wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We consider two optimization goals: either minimizing the number of line segments or minimizing the number of bend points in the simplification. By reduction from Minimum-Independent-Dominating-Set, we show that both of these optimization problems are NP-hard to approximate within a factor n^{1/3 - \varepsilon} for any \varepsilon > 0 where n is the number of bend points in the polyline bundle.
Revised:  We consider two optimization goals: either minimizing the number of line segments or minimizing the number of bend points in the simplified bundle with respect to some error tolerance \delta (measuring Fr\'echet distance) but under the additional constraint that shared parts have to be simplified consistently. We show that polyline bundle simplification is NP-hard to approximate within a factor n^{1/3 - \varepsilon} for any \varepsilon > 0 where n is the number of bend points in the polyline bundle.
Edit intent: meaning-changed
Original:  Moreover, we outline that both problems remain NP-hard even if the input is planar. On the positive side, we give a polynomial-size integer linear program and show fixed-parameter tractability in the number of shared bend points.
Revised:  This inapproximability even applies to instances with only \ell=2 polylines. However, we identify the sensitivity of the solution to the choice of \delta as a reason for this strong inapproximability. In particular, we prove that if we allow \delta to be exceeded by a factor of 2 in our solution, we can find a simplified polyline bundle with no more than O(\log (\ell + n)) \cdot OPT bend points in polytime, providing us with an efficient bi-criteria approximation. As a further result, we show fixed-parameter tractability in the number of shared bend points.
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Original: We show that, for a class of mean-reverting models , the correlation function of stochastic variance (squared volatility) contains only one -- relaxation -- parameter. We generalize and simplify the expression for leverage for this class of models. We apply our results to specific examples of such models -- multiplicative, Heston , and combined multiplicative-Heston -- and use historic stock market data to obtain parameters of their steady-state distributions and cross-correlations between Weiner processes in the models for stock returns and stochastic variance .
Revised: We show that, for a class of mean-reverting models for stochastic variances. We derive closed-form expressions for the correlation functions and leverage for a general form of the stochastic term. We also discuss correlation functions and leverage for three specific models -- multiplicative, Heston , and combined multiplicative-Heston -- and use historic stock market data to obtain parameters of their steady-state distributions and cross-correlations between Weiner processes in the models for stock returns and stochastic variance .
Edit intent: clarity
Original:  We apply our results to specific examples of such models -- multiplicative, Heston , and combined multiplicative-Heston -- and use historic stock market data to obtain parameters of their steady-state distributions and cross-correlations between Weiner processes in the models for stock returns and stochastic variance .
Revised:  We apply our results to specific examples of such models -- multiplicative, Heston (Cox-Ingersoll-Ross) and combined multiplicative-Heston -- and use historic stock market data to obtain parameters of their steady-state distributions and cross-correlations between Weiner processes in the models for stock returns and stochastic variance .
Edit intent: meaning-changed
Original:  We apply our results to specific examples of such models -- multiplicative, Heston , and combined multiplicative-Heston -- and use historic stock market data to obtain parameters of their steady-state distributions and cross-correlations between Weiner processes in the models for stock returns and stochastic variance .
Revised:  We apply our results to specific examples of such models -- multiplicative, Heston , and combined multiplicative-Heston -- whose steady-state distributions and cross-correlations between Weiner processes in the models for stock returns and stochastic variance .
Edit intent: clarity
Original:  We apply our results to specific examples of such models -- multiplicative, Heston , and combined multiplicative-Heston -- and use historic stock market data to obtain parameters of their steady-state distributions and cross-correlations between Weiner processes in the models for stock returns and stochastic variance .
Revised:  We apply our results to specific examples of such models -- multiplicative, Heston , and combined multiplicative-Heston -- and use historic stock market data to obtain parameters of their steady-state probability density functions are Gamma, Inverse Gamma and Beta Prime respectively, the latter two exhibiting "fat" tails. For the Heston model, we apply the eigenvalue analysis of the Fokker-Planck equation to derive the correlation function -- in agreement with the general analysis -- and to identify a series of time scales, which are observable in relaxation of cumulants on approach to the steady state. We test our findings on a very large set of historic financial markets data .
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Original:  Finally, we present a novel software framework ( warfit-learn ) for standardising future research by leveraging the most successful techniques in preprocessing, imputation, and evaluation, with the goal of making results more reproducible in this domain.
Revised:  Finally, we present a novel software framework ( Warfit-learn ) for standardising future research by leveraging the most successful techniques in preprocessing, imputation, and evaluation, with the goal of making results more reproducible in this domain.
Edit intent: fluency
Original:  Finally, we present a novel software framework ( warfit-learn ) for standardising future research by leveraging the most successful techniques in preprocessing, imputation, and evaluation, with the goal of making results more reproducible in this domain.
