wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Pooling and sharing resources reduces fluctuations, which leaves ensemble averages unchanged but -- contrary to common perception -- increases the time-average growth rate for each cooperator .
Revised:  Pooling and sharing resources reduces fluctuations, which leaves ensemble averages unchanged but -- contrary to common perception -- increases the time-average growth rate for cooperating entities, meaning that cooperators outgrow similar non-cooperators. We identify this increase in growth rate as the net fitness gain, consistent with the concept of geometric mean fitness in the biological literature. This constitutes a fundamental mechanism for the evolution of cooperation. Its minimal assumptions make it a candidate explanation of cooperation in settings too simple for other fitness gains, such as emergent function and specialization, to be probable. One such example is the transition from single cells to early multicellular life .
Edit intent: meaning-changed
Original:  This concept is evaluated by solving the rotational shallow water equations parallel-in-time: We provide speedup benchmarks and an in-depth analysis of our results based on state-plots and a performance model. This allows us to show the applicability of the Parareal approach with the rotational shallow water equations and also to evaluate the limitations of Parareal .
Revised:  This concept is evaluated by a prototypical solver for the rotational shallow-water equations which we use as a representative black-box solver .
Edit intent: meaning-changed
Original:  Changes in DNA methylation patterns reflect environmental risk factors, and are amongst the first pre-disease changes in cancer. Hence, DNA methylation appears highly promising as a basis on which to develop minimally invasive, DNA-based measures of disease risk and prognosis. DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genomic regulatory interactions.
Revised:  DNA methylation is a gene-regulatory pattern, and hence provides a means by which to assess genomic regulatory interactions.
Edit intent: clarity
Original:  The utility of network models also increases as the quantity of data and number of variables increase, as continues to happen in the genome-wide era of biomedical science. We present a DNA methylation-based measure of genomic interaction and association , and we show how to use it to infer prognostic genomic networks.
Revised:  The utility of network models also increases as the quantity of data and number of variables increase, making them increasingly relevant to large-scale genomic studies. We propose methodology to infer prognostic genomic networks from a DNA methylation-based measure of genomic interaction and association , and we show how to use it to infer prognostic genomic networks.
Edit intent: clarity
Original:  We present a DNA methylation-based measure of genomic interaction and association , and we show how to use it to infer prognostic genomic networks. We show how to identify prognostic biomarkers from such networks, which we term `network community oncomarkers'.
Revised:  We present a DNA methylation-based measure of genomic interaction and association . We then show how to identify prognostic biomarkers from such networks, which we term `network community oncomarkers'.
Edit intent: coherence
Original:  By solving the genotype-phentoype (GP) map for RNA secondary structure for systems up to length L=126 nucleotides (where the set of all possible RNA strands would weigh more than the mass of the visible universe) we show that the GP map strongly constrains the evolution of non-coding RNA (ncRNA).
Revised:  By solving the genotype-phenotype (GP) map for RNA secondary structure for systems up to length L=126 nucleotides (where the set of all possible RNA strands would weigh more than the mass of the visible universe) we show that the GP map strongly constrains the evolution of non-coding RNA (ncRNA).
Edit intent: fluency
Original:  Remarkably, simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA  . Since we ignore natural selection, this  close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Revised:  Simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA . Since we ignore natural selection, this close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Edit intent: coherence
Original:  Remarkably, simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA  . Since we ignore natural selection, this  close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Revised:  Remarkably, simple random sampling over genotypes predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA . Since we ignore natural selection, this close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Edit intent: clarity
Original:  Remarkably, simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA  . Since we ignore natural selection, this  close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Revised:  Remarkably, simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA with surprising accuracy . Since we ignore natural selection, this close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Edit intent: clarity
Original:  Remarkably, simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA  . Since we ignore natural selection, this  close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Revised:  Remarkably, simple random sampling over genotypes accurately predicts the distribution of properties such as the mutational robustness or the number of stems per secondary structure found in naturally occurring ncRNA . Since we ignore natural selection, this strikingly close correspondence with the mapping suggests that structures allowing for functionality are easily discovered, despite the enormous size of the genetic spaces.
Edit intent: clarity
Original:  Such strong constraints provide a non-adaptive explanation for the convergent evolution of structures such as the hammerhead ribozyme. ncRNA presents a particularly clear example of bias in the arrival of variation strongly shaping evolutionary outcomes  .
Revised:  Such strong constraints provide a non-adaptive explanation for the convergent evolution of structures such as the hammerhead ribozyme. These results presents a particularly clear example of bias in the arrival of variation strongly shaping evolutionary outcomes .
