wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The regime of large deviations is separated from the regime of typical fluctuations by a phase transition where the symmetry between the points in the sample is %DIFDELCMD < {\em %%% spontaneously broken .
Revised:  The regime of large deviations is separated from the regime of typical fluctuations by a phase transition where the symmetry between the points in the sample is %DIFDELCMD < {\em %%% .
Edit intent: clarity
Original:  The regime of large deviations is separated from the regime of typical fluctuations by a phase transition where the symmetry between the points in the sample is %DIFDELCMD < {\em %%% spontaneously broken . This phenomenon has been discussed in the context of mass transport models in physics, where it takes the form of a condensation phase transition. Yet, the phenomenon is way more general. For example, in risk management of large portfolios, it suggests that one should expect losses to concentrate on a single asset: when extremely bad things happen, it is likely that there is a single factor on which bad luck concentrates.
Revised:  The regime of large deviations is separated from the regime of typical fluctuations by a phase transition where the symmetry between the points in the sample is %DIFDELCMD < {\em %%% spontaneously broken spontaneously broken. For stochastic processes with a fat tailed microscopic noise, this implies that while typical realizations are well described by a diffusion process with continuous sample paths, large deviation paths are typically discontinuous. For eigenvalues of random matrices with fat tailed distributed elements, a large deviation where the trace of the matrix is anomalously large concentrates on just a single eigenvalue, whereas in the thin tailed world the large deviation affects the whole distribution. These results find a natural application to finance. Since the price dynamics of financial stocks is characterized by fat tailed increments, large fluctuations of stock prices are expected to be realized by discrete jumps. Interestingly, we find that large portfolios, it suggests that one should expect losses to concentrate on a single asset: when extremely bad things happen, it is likely that there is a single factor on which bad luck concentrates.
Edit intent: meaning-changed
Original:  For example, in risk management of large portfolios, it suggests that one should expect losses to concentrate on a single asset: when extremely bad things happen, it is likely that there is a single factor on which bad luck concentrates. Along similar lines, one should expect that bubbles in financial markets do not gradually deflate, but rather burst abruptly and that in the most rainy day of a year, precipitation concentrate on a given spot. Analogously, when applied to biological evolution, we 're lead to infer that , if fitness changes for individual mutations have a broad distribution, those large deviations that lead to better fit species are not likely to result from the accumulation of small positive mutations. Rather they are likely to arise from large rare jumps .
Revised:  For example, in risk management of large excursions of prices are more likely realized by continuous drifts rather than by discontinuous jumps. Indeed, auto-correlations suppress the concentration of large deviations. Financial covariance matrices also exhibit an anomalously large eigenvalue, the market mode, as compared to the prediction of random matrix theory. We show that this is explained by a large deviation with excess covariance rather than by one with excess volatility .
Edit intent: meaning-changed
Original:   We investigate here  recent data on illicit financial flows from developing countries and reveal that the data does submit to Benford's law . Further, the general improvement in the statistical accuracy we find here supports the applicability of the two stage normalization in filtering out countries from database for which illicit financial flows are not substantial .
Revised:  Particularly, the law is known to hold for different types of financial data. The Illicit Financial Flows (IFFs) exiting the developing countries are frequently discussed as hidden resources which could have been otherwise properly utilized for their development. We investigate here recent data on illicit financial flows from developing countries and reveal that the data does submit to Benford's law . Further, the general improvement in the statistical accuracy we find here supports the applicability of the two stage normalization in filtering out countries from database for which illicit financial flows are not substantial .
Edit intent: meaning-changed
Original:   We investigate here  recent data on illicit financial flows from developing countries and reveal that the data does submit to Benford's law . Further, the general improvement in the statistical accuracy we find here supports the applicability of the two stage normalization in filtering out countries from database for which illicit financial flows are not substantial .
Revised:  We investigate here the distribution of the leading digits in the recent data on illicit financial flows from developing countries and reveal that the data does submit to Benford's law . Further, the general improvement in the statistical accuracy we find here supports the applicability of the two stage normalization in filtering out countries from database for which illicit financial flows are not substantial .
Edit intent: meaning-changed
Original:   We investigate here  recent data on illicit financial flows from developing countries and reveal that the data does submit to Benford's law . Further, the general improvement in the statistical accuracy we find here supports the applicability of the two stage normalization in filtering out countries from database for which illicit financial flows are not substantial .
