wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In  [ 14 ] and [ 15 ] the authors showed  with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Revised:  In the reverse sense attaching meaningful dynamics to given 'interaction diagrams' is a central bottleneck problem in many application areas, especially in cell biology where various such diagrams with different conventions describing molecular regulation are presently in use. In most situations these diagrams can only be interpreted by the use of both discrete and continuous variables during the modelling process, corresponding to both deterministic and stochastic hybrid dynamics. The conventions in genetics are well-known, and therefore we use this field for illustration purposes. In [ 14 ] and [ 15 ] the authors showed with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Edit intent: meaning-changed
Original:  In  [ 14 ] and [ 15 ] the authors showed  with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Revised:  In [ 25 ] and [ 15 ] the authors showed with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Edit intent: meaning-changed
Original:  In  [ 14 ] and [ 15 ] the authors showed  with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Revised:  In [ 14 ] and [ 26 ] the authors showed with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Edit intent: meaning-changed
Original:  In  [ 14 ] and [ 15 ] the authors showed  with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Revised:  In [ 14 ] and [ 15 ] the authors showed that with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Edit intent: fluency
Original:  In  [ 14 ] and [ 15 ] the authors showed  with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Revised:  In [ 14 ] and [ 15 ] the authors showed with the help of a multi-scale analysis stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Edit intent: fluency
Original:  In  [ 14 ] and [ 15 ] the authors showed  with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Revised:  In [ 14 ] and [ 15 ] the authors showed with the help of a multi-scale anal- ysis that stochastic systems with both continuous variables and finite state spaces can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Edit intent: meaning-changed
Original:  In  [ 14 ] and [ 15 ] the authors showed  with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordi- nary differential equations and Markov chains.
Revised:  In [ 14 ] and [ 15 ] the authors showed with the help of a multi-scale anal- ysis that stochastic systems with infinite and finite degrees of freedom can be approximated by dynamical systems whose leading order time evolution is given by a combination of ordinary differential equations (ODEs) and Markov chains.
Edit intent: clarity
Original:  The leading order term in these dynamical systems is called average dynamics and turns out to be an adequate concept to analyse such systemswith discrete and continuous state spaces. There are many complex systems that can be conveniently described inside such a state space setting, for example bio-molecular interactions. Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the  introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems  [ 4 ].
Revised:  The leading order term in these dynamical systems is called average dynamics and turns out to be an adequate concept to analyse a class of simplified hybrid systems. Once the dynamics is defifined the mutual interaction of both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems [ 4 ].
Edit intent: meaning-changed
Original:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the  introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems  [ 4 ].
Revised:  Both ODEs and Markov chains can be analysed through the introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems [ 4 ].
Edit intent: clarity
Original:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the  introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems  [ 4 ].
Revised:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the (reverse) introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems [ 4 ].
Edit intent: meaning-changed
Original:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the  introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems  [ 4 ].
Revised:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the introduction of the so called Interaction Graph, a concept originally invented for time-continuous dynamical systems [ 4 ].
Edit intent: fluency
Original:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the  introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems  [ 4 ].
Revised:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems , see [ 4 ].
Edit intent: coherence
Original:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the  introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems  [ 4 ].
Revised:  Both ODEs and Markov chains and their mutual interaction can be better understood and analysed through the introduction of the so called Interaction Graph, a con- cept originally invented for time-continuous dynamical systems [ 5 ].
Edit intent: meaning-changed
Original:  In this paper the average dynamics  is introduced as an heuristic tool to construct models of reaction networks with their qualitative properties explored in terms of connectivity properties of the Interaction Graphs for the ODEs, the Markov chain and the resulting average dynamics.
Revised:  Here we transfer this graph concept to the average dynamics is introduced as an heuristic tool to construct models of reaction networks with their qualitative properties explored in terms of connectivity properties of the Interaction Graphs for the ODEs, the Markov chain and the resulting average dynamics.
Edit intent: meaning-changed
Original:  In this paper the average dynamics  is introduced as an heuristic tool to construct models of reaction networks with their qualitative properties explored in terms of connectivity properties of the Interaction Graphs for the ODEs, the Markov chain and the resulting average dynamics.
Revised:  In this paper the average dynamics , which itself is introduced as an heuristic tool to construct models of reaction networks with their qualitative properties explored in terms of connectivity properties of the Interaction Graphs for the ODEs, the Markov chain and the resulting average dynamics.
Edit intent: meaning-changed
Original:  In this paper the average dynamics  is introduced as an heuristic tool to construct models of reaction networks with their qualitative properties explored in terms of connectivity properties of the Interaction Graphs for the ODEs, the Markov chain and the resulting average dynamics.
