wanyu/IteraTeR_full_sent huggingface dataset:
Original:  More specifically, we analyze three types of available information on the dependence structure: First, we consider the case where extreme value information, such as  distributions of partial minima and maxima of \mathbf X are known .
Revised:  More specifically, we analyze three types of available information on the dependence structure: First, we consider the case where extreme value information, such as the distributions of partial minima and maxima of \mathbf X are known .
Edit intent: fluency
Original:  More specifically, we analyze three types of available information on the dependence structure: First, we consider the case where extreme value information, such as  distributions of partial minima and maxima of \mathbf X are known .
Revised:  More specifically, we analyze three types of available information on the dependence structure: First, we consider the case where extreme value information, such as distributions of partial minima and maxima of \mathbf X , is available .
Edit intent: clarity
Original:  Second, we assume that the copula of \mathbf X is known only on a subset of its domain, and finally we consider the case where the copula of \mathbf X lies in the vicinity of a reference copula as measured by a statistical distance.
Revised:  Second, we assume that the copula of \mathbf X is known on a subset of its domain, and finally we consider the case where the copula of \mathbf X lies in the vicinity of a reference copula as measured by a statistical distance.
Edit intent: clarity
Original:  In order to include this information in the computation of Value-at-Risk bounds, we establish a reduction principle that relates this problem to an optimization problem over a standard Fr\'echet class  . Second, we assume that the copula of \mathbf X is known on a subset of its domain, and finally we consider the case where the copula of \mathbf X lies in the vicinity of a reference copula as measured by a statistical distance.
Revised:  In order to include this information in the computation of Value-at-Risk bounds, we establish a reduction principle that relates this problem to an optimization problem over a standard Fr\'echet class , which can then be solved by means of the well-known standard bounds or the rearrangement algorithm . Second, we assume that the copula of \mathbf X is known on a subset of its domain, and finally we consider the case where the copula of \mathbf X lies in the vicinity of a reference copula as measured by a statistical distance.
Edit intent: meaning-changed
Original:  In order to derive Value-at-Risk bounds in the latter situations, we first improve the Fr\'echet-Hoeffding \FH bounds on copulas so as to include this additional information on the dependence structure.
Revised:  In order to derive Value-at-Risk bounds in the latter situations, we first improve the \FH bounds on copulas so as to include this additional information on the dependence structure.
Edit intent: coherence
Original:  In order to include this information in the computation of Value-at-Risk bounds, we establish a reduction principle that relates this problem to an optimization problem over a standard Fr\'echet class, which can then be solved by means of the well-known standard bounds or the rearrangement algorithm .
Revised:  In order to include this information in the computation of Value-at-Risk bounds, we utilize a reduction principle that relates this problem to an optimization problem over a standard Fr\'echet class, which can then be solved by means of the well-known standard bounds or the rearrangement algorithm .
Edit intent: clarity
Original:  In order to include this information in the computation of Value-at-Risk bounds, we establish a reduction principle that relates this problem to an optimization problem over a standard Fr\'echet class, which can then be solved by means of the well-known standard bounds or the rearrangement algorithm .
Revised:  In order to include this information in the computation of Value-at-Risk bounds, we establish a reduction principle that relates this problem to an optimization problem over a standard Fr\'echet class, which can then be solved by means of the rearrangement algorithm or using analytical results .
Edit intent: meaning-changed
Original:  In order to derive Value-at-Risk bounds in the latter situations, we first improve the %DIFDELCMD < \FH %%%  bounds on copulas so as to include this additional information on the dependence structure.
Revised:  In order to derive Value-at-Risk bounds in the latter situations, we first improve the %DIFDELCMD < \FH %%% Fr\'echet--Hoeffding bounds on copulas so as to include this additional information on the dependence structure.
Edit intent: meaning-changed
Original: Several studies pointed out the relevance of extrinsic noise in molecular networks in shaping cell decision making and differentiation . Interestingly, bimodal distributions of gene expression levels, that may be a feature of phenotypic differentiation, are a common phenomenon in gene expression data.