Revised:  Finally, we present a novel software framework ( warfit-learn ) for standardising future research by leveraging the most successful techniques in preprocessing, imputation, and evaluation--with the goal of making results more reproducible in this domain.
Edit intent: fluency
Original:  Finally, we present a novel software framework (Warfit-learn) for standardising future researchby leveraging the most successful techniques in preprocessing, imputation, and evaluation--with the goal of making results more reproducible in this domain  .
Revised:  Finally, we present a novel software framework (Warfit-learn) for warfarin dosing research. It leverages the most successful techniques in preprocessing, imputation, and evaluation--with the goal of making results more reproducible in this domain .
Edit intent: meaning-changed
Original:  Finally, we present a novel software framework (Warfit-learn) for standardising future researchby leveraging the most successful techniques in preprocessing, imputation, and evaluation--with the goal of making results more reproducible in this domain  .
Revised:  Finally, we present a novel software framework (Warfit-learn) for standardising future researchby leveraging the most successful techniques in preprocessing, imputation, and parallel evaluation, with the goal of making results more reproducible in this domain .
Edit intent: clarity
Original:  Finally, we present a novel software framework (Warfit-learn) for standardising future researchby leveraging the most successful techniques in preprocessing, imputation, and evaluation--with the goal of making results more reproducible in this domain  .
Revised:  Finally, we present a novel software framework (Warfit-learn) for standardising future researchby leveraging the most successful techniques in preprocessing, imputation, and evaluation--with the goal of accelerating research and making results in this domain .
Edit intent: clarity
Original:  Finally, we present a novel software framework (Warfit-learn) for standardising future researchby leveraging the most successful techniques in preprocessing, imputation, and evaluation--with the goal of making results more reproducible in this domain  .
Revised:  Finally, we present a novel software framework (Warfit-learn) for standardising future researchby leveraging the most successful techniques in preprocessing, imputation, and evaluation--with the goal of making results more reproducible in this domain more reproducible .
Edit intent: meaning-changed
Original:   Using some known states of a dynamical system that represents the evolution of a financial market, we use our technique to simulate new states , that we call ``dreams" .
Revised:  Specifically, the McShane and Whitney extensions are considered for a reward function which is defined by the total evaluation of the benefits produced by the investment decision at a given time. We define the metric as a linear combination of a Euclidean distance and an angular metric component. All information about the evolution of the system from the beginning of the time interval is used to support the extension of the reward function, but in addition this data set is enriched by adding some artificially produced states. Thus, the main novelty of our method is the way we produce more states -- which we call "dreams" -- to enrich learning. Using some known states of a dynamical system that represents the evolution of a financial market, we use our technique to simulate new states , that we call ``dreams" .
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Original: We study the multi-level order-flow imbalance (MLOFI) , which measures the net flow of buy and sell orders at different price levels in a limit order book (LOB).
Revised: We study the multi-level order-flow imbalance (MLOFI) multi-level order-flow imbalance (MLOFI), which is a vector quantity that measures the net flow of buy and sell orders at different price levels in a limit order book (LOB).
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Original:  For all 6 stocks that we study, we find that the  goodness-of-fit of the relationship improves with each additional price level that we include in the MLOFI vector.
Revised:  For all 6 stocks that we study, we find that the out-of-sample goodness-of-fit of the relationship improves with each additional price level that we include in the MLOFI vector.
Edit intent: meaning-changed
Original:  Specifically, using data for select U.S. cities over the past half-century, we compare the evolving time series dynamics of the average temperature level, AVG, and the diurnal temperature range, DTR (the difference between the daily maximum and minimum temperatures at a given location ).
Revised:  Specifically, using data for select U.S. cities over the past half-century, we compare the evolving time series dynamics of the average temperature level, AVG, and the diurnal temperature range, DTR (the difference between the daily maximum and minimum temperatures ).
Edit intent: clarity
Original: We provide here a general mathematical framework to model attitudes towards ambiguity which uses the formalism of quantum theory as a " purely mathematical formalism, detached from any physical interpretation " . We show that the quantum-theoretic framework enables modelling of the "Ellsberg paradox", but it also successfully applies to more concrete human decision-making (DM) tests involving financial, managerial and medical decisions.
Revised: We provide here a general mathematical framework to model attitudes towards ambiguity which uses the formalism of quantum theory as a `` purely mathematical formalism, detached from any physical interpretation " . We show that the quantum-theoretic framework enables modelling of the "Ellsberg paradox", but it also successfully applies to more concrete human decision-making (DM) tests involving financial, managerial and medical decisions.