Revised:  In this paper the average dynamics is introduced as an heuristic tool to construct models of reaction or contact networks. The graphical concepts introduced form the basis for any subsequent study of the qualitative properties of hybrid models in terms of connectivity properties of the Interaction Graphs for the ODEs, the Markov chain and the resulting average dynamics.
Edit intent: meaning-changed
Original:  In this paper the average dynamics  is introduced as an heuristic tool to construct models of reaction networks with their qualitative properties explored in terms of connectivity properties of the Interaction Graphs for the ODEs, the Markov chain and the resulting average dynamics. In a second paper the question of stability, robustness and a possible modular structure of the complex system defined by the average dynamics is addressed .
Revised:  In this paper the average dynamics is introduced as an heuristic tool to construct models of reaction networks with their qualitative properties explored in terms of connectivity and (feedback) loop formation .
Edit intent: clarity
Original:  Our agents form opinions and invest , based on three sources of information, (i) public information, i.e. news, (ii) information from their "friendship" network , promoting imitation and (iii) private information.
Revised:  Our agents invest according to their opinion on future price movements, which is based on three sources of information, (i) public information, i.e. news, (ii) information from their "friendship" network , promoting imitation and (iii) private information.
Edit intent: meaning-changed
Original:  Our agents form opinions and invest , based on three sources of information, (i) public information, i.e. news, (ii) information from their "friendship" network , promoting imitation and (iii) private information.
Revised:  Our agents form opinions and invest , based on three sources of information, (i) public information, i.e. news, (ii) information from their "friendship" network and (iii) private information.
Edit intent: coherence
Original:  We find that rallies and crashes occur as amplifications of random lucky or unlucky streak of news, due to the feedback of  these news on the agents' strategies into collective transient herding regimes.
Revised:  We find that rallies and crashes occur as amplifications of random lucky or unlucky streak of news, due to the feedback of positive news, which, due to a feedback mechanism of these news on the agents' strategies into collective transient herding regimes.
Edit intent: meaning-changed
Original:  We find that rallies and crashes occur as amplifications of random lucky or unlucky streak of news, due to the feedback of  these news on the agents' strategies into collective transient herding regimes.
Revised:  We find that rallies and crashes occur as amplifications of random lucky or unlucky streak of news, due to the feedback of these news on the agents' strategies develop into a transient collective herding regime. After this self-amplified exuberance, the price has reached an unsustainable high value, being corrected by a crash, which brings the price even below its fundamental value.
Edit intent: meaning-changed
Original:  Paradoxically, it is the attempt for investors to learn the level of relevance of the news on the price formation which leads to a dramatic amplification of the price volatility due to their collective search for the "truth" . A positive feedback loop is created by the two dominating mechanisms ( Bayesian learning and imitation) which, by reinforcing each other, result in rallies and crashes.
Revised:  Paradoxically, it is the attempt for investors to adapt to the current market regime which leads to a dramatic amplification of the price volatility due to their collective search for the "truth" . A positive feedback loop is created by the two dominating mechanisms ( Bayesian learning and imitation) which, by reinforcing each other, result in rallies and crashes.
Edit intent: clarity
Original:  Paradoxically, it is the attempt for investors to learn the level of relevance of the news on the price formation which leads to a dramatic amplification of the price volatility due to their collective search for the "truth" . A positive feedback loop is created by the two dominating mechanisms ( Bayesian learning and imitation) which, by reinforcing each other, result in rallies and crashes.
Revised:  Paradoxically, it is the attempt for investors to learn the level of relevance of the news on the price formation which leads to a dramatic amplification of the price volatility . A positive feedback loop is created by the two dominating mechanisms ( Bayesian learning and imitation) which, by reinforcing each other, result in rallies and crashes.
Edit intent: clarity
Original:  Paradoxically, it is the attempt for investors to learn the level of relevance of the news on the price formation which leads to a dramatic amplification of the price volatility due to their collective search for the "truth" . A positive feedback loop is created by the two dominating mechanisms ( Bayesian learning and imitation) which, by reinforcing each other, result in rallies and crashes.
Revised:  Paradoxically, it is the attempt for investors to learn the level of relevance of the news on the price formation which leads to a dramatic amplification of the price volatility due to their collective search for the "truth" . A positive feedback loop is created by the two dominating mechanisms ( adaptation and imitation) which, by reinforcing each other, result in rallies and crashes.
Edit intent: meaning-changed
Original:  Paradoxically, it is the attempt for investors to learn the level of relevance of the news on the price formation which leads to a dramatic amplification of the price volatility due to their collective search for the "truth" . A positive feedback loop is created by the two dominating mechanisms ( Bayesian learning and imitation) which, by reinforcing each other, result in rallies and crashes.
