wanyu/IteraTeR_full_sent huggingface dataset:
Original: Financial data give an opportunity to uncover the non-stationarity which may be hidden in many single time-series. Five years of daily Euro/ Dollar trading records in the about three hours following the New York opening session are shown to give an accurate ensemble representation of the self-similar ,
Revised: A central problem of Quantitative Finance is that of formulating a probabilistic model of the time evolution of asset prices allowing reliable predictions on their future volatility. As in several natural phenomena, the predictions of such a model must be compared with the data of a single process realization in our records. In order to give statistical significance to such a comparison, assumptions of stationarity for some quantities extracted from the single historical time series, like the distribution of the returns over a given time interval, cannot be avoided. Such assumptions entail the risk of masking or misrepresenting non-stationarities of the underlying process, and of giving an incorrect account of its correlations. Here we overcome this difficulty by showing that five years of daily Euro/ Dollar trading records in the about three hours following the New York opening session are shown to give an accurate ensemble representation of the self-similar ,
Edit intent: coherence
Original:  Five years of daily Euro/ Dollar trading records in the about three hours following the New York opening session are shown to give an accurate ensemble representation of the self-similar ,
Revised:  Five years of daily Euro/ Dollar trading records in the about three hours following the New York market opening, provide a rich enough ensemble of histories. The statistics of this ensemble allows to propose and test an adequate model of the stochastic process driving the exchange rate. This turns out to be a non-Markovian, self-similar ,
Edit intent: meaning-changed
Original:  Five years of daily Euro/ Dollar trading records in the about three hours following the New York opening session are shown to give an accurate ensemble representation of the self-similar , non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Revised:  Five years of daily Euro/ Dollar trading records in the about three hours following the New York opening session are shown to give an accurate ensemble representation of the self-similar PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Edit intent: clarity
Original:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Revised:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics %DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Edit intent: clarity
Original:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Revised:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Edit intent: clarity
Original:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Revised:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 %DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Edit intent: coherence
Original:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Revised:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% process with non-stationary returns. The empirical ensemble correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Edit intent: clarity
Original:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in remarkable agreement with model predictions based only on the anomalous scaling form of the logarithmic return distribution.
Revised:  non-Markovian stochastic process with nonstationary increments recently conjectured to generally underlie financial assets dynamics PNAS%DIFDELCMD < {\bf %%% 104 , 19741 (2007)%DIFDELCMD < ]%%% . Introducing novel quantitative tools in the analysis of non-Markovian time-series we show that empirical non-linear correlators are in agreement with the predictions of this model, which is constructed on the basis of the time-inhomogeneous, anomalous scaling obeyed by the return distribution.
Edit intent: clarity
Original:  In this paper we "decode" the index option market and extract this correlation information in order to extend the multi-asset version of Dupire's "local volatility" model  . A "local correlation" model (LCM) is introduced for the pricing of multi-asset derivatives  . LCM achieves consistency with both the constituent- and index option markets by construction while preserving the efficiency and easy implementation of Dupire's model.
Revised:  In this paper we "decode" the index option market and extract this correlation information in order to extend the multi-asset version of Dupire's "local volatility" model by making correlations a dynamic variable of the market . A "local correlation" model (LCM) is introduced for the pricing of multi-asset derivatives . LCM achieves consistency with both the constituent- and index option markets by construction while preserving the efficiency and easy implementation of Dupire's model.
Edit intent: meaning-changed
Original:  In this paper we "decode" the index option market and extract this correlation information in order to extend the multi-asset version of Dupire's "local volatility" model  . A "local correlation" model (LCM) is introduced for the pricing of multi-asset derivatives  . LCM achieves consistency with both the constituent- and index option markets by construction while preserving the efficiency and easy implementation of Dupire's model.
Revised:  In this paper we "decode" the index option market and extract this correlation information in order to extend the multi-asset version of Dupire's "local volatility" model . A "local correlation" model (LCM) is introduced for the pricing of multi-asset derivatives . We show how consistency with the index volatility data can be achieved by construction . LCM achieves consistency with both the constituent- and index option markets by construction while preserving the efficiency and easy implementation of Dupire's model.