Edit intent: clarity
Original:  Such strong constraints provide a non-adaptive explanation for the convergent evolution of structures such as the hammerhead ribozyme. ncRNA presents a particularly clear example of bias in the arrival of variation strongly shaping evolutionary outcomes  .
Revised:  Such strong constraints provide a non-adaptive explanation for the convergent evolution of structures such as the hammerhead ribozyme. ncRNA presents a particularly clear example of bias in the arrival of variation strongly shaping evolutionary outcomes and may be relevant to Mayr's distinction between proximate and ultimate causes in evolutionary biology .
Edit intent: meaning-changed
Original:  Using this property, in this article, we describe a quantum computing approach for the alignment of protein-protein interaction networks by following the classical algorithm IsoRank which uses the principal eigenvector of the stochastic matrix representing the Kronecker product of the normalized adjacency matrices of networks for the pairwise alignment.
Revised:  Using the quantum phase estimation algorithm, we introduce a quantum computing approach for the alignment of protein-protein interaction networks by following the classical algorithm IsoRank which uses the principal eigenvector of the stochastic matrix representing the Kronecker product of the normalized adjacency matrices of networks for the pairwise alignment.
Edit intent: meaning-changed
Original:  The properties of the resulting GPO-ABC method are studied using stochastic volatility (SV) models with both synthetic and real-world data . We conclude that the algorithm enjoys: good accuracy comparable to particle Markov chain Monte Carlo with a significant reduction in computational cost and better robustness to noise in the estimates compared with a gradient-based optimisation algorithm. Finally, we make use of GPO-ABC to estimate the Value-at-Risk for a portfolio using a copula model with SV models for the margins .
Revised:  The properties of the resulting GPO-ABC method are studied using stochastic volatility (SV) models with both synthetic and real-world data as well as for estimating the Value-at-Risk for a portfolio using a copula model with SV models for the margins .
Edit intent: coherence
Original:  Finally, we make use of GPO-ABC to estimate the Value-at-Risk for a portfolio using a copula model with SV models for the margins .
Revised:  Finally, we make use of GPO-ABC to estimate the Value-at-Risk for a portfolio using a copula model . We document speed-ups of between one and two orders of magnitude compared to state-of-the-art algorithms for posterior inference .
Edit intent: meaning-changed
Original:  While several recent papers have approached the general question of which properties are elicitable, we suggest that this is the wrong question---all properties are elicitable by first eliciting the entire distribution or data set, and thus the important questionis how elicitable.
Revised:  While several recent papers have asked which properties are elicitable, we suggest that this is the wrong question---all properties are elicitable by first eliciting the entire distribution or data set, and thus the important questionis how elicitable.
Edit intent: clarity
Original:  While several recent papers have approached the general question of which properties are elicitable, we suggest that this is the wrong question---all properties are elicitable by first eliciting the entire distribution or data set, and thus the important questionis how elicitable. Specifically, what is the minimum number of regression parameters needed to compute the property?
Revised:  While several recent papers have approached the general question of which properties are elicitable, we instead advocate for a more nuanced question: how many dimensions are required to indirectly elicit a given property? This number is called the elicitation complexity of the minimum number of regression parameters needed to compute the property?
Edit intent: clarity
Original:  Building on previous work, we introduce a new notion of elicitation complexity and lay the foundations for a calculus of elicitation. We establish several general results and techniques for proving upper and lower bounds on elicitation complexity.
Revised:  Building on previous work, we introduce a new notion of elicitation complexity , including several basic results about how elicitation complexity behaves, and the complexity of standard properties of interest. Building on this foundation, we establish several upper and lower bounds on elicitation complexity.
Edit intent: meaning-changed
Original:  We establish several general results and techniques for proving upper and lower bounds on elicitation complexity. These results provide tight bounds for eliciting the Bayes risk of any loss, a large class of propertieswhich includes spectral risk measuresand several new properties of interest.
Revised:  We establish several general results and techniques for proving upper and lower bounds for the broad class of Bayes risks. We apply these results by proving tight complexity bounds, with respect to identifiable properties, for variance, financial risk measures, entropy, norms, and new properties of interest.
Edit intent: meaning-changed
Original:  We apply these results by proving tight complexity bounds, with respect to identifiable properties , for variance, financial risk measures, entropy, norms, and new properties of interest. We then show how some of these bounds can extend to other practical classes of properties, and conclude with a discussion of open directions.
