wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The expansion is performed around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors before in a Brownian setup  .
Revised:  The expansion is performed around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors in a Brownian setup .
Edit intent: coherence
Original:  The expansion is performed around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors before in a Brownian setup  .
Revised:  The expansion is performed around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors before in a Brownian setup before .
Edit intent: coherence
Original:  In the case of a finite jump measure with a bounded intensity, one can also handle a state-dependent intensity process, which is quite relevant for many practical applications  .
Revised:  In the case of a finite jump measure with a bounded intensity, one can also handle a state-dependent intensity process, which is quite relevant for many practical applications . Based on the stability result, we also provide a rigorous justification to use arbitrarily smooth coefficients in FBSDEs for any approximation purpose whenever rather mild conditions are satisfied .
Edit intent: meaning-changed
Original: The paper develops an asymptotic expansion method for forward-backward SDEs (FBSDEs)  driven by the random Poisson measures with sigma-finite compensators .
Revised: The paper develops an asymptotic expansion method for forward-backward SDEs (FBSDEs) with jumps. In particular, we construct an asymptotic expansion method for FBSDEs driven by the random Poisson measures with sigma-finite compensators .
Edit intent: meaning-changed
Original: The paper develops an asymptotic expansion method for forward-backward SDEs (FBSDEs)  driven by the random Poisson measures with sigma-finite compensators . The expansion is performed  around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors in a Brownian setup before.
Revised: The paper develops an asymptotic expansion method for forward-backward SDEs (FBSDEs) driven by the random Poisson measures with \sigma around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors in a Brownian setup before.
Edit intent: clarity
Original:  The expansion is performed  around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors in a Brownian setup before.
Revised:  The expansion is performed -finite compensators as well as the standard Brownian motions around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors in a Brownian setup before.
Edit intent: meaning-changed
Original:  The expansion is performed  around the small-variance limit of the forward SDE and does not necessarily require a small size of the non-linearity in the BSDE's driver, which was actually the case for the linearization method proposed by the current authors in a Brownian setup before. A semi-analytic solution technique , which only requires a system of ODEs (one is non-linear and the others are linear) to be solved, as well as its error estimate are provided .
Revised:  The expansion is performed around the small-variance limit of the forward SDE . We provide a semi-analytic solution technique , which only requires a system of ODEs (one is non-linear and the others are linear) to be solved, as well as its error estimate are provided .
Edit intent: coherence
Original:  A semi-analytic solution technique , which only requires a system of ODEs (one is non-linear and the others are linear) to be solved, as well as its error estimate are provided .
Revised:  A semi-analytic solution technique , which only requires a system of ODEs (one is non-linear and the others are linear) to be solved, as well as its error estimate for which we only need to solve essentially a system of linear ODEs .
Edit intent: meaning-changed
Original:  In the case of a finite jump measure with a bounded intensity, the method can also handle sate-dependent ( and hence non-Poissonian ) jumps, which are quite relevant for many practical applications . Based on the stability result, we also provide a rigorous justification to use arbitrarily smooth coefficients in FBSDEs for any approximation purpose whenever rather mild conditions are satisfied .
Revised:  In the case of a finite jump measure with a bounded intensity, the method can also handle state-dependent and hence non-Poissonian ) jumps, which are quite relevant for many practical applications . Based on the stability result, we also provide a rigorous justification to use arbitrarily smooth coefficients in FBSDEs for any approximation purpose whenever rather mild conditions are satisfied .
Edit intent: fluency
Original:  In the case of a finite jump measure with a bounded intensity, the method can also handle sate-dependent ( and hence non-Poissonian ) jumps, which are quite relevant for many practical applications . Based on the stability result, we also provide a rigorous justification to use arbitrarily smooth coefficients in FBSDEs for any approximation purpose whenever rather mild conditions are satisfied .
Revised:  In the case of a finite jump measure with a bounded intensity, the method can also handle sate-dependent ( and hence non-Poissonian jumps, which are quite relevant for many practical applications . Based on the stability result, we also provide a rigorous justification to use arbitrarily smooth coefficients in FBSDEs for any approximation purpose whenever rather mild conditions are satisfied .
Edit intent: fluency
Original:  In the case of a finite jump measure with a bounded intensity, the method can also handle sate-dependent ( and hence non-Poissonian ) jumps, which are quite relevant for many practical applications . Based on the stability result, we also provide a rigorous justification to use arbitrarily smooth coefficients in FBSDEs for any approximation purpose whenever rather mild conditions are satisfied .