Edit intent: fluency
Original: We provide here a general mathematical framework to model attitudes towards ambiguity which uses the formalism of quantum theory as a " purely mathematical formalism, detached from any physical interpretation " . We show that the quantum-theoretic framework enables modelling of the "Ellsberg paradox", but it also successfully applies to more concrete human decision-making (DM) tests involving financial, managerial and medical decisions.
Revised: We provide here a general mathematical framework to model attitudes towards ambiguity which uses the formalism of quantum theory as a " purely mathematical formalism, detached from any physical interpretation '' . We show that the quantum-theoretic framework enables modelling of the "Ellsberg paradox", but it also successfully applies to more concrete human decision-making (DM) tests involving financial, managerial and medical decisions.
Edit intent: fluency
Original:  In particular, we provide a faithful mathematical representation of various empirical studies which reveal that attitudes of managers towards uncertainty shift from "ambiguity seeking" to "ambiguity aversion", and viceversa, thus exhibiting "hope effects" and "fear effects" in management decisions .
Revised:  In particular, we elaborate a mathematical representation of various empirical studies which reveal that attitudes of managers towards uncertainty shift from "ambiguity seeking" to "ambiguity aversion", and viceversa, thus exhibiting "hope effects" and "fear effects" in management decisions .
Edit intent: clarity
Original:  In particular, we provide a faithful mathematical representation of various empirical studies which reveal that attitudes of managers towards uncertainty shift from "ambiguity seeking" to "ambiguity aversion", and viceversa, thus exhibiting "hope effects" and "fear effects" in management decisions .
Revised:  In particular, we provide a faithful mathematical representation of various empirical studies which reveal that attitudes of managers towards uncertainty shift from "ambiguity seeking" to "ambiguity aversion", and viceversa, thus exhibiting "hope effects" and "fear effects" .
Edit intent: clarity
Original:  The test compares the likelihood ratio statistic to a chi-squared critical value, where the degrees of freedom is the rank of the active inequalities . This test requires no tuning parameters or simulations, and therefore is computationally fast , even with many inequalities.
Revised:  The test compares the likelihood ratio statistic to a chi-squared critical value, where the degrees of freedom is the rank of the inequalities that are active in finite samples. The test requires no tuning parameters or simulations, and therefore is computationally fast , even with many inequalities.
Edit intent: meaning-changed
Original:  This test requires no tuning parameters or simulations, and therefore is computationally fast , even with many inequalities. Further, it does not require an estimate of the number of binding or close-to-binding inequalities. To show that this test is uniformly valid, we establish a new bound on the probability of translations of cones under the multivariate normal distribution that may be of independent interest. The leading application of our test is inference in moment inequality models. We also consider testing affine inequalities in the multivariate normal model and testing nonlinear inequalities in general asymptotically normal models .
Revised:  This test requires no tuning parameters or simulations, and therefore is computationally fast and especially suitable for constructing confidence sets for parameters by test inversion. It uses no tuning parameter for moment selection and yet still adapts to the slackness of the moment inequalities. Furthermore, we show how the test can be easily adapted for inference on subvectors for the common empirical setting of conditional moment inequalities with nuisance parameters entering linearly .
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Original:  We prove the convergence of the well-known Longstaff and Schwartz algorithm when the standard least-square regression is replaced by a neural network approximation  .
Revised:  We prove the convergence of the well-known Longstaff and Schwartz algorithm when the standard least-square regression is replaced by a neural network approximation . We illustrate the numerical efficiency of neural networks as an alternative to standard regression methods for approximating conditional expectations on several numerical examples .
Edit intent: meaning-changed
Original:  We also show that every set S of n points contains two points such that either (i) every circle passing through them encloses at least%DIFDELCMD < \frac{n-2{3}}\rfloor %%% points of S, or (ii) every circle passing through them encloses at most%DIFDELCMD < \frac{2n-5{3}}%%% \frac{2n-3{3}} \rfloor points of S.
Revised:  We also show that every set S of n points contains two points such that every circle passing through them encloses at least%DIFDELCMD < \frac{n-2{3}}\rfloor %%% points of S, or (ii) every circle passing through them encloses at most%DIFDELCMD < \frac{2n-5{3}}%%% \frac{2n-3{3}} \rfloor points of S.
Edit intent: coherence
Original:  We also show that every set S of n points contains two points such that either (i) every circle passing through them encloses at least%DIFDELCMD < \frac{n-2{3}}\rfloor %%% points of S, or (ii) every circle passing through them encloses at most%DIFDELCMD < \frac{2n-5{3}}%%% \frac{2n-3{3}} \rfloor points of S.
Revised:  We also show that every set S of n points contains two points such that either (i) every circle passing through them encloses at %DIFDELCMD < \frac{n-2{3}}\rfloor %%% points of S, or (ii) every circle passing through them encloses at most%DIFDELCMD < \frac{2n-5{3}}%%% \frac{2n-3{3}} \rfloor points of S.