Revised:  We apply these results by proving tight complexity bounds, with respect to identifiable properties , for variance, financial risk measures, entropy, norms, and several classes of financial risk measures. We conclude with discussion and open directions.
Edit intent: clarity
Original: Martingality plays a crucial role in mathematical finance, in particular arbitrage-freeness of a financial model is guaranteed by the local martingale property of discounted price processes. However, in order to compute prices as conditional expectations the discounted price process has to be a true martingale. If this is not the case, the market and the fundamental (computed) prices deviate, which is interpreted as financial bubble. Moreover, if the discounted price process is a true martingale it can be used to define an equivalent change of measure. Based on general conditions in Kallsen and Shiryaev (2002), we derive explicit sufficient conditions for the true martingality of a wide class of exponentials of semimartingales.
Revised: We give a collection of explicit sufficient conditions for the true martingality of a wide class of exponentials of semimartingales.
Edit intent: coherence
Original:  We illustrate their use for stochastic volatility asset price models driven by semimartingales. Finally, we prove the well-definedness of semimartingale Libor models given by a backward construction .
Revised:  This turns out to be very convenient in financial modeling in general. Especially it allows us to carefully discuss the question of well-definedness of semimartingale Libor models given by a backward construction .
Edit intent: coherence
Original:  For any spectrally negative L\'evy process whose scale functions are well-behaved at 0+, we then study the asymptotics of drawdown quantities when the threshold of drawdown magnitude approaches zero.
Revised:  For any spectrally negative L\'{e process whose scale functions are well-behaved at 0+, we then study the asymptotics of drawdown quantities when the threshold of drawdown magnitude approaches zero.
Edit intent: meaning-changed
Original:  Finally, thanks to the asymptotic results and some recent works on the running maximum of L\'evy processes, we derive the law of duration of drawdowns for a large class of L\'evy processes (with a general spectrally negative part plus a positive compound Poisson structure).
Revised:  Finally, thanks to the asymptotic results and some recent works on the running maximum of L\'{e processes, we derive the law of duration of drawdowns for a large class of L\'evy processes (with a general spectrally negative part plus a positive compound Poisson structure).
Edit intent: meaning-changed
Original:  Finally, thanks to the asymptotic results and some recent works on the running maximum of L\'evy processes, we derive the law of duration of drawdowns for a large class of L\'evy processes (with a general spectrally negative part plus a positive compound Poisson structure).
Revised:  Finally, thanks to the asymptotic results and some recent works on the running maximum of L\'evy processes, we derive the law of duration of drawdowns for a large class of L\'{e processes (with a general spectrally negative part plus a positive compound Poisson structure).
Edit intent: fluency
Original:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Revised:  The total cost of the clearance framework for a clearing member , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Edit intent: clarity
Original:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Revised:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Edit intent: clarity
Original:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Revised:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of its losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Edit intent: fluency
Original:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Revised:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other member, an MVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Edit intent: meaning-changed
Original:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Revised:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its margins and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term).
Edit intent: clarity
Original:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of regulatory capital (and for completeness we also incorporate a DVA term). This framework can be used by a clearinghouse to assess the right balance between initial margins and default fund in order to minimize the CCVA, hence optimize its costs for a given level of resilience. A clearinghouse can also use it to analyze the benefit for a dealer to trade centrally as a member, rather than on a bilateral basis, or to help clearing members risk manage their CCVA. The potential netting benefit of central clearing and the impact of the credit risk of the members are illustrated numerically .
Revised:  The total cost of the clearance framework for a member of the clearinghouse , called CCVA for central clearing valuation adjustment, is decomposed into a nonstandard CVA corresponding to the cost of the losses on the default fund in case of defaults of other members, an FVA corresponding to the cost of funding its position (including all the margins ) and a KVA corresponding to the cost of the regulatory capital and also of the capital at risk that the member implicitly provides to the CCP through its default fund contribution. In the end the structure of the XVA equations for bilateral and cleared portfolios is similar, but the input data to these equations are not the same, reflecting different financial network structures. The resulting XVA numbers differ, but, interestingly enough, they become comparable after scaling by a suitable netting ratio .
Edit intent: coherence
Original:  In fact, dissipation can be reduced by the effect of noise, as occurs in a simplified core model of metabolism that we employ to illustrate our findings  .
Revised:  In fact, dissipation can be reduced by the effect of noise, as occurs in a simplified core model of metabolism that we employ to illustrate our findings . This phenomenon highlights that there is a close interplay between deficiency and the activation of new dissipative pathways at low molecule numbers .
Edit intent: meaning-changed