wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In order to find an infinite dimensional variational formulation of the pricing problem , we extend some results on infinite dimensional optimal stopping and variational inequalities recently obtained in 8%DIFDELCMD < ]%%% . The proof goes through three main steps. First we regularize the American bond option's payoff by adopting usual smoothing arguments. Next we approximate the infinite dimensional dynamics by finite dimensional ones to which we associate suitable optimal stopping problems in R^n.
Revised:  In order to find an infinite dimensional variational formulation of the pricing problem , we extend some results on infinite dimensional optimal stopping and variational inequalities recently obtained in 8%DIFDELCMD < ]%%% by approximating the original optimal stopping problem by finite dimensional ones to which we associate suitable optimal stopping problems in R^n.
Edit intent: clarity
Original:  Next we approximate the infinite dimensional dynamics by finite dimensional ones to which we associate suitable optimal stopping problems in R^n. Then, by taking the limit as n goes to infinity and by removing the smoothing on the payoff, we obtain an infinite dimensional variational inequality for the price of the American bond option. Moreover ,  the  first time at which the price of the American bond option equals the payoff turns out to be an optimalexercise time .
Revised:  Next we approximate the infinite dimensional dynamics by finite dimensional ones , the first time at which the price of the American bond option equals the payoff turns out to be an optimalexercise time .
Edit intent: coherence
Original:  We infer  from exhaustive numerical explorations  that the enhanced BPLS equations have a unique attractor fixed point for physiologically acceptable ranges of mitochondrial variables and respiration inputs  .
Revised:  We infer from exhaustive numerical explorations that the enhanced BPLS equations have a unique attractor fixed point for physiologically acceptable ranges of mitochondrial variables and respiration inputs , as one would indeed expect from homeostasis .
Edit intent: meaning-changed
Original:  We find out, however, an interesting non-stationary effect : the inertia of the model tends to increase considerably for high concentrations of calcium {\rm   .
Revised:  We find out, however, an interesting non-stationary effect : the inertia of the model tends to increase considerably for high concentrations of calcium and/or FBP. In particular, the larger the concentrations of{\rm .
Edit intent: meaning-changed
Original:  While a class of O(n^{2.5} \log n) complexity hybrid scheduling policies are recently developed to guarantee both rate-function delay optimality (in the many-channel many-user asymptotic regime) and throughput optimality (in  general non-asymptotic setting), their practical complexity is typically high.
Revised:  While a class of O(n^{2.5} \log n) -complexity hybrid scheduling policies are recently developed to guarantee both rate-function delay optimality (in the many-channel many-user asymptotic regime) and throughput optimality (in general non-asymptotic setting), their practical complexity is typically high.
Edit intent: fluency
Original:  While a class of O(n^{2.5} \log n) complexity hybrid scheduling policies are recently developed to guarantee both rate-function delay optimality (in the many-channel many-user asymptotic regime) and throughput optimality (in  general non-asymptotic setting), their practical complexity is typically high.
Revised:  While a class of O(n^{2.5} \log n) complexity hybrid scheduling policies are recently developed to guarantee both rate-function delay optimality (in the many-channel many-user asymptotic regime) and throughput optimality (in the general non-asymptotic setting), their practical complexity is typically high.
Edit intent: fluency
Original:  To address this issue, we develop a simple greedy policy called Delay-based Server-Side-Greedy (D-SSG) with a lower complexity O(n^2) \lower  , and rigorously prove that D-SSG not only achieves throughput optimality, but also guarantees near-optimal rate-function-based delay performance.
Revised:  To address this issue, we develop a simple greedy policy called Delay-based Server-Side-Greedy (D-SSG) with a \lower , and rigorously prove that D-SSG not only achieves throughput optimality, but also guarantees near-optimal rate-function-based delay performance.
