wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The SDR combines the Expected Shortfall (ES) and the Shortfall Deviation (SD), which we also introduce, contemplating the two fundamental pillars of the risk concept, the probability of adverse events (ES) and the variability of an expectation (SD), and considers extreme results.
Revised:  The SDR combines the Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating the two fundamental pillars of the risk concept, the probability of adverse events (ES) and the variability of an expectation (SD), and considers extreme results.
Edit intent: fluency
Original:  The SDR combines the Expected Shortfall (ES) and the Shortfall Deviation (SD), which we also introduce, contemplating the two fundamental pillars of the risk concept, the probability of adverse events (ES) and the variability of an expectation (SD), and considers extreme results.
Revised:  The SDR combines the Expected Shortfall (ES) and the Shortfall Deviation (SD), which we also introduce, contemplating two fundamental pillars of the risk concept, the probability of adverse events (ES) and the variability of an expectation (SD), and considers extreme results.
Edit intent: fluency
Original:  We achieve the dual representation of the SDR, and we discuss issues such as its representation by a weighted ES, acceptance sets, convexity, continuity and the relationship with stochastic dominance . Illustrations using Monte Carlo simulation and real data indicate that the SDR offers greater protection to measure risk than other measures, especially in turbulent times .
Revised:  We achieve the dual representation of the SDR, and we discuss issues such as its representation by a weighted ES, acceptance sets, convexity, continuity and the relationship with stochastic dominance .
Edit intent: coherence
Original:  SDR combines Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating two fundamental pillars of the risk concept, the probability of adverse events (ES) and the variability of an expectation (SD) , and considers extreme results.
Revised:  SDR combines Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating two fundamental pillars of the risk concept, the probability of adverse events and the variability of an expectation (SD) , and considers extreme results.
Edit intent: fluency
Original:  SDR combines Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating two fundamental pillars of the risk concept, the probability of adverse events (ES) and the variability of an expectation (SD) , and considers extreme results.
Revised:  SDR combines Expected Shortfall (ES) and Shortfall Deviation (SD), which we also introduce, contemplating two fundamental pillars of the risk concept, the probability of adverse events (ES) and the variability of an expectation , and considers extreme results.
Edit intent: fluency
Original:  We achieve the dual representation of SDR, and we discuss issues such as its representation by a weighted ES, acceptance sets, convexity, continuity and  relationship with stochastic dominance  .
Revised:  We achieve the dual representation of SDR, and we discuss issues such as its representation by a weighted ES, acceptance sets, convexity, continuity and relationship with stochastic dominance . Illustrations with real and simulated data allow us to conclude that SDR offers greater protection in risk measurement compared with VaR and ES, especially in times of significant turbulence in riskier scenarios .
Edit intent: meaning-changed
Original:  In particular we assume the alphabet consists of complementary pairs, for instance the Watson-Crick pairs A-U and C-G of RNA. Given a word in the alphabet, a valid plane tree is a tree for which, when the word is folded around the tree, each edge matchs two complementary letters.
Revised:  In particular we assume the alphabet consists of complementary pairs, for instance the Watson-Crick pairs A-U and C-G of RNA. Given a word in the alphabet, a valid plane tree is a tree for which, when the word is folded around the tree, each edge matches two complementary letters.
Edit intent: fluency
Original:   Using large deviations techniques, we determine the asymptotic shape of the implied volatility surface in any regime of small maturity t \to 0 or extreme log-strike |\kappa| \to \infty (with bounded maturity).
Revised:  The volatility evolves according to a generalized Ornstein-Uhlenbeck processes with super-linear mean reversion. Using large deviations techniques, we determine the asymptotic shape of the implied volatility surface in any regime of small maturity t \to 0 or extreme log-strike |\kappa| \to \infty (with bounded maturity).
Edit intent: meaning-changed
Original:  Even if the price has continuous paths, we show that out-of-the-money implied volatility diverges for small maturity, producing a very pronounced smile . When |\kappa| is much larger than t, the implied volatility is asymptotically an explicit function of just the ratio (\kappa / t) .
