:  Then using techniques in branching process theory, we derive primitive conditions for stabilization in the following applications: static and dynamic models of strategic network formation , network regressions, and treatment effects with network spillovers. Finally, we suggest some practical methods for inference .
Revised:  Then using techniques in branching process theory, we derive primitive conditions for stabilization in the following applications: static and dynamic models of network formation and discuss how it can be used more broadly .
Edit intent: meaning-changed
Original:  We consider an asymptotic framework in which the size of the network tends to infinity , which is useful for inference in the typical setting in which the sample consists of a single large network .
Revised:  Since data often consists of observations on a single large network, we consider an asymptotic framework in which the size of the network tends to infinity , which is useful for inference in the typical setting in which the sample consists of a single large network .
Edit intent: meaning-changed
Original:  Our first contribution is to prove a CLT for a large class of network moments satisfying stabilization and a moment condition. Our second contribution is a methodology for deriving primitive sufficient conditions for stabilization using results in branching process theory.
Revised:  Our first contribution is to prove a CLT for a large class of network moments satisfying stabilization and provide a methodology for deriving primitive sufficient conditions for stabilization using results in branching process theory.
Edit intent: clarity
Original:  the IV must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, and is allowed to be correlated with the misclassification error.
Revised:  the instrumental variable must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, and is allowed to be correlated with the misclassification error.
Edit intent: clarity
Original:  the IV must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, and is allowed to be correlated with the misclassification error.
Revised:  the IV must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, but is allowed to be correlated with the misclassification error.
Edit intent: coherence
Original:  Here, we show that transient thermal transport reflects macromolecular energy landscape architecture through both (i) the topological characteristics of the conformational ensemble and (ii) the nonlinear processes that mediate dynamics.
Revised:  Here, we show that transient thermal transport reflects macromolecular energy landscape architecture through the topological characteristics of the conformational ensemble and (ii) the nonlinear processes that mediate dynamics.
Edit intent: coherence
Original:  While the former determines transport pathways via molecular contacts , the latter reflects the ruggedness of the landscape for local motion of atoms and molecular fragments . Unlike transport through small-molecule systems, such as alkanes, nonlinearity dominates over coherent processes at even quite short time- and length-scales.
Revised:  While the former determines transport pathways via pairwise interactions , the latter reflects the ruggedness of the landscape for local motion of atoms and molecular fragments . Unlike transport through small-molecule systems, such as alkanes, nonlinearity dominates over coherent processes at even quite short time- and length-scales.
Edit intent: clarity
Original:  While the former determines transport pathways via molecular contacts , the latter reflects the ruggedness of the landscape for local motion of atoms and molecular fragments . Unlike transport through small-molecule systems, such as alkanes, nonlinearity dominates over coherent processes at even quite short time- and length-scales.
Revised:  While the former determines transport pathways via molecular contacts , the latter reflects frustration within the landscape for local motion of atoms and molecular fragments . Unlike transport through small-molecule systems, such as alkanes, nonlinearity dominates over coherent processes at even quite short time- and length-scales.
Edit intent: clarity
Original:  While the former determines transport pathways via molecular contacts , the latter reflects the ruggedness of the landscape for local motion of atoms and molecular fragments . Unlike transport through small-molecule systems, such as alkanes, nonlinearity dominates over coherent processes at even quite short time- and length-scales.
Revised:  While the former determines transport pathways via molecular contacts , the latter reflects the ruggedness of the landscape for local conformational rearrangements . Unlike transport through small-molecule systems, such as alkanes, nonlinearity dominates over coherent processes at even quite short time- and length-scales.
Edit intent: clarity
Original:  The dual characterization of  super-replication cost is provided. It is shown that the reservation prices of investors converge to this cost as their respective risk-aversion tends to infinity.
Revised:  The dual characterization of super-replication cost . Then, we show the existence of optimal strategies for investors maximizing their expected utility and the convergence of their reservation prices to the super-replication cost as their respective risk-aversion tends to infinity.
Edit intent: meaning-changed
Original:  Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make predictions concerning future states for new patients. HMMs can be trained using longitudinal observations of subjects from large-scale cohort studies, clinical trials, and electronic health records.
Revised:  Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients.
Edit intent: clarity
Original:  To tackle this problem , we conducted a design study with physician scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of certain chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD).
Revised:  To tackle these problems , we conducted a design study with physician scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of certain chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD).
Edit intent: fluency
Original:  To tackle this problem , we conducted a design study with physician scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of certain chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD).
Revised:  To tackle this problem , we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of certain chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD).
Edit intent: clarity
Original:  To tackle this problem , we conducted a design study with physician scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of certain chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD).
Revised:  To tackle this problem , we conducted a design study with physician scientists, statisticians, and visualization experts, with the goal to investigate disease progression pathways of chronic diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's disease, and chronic obstructive pulmonary disease (COPD).
Edit intent: clarity
Original:  In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and designing and comparing clinically relevant subgroup cohorts by introducing a case study on observation data from clinical studies of T1D .
Revised:  In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and building, analyzing, and comparing clinically relevant subgroup cohorts by introducing a case study on observation data from clinical studies of T1D .
Edit intent: clarity
Original:  In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and designing and comparing clinically relevant subgroup cohorts by introducing a case study on observation data from clinical studies of T1D .
Revised:  In this study, we demonstrate that DPVis is successful in evaluating disease progression models, visually summarizing disease states, interactively exploring disease progression patterns, and designing and comparing clinically relevant patient subgroups .
Edit intent: clarity