wanyu/IteraTeR_full_sent huggingface dataset:
Original:  A theoretical model of technological change and turnover is presented, intended as a methodological paradigm shift from widely used conventional modelling approaches such as cost optimisation. It follows the tradition of evolutionary economics and evolutionary game theory, using ecological population growth dynamics to represent the evolution of technology populations in the marketplace, with substitutions taking place at the level of the decision-maker.
Revised:  Many, if not most, contemporary modelling approaches for future technology pathways overlook most aspects of transitions theory, for instance dimensions of investor choices, dynamic rates of diffusion and the profile of transitions. A significant body of literature however exists that demonstrates how transitions follow S-shaped diffusion curves or Lotka-Volterra systems of equations. This framework is used ex-post since timescales can only be reliably obtained in cases where the transitions have already occurred, precluding its use for studying cases of interest where nascent innovations in protective niches await favourable conditions for their diffusion. Scaling parameters of transitions can in principle be derived from industrial dynamics, technology turnover rates and technology characteristics. In this context, this paper presents a theory framework for calculating the decision-maker.
Edit intent: meaning-changed
Original:  It follows the tradition of evolutionary economics and evolutionary game theory, using ecological population growth dynamics to represent the evolution of technology populations in the marketplace, with substitutions taking place at the level of the decision-maker. Extended to use principles of human demography or the age structured evolution of species in interacting ecosystems, this theory is built from first principles, and through an appropriate approximation, reduces to a form identical to empirical models of technology diffusion common in the technology transitions literature. Using an age structure, it provides the appropriate groundwork and theoretical framework to understand interacting technologies, their birth, ageing and mutual substitution. This analysis provides insight in explaining the nature and origin of observed timescales of technology transitions , in terms of technology life expectancies, the dynamic process of production capacity expansion or collapse and its timescales, in what is termed a `demographic phase'. While this model contributes to the general understanding of technological change, the information in this work is intended to be used practically for the parameterisation of technology diffusion in large scale models of technology systems when measured data is unknown or uncertain, as is the case for new technologies, notably for modelling future energy systems and greenhouse gas emissions .
Revised:  It follows the tradition of evolutionary economics and evolutionary game theory, using ecological population growth dynamics to represent the evolution of technology populations in the marketplace, with substitutions taking place at the level of the parameterisation of technology diffusion in large scale models of technology systems when measured data is unknown or uncertain, as is the case for new technologies, notably for modelling future energy systems and greenhouse gas emissions .
Edit intent: coherence
Original:  While this model contributes to the general understanding of technological change, the information in this work is intended to be used practically for the parameterisation of technology diffusion in large scale models of technology systems when measured data is unknown or uncertain, as is the case for new technologies, notably for modelling future energy systems and greenhouse gas emissions .
Revised:  While this model contributes to the general understanding of technological change, the information in this work is intended to be used practically for the parameterisation of S-shaped diffusion curves for use in simulation models of technology systems when measured data is unknown or uncertain, as is the case for new technologies, notably for modelling future energy systems and greenhouse gas emissions .
Edit intent: meaning-changed
Original:  While this model contributes to the general understanding of technological change, the information in this work is intended to be used practically for the parameterisation of technology diffusion in large scale models of technology systems when measured data is unknown or uncertain, as is the case for new technologies, notably for modelling future energy systems and greenhouse gas emissions .
Revised:  While this model contributes to the general understanding of technological change, the information in this work is intended to be used practically for the parameterisation of technology diffusion in large scale models of technology transitions without the involvement of historical data fitting, making use of standard demography theory applied to technology at the unit level. The classic Lotka-Volterra competition system emerges from first principles from demography theory, its timescales explained in terms of technology lifetimes and industrial dynamics. The theory is discussed in the context of the multi-level perspective on technology transitions, where innovation and the diffusion of new socio-technical regimes take a prominent place, offering a bridge between qualitative and quantitative descriptions .
Edit intent: meaning-changed
Original:  In this context, this paper presents a theory framework for calculating the parameterisation of S-shaped diffusion curves for use in simulation models of technology transitions without the involvement of historical data fitting, making use of standard demography theory applied to technology at the unit level.
Revised:  In this context, this paper presents a theory framework for evaluating the parameterisation of S-shaped diffusion curves for use in simulation models of technology transitions without the involvement of historical data fitting, making use of standard demography theory applied to technology at the unit level.