Revised:  Paradoxically, it is the attempt for investors to learn the level of relevance of the news on the price formation which leads to a dramatic amplification of the price volatility due to their collective search for the "truth" . A positive feedback loop is created by the two dominating mechanisms ( Bayesian learning and imitation) which, by reinforcing each other, result in bubbles and crashes.
Edit intent: clarity
Original:  The model offers a simple reconciliation of the two opposite (herding versus fundamental) proposals for the origin of crashes within a single framework and justifies the existence of two populations in the distribution of returns, exemplifying the concept that rallies and crashes are qualitatively different from the rest of the price moves.
Revised:  The model offers a simple reconciliation of the two opposite (herding versus fundamental) proposals for the origin of crashes within a single framework and justifies the existence of two populations in the distribution of returns, exemplifying the concept that crashes are qualitatively different from the rest of the price moves.
Edit intent: clarity
Original:  Although the enzymes involved in multiple glycan substrates indicate cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis could be confined within a moderate range by transcriptional-level control.
Revised:  Although the enzymes act on multiple glycan substrates indicate cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis could be confined within a moderate range by transcriptional-level control.
Edit intent: clarity
Original:  Although the enzymes involved in multiple glycan substrates indicate cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis could be confined within a moderate range by transcriptional-level control.
Revised:  Although the enzymes involved in multiple glycan substrates , indicating cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis could be confined within a moderate range by transcriptional-level control.
Edit intent: fluency
Original:  Although the enzymes involved in multiple glycan substrates indicate cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis could be confined within a moderate range by transcriptional-level control.
Revised:  Although the enzymes involved in multiple glycan substrates indicate cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis may be confined within a moderate range by transcriptional-level control.
Edit intent: fluency
Original:  Although the enzymes involved in multiple glycan substrates indicate cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis could be confined within a moderate range by transcriptional-level control. Our findings give the experimentally-testable predictions for glycosylation processes, and can be potentially applied to the engineering of therapeutic glycoproteins .
Revised:  Although the enzymes involved in multiple glycan substrates indicate cross-talk between modules, the impact of the cross-talk on the module-specific enhancement of glycan synthesis could be confined within a moderate range by transcription-level control. The findings of the present study provide experimentally-testable predictions for glycosylation processes, and can be potentially applied to the engineering of therapeutic glycoproteins .
Edit intent: clarity
Original:  Asset backed securities have been extensively criticised for creating a moral hazard in loan issuance by removing any incentive on the lender to ensure that the recipient of such loans could repay them.
Revised: Analysis of the 2007-8 credit crisis has concentrated on issues of relaxed lending standards, and the perception of irrational behaviour by speculative investors in real estate and other assets. Asset backed securities have been extensively criticised for creating a moral hazard in loan issuance by removing any incentive on the lender to ensure that the recipient of such loans could repay them.
Edit intent: meaning-changed
Original:  Asset backed securities have been extensively criticised for creating a moral hazard in loan issuance by removing any incentive on the lender to ensure that the recipient of such loans could repay them.
Revised:  Asset backed securities have been extensively criticised for creating a moral hazard in loan issuance and an associated increase in default risk, by removing the immediate lender's incentive to ensure that the recipient of such loans could repay them.
Edit intent: meaning-changed
Original:  Asset backed securities have been extensively criticised for creating a moral hazard in loan issuance by removing any incentive on the lender to ensure that the recipient of such loans could repay them. However the inter-relationship between money and loans within the commercial banking system, also suggests that deeper systemic issues can accompany any form of unlimited commercial bank lending .
Revised:  Asset backed securities have been extensively criticised for creating a moral hazard in loan issuance by removing any incentive on the lender to ensure that the underlying loans could be repaid. However significant monetary issues can accompany any form of unlimited commercial bank lending .
Edit intent: clarity
Original:  However the inter-relationship between money and loans within the commercial banking system, also suggests that deeper systemic issues can accompany any form of unlimited commercial bank lending . This paper examines the question of why speculative demand for credit during credit bubbles is not naturally constrained by regulated limits within the banking system on the supply of loans. In particular, we propose an explanation for the current credit crisis, as a fundamental, systemic failure in the system of rules governing the behaviour of the reserve based banking system, which have resulted in the effective removal of central bank control over money and loan supply expansion.
Revised:  However the inter-relationship between money and loans within the commercial banking system, also suggests that deeper systemic issues can accompany any form of unlimited commercial bank lending , and these appear to have been overlooked by this analysis. In this paper we propose a general explanation for credit crises based on an examination of the mechanics of the banking system, which have resulted in the effective removal of central bank control over money and loan supply expansion.
Edit intent: clarity