Edit intent: clarity
Original:  To address this issue, we develop a simple greedy policy called Delay-based Server-Side-Greedy (D-SSG) with a lower complexity O(n^2) \lower  , and rigorously prove that D-SSG not only achieves throughput optimality, but also guarantees near-optimal rate-function-based delay performance.
Revised:  To address this issue, we develop a simple greedy policy called Delay-based Server-Side-Greedy (D-SSG) with a lower complexity O(n^2) \lower complexity 2n^2+2n , and rigorously prove that D-SSG not only achieves throughput optimality, but also guarantees near-optimal rate-function-based delay performance.
Edit intent: meaning-changed
Original:  To address this issue, we develop a simple greedy policy called Delay-based Server-Side-Greedy (D-SSG) with a lower complexity O(n^2) \lower  , and rigorously prove that D-SSG not only achieves throughput optimality, but also guarantees near-optimal rate-function-based delay performance.
Revised:  To address this issue, we develop a simple greedy policy called Delay-based Server-Side-Greedy (D-SSG) with a lower complexity O(n^2) \lower , and rigorously prove that D-SSG not only achieves throughput optimality, but also guarantees near-optimal asymptotic delay performance.
Edit intent: meaning-changed
Original:  Specifically,  the rate-function attained by D-SSG for any fixed integer threshold b>0 , is no smaller than the maximum achievable rate-function by any scheduling policy for threshold b-1.
Revised:  Specifically, we show that the rate-function attained by D-SSG for any fixed integer threshold b>0 , is no smaller than the maximum achievable rate-function by any scheduling policy for threshold b-1.
Edit intent: clarity
Original:  Specifically,  the rate-function attained by D-SSG for any fixed integer threshold b>0 , is no smaller than the maximum achievable rate-function by any scheduling policy for threshold b-1.
Revised:  Specifically, the rate-function attained by D-SSG for any delay-violation threshold b , is no smaller than the maximum achievable rate-function by any scheduling policy for threshold b-1.
Edit intent: meaning-changed
Original:  Thus, we are able to achieve a reduction in complexity (from O(n^{2.5} \log n) of the hybrid policies to O(n^2 ) ) with a minimal drop in the delay performance.
Revised:  Thus, we are able to achieve a reduction in complexity (from O(n^{2.5} \log n) of the hybrid policies to 2n^2 + 2n ) with a minimal drop in the delay performance.
Edit intent: meaning-changed
Original:  Here we generalize the class of FGPs in several ways: (1) they may be specified over any strictly positive wealth processes resulting from investment in the tradeable assets, (2) the num\'eraire may be any strictly positive wealth process, (3 ) generating functions may be stochastically dynamic, adjusting to changing market conditions through an auxiliary continuous-path stochastic argument of finite variation.
Revised:  Here we generalize the class of FGPs in several ways: (1) the num\'eraire may be any strictly positive wealth process, (3 ) generating functions may be stochastically dynamic, adjusting to changing market conditions through an auxiliary continuous-path stochastic argument of finite variation.
Edit intent: clarity
Original:  Here we generalize the class of FGPs in several ways: (1) they may be specified over any strictly positive wealth processes resulting from investment in the tradeable assets, (2) the num\'eraire may be any strictly positive wealth process, (3 ) generating functions may be stochastically dynamic, adjusting to changing market conditions through an auxiliary continuous-path stochastic argument of finite variation.
Revised:  Here we generalize the class of FGPs in several ways: (1) they may be specified over any strictly positive wealth processes resulting from investment in the tradeable assets, (2) the num\'eraire may be any strictly positive wealth process, not necessarily the market portfolio or even a passive portfolio; (2 ) generating functions may be stochastically dynamic, adjusting to changing market conditions through an auxiliary continuous-path stochastic argument of finite variation.
Edit intent: meaning-changed
Original:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Revised:  From an online machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Edit intent: fluency
Original:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Revised:  From an on-line machine learning perspective, we first formulate online portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Edit intent: fluency
Original:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Revised:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as a sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Edit intent: fluency
Original:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Revised:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Edit intent: clarity
Original:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and meta-learning algorithms .