Edit intent: clarity
Original:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Revised:  At the biological level, such membrane domains have been shown to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Edit intent: fluency
Original:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Revised:  At the biological level, such membrane domains have been demonstrated to be specialized, in order to perform a determined biological task, in the sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Edit intent: clarity
Original:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Revised:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they gather one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Edit intent: fluency
Original:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Revised:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species out of the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Edit intent: clarity
Original:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Revised:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different ones that a cell membrane may contain , in order to perform a determined biological task .
Edit intent: clarity
Original:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain , in order to perform a determined biological task .
Revised:  At the biological level, such membrane domains have been demonstrated to be specialized, in that sense that they congregate one or a few protein species amongst the hundreds of different species that a cell membrane may contain .
Edit intent: clarity
Original:  By analyzing the balance between mixing entropy and protein affinities, we elucidate by statistical mechanics arguments how such protein sorting in distinct domains can be explained without appealing to pre-existing lipidic micro-phase separations, as in the lipid raft scenario.
Revised:  By analyzing the balance between mixing entropy and protein affinities, we propose that such protein sorting in distinct domains can be explained without appealing to pre-existing lipidic micro-phase separations, as in the lipid raft scenario.
Edit intent: clarity
Original: We derive probabilistic representations for the probability density function of the arbitrage price of a financial asset and the price of European call and put options in a linear stochastic volatility model with correlated Brownian noises.
Revised: We derive probabilistic representations for the probability density function of the arbitrage price of a financial asset and the price of European call and put options in a linear stochastic volatility models with correlated Brownian noises.
Edit intent: fluency
Original:  Examples of such models are considered, including a log-normal stochastic volatility model . In all examples a closed formula for the density function is given. In the Appendix we present a conditional version of the Donati-Martin and Yor formula .
Revised:  Examples of such models are considered, including a log-normal stochastic volatility model and Heston stochastic volatility model. For a linear stochastic volatility model we derive representations for the probability density function of the arbitrage price of a financial asset and the prices of European call and put options. A closed-form formulae for the density function and the prices of European call and put options are given for log-normal stochastic volatility model. We also obtain present some new results for Heston and extended Heston stochastic volatility models .
Edit intent: meaning-changed
Original: Boolean networks are discrete dynamical systems in which the state (zero or one) of each network node at time t is updated to a state determined by the states at time t-1 of those nodes that have links to it.
Revised: Boolean networks are discrete dynamical systems in which the state (zero or one) of each node is updated at each time t to a state determined by the states at time t-1 of those nodes that have links to it.
Edit intent: clarity
Original:  A canalizing update rule is one for which the node state at time t is determined by the state at time t-1 of a particular one of its inputs when that input is in its canalizing state.
Revised:  A canalizing rule is one for which the node state at time t is determined by the state at time t-1 of a particular one of its inputs when that input is in its canalizing state.
Edit intent: clarity
Original:  A canalizing update rule is one for which the node state at time t is determined by the state at time t-1 of a particular one of its inputs when that input is in its canalizing state.
Revised:  A canalizing update rule is one for which a node state at time t is determined by the state at time t-1 of a particular one of its inputs when that input is in its canalizing state.
Edit intent: fluency
Original:  A canalizing update rule is one for which the node state at time t is determined by the state at time t-1 of a particular one of its inputs when that input is in its canalizing state.
Revised:  A canalizing update rule is one for which the node state at time t is determined by the state at time t-1 of a single one of its inputs when that input is in its canalizing state.
Edit intent: clarity
Original:  A canalizing update rule is one for which the node state at time t is determined by the state at time t-1 of a particular one of its inputs when that input is in its canalizing state.
Revised:  A canalizing update rule is one for which the node state at time t is determined by the state at time t-1 of a particular one of its inputs when that inputting node is in its canalizing state.
Edit intent: clarity
Original:  In this paper, we introduce a generalized concept of canalization that we believe offers a significant enhancement in biological relevance, and we obtain a simple general network stability criterion for Boolean networks with generalized canalization for a broad class of network topologies .