Revised: Several studies pointed out the relevance of extrinsic noise in molecular networks in shaping cell decision making and differentiation in molecular networks. Experimental evidences of phenotypic differentiation are given by the presence of bimodal distributions of gene expression levels, that may be a feature of phenotypic differentiation, are a common phenomenon in gene expression data.
Edit intent: meaning-changed
Original:  We study the fluctuating miRNA transcription case to probe the effects of  extrinsic noise on the system .
Revised:  We study the fluctuating miRNA transcription case to probe the effects of this interaction is titrative and induces a threshold effect: below a given target transcription rate no mRNAs are free and available for translation. We investigate the effect of extrinsic noise on the system .
Edit intent: meaning-changed
Original:  bimodal target distributions can be obtained exploiting a noisy environment even in case of small miRNA-target interaction strength , (ii) an increase in the extrinsic noise shifts the range of target transcription rates that allow bimodality towards higher values, (iii) the protein half-life may buffer bimodal mRNA preventing its distribution from becoming bimodal and that (iv) in a noisy environment different targets may cross-regulate each other's bimodal distribution when competing for a shared pool of miRNAs even if the miRNA regulation is small .
Revised:  bimodal target distributions can be obtained exploiting a noisy environment even in case of small miRNA-target interaction strength . Our results suggest that combining threshold-inducing interactions with extrinsic noise provides a simple and robust mechanism for obtaining bimodal populations not requiring fine tuning. We furthermore characterise the protein distributions dependence on protein half-life may buffer bimodal mRNA preventing its distribution from becoming bimodal and that (iv) in a noisy environment different targets may cross-regulate each other's bimodal distribution when competing for a shared pool of miRNAs even if the miRNA regulation is small .
Edit intent: meaning-changed
Original:  an increase in the extrinsic noise shifts the range of target transcription rates that allow bimodality towards higher values, (iii) the protein half-life may buffer bimodal mRNA preventing its distribution from becoming bimodal and that (iv) in a noisy environment different targets may cross-regulate each other's bimodal distribution when competing for a shared pool of miRNAs even if the miRNA regulation is small .
Revised:  an increase in the extrinsic noise shifts the range of target transcription rates that allow bimodality towards higher values, (iii) the protein half-life .
Edit intent: coherence
Original:  The Gini (g) index, giving the overall inequality is the most common , while the recently introduced Kolkata (k) index gives a measure of 1-k fraction of population who possess top k fraction of wealth in the society.
Revised:  The Gini (g) index, giving the overall inequality is the most commonly used , while the recently introduced Kolkata (k) index gives a measure of 1-k fraction of population who possess top k fraction of wealth in the society.
Edit intent: clarity
Original:  We assume the option price is a solution to a stationary generalized Black-Scholes equation in which the volatility may depend on the second derivative of the option price itself. We prove existence and uniqueness of a solution to the free boundary problem. We derive a single implicit equation for the free boundary position and the closed form formula for the option price. It is a generalization of the well-known explicit closed form solution derived by Merton for the case of a constant volatility. We also present results of numerical computations of the free boundary position, option price and their dependence on model parameters. is a solution to a stationary generalized Black-Scholes equation in which the volatility  may depend on the second derivative of the option price itself.
Revised:  We assume the option price is a solution to the stationary generalized Black-Scholes equation in which the volatility may depend on the second derivative of the option price itself.
Edit intent: meaning-changed
Original:  We also present results of numerical computations of the free boundary position, option price and their dependence on model parameters. is a solution to a stationary generalized Black-Scholes equation in which the volatility  may depend on the second derivative of the option price itself.
Revised:  We also present results of numerical computations of the free boundary position, option price and their dependence on model parameters. is a solution to a stationary generalized Black-Scholes equation in which the volatility function may depend on the second derivative of the option price itself.