Revised:  From an on-line machine learning perspective, we first formulate on-line portfolio selection as an on-line sequential decision problem, and then survey a variety of state-of-the-art approaches in literature , which are grouped into several major categories, including benchmarks, "Follow-the-Winner" approaches, "Follow-the-Loser" approaches, "Pattern-Matching" based approaches, and "Meta-Learning Algorithms" .
Edit intent: fluency
Original:  This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in  financial industry to help them understand the state of the art and facilitate their research or practical applications.
Revised:  This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in the financial industry to help them understand the state of the art and facilitate their research or practical applications.
Edit intent: fluency
Original:  This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in  financial industry to help them understand the state of the art and facilitate their research or practical applications.
Revised:  This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in financial industry to help them understand the state-of-the-art and facilitate their research or practical applications.
Edit intent: fluency
Original:  This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in  financial industry to help them understand the state of the art and facilitate their research or practical applications.
Revised:  This article aims to provide a timely and comprehensive survey for both machine learning and data mining researchers in academia and quantitative portfolio managers in financial industry to help them understand the state of the art and facilitate their research and practical applications.
Edit intent: fluency
Original:  Moreover, exploiting the theory of sublinear expectations, we define a nonlinear Snell envelope Y and prove that the first hitting time \inf{t:\, Y_t=X_t is an optimal stopping time.
Revised:  Moreover, exploiting the theory of sublinear expectations, we define a nonlinear Snell envelope Y and prove that the first hitting time \inf\{t:Y_t=X_t\ is an optimal stopping time.
Edit intent: meaning-changed
Original: In this paper we derive an easily computed approximation of Rogers and Shi's lower bound for a local volatility jump-diffusion model and then use it to approximate European basket option values .
Revised: In this paper we derive an easily computed approximation of Rogers and Shi's lower bound for a local volatility jump-diffusion model . We apply the asymptotic expansion method to find the approximate value of the lower bound of European basket call prices .
Edit intent: meaning-changed
Original:  While dynamical wGHE computed on synthetic MRW series is consistent with a scenario where multifractality is constant over time, fluctuations in the dynamical wGHE observed in empirical data fail to be in agreement with a MRW with constant intermittency parameter.
Revised:  While dynamical wGHE computed on synthetic MRW series is consistent with a scenario where multifractality is constant over time, fluctuations in the dynamical wGHE observed in empirical data are not in agreement with a MRW with constant intermittency parameter.
Edit intent: clarity
Original:  This is a strong argument to claim that observed variations of multifractality in financial time series are to be ascribed to a structural breakdown in the temporal covariance structure of stock returns series. As a consequence, multi fractal models with a constant intermittency parameter may not always be satisfactory in reproducing financial market behaviour .
Revised:  We test these hypotheses of constant multifractality considering different specifications of MRW model with fatter tails: in all cases considered, although the thickness of the tails accounts for most of anomalous fluctuations of multifractality, still cannot fully explain the observed fluctuations .
Edit intent: meaning-changed
Original: Studies of coevolution of amino acids within and between proteins have revealed two types of coevolving units: coevolving contacts, which are pairs of amino acids distant along the sequence but in contact in the three-dimensional structure, and sectors, which are larger groups of structurally connected amino acids that underly the biochemical properties of proteins.
Revised: Studies of coevolution of amino acids within and between proteins have revealed two types of coevolving units: coevolving contacts, which are pairs of amino acids distant along the sequence but in contact in the three-dimensional structure, and sectors, which are larger groups of structurally connected amino acids that underlie the biochemical properties of proteins.
Edit intent: fluency
Original:  By reconciling two approaches for analyzing correlations in multiple sequence alignments, we uncover a new class of coevolving units , called 'sectons'. Sectons provide a conceptual link between coevolving contacts and sectors. The methods and results that we present are general , and relevant beyond protein structures .