Revised:  Previous work on the order/disorder transition in Boolean networks considered complex, non-random network topology. In the current paper we extend this previous work to account for canalizing behavior .
Edit intent: meaning-changed
Original: Human olfactory receptor , OR1D2, binds to Bourgeonal , a chemical constituent of the mythical flower, Lily of the valley or Our Lady's tears (1) . OR1D2, OR1D4 and OR1D5 are three full length olfactory receptors present in an olfactory locus in human genome.
Revised: Ligands for only two human olfactory receptors are known. One of them , OR1D2, binds to Bourgeonal , a chemical constituent of the mythical flower, Lily of the valley or Our Lady's tears (1) . OR1D2, OR1D4 and OR1D5 are three full length olfactory receptors present in an olfactory locus in human genome.
Edit intent: meaning-changed
Original: Human olfactory receptor , OR1D2, binds to Bourgeonal , a chemical constituent of the mythical flower, Lily of the valley or Our Lady's tears (1) . OR1D2, OR1D4 and OR1D5 are three full length olfactory receptors present in an olfactory locus in human genome.
Revised: Human olfactory receptor , OR1D2, binds to Bourgeonal Malnic B, Godfrey P-A, Buck L-B (2004) The human olfactory receptor gene family. Proc. Natl. Acad. Sci U. S. A. 101: 2584-2589 and Erratum in: Proc Natl Acad Sci U. S. A. (2004) 101: 7205 . OR1D2, OR1D4 and OR1D5 are three full length olfactory receptors present in an olfactory locus in human genome.
Edit intent: meaning-changed
Original:  Apparently, these mismatch positions show no striking pattern using computer pattern recognition tools. In an attempt to find a mathematical rule in those mismatches, we find that L-system generated sequence can be inserted into the OR1D2 subfamily specific star model and novel full length olfactory receptors can be generated.
Revised:  We have used L-system generated sequence can be inserted into the OR1D2 subfamily specific star model and novel full length olfactory receptors can be generated.
Edit intent: coherence
Original:  In an attempt to find a mathematical rule in those mismatches, we find that L-system generated sequence can be inserted into the OR1D2 subfamily specific star model and novel full length olfactory receptors can be generated. This remarkable mathematical principle could be utilized for making new subfamily OR members from any OR subfamily. Aroma and electronic nose industry might utilize this rule in a large way in near future .
Revised:  In an attempt to find a mathematical rule in those mismatches, we find that L-system mathematics and have been able to show a closely related subfamily of OR1D2, OR1D4 and OR1D5 .
Edit intent: meaning-changed
Original:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 7, 8, 10 ] for short time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price  .
Revised:  To obtain the formulas we use the Dyson-Taylor commutator method that we have recently developed in [ 7, 8, 10 ] for short time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price .
Edit intent: clarity
Original:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 7, 8, 10 ] for short time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price  .
Revised:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 5, 6, 8 ] for short time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price .
Edit intent: meaning-changed
Original:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 7, 8, 10 ] for short time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price  .
Revised:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 7, 8, 10 ] for short-time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price .
Edit intent: fluency
Original:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 7, 8, 10 ] for short time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price  .
Revised:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 7, 8, 10 ] for short time asymptotic expansions of heat kernels, and obtain a family of general closed-form approximate solutions for both the pricing kernel and derivative price .
Edit intent: clarity
Original:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 7, 8, 10 ] for short time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price  .
Revised:  To obtain the formulas we use the Dyson-Taylor commutator method re- cently developed in [ 7, 8, 10 ] for short time asymptotic expansions of heat kernels, and obtain a family of general explicit closed form approx- imate solutions for both the pricing kernel and derivative price . A bootstrap scheme allows us to extend our method to large time .
Edit intent: meaning-changed
Original:  For utility functions finite on the  real line, our class represents a minimal set containing simple strategies which also contains the optimizer, under conditions that are substantially milder than the celebrated reasonable asymptotic elasticity condition on the utility function .
Revised:  For utility functions finite on the whole real line, our class represents a minimal set containing simple strategies which also contains the optimizer, under conditions that are substantially milder than the celebrated reasonable asymptotic elasticity condition on the utility function .
Edit intent: meaning-changed