Revised:  We investigate here recent data on estimates of IFFs to look for the existence of a pattern as predicted by Benford's law . Further, the general improvement in the statistical accuracy we find here supports the applicability of the two stage normalization in filtering out countries from database for which illicit financial flows are not substantial .
Edit intent: clarity
Original:   We investigate here  recent data on illicit financial flows from developing countries and reveal that the data does submit to Benford's law . Further, the general improvement in the statistical accuracy we find here supports the applicability of the two stage normalization in filtering out countries from database for which illicit financial flows are not substantial .
Revised:  We investigate here recent data on illicit financial flows from developing countries and reveal that the data does submit to Benford's law and establish that the frequency of occurrence of the leading digits in these estimates does closely follow the law .
Edit intent: meaning-changed
Original: Markov state models (MSMs) are a powerful means of understanding the structure and function of biomolecules by describing their free energy landscapes as a set of local minima (a.k.a. states) and the probabilities of transitioning between them.
Revised: Markov state models (MSMs) ---or discrete-time master equation models---are a powerful way of understanding the structure and function of biomolecules by describing their free energy landscapes as a set of local minima (a.k.a. states) and the probabilities of transitioning between them.
Edit intent: meaning-changed
Original: Markov state models (MSMs) are a powerful means of understanding the structure and function of biomolecules by describing their free energy landscapes as a set of local minima (a.k.a. states) and the probabilities of transitioning between them. Unfortunately, it can be difficult to gain an intuition for an MSM because they typically must have tens of thousands of sates to quantitatively describe the rugged landscapes of most biomolecules .
Revised: Markov state models (MSMs) are a powerful means of understanding the structure and function of proteins and other molecular systems. However, they are typically too complicated to understand .
Edit intent: clarity
Original:  Here, I derive a Bayesian agglomerative clustering engine (BACE) for coarse-graining MSMs, making them suitable for extracting human understanding. BACE considerably outperforms existing methods by iteratively lumping together the most kinetically similar states while taking into account model uncertainty.
Revised:  Here, I derive a Bayesian agglomerative clustering engine (BACE) for coarse-graining Markov chains---as well as a more general class of probabilistic models---making them more comprehensible while remaining as faithful as possible to the original kinetics by accounting for model uncertainty.
Edit intent: meaning-changed
Original:  I also present an extremely efficient expression for Bayesian model comparison that can be used to identify the most meaningful levels of the hierarchy of models from BACE.
Revised:  The closed-form expression I derive here for determining which states to merge is equivalent to the generalized Jensen-Shannon divergence, an important measure from information theory that is related to the relative entropy. Therefore, the method has an appealing information theoretic interpretation. I also present an extremely efficient expression for Bayesian model comparison that can be used to identify the most meaningful levels of the hierarchy of models from BACE.
Edit intent: meaning-changed
Original: Markov state models (MSMs)---or discrete-time master equation models---are a powerful way of understanding the structure and function of proteins and other molecular systems . However, they are typically too complicated to understand.
Revised: Markov state models (MSMs)---or discrete-time master equation models---are a powerful way of understanding the structure and function of molecular systems like proteins. Unfortunately, MSMs with sufficiently many states to make a quantitative connection with experiments (often tens of thousands of states even for small systems) are generally too complicated to understand.
Edit intent: meaning-changed
Original:  Here, I present a Bayesian agglomerative clustering engine (BACE) for coarse-graining Markov chains---as well as a more general class of probabilistic models---making them more comprehensible while remaining as faithful as possible to the original kinetics by accounting for model uncertainty .
Revised:  Here, I present a Bayesian agglomerative clustering engine (BACE) for coarse-graining such Markov models, thereby reducing their complexity and making them more comprehensible while remaining as faithful as possible to the original kinetics by accounting for model uncertainty .
Edit intent: clarity
Original:  Here, I present a Bayesian agglomerative clustering engine (BACE) for coarse-graining Markov chains---as well as a more general class of probabilistic models---making them more comprehensible while remaining as faithful as possible to the original kinetics by accounting for model uncertainty .