Revised:  Even if the price has continuous paths, we show that out-of-the-money implied volatility diverges for small maturity, producing a very pronounced smile .
Edit intent: coherence
Original:  We conduct a horse race of conventional statistical methods and more recent machine learning methods . As early-warning models based upon one approach are oftentimes built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wide variety of methods.
Revised:  We conduct a horse race of conventional statistical methods and more recent machine learning methods as early-warning models. As individual models are in the literature most often built in isolation of other methods, the exercise is of high relevance for assessing the relative performance of a wide variety of methods.
Edit intent: meaning-changed
Original:  Traditional clustering methods assign each component to one group. Noisy measurements are common in high-throughput biological datasets. These two limitations reduce our ability to accurately define clusters in biological datasets and to interpret their biological functions.
Revised:  However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to accurately define clusters in biological datasets and to interpret their biological functions.
Edit intent: meaning-changed
Original:  To address these limitations , we designed an algorithm called SpeakEasy , which detects overlapping or non-overlapping communities in biological networks. Input to SpeakEasy can be physical networks, such as molecular interactions, or inferred networks, such as gene coexpression networks. The networks can be directed or undirected, and may contain negative links. SpeakEasy combines traditional bottom-up and top-down approaches to clustering, by creating competition between clusters.
Revised:  To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down approaches to clustering, by creating competition between clusters.
Edit intent: clarity
Original:  SpeakEasy combines traditional bottom-up and top-down approaches to clustering, by creating competition between clusters. Nodes that oscillate between multiple clusters in this competition are classified as multi-community nodes. SpeakEasy can quickly process networks with tens of thousands of nodes , quantify the stability of each cluster and detect an optimal number of clusters without requiring user preset parameter.
Revised:  SpeakEasy combines traditional bottom-up and top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds of thousands of nodes , quantify the stability of each cluster and detect an optimal number of clusters without requiring user preset parameter.
Edit intent: meaning-changed
Original:  SpeakEasy can quickly process networks with tens of thousands of nodes , quantify the stability of each cluster and detect an optimal number of clusters without requiring user preset parameter. Clustering networks derived from gene microarrays, protein affinity , sorted cell populations, electrophysiology , functional magnetic resonance imaging of resting-state brain activity, and synthetic datasets validate the ability of SpeakEasy to facilitate biological insights.
Revised:  SpeakEasy can quickly process networks with tens of thousands of nodes . SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein affinity , sorted cell populations, electrophysiology , functional magnetic resonance imaging of resting-state brain activity, and synthetic datasets validate the ability of SpeakEasy to facilitate biological insights.
Edit intent: clarity
Original:  Clustering networks derived from gene microarrays, protein affinity , sorted cell populations, electrophysiology , functional magnetic resonance imaging of resting-state brain activity, and synthetic datasets validate the ability of SpeakEasy to facilitate biological insights.
Revised:  Clustering networks derived from gene microarrays, protein interactions , sorted cell populations, electrophysiology , functional magnetic resonance imaging of resting-state brain activity, and synthetic datasets validate the ability of SpeakEasy to facilitate biological insights.
Edit intent: clarity
Original:  Clustering networks derived from gene microarrays, protein affinity , sorted cell populations, electrophysiology , functional magnetic resonance imaging of resting-state brain activity, and synthetic datasets validate the ability of SpeakEasy to facilitate biological insights. For instance, we can identify overlapping co-regulated genes sets, multi-complex proteins and robust changes to the community structure of co-active brain regions in Parkinson disease. These insights rely on a robust overlapping clustering approach that enables a more realistic interpretation of common high-throughput datasets .
Revised:  Clustering networks derived from gene microarrays, protein affinity , sorted cell populations, electrophysiology and fMRI brain imaging .
Edit intent: clarity
Original:  Focusing on the empirically relevant specification where both the risk aversion and the elasticity of intertemporal substitution are in excess of one, we characterize optimal consumption and investment strategies via backward stochastic differential equations.
Revised:  Focusing on the empirically relevant specification where both risk aversion and the elasticity of intertemporal substitution are in excess of one, we characterize optimal consumption and investment strategies via backward stochastic differential equations.