Revised:  By reconciling two approaches for analyzing correlations in multiple sequence alignments, we link these two findings together and with coevolving units of intermediate size, called `sectons', which are shown to provide additional information. By extending the analysis to the co-occurrence of orthologous genes in bacterial genomes, we also show that the methods and results that we present are general , and relevant beyond protein structures .
Edit intent: meaning-changed
Original:  The Hopfield-Potts model allows us to identify relevant 'patterns' of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue Pearson correlation matrix.
Revised:  The Hopfield-Potts model allows us to identify relevant 'patterns' of residues from the knowledge of the eigenmodes and eigenvalues of the residue-residue correlation matrix.
Edit intent: clarity
Original:   In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact on the three-dimensional protein fold .
Revised:  This dimensional reduction allows us to avoid overfitting and to extract contact information from multiple-sequence alignments of reduced size. In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact on the three-dimensional protein fold .
Edit intent: meaning-changed
Original:   In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact on the three-dimensional protein fold .
Revised:  In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact in the three-dimensional protein fold .
Edit intent: clarity
Original:   In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact on the three-dimensional protein fold . We also explain why these low-eigenvalue modes, in contrast to the standard principal components, are able to efficiently encode compensatory mutations between pairs of residues .
Revised:  In addition, we show that low-eigenvalue correlation modes, discarded by PCA, are important to recover structural information: the corresponding patterns are highly localized, that is, they are concentrated in few sites, which we find to be in close contact on the three-dimensional protein fold .
Edit intent: coherence
Original:  A system of interest, when exposed to a stimulus S, adopts a deterministic response R of which a  noisy measurement M is made. Given a large number of measurements and corresponding stimuli , we wish to identify the correct " response function " relating R to S . However the "noise function" relating M to R is unknown a priori.
Revised:  A system of interest, when exposed to a stimulus S, adopts a deterministic response R of which a noisy measurement M . From a large number of measurements and corresponding stimuli , we wish to identify the correct " response function " relating R to S . However the "noise function" relating M to R is unknown a priori.
Edit intent: clarity
Original:  Given a large number of measurements and corresponding stimuli , we wish to identify the correct " response function " relating R to S . However the "noise function" relating M to R is unknown a priori.
Revised:  Given a large number of measurements and corresponding stimuli , we wish to identify the correct " response function " that maps S to R. However, the standard method for solving such problems, likelihood-based inference, requires perfect a priori knowledge of the "noise function" relating M to R is unknown a priori.
Edit intent: meaning-changed
Original:  Given a large number of measurements and corresponding stimuli , we wish to identify the correct " response function " relating R to S . However the "noise function" relating M to R is unknown a priori.
Revised:  Given a large number of measurements and corresponding stimuli , we wish to identify the correct " response function " relating R to S . However the "noise function" mapping R to M. In practice such noise functions are usually known only approximately, if at all, and using an incorrect noise function will typically bias the inferred filter.
Edit intent: meaning-changed
Original:  Here we show that maximizing likelihood over both response functions and noise functions is equivalent to simply identifying maximally informative response functions -- ones that maximize the mutual information I[ R;M ] between predicted responses and corresponding measurements .
Revised:  Here we show that , in the large data limit, this need for a pre-characterized noise function can be circumvented by searching for filters that instead maximize the mutual information I[ R;M ] between predicted responses and corresponding measurements .
Edit intent: clarity
Original:  Here we show that maximizing likelihood over both response functions and noise functions is equivalent to simply identifying maximally informative response functions -- ones that maximize the mutual information I[ R;M ] between predicted responses and corresponding measurements .
Revised:  Here we show that maximizing likelihood over both response functions and noise functions is equivalent to simply identifying maximally informative response functions -- ones that maximize the mutual information I[ M;R ] between predicted responses and corresponding measurements .
Edit intent: clarity