Revised:  Here, I present a Bayesian agglomerative clustering engine (BACE) for coarse-graining Markov chains---as well as a more general class of probabilistic models---making them more comprehensible . An important feature of this algorithm is its ability to explicitly account for statistical uncertainty in model parameters that arises from finite sampling. This advance builds on a number of recent works highlighting the importance of accounting for uncertainty in the analysis of MSMs and provides significant advantages over existing methods for coarse-graining Markov state models .
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Original:  The conclusions are that reducing fossil fuel consumption may hamper economic growth, and that it is unlikely that nonfossil energy will substantially replace fossil fuels . This paper also examines causal linkages between the variables using a trivariate model, and obtains the same results as those from the bivariate model .
Revised:  The conclusions are that reducing fossil fuel consumption may hamper economic growth, and that it is unlikely that nonfossil energy will substantially replace fossil fuels .
Edit intent: coherence
Original: This paper analyzes cointegration and causality between fossil fuel consumption and economic growth in the world over the period 1971--2008. The estimation results indicate that fossil fuel consumption and GDP are cointegrated and there exists long-run unidirectional causality from fossil fuel consumption to GDP. This paper also investigates the nexus between nonfossil energy consumption and GDP, and shows that there is no causality between the variables. The conclusions are that reducing fossil fuel consumption may hamper economic growth, and that it is unlikely that nonfossil energy will substantially replace fossil fuels .
Revised: This paper has been withdrawn by the author due to some inaccurate descriptions in the section of INTRODUCTION and CONCLUSIONS .
Edit intent: meaning-changed
Original:  Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and interaction among these components are given by logical rules representing the biochemical mechanisms governing their regulation ; stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update function guarantee activation or degradation there is a probability that the process will not occur due to stochasticity in the process, for instance, some of the chemical reactions encoded by the function fail to occur which lead to a function failure .
Revised:  Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update function guarantee activation or degradation there is a probability that the process will not occur due to stochasticity in the process, for instance, some of the chemical reactions encoded by the function fail to occur which lead to a function failure .
Edit intent: clarity
Original:  stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update function guarantee activation or degradation there is a probability that the process will not occur due to stochasticity in the process, for instance, some of the chemical reactions encoded by the function fail to occur which lead to a function failure .
Revised:  stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochasticity in the process, for instance, some of the chemical reactions encoded by the function fail to occur which lead to a function failure .
Edit intent: clarity
Original:  stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update function guarantee activation or degradation there is a probability that the process will not occur due to stochasticity in the process, for instance, some of the chemical reactions encoded by the function fail to occur which lead to a function failure .
Revised:  stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update function guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects .
Edit intent: clarity
Original: Wolfe-Simon et al. reported isolation of a strain of Halomonas bacteria, GFAJ-1, which could use arsenic as a nutrient when phosphate is limiting, and which could specifically incorporate arsenic into its DNA in place of phosphorus.
Revised: A strain of Halomonas bacteria, GFAJ-1, which could use arsenic as a nutrient when phosphate is limiting, and which could specifically incorporate arsenic into its DNA in place of phosphorus.
Edit intent: coherence
Original: Wolfe-Simon et al. reported isolation of a strain of Halomonas bacteria, GFAJ-1, which could use arsenic as a nutrient when phosphate is limiting, and which could specifically incorporate arsenic into its DNA in place of phosphorus.
Revised: Wolfe-Simon et al. reported isolation of a strain of Halomonas bacteria, GFAJ-1, has been reported to be able to use arsenate as a nutrient when phosphate is limiting, and which could specifically incorporate arsenic into its DNA in place of phosphorus.
Edit intent: meaning-changed
Original: Wolfe-Simon et al. reported isolation of a strain of Halomonas bacteria, GFAJ-1, which could use arsenic as a nutrient when phosphate is limiting, and which could specifically incorporate arsenic into its DNA in place of phosphorus.
Revised: Wolfe-Simon et al. reported isolation of a strain of Halomonas bacteria, GFAJ-1, which could use arsenic as a nutrient when phosphate is limiting, and to specifically incorporate arsenic into its DNA in place of phosphorus.
Edit intent: fluency
Original:  We have found that arsenate is not needed for growth of GFAJ-1 when phosphate is limiting . Additionally, we used mass spectrometry to show that DNA purified from cells grown with limiting phosphate and abundant arsenate does not contain detectable arsenate.