Edit intent: clarity
Original:  Focusing on the empirically relevant specification where both the risk aversion and the elasticity of intertemporal substitution are in excess of one, we characterize optimal consumption and investment strategies via backward stochastic differential equations.
Revised:  Focusing on the empirically relevant specification where both the risk aversion and elasticity of intertemporal substitution are in excess of one, we characterize optimal consumption and investment strategies via backward stochastic differential equations.
Edit intent: fluency
Original: In this paper we perform a statistical analysis over the returns and relative prices of the CAC 40 and the S  P 500 with the purpose of analysing the intra-day seasonalities of single and cross-sectional stock dynamics.
Revised: In this paper we perform a statistical analysis over the returns and relative prices of the CAC 40 and the S \& P 500 with the purpose of analysing the intra-day seasonalities of single and cross-sectional stock dynamics.
Edit intent: fluency
Original: In this paper we perform a statistical analysis over the returns and relative prices of the CAC 40 and the S  P 500 with the purpose of analysing the intra-day seasonalities of single and cross-sectional stock dynamics.
Revised: In this paper we perform a statistical analysis over the returns and relative prices of the CAC 40 and the S P 500 with the purpose of analyzing the intra-day seasonalities of single and cross-sectional stock dynamics.
Edit intent: fluency
Original:  Finally, we show how this bin size independence could be used in order to define and characterize " anomalous behaviours" in stocks and " atypical days" for indexes  .
Revised:  Finally, we show how this bin size independence could be used in order to define and characterize " anomalous behaviours" in stocks and " atypical days" for indexes and "anomalous behaviours" in stocks .
Edit intent: meaning-changed
Original:  We compute the optimal ratio of senior to junior debts, which we call the optimal seniority ratio, for two uncorrelated Erd\H{os-R\'{e}nyi }  networks.
Revised:  We compute the optimal ratio of senior to junior debts, which we call the optimal seniority ratio, for two uncorrelated Erd\H{os-R\'{e}nyi } Erdos-Renyi networks.
Edit intent: meaning-changed
Original:  Rotation of 2-D images is usually done with the assumption that the magnification of the electron microscope is the same in all directions , a condition that has been found to be untrue with some electron microscopes when used with the settings necessary for cryo-EM with a direct detector device (DDD) camera (Grant and Grigorieff, in preparation).
Revised:  Rotation of 2-D images is usually done with the assumption that the magnification of the electron microscope is the same in all directions . However, due to electron optical aberrations, this condition is not met with some electron microscopes when used with the settings necessary for cryo-EM with a direct detector device (DDD) camera (Grant and Grigorieff, in preparation).
Edit intent: meaning-changed
Original:  Cryo-EM data analysis requires electron microscope images of randomly oriented ice-embedded protein particles to be rotated and translated to allow for coherent averaging when calculating three-dimensional ( 3-D ) structures.
Revised:  Cryo-EM data analysis requires electron microscope images of randomly oriented ice-embedded protein particles to be rotated and translated to allow for coherent averaging when calculating three-dimensional ( 3D ) structures.
Edit intent: fluency
Original:  However, due to electron optical aberrations, this condition is not met with some electron microscopes when used with the settings necessary for cryo-EM with a direct detector device (DDD) camera (Grant and Grigorieff, in preparation) .
Revised:  However, due to electron optical aberrations, this condition is not met with some electron microscopes when used with the settings necessary for cryo-EM with a direct detector device (DDD) camera .
Edit intent: coherence
Original:  The key idea is to compress the  signal samples on the mote platform by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain.
Revised:  The key idea is to compress the ranging signal samples on the mote platform by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain.
Edit intent: meaning-changed
Original:  The key idea is to compress the  signal samples on the mote platform by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain.
Revised:  The key idea is to compress the signal samples on the mote by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain.
Edit intent: clarity
Original:  The key idea is to compress the  signal samples on the mote platform by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain.
Revised:  The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device ; where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain.
Edit intent: fluency
Original:  The key idea is to compress the  signal samples on the mote platform by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting its sparsity in our proposed correlation domain. Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework , together with the proposed correlation domain achieved up to two orders of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling.