Revised:  In the case of a finite jump measure with a bounded intensity, the method can also handle sate-dependent ( and hence non-Poissonian ) jumps, which are quite relevant for many practical applications .
Edit intent: coherence
Original:  While randomly selecting a subset of securities from the index increases the chance of outperforming the index, it also increases the chance of underperforming the index , with the frequency of underperformance being larger than the frequency of overperformance .
Revised:  Randomly selecting a subset of securities from the index increases the chance of outperforming the index, it also increases the chance of underperforming the index , with the frequency of underperformance being larger than the frequency of overperformance .
Edit intent: coherence
Original:  While randomly selecting a subset of securities from the index increases the chance of outperforming the index, it also increases the chance of underperforming the index , with the frequency of underperformance being larger than the frequency of overperformance .
Revised:  While randomly selecting a subset of securities from the index may dramatically increase the chance of outperforming the index, it also increases the chance of underperforming the index , with the frequency of underperformance being larger than the frequency of overperformance .
Edit intent: clarity
Original:  While randomly selecting a subset of securities from the index increases the chance of outperforming the index, it also increases the chance of underperforming the index , with the frequency of underperformance being larger than the frequency of overperformance .
Revised:  While randomly selecting a subset of securities from the index increases the chance of underperforming the index , with the frequency of underperformance being larger than the frequency of overperformance .
Edit intent: clarity
Original: Coverage prediction is one of the most important aspects of cellular network optimization for a mobile operator . Spatial statistics can be used for coverage prediction. This approach is based on the collected geo-located measurements performed usually by drive test campaigns.
Revised: Coverage optimization is an important process for the operator as it is a crucial prerequisite towards offering a satisfactory quality of service to the end-users. The first step of this process is coverage prediction, which can be performed by interpolating geo-located measurements performed usually by drive test campaigns.
Edit intent: clarity
Original:  This approach is based on the collected geo-located measurements performed usually by drive test campaigns. Notice that uncertainty in reporting the location can result in inaccurate coverage prediction . In urban environments the location error can reach important levels up to 30 m using the Global Positioning System (GPS) . In this paper, we propose to consider the location uncertainty in the spatial prediction technique.
Revised:  This approach is based on the collected geo-located measurements reported to the network by mobile users' equipments. In previous works, we proposed a low complexity coverage prediction algorithm based on the adaptation of the Geo-statistics Fixed Rank Kriging (FRK) algorithm. We supposed that the geo-location information reported with the radio measurements was perfect, which is not the case in reality. In this paper, we propose to consider the location uncertainty in the spatial prediction technique.
Edit intent: meaning-changed
Original:  In urban environments the location error can reach important levels up to 30 m using the Global Positioning System (GPS) . In this paper, we propose to consider the location uncertainty in the spatial prediction technique. We focus also on the complexity problem. We therefore propose to use the Fixed Rank Kriging (FRK) as spatial prediction technique .
Revised:  In urban environments the location error can reach important levels up to 30 m using the Global Positioning System (GPS) . In this paper, we study the impact of location uncertainty on the coverage prediction accuracy and we extend the previously proposed algorithm to include geo-location error in the prediction model .
Edit intent: clarity
Original:  We validate the model using a field-like dataset obtained from a sophisticated simulation/planning tool. With the location uncertainty , the FRK proves to reduce the computational complexity of the spatial interpolation while keeping an acceptable prediction error .
Revised:  We validate the proposed algorithm using both simulated and real field measurements. The FRK extended to take into account the location uncertainty , the FRK proves to reduce the computational complexity of the spatial interpolation while keeping an acceptable prediction error .
Edit intent: meaning-changed
Original:  In this work we propose an alternative method to measure the dipolar repulsion for neutral lipid monolayers. The procedure is based on the comparison of the radial distribution function , g(r), from experiments and Brownian dynamic (BD) simulations .
Revised:  In this work we propose an alternative method to measure the dipolar repulsion using a passive method that involves the analysis of images of the monolayer with phase coexistence. The method is based on the comparison of the radial distribution function , g(r), from experiments and Brownian dynamic (BD) simulations .
Edit intent: meaning-changed
Original:  The procedure is based on the comparison of the radial distribution function , g(r), from experiments and Brownian dynamic (BD) simulations . The domains were modeled as disks with surface dipolar density, whose strength was varied to best describe the experimentally determined monolayer structure. For comparison, the point dipole approximation was also studied.