Revised:  We have found that arsenate is not needed for growth of GFAJ-1 when phosphate is limiting and that DNA purified from cells grown with limiting phosphate and abundant arsenate does not contain detectable arsenate.
Edit intent: coherence
Original:  Additionally, we used mass spectrometry to show that DNA purified from cells grown with limiting phosphate and abundant arsenate does not contain detectable arsenate.
Revised:  Additionally, we used mass spectrometry to show that DNA purified from cells grown with limiting phosphate and abundant arsenate does not exhibit the spontaneous hydrolysis expected of arsenate ester bonds. Furthermore, mass spectrometry showed that this DNA contains only trace amounts of free arsenate and no detectable covalently bound arsenate.
Edit intent: meaning-changed
Original: In this paper, we take up the analysis of a principal/agent model with moral hazard introduced in \mbox{%DIFAUXCMD pages , with optimal contracting between competitive investors and an impatient bank monitoring a pool of long-term loans subject to Markovian contagion.
Revised: In this paper, we take up the analysis of a principal/agent model with moral hazard introduced in 17 , with optimal contracting between competitive investors and an impatient bank monitoring a pool of long-term loans subject to Markovian contagion.
Edit intent: meaning-changed
Original:  We provide here a comprehensive mathematical formulation of the model and show using martingale arguments in the spirit of Sannikov \mbox{%DIFAUXCMD san how the maximization problem with implicit constraints faced by investors can be reduced to a classic stochastic control problem.
Revised:  We provide here a comprehensive mathematical formulation of the model and show using martingale arguments in the spirit of Sannikov 18 how the maximization problem with implicit constraints faced by investors can be reduced to a classic stochastic control problem.
Edit intent: meaning-changed
Original:  We provide here a comprehensive mathematical formulation of the model and show using martingale arguments in the spirit of Sannikov \mbox{%DIFAUXCMD san how the maximization problem with implicit constraints faced by investors can be reduced to a classic stochastic control problem.
Revised:  We provide here a comprehensive mathematical formulation of the model and show using martingale arguments in the spirit of Sannikov \mbox{%DIFAUXCMD san how the maximization problem with implicit constraints faced by investors can be reduced to a classical stochastic control problem.
Edit intent: fluency
Original:  The approach has the advantage of avoiding the more general techniques based on forward-backward stochastic differential equations described in \mbox{%DIFAUXCMD cviz and leads to a simple recursive system of Hamilton-Jacobi-Bellman equations.
Revised:  The approach has the advantage of avoiding the more general techniques based on forward-backward stochastic differential equations described in 6 and leads to a simple recursive system of Hamilton-Jacobi-Bellman equations.
Edit intent: meaning-changed
Original:  Recently, statistical mechanical network properties have been studied in the context of cancer networks, yet it is unclear which properties best characterise the cancer phenotype. In this work we take a step in this direction by comparing two different types of molecular entropy in their ability to discriminate cancer from the normal phenotype. One entropy measure (flux entropy) is dynamical in the sense that it is derived from a stochastic process. The second measure (covariance entropy) does not depend on the interaction network and is thus "static". Using multiple gene expression data sets of normal and cancer tissue, we demonstrate that flux entropy is a better discriminator of the cancer phenotype than covariance entropy. Specifically, we show that local flux entropy is always increased in cancer relative to normal tissue while the local covariance entropy is not. We show that gene expression differences between normal and cancer tissue are anticorrelated with local flux entropy changes, thus providing a systemic link between gene expression changes  and their local information flux dynamics.
Revised:  By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local flux entropy changes, thus providing a systemic link between gene expression changes and their local information flux dynamics.
Edit intent: clarity
Original:  We show that gene expression differences between normal and cancer tissue are anticorrelated with local flux entropy changes, thus providing a systemic link between gene expression changes  and their local information flux dynamics. We also show that genes located in the intracellular domain demonstrate preferential increases in flux entropy, while the entropy of genes encoding membrane receptors and secreted factors is preferentially reduced. Thus, these results elucidate intrinsic network properties of cancer and support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamical network entropy allowing cells to adapt to extracellular stresses .
Revised:  We show that gene expression differences between normal and cancer tissue are anticorrelated with local flux entropy changes, thus providing a systemic link between gene expression changes and their local network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamical network entropy allowing cells to adapt to extracellular stresses .
Edit intent: clarity