Revised:  The key idea is to compress the signal samples on the mote platform by efficient random projections and transfer them to a central device , where a convex optimization process estimates the range by exploiting the sparse signal structure in the proposed correlation dictionary. Through theoretical validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework , together with the proposed correlation domain achieved up to two orders of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling.
Edit intent: clarity
Original:  Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework , together with the proposed correlation domain achieved up to two orders of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling.
Revised:  Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework can achieve up to two orders of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling.
Edit intent: coherence
Original:  Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework , together with the proposed correlation domain achieved up to two orders of magnitude better performance compared to naive approaches such as working on DCT domain and downsampling.
Revised:  Through sparse representation theory validation, extensive empirical studies and experiments on an end-to-end acoustic ranging system implemented on resource limited off-the-shelf sensor nodes, we show that the proposed framework , together with the proposed correlation domain achieved up to two orders of magnitude better performance compared to other approaches such as working on DCT domain and downsampling.
Edit intent: clarity
Original:  Compared to  standard cross-correlation, SparseXcorr uses only 30-40\% measurements to obtain precise range estimates with an additional bias of only 2-6 cm for applications with high accuracy requirement;
Revised:  Compared to the standard cross-correlation, SparseXcorr uses only 30-40\% measurements to obtain precise range estimates with an additional bias of only 2-6 cm for applications with high accuracy requirement;
Edit intent: fluency
Original:  Compared to  standard cross-correlation, SparseXcorr uses only 30-40\% measurements to obtain precise range estimates with an additional bias of only 2-6 cm for applications with high accuracy requirement; whereas for applications with less constrained accuracy levels, only 5\% measurements are adequate to achieve approximately 100cm ranging precision.
Revised:  Compared to standard cross-correlation, SparseXcorr uses only 30-40\% measurements to obtain precise range estimates with a bias of 2-6cm with 30\% and approximately 100cm with 5\% measurements are adequate to achieve approximately 100cm ranging precision.
Edit intent: coherence
Original:  whereas for applications with less constrained accuracy levels, only 5\% measurements are adequate to achieve approximately 100cm ranging precision. We also present StructSparseXcorr: cross-correlation via structured sparse representation, an addendum to the proposed computing framework to overcome shortcoming due to dictionary coherence. For cases of high compression factor and low signal-to-noise ratio , we show that StructSparseXcorr improves the performance of SparseXcorr by approximately 40\% .
Revised:  whereas for applications with less constrained accuracy levels, only 5\% compressed measurements. Its structured sparsity model is able to improve the ranging accuracy by 40\% under challenging recovery conditions (such as high compression factor and low signal-to-noise ratio , we show that StructSparseXcorr improves the performance of SparseXcorr by approximately 40\% .
Edit intent: clarity
Original: Mathematical models, with some simplifying assumptions, can provide clarity into the workings of complex biological systems. Despite their seeming simplicity, even so-called 'minimal' models can exhibit a diverse range of behavior in various regions of the parameter space. These models can be studied using simulations and analytical methods. Simulations are often parameter dependent and fall short of providing a complete understanding of the full range of  system responses.
Revised: Mathematical modeling is now used commonly in the analysis of signaling networks. With advances in high resolution microscopy, the spatial location of different signaling molecules and the spatio-temporal dynamics of signaling microdomains are now widely acknowledged as key features of biochemical signal transduction. Reaction-diffusion mechanisms are commonly used to model such features, often with a heavy reliance on numerical simulations to obtain results. However, simulations are parameter dependent and fall short of providing a complete understanding of the full range of system responses.
Edit intent: meaning-changed
Original:  Here, we outline a largely analytical method for studying reaction-diffusion models . Using a prototypic model of cell polarization, we characterize protein activation in response to an external stimulus and highlight the importance of phase-space analysis.
Revised:  In this tutorial, we provide a largely analytical method for studying reaction-diffusion models . Using a prototypic model of cell polarization, we characterize protein activation in response to an external stimulus and highlight the importance of phase-space analysis.
Edit intent: clarity