Edit intent: clarity
Original:  The theory is discussed in the context of the multi-level perspective on technology transitions, where innovation and the diffusion of new socio-technical regimes take a prominent place, offering a bridge between qualitative and quantitative descriptions .
Revised:  The theory is placed in the context of the multi-level perspective on technology transitions, where innovation and the diffusion of new socio-technical regimes take a prominent place, offering a bridge between qualitative and quantitative descriptions .
Edit intent: clarity
Original:  The theory is discussed in the context of the multi-level perspective on technology transitions, where innovation and the diffusion of new socio-technical regimes take a prominent place, offering a bridge between qualitative and quantitative descriptions .
Revised:  The theory is discussed in the context of the multi-level perspective on technology transitions, where innovation and the diffusion of new socio-technical regimes take a prominent place, as well as discrete choice theory, the primary theoretical framework for introducing agent diversity .
Edit intent: meaning-changed
Original:  The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of amino acids and proteins in the integration and regulation of complex biological systems.
Revised:  The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of nodes in the integration and regulation of complex biological systems.
Edit intent: clarity
Original:  The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of amino acids and proteins in the integration and regulation of complex biological systems.
Revised:  The efficiency of single, initially defecting nodes to convert the cooperation of other nodes to defection in social dilemma games may be an important measure to predict the importance of amino acids and proteins in the integration and regulation of complex systems. Game centrality may help to design more efficient interventions to cellular networks (in forms of drugs), to ecosystems and social networks.
Edit intent: meaning-changed
Original:  In this paper, we investigate defending mechanisms against false-data injection attacks . By protecting carefully selected meter measurements and (or) power network topological information, we show that no false-data injection attack can be formulated to compromise any set of state estimates.
Revised:  In this paper, we investigate defending mechanisms against false-data injection attacks on power system state estimation. By securing carefully selected meter measurements and (or) power network topological information, we show that no false-data injection attack can be formulated to compromise any set of state estimates.
Edit intent: clarity
Original:  We characterize the optimal protection problem  as a variant Steiner tree problem in a graph , and propose both exact and reduced-complexity approximation algorithms to derive the optimal protection strategy that achieves the protection objective with minimum cost.
Revised:  We characterize the optimal protection problem , which protects the state estimates with minimum number of measurements, as a variant Steiner tree problem in a graph , and propose both exact and reduced-complexity approximation algorithms to derive the optimal protection strategy that achieves the protection objective with minimum cost.
Edit intent: meaning-changed
Original:  We characterize the optimal protection problem  as a variant Steiner tree problem in a graph , and propose both exact and reduced-complexity approximation algorithms to derive the optimal protection strategy that achieves the protection objective with minimum cost.
Revised:  We characterize the optimal protection problem as a variant Steiner tree problem in a graph . Based on the graphical characterization, we propose both exact and reduced-complexity approximation algorithms to derive the optimal protection strategy that achieves the protection objective with minimum cost.
Edit intent: meaning-changed
Original:  We characterize the optimal protection problem  as a variant Steiner tree problem in a graph , and propose both exact and reduced-complexity approximation algorithms to derive the optimal protection strategy that achieves the protection objective with minimum cost. For practical implementation, we also develop a unified defending strategy that efficiently utilizes both secure meter measurements and covert topological information .
Revised:  We characterize the optimal protection problem as a variant Steiner tree problem in a graph , and propose both exact and reduced-complexity approximation algorithms .
Edit intent: coherence
Original:  Further, we show analytically and verify numerically that when enzyme copy-numbers fluctuate  the strength of substrate fluctuations increases linearly with the RNA transcription rate.
Revised:  In this case, solely fluctuations in the timing of RNA processing lead to variation in the number of RNA molecules. However, we show analytically and verify numerically that when enzyme copy-numbers fluctuate the strength of substrate fluctuations increases linearly with the RNA transcription rate.
Edit intent: meaning-changed
Original:  Lastly, we suggest four solutions that  URLanization can take: (1) benchmark URLanization to know where you are, (2) de-bias your IT project decision-making, (3) reduce the complexities of your IT projects, and (4) develop Masterbuilders to learn from the best in the field.
Revised:  Lastly, we suggest four solutions that public URLanization can take: (1) benchmark URLanization to know where you are, (2) de-bias your IT project decision-making, (3) reduce the complexities of your IT projects, and (4) develop Masterbuilders to learn from the best in the field.
Edit intent: meaning-changed
Original:  By using a probabilistic cellular automaton approach, we model the input-output response of large active dendritic arbors subjected to complex spatio-temporal inputs , and exhibiting non-stereotyped dendritic spikes.