Edit intent: clarity
Original:  We also show that every set S of n points contains two points such that either (i) every circle passing through them encloses at least%DIFDELCMD < \frac{n-2{3}}\rfloor %%% points of S, or (ii) every circle passing through them encloses at most%DIFDELCMD < \frac{2n-5{3}}%%% \frac{2n-3{3}} \rfloor points of S.
Revised:  We also show that every set S of n points contains two points such that either (i) every circle passing through them encloses at least%DIFDELCMD < \frac{n-2{3}}\rfloor %%% %DIFDELCMD < \frac{2n-5{3}}%%% \frac{2n-3{3}} \rfloor points of S.
Edit intent: coherence
Original:  We also show that every set S of n points contains two points such that either (i) every circle passing through them encloses at least%DIFDELCMD < \frac{n-2{3}}\rfloor %%% points of S, or (ii) every circle passing through them encloses at most%DIFDELCMD < \frac{2n-5{3}}%%% \frac{2n-3{3}} \rfloor points of S.
Revised:  We also show that every set S of n points contains two points such that either (i) every circle passing through them encloses at least%DIFDELCMD < \frac{n-2{3}}\rfloor %%% points of S, or (ii) every circle passing through them encloses at most%DIFDELCMD < \frac{2n-5{3}}%%% most\frac{2n-3{3}} \rfloor points of S.
Edit intent: style
Original:  Quant GANs consist of a generator and discriminator function  which utilize temporal convolutional networks (TCNs) and thereby achieve to capture longer-ranging dependencies such as the presence of volatility clusters.
Revised:  Quant GANs consist of a generator and discriminator function , which utilize temporal convolutional networks (TCNs) and thereby achieve to capture longer-ranging dependencies such as the presence of volatility clusters.
Edit intent: fluency
Original:  Quant GANs consist of a generator and discriminator function  which utilize temporal convolutional networks (TCNs) and thereby achieve to capture longer-ranging dependencies such as the presence of volatility clusters.
Revised:  Quant GANs consist of a generator and discriminator function which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters.
Edit intent: fluency
Original: Interpretable Machine Learning (IML) has become increasingly important in many  applications, such as autonomous cars and medical diagnosis, where explanations are  preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.
Revised: Interpretable Machine Learning (IML) has become increasingly important in many real-world applications, such as autonomous cars and medical diagnosis, where explanations are preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.
Edit intent: clarity
Original: Interpretable Machine Learning (IML) has become increasingly important in many  applications, such as autonomous cars and medical diagnosis, where explanations are  preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.
Revised: Interpretable Machine Learning (IML) has become increasingly important in many applications, such as autonomous cars and medical diagnosis, where explanations are significantly preferred to help people better understand how machine learning systems work and further enhance their trust towards systems.
Edit intent: clarity
Original:  To benchmark  evaluation in IML, in this paper , we rigorously define the problem of evaluating explanations, and systematically review the existing efforts  . Specifically, we summarize three general aspects of explanation (i.e., predictability , fidelity and persuasibility) with formal definitions, and respectively review the representative methodologies for each of them under different tasks.
Revised:  To benchmark the evaluation in IML, in this paper , we rigorously define the problem of evaluating explanations, and systematically review the existing efforts . Specifically, we summarize three general aspects of explanation (i.e., predictability , fidelity and persuasibility) with formal definitions, and respectively review the representative methodologies for each of them under different tasks.
Edit intent: coherence
Original:  To benchmark  evaluation in IML, in this paper , we rigorously define the problem of evaluating explanations, and systematically review the existing efforts  . Specifically, we summarize three general aspects of explanation (i.e., predictability , fidelity and persuasibility) with formal definitions, and respectively review the representative methodologies for each of them under different tasks.
Revised:  To benchmark evaluation in IML, in this article , we rigorously define the problem of evaluating explanations, and systematically review the existing efforts . Specifically, we summarize three general aspects of explanation (i.e., predictability , fidelity and persuasibility) with formal definitions, and respectively review the representative methodologies for each of them under different tasks.
Edit intent: fluency
Original:  To benchmark  evaluation in IML, in this paper , we rigorously define the problem of evaluating explanations, and systematically review the existing efforts  . Specifically, we summarize three general aspects of explanation (i.e., predictability , fidelity and persuasibility) with formal definitions, and respectively review the representative methodologies for each of them under different tasks.
Revised:  To benchmark evaluation in IML, in this paper , we rigorously define the problem of evaluating explanations, and systematically review the existing efforts from state-of-the-arts . Specifically, we summarize three general aspects of explanation (i.e., predictability , fidelity and persuasibility) with formal definitions, and respectively review the representative methodologies for each of them under different tasks.
Edit intent: meaning-changed