Revised:  The procedure is based on the comparison of the pair correlation function obtained from experiments with that obtained from Brownian dynamics simulations of a model system.
Edit intent: meaning-changed
Original:  As an example, we applied the method for mixed monolayers with different proportions of distearoylphosphatidylcholine (DSPC) and dimyristoylphosphatidylcholine (DMPC ) and obtained the excess dipolar density, which were in agreement with those obtained from surface potential measurements .
Revised:  As an example, we determined the difference in dipolar density of a binary monolayer of DSPC/DMPC at the air-water interface from the analysis of the radial distribution of domains, and the results are compared with those obtained from surface potential measurements .
Edit intent: meaning-changed
Original:  As an example, we applied the method for mixed monolayers with different proportions of distearoylphosphatidylcholine (DSPC) and dimyristoylphosphatidylcholine (DMPC ) and obtained the excess dipolar density, which were in agreement with those obtained from surface potential measurements .
Revised:  As an example, we applied the method for mixed monolayers with different proportions of distearoylphosphatidylcholine (DSPC) and dimyristoylphosphatidylcholine (DMPC ) and obtained the excess dipolar density, which were in agreement with those obtained by surface potential determinations .
Edit intent: clarity
Original:  Then, we study several structural properties of the minimum cost path of the maximum queue length . Given these properties, we prove that the maximum queue length follows a sample path with linear increment.
Revised:  Then, we study several structural properties of the minimum-cost-path to overflow of the queue with the largest length, which is basically equivalent to the decay rate of the largest queue-overflow probability . Given these properties, we prove that the maximum queue length follows a sample path with linear increment.
Edit intent: meaning-changed
Original: In this paper, we define weighted directed networks for large panels of financial time series where the edges and the associated weights are reflecting the dynamic conditional correlation structure of the panel. Those networks produce a most informative picture of the interconnections among the various series in the panel .
Revised: In this paper, we define weighted directed networks for analysing, over the period 2000-2013, the interdependencies between volatilities of a large panel of stocks belonging to the S\&P100 index .
Edit intent: meaning-changed
Original:  In particular, we are combining this network-based analysis and a general dynamic factor decomposition in a study of the volatilities of the stocks of the Standard \&Poor's 100 index over the period 2000-2013. This approach allows us to decompose the panel into two components which represent the two main sources of variation of financial time series: common or market shocks, and the stock-specific or idiosyncratic ones. While the common components, driven by market shocks, are related to the non-diversifiable or {\it systematic } components of risk, the idiosyncratic components show important interdependencies which are nicely described through network structures.
Revised:  In particular, we focus on the so-called {\it systematic } components of risk, the idiosyncratic components show important interdependencies which are nicely described through network structures.
Edit intent: clarity
Original:  While the common components, driven by market shocks, are related to the non-diversifiable or {\it systematic } components of risk, the idiosyncratic components show important interdependencies which are nicely described through network structures.
Revised:  While the common components, driven by market shocks, are related to the non-diversifiable or {\it Long-Run Variance Decomposition Network } components of risk, the idiosyncratic components show important interdependencies which are nicely described through network structures.
Edit intent: meaning-changed
Original:  While the common components, driven by market shocks, are related to the non-diversifiable or {\it systematic } components of risk, the idiosyncratic components show important interdependencies which are nicely described through network structures. Those networks shed some light on the contagion phenomenons associated with financial crises, and help assessing how {\it systemic} a given firm islikely to be.
Revised:  While the common components, driven by market shocks, are related to the non-diversifiable or {\it systematic } (LVDN), where the nodes are stocks, and the weight associated with edge (i,j) represents the proportion of h-step-ahead forecast error variance of variable i accounted for by variable j's innovations. To overcome the curse of dimensionality, we decompose the panel into a component driven by few global, market-wide, factors, and an idiosyncratic one modelled by means of a sparse vector autoregression (VAR) model. Inversion of the VAR together with suitable identification restrictions, produces the estimated network, by means of which we can assess how {\it systemic} a given firm islikely to be.
Edit intent: meaning-changed
Original:  Those networks shed some light on the contagion phenomenons associated with financial crises, and help assessing how {\it systemic} a given firm islikely to be.We show how to estimate them by combining dynamic principal components and sparse VAR techniques. The results provide evidence of high positive intra-sectoral and lower, but nevertheless quite important, negative inter-sectoral, dependencies, the Energy and Financials sectors being the most interconnected ones. In particular, the Financials stocks appear to be the most central vertices in the network, making them the main source of contagion .