Revised:  By using a probabilistic cellular automaton approach, we model the input-output response of large active dendritic arbors subjected to complex spatio-temporal inputs and exhibiting non-stereotyped dendritic spikes.
Edit intent: fluency
Original: A celebrated financial application of convex duality theory gives an explicit relation between the following two quantities: %DIFDELCMD < myenumerate \item %%%  The optimal terminal wealth X^*(T) : = X_{\varphi^*}(T) of the classical problem to maximize the expected U-utility of the terminal wealth X_{\varphi}(T) generated by admissible portfolios \varphi(t) ;
Revised: A celebrated financial application of convex duality theory gives an explicit relation between the following two quantities: %DIFDELCMD < myenumerate \item %%% (i) The optimal terminal wealth X^*(T) : = X_{\varphi^*}(T) of the classical problem to maximize the expected U-utility of the terminal wealth X_{\varphi}(T) generated by admissible portfolios \varphi(t) ;
Edit intent: meaning-changed
Original: A celebrated financial application of convex duality theory gives an explicit relation between the following two quantities: %DIFDELCMD < myenumerate \item %%%  The optimal terminal wealth X^*(T) : = X_{\varphi^*}(T) of the classical problem to maximize the expected U-utility of the terminal wealth X_{\varphi}(T) generated by admissible portfolios \varphi(t) ;
Revised: A celebrated financial application of convex duality theory gives an explicit relation between the following two quantities: %DIFDELCMD < myenumerate \item %%% The optimal terminal wealth X^*(T) : = X_{\varphi^*}(T) of the problem to maximize the expected U-utility of the terminal wealth X_{\varphi}(T) generated by admissible portfolios \varphi(t) ;
Edit intent: clarity
Original: A celebrated financial application of convex duality theory gives an explicit relation between the following two quantities: %DIFDELCMD < myenumerate \item %%%  The optimal terminal wealth X^*(T) : = X_{\varphi^*}(T) of the classical problem to maximize the expected U-utility of the terminal wealth X_{\varphi}(T) generated by admissible portfolios \varphi(t) ;
Revised: A celebrated financial application of convex duality theory gives an explicit relation between the following two quantities: %DIFDELCMD < myenumerate \item %%% The optimal terminal wealth X^*(T) : = X_{\varphi^*}(T) of the classical problem to maximize the expected U-utility of the terminal wealth X_{\varphi}(T) generated by admissible portfolios \varphi(t) ,
Edit intent: fluency
Original:  0 \leq t \leq T in a market with the risky asset price process modeled as a semimartingale %DIFDELCMD < \item %%%  The optimal scenario dQ^*{dP} of the dual problem to minimize the expected V-value of dQ{dP} over a family of equivalent local martingale measures Q . Here V is the convex dual function of the concave function U.
Revised:  0 \leq t \leq T in a market with the risky asset price process modeled as a semimartingale %DIFDELCMD < \item %%% ; (ii) The optimal scenario dQ^*{dP} of the dual problem to minimize the expected V-value of dQ{dP} over a family of equivalent local martingale measures Q . Here V is the convex dual function of the concave function U.
Edit intent: meaning-changed
Original:  0 \leq t \leq T in a market with the risky asset price process modeled as a semimartingale %DIFDELCMD < \item %%%  The optimal scenario dQ^*{dP} of the dual problem to minimize the expected V-value of dQ{dP} over a family of equivalent local martingale measures Q . Here V is the convex dual function of the concave function U.
Revised:  0 \leq t \leq T in a market with the risky asset price process modeled as a semimartingale %DIFDELCMD < \item %%% The optimal scenario dQ^*{dP} of the dual problem to minimize the expected V-value of dQ{dP} over a family of equivalent local martingale measures Q , where V is the convex dual function of the concave function U.
Edit intent: fluency
Original:  0 \leq t \leq T in a market with the risky asset price process modeled as a semimartingale %DIFDELCMD < \item %%%  The optimal scenario dQ^*{dP} of the dual problem to minimize the expected V-value of dQ{dP} over a family of equivalent local martingale measures Q . Here V is the convex dual function of the concave function U.
Revised:  0 \leq t \leq T in a market with the risky asset price process modeled as a semimartingale %DIFDELCMD < \item %%% The optimal scenario dQ^*{dP} of the dual problem to minimize the expected V-value of dQ{dP} over a family of equivalent local martingale measures Q . Here V is the convex conjugate function of the concave function U.