Edit intent: clarity
Original: Natural genetic variation between individuals in a population leads to variations in gene expression that are informative for the inference of gene regulatory networks. Particularly, genome-wide genotype and transcriptome data from the same samples allow for causal inference between gene expression traits using the DNA variations in cis-regulatory regions as causal anchors .
Revised: Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using the DNA variations in cis-regulatory regions as causal anchors .
Edit intent: meaning-changed
Original:  Particularly, genome-wide genotype and transcriptome data from the same samples allow for causal inference between gene expression traits using the DNA variations in cis-regulatory regions as causal anchors . However, existing causal inference programs are not efficient enough for contemporary datasets, and unrealistically assume the absence of hidden confounders affecting the coexpression of causally related gene pairs. Here we propose alternative statistical tests to infer causal effects in the presence of confounding and weak regulations , and implemented both the novel and the traditional causal inference tests in the software package Findr (Fast Inference of Networks from Directed Regulations), achieving thousands to millions of times of speedup due to analytical false positive rate estimation and implementational optimizations.
Revised:  Particularly, genome-wide genotype and transcriptome data from the same samples allow for causal inference between gene expression traits using the DNA variations in cis-regulatory regions as causal anchors , which improves current methods by taking into account hidden confounders and weak regulations , and implemented both the novel and the traditional causal inference tests in the software package Findr (Fast Inference of Networks from Directed Regulations), achieving thousands to millions of times of speedup due to analytical false positive rate estimation and implementational optimizations.
Edit intent: clarity
Original:  Here we propose alternative statistical tests to infer causal effects in the presence of confounding and weak regulations , and implemented both the novel and the traditional causal inference tests in the software package Findr (Fast Inference of Networks from Directed Regulations), achieving thousands to millions of times of speedup due to analytical false positive rate estimation and implementational optimizations. A systematic evaluation using simulated data from the DREAM5 Systems Genetics challenge demonstrated that the novel tests outperformed existing causal inference methods as well as all challenge submissions.
Revised:  Here we propose alternative statistical tests to infer causal effects in the presence of confounding and weak regulations . Findr outperformed existing methods on the DREAM5 Systems Genetics challenge demonstrated that the novel tests outperformed existing causal inference methods as well as all challenge submissions.
Edit intent: clarity
Original:  A systematic evaluation using simulated data from the DREAM5 Systems Genetics challenge demonstrated that the novel tests outperformed existing causal inference methods as well as all challenge submissions. We confirmed these results using siRNA silencing, ChIP-sequencing and microRNA target data to validate causal gene-gene and microRNA-gene interactions inferred from genotype, microRNA and mRNA sequencing data of nearly 400 human individuals from the Geuvadis study.
Revised:  A systematic evaluation using simulated data from the DREAM5 Systems Genetics challenge and microRNA-gene interactions inferred from genotype, microRNA and mRNA sequencing data of nearly 400 human individuals from the Geuvadis study.
Edit intent: clarity
Original:  We confirmed these results using siRNA silencing, ChIP-sequencing and microRNA target data to validate causal gene-gene and microRNA-gene interactions inferred from genotype, microRNA and mRNA sequencing data of nearly 400 human individuals from the Geuvadis study. Findr provides the community with the first efficient and accurate causal inference tool for modern datasets of tens of thousands of RNA expression traits and genotypes from hundreds or more human individuals . Findr is publicly available at URL
Revised:  We confirmed these results using siRNA silencing, ChIP-sequencing and microRNA target data to validate causal gene-gene and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster . Findr is publicly available at URL
Edit intent: meaning-changed
Original:  It is well known  the market values of the main strategic resources such as oil, natural gas, uranium, copper,..., etc, fluctuate randomly following global and seasonal macro-economic parameters, these values are modeled using Markov switching L\'evy processes.
Revised:  It is well known that the market values of the main strategic resources such as oil, natural gas, uranium, copper,..., etc, fluctuate randomly following global and seasonal macro-economic parameters, these values are modeled using Markov switching L\'evy processes.