Revised:  Those networks shed some light on the contagion phenomenons associated with financial crises, and help assessing how {\it systemic} each firm is.~Our analysis demonstrates the prominent role of financial firms as sources of contagion, especially during the~2007-2008 crisis .
Edit intent: clarity
Original: We consider the problem of finding investment strategies that maximize the average growth-rate of the capital of an investor. This is usually achieved through the so-called Kelly criterion, which in a dynamic setting where investment decisions are adjusted over time, prescribes a constant optimal fraction of capital that should be re-invested at each time, i.e. the investor's optimal leverage .
Revised: We consider the problem of finding investment strategies that maximize the average growth-rate of multiplicative stochastic processes. For a geometric Brownian motion the problem is solved through the so-called Kelly criterion, which in a dynamic setting where investment decisions are adjusted over time, prescribes a constant optimal fraction of capital that should be re-invested at each time, i.e. the investor's optimal leverage .
Edit intent: meaning-changed
Original:  This is usually achieved through the so-called Kelly criterion, which in a dynamic setting where investment decisions are adjusted over time, prescribes a constant optimal fraction of capital that should be re-invested at each time, i.e. the investor's optimal leverage .
Revised:  This is usually achieved through the so-called Kelly criterion, according to which the optimal growth rate is achieved by investing a constant given fraction of resources at any step of the dynamics .
Edit intent: clarity
Original:  We generalize this problem by accounting for the effects of market impact, that is the fact that prices respond to trading activity. In particular, we assume that the value of an investment portfolio should be measured in terms of the cash-flow that can be generated by liquidating the portfolio, rather than by its mark-to-market value .
Revised:  We generalize these finding to the case of dynamical equations with finite carrying capacity, which can find applications in biology, mathematical ecology, and finance .
Edit intent: meaning-changed
Original: We prove that the Omega measure, which considers all moments when assessing portfolio performance, is equivalent to the widely used Sharpe ratio under a jointly normal distribution . Portfolio optimization of the Sharpe ratio is  explored, with a novel active-set algorithm presented for markets prohibiting short sales.
Revised: We prove that the Omega measure, which considers all moments when assessing portfolio performance, is equivalent to the widely used Sharpe ratio under jointly elliptic distributions of returns . Portfolio optimization of the Sharpe ratio is explored, with a novel active-set algorithm presented for markets prohibiting short sales.
Edit intent: meaning-changed
Original: We prove that the Omega measure, which considers all moments when assessing portfolio performance, is equivalent to the widely used Sharpe ratio under a jointly normal distribution . Portfolio optimization of the Sharpe ratio is  explored, with a novel active-set algorithm presented for markets prohibiting short sales.
Revised: We prove that the Omega measure, which considers all moments when assessing portfolio performance, is equivalent to the widely used Sharpe ratio under a jointly normal distribution . Portfolio optimization of the Sharpe ratio is then explored, with a novel active-set algorithm presented for markets prohibiting short sales.
Edit intent: coherence
Original: We prove that the Omega measure, which considers all moments when assessing portfolio performance, is equivalent to the widely used Sharpe ratio under a jointly normal distribution . Portfolio optimization of the Sharpe ratio is  explored, with a novel active-set algorithm presented for markets prohibiting short sales.
Revised: We prove that the Omega measure, which considers all moments when assessing portfolio performance, is equivalent to the widely used Sharpe ratio under a jointly normal distribution . Portfolio optimization of the Sharpe ratio is explored, with an active-set algorithm presented for markets prohibiting short sales.
Edit intent: clarity
Original: The direct-coupling analysis is a powerful method for protein contact prediction, and enables us to extract ``direct'' correlations between distant sites that are latent in ``indirect'' correlations observed in a protein multiple-sequence alignment.
Revised: The direct-coupling analysis is a powerful method for protein contact prediction, and enables us to extract "direct" correlations between distant sites that are latent in ``indirect'' correlations observed in a protein multiple-sequence alignment.
Edit intent: fluency
Original: The direct-coupling analysis is a powerful method for protein contact prediction, and enables us to extract ``direct'' correlations between distant sites that are latent in ``indirect'' correlations observed in a protein multiple-sequence alignment.
Revised: The direct-coupling analysis is a powerful method for protein contact prediction, and enables us to extract ``direct'' correlations between distant sites that are latent in "indirect" correlations observed in a protein multiple-sequence alignment.
Edit intent: fluency