Edit intent: clarity
Original:  In this paper we consider markets modeled by It\^o-L\'evy processes , and in the first part we give a new proof of the above result in this setting, based on the maximum principle in stochastic control theory .
Revised:  In this paper we consider markets modeled by It\^o-L\'evy processes . In the first part we give a new proof of the above result in this setting, based on the maximum principle in stochastic control theory .
Edit intent: coherence
Original:  In this paper we consider markets modeled by It\^o-L\'evy processes , and in the first part we give a new proof of the above result in this setting, based on the maximum principle in stochastic control theory .
Revised:  In this paper we consider markets modeled by It\^o-L\'evy processes , and in the first part we use the maximum principle in stochastic control theory .
Edit intent: coherence
Original:  In this paper we consider markets modeled by It\^o-L\'evy processes , and in the first part we give a new proof of the above result in this setting, based on the maximum principle in stochastic control theory . An advantage with our approach is that it also gives\emph{ [  an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Revised:  In this paper we consider markets modeled by It\^o-L\'evy processes , and in the first part we give a new proof of the above result in this setting, based on the maximum principle in stochastic control theory to extend the above relation to a\emph{ [ an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Edit intent: coherence
Original:  An advantage with our approach is that it also gives\emph{ [  an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Revised:  An advantage with our approach is that it also gives\emph{dynamic [ an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Edit intent: meaning-changed
Original:  An advantage with our approach is that it also gives\emph{ [  an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Revised:  An advantage with our approach is that it also gives\emph{ relation, valid for all t \in[ an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Edit intent: meaning-changed
Original:  An advantage with our approach is that it also gives\emph{ [  an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Revised:  An advantage with our approach is that it also gives\emph{ [0,T an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Edit intent: meaning-changed
Original:  An advantage with our approach is that it also gives\emph{ [  an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Revised:  An advantage with our approach is that it also gives\emph{ [ . We prove in particular that the optimal adjoint process for the primal problem coincides with the optimal density process, and that the optimal adjoint process for the dual problem coincides with the optimal wealth process, 0 \leq t \leq T. In the terminal time case t=T we recover the classical duality connection above. We get moreover an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Edit intent: meaning-changed
Original:  An advantage with our approach is that it also gives\emph{ [  an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* , in terms of backward stochastic differential equations .
Revised:  An advantage with our approach is that it also gives\emph{ [ an explicit relation between the optimal portfolio \varphi^* and the optimal measure Q^* . We also obtain that the existence of an optimal scenario is equivalent to the replicability of a related T-claim .
Edit intent: meaning-changed
Original:  Furthermore, we provide a rigorous veri?cation for that the free boundary for this problem is concave. As an application to the American option pricing problem, our results imply that the early exercise boundary of an American call is a strictly decreasing concave function. This result provides a useful information to obtain an asymptotic formula for the early exercise boundary .
Revised:  Furthermore, we provide a rigorous veri?cation for that the free boundary for this problem is convex .
Edit intent: clarity
Original: In this paper , we study a parabolic free boundary problem which shows that the solutions of this free boundary problem are increasing functions. Furthermore, we provide a rigorous verification for that the free boundary for this problem is convex .
Revised: In this paper , we study a parabolic free boundary problem arising from pricing American-style put options on an asset whose index follows a time-homogeneous diffusion process. The time-homogeneous diffusion process in this paper includes the geometric Brownian motion process, the CEV process, the mean-reverting Gaussian process or the Vasicek model, and the mean-reverting square root process or the CIR model. The contributions are to provide rigorous proofs of following facts. The value of an American-style put option increases with an increase in the time-to-maturity and decreases with an increase in the underlying asset index. The early exercise boundary is a strictly decreasing convex function of the time-to-maturity under the given conditions .
Edit intent: meaning-changed
Original: This paper studies the parabolic free boundary problem arising from pricing American-style put options on an asset whose index follows a time-homogeneous diffusion process. The time-homogeneous diffusion process in this paper includes the geometric Brownian motion process , the CEV process, the mean-reverting Gaussian process or the Vasicek model, and the mean-reverting square root process or the CIR model.
Revised: This paper studies the parabolic free boundary problem arising from pricing American-style put options on an asset whose index follows a geometric Brownian motion process , the CEV process, the mean-reverting Gaussian process or the Vasicek model, and the mean-reverting square root process or the CIR model.
Edit intent: coherence