Edit intent: fluency
Original:  It is well known  the market values of the main strategic resources such as oil, natural gas, uranium, copper,..., etc, fluctuate randomly following global and seasonal macro-economic parameters, these values are modeled using Markov switching L\'evy processes.
Revised:  It is well known the market values of the main strategic resources such as oil, natural gas, uranium, copper,..., etc, fluctuate randomly following global and seasonal macroeconomic parameters, these values are modeled using Markov switching L\'evy processes.
Edit intent: fluency
Original:  We formulate this problem as a differential game where the two players are the mining company whose aim is to maximize the revenues generated from its extracting activities and the government agency in charge of regulating and taxing natural resources.
Revised:  We formulate this problem as a differential game . The two players of this differential game are the mining company whose aim is to maximize the revenues generated from its extracting activities and the government agency in charge of regulating and taxing natural resources.
Edit intent: clarity
Original:  We prove the existence of a Nash equilibrium and characterize the value functions of this differential game as the unique viscosity solutions of the corresponding Hamilton Jacobi Isaacs equations.
Revised:  We prove the existence of a Nash equilibrium . The corresponding Hamilton Jacobi Isaacs equations are completely solved and the value functions of this differential game as the unique viscosity solutions of the corresponding Hamilton Jacobi Isaacs equations.
Edit intent: meaning-changed
Original:  We prove the existence of a Nash equilibrium and characterize the value functions of this differential game as the unique viscosity solutions of the corresponding Hamilton Jacobi Isaacs equations. Furthermore, optimal extraction and taxation policies that should be applied when the equilibrium is reached are derived .
Revised:  We prove the existence of a Nash equilibrium and characterize the value functions as well as the optimal extraction and taxation policies that should be applied when the equilibrium is reached are derived .
Edit intent: clarity
Original:  These updates are : i) the possibility to work on asynchronous Boolean networks, ii) a smarter search for therapeutic targets , and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system correspond to the phenotypes of the modeled biological system.
Revised:  These improvements are i) the possibility to work on asynchronous Boolean networks, ii) a smarter search for therapeutic targets , and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system correspond to the phenotypes of the modeled biological system.
Edit intent: clarity
Original:  These updates are : i) the possibility to work on asynchronous Boolean networks, ii) a smarter search for therapeutic targets , and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system correspond to the phenotypes of the modeled biological system.
Revised:  These updates are : i) the possibility to work on asynchronous Boolean networks, ii) a finer assessment of therapeutic targets and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system correspond to the phenotypes of the modeled biological system.
Edit intent: clarity
Original:  These updates are : i) the possibility to work on asynchronous Boolean networks, ii) a smarter search for therapeutic targets , and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system correspond to the phenotypes of the modeled biological system.
Revised:  These updates are : i) the possibility to work on asynchronous Boolean networks, ii) a smarter search for therapeutic targets , and iii) the possibility to use multivalued logic. kali assumes that the attractors of a dynamical system , such as a Boolean network, are associated with the phenotypes of the modeled biological system.
Edit intent: clarity
Original:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Revised:  Given a logic-based model of pathological mechanisms, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Edit intent: clarity
Original:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Revised:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for therapeutic targets able to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Edit intent: clarity
Original:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Revised:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing their likeliness . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Edit intent: clarity
Original:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Revised:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on an example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Edit intent: clarity
Original:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and shows that it can find therapeutic targets able to reduce the likeliness of pathological phenotypes .
Revised:  Given a logical model of a pathophysiology, either Boolean or multivalued, kali searches for which biological components should be therapeutically disturbed in order to reduce the reachability of the attractors associated with pathological phenotypes, thus reducing the likelinessof pathological phenotypes . kali is illustrated on a simple example network and used on a biological case study. This case study is a published logic-based model of bladder tumorigenesis from which kali returns consistent results .
Edit intent: meaning-changed