wanyu/IteraTeR_full_sent huggingface dataset:
Original: This paper considers an initial market model , specified by the pair (S,\mathbb F) where S is its discounted assets' price process and \mathbb F its flow of information , and an arbitrary random time \tau.
Revised: This paper focuses on num\'eraire portfolio and log-optimal portfolio (portfolio with finite expected utility that maximizes the expected logarithm utility from terminal wealth), when a market model (S,\mathbb F) where S is its discounted assets' price process and \mathbb F its flow of information , and an arbitrary random time \tau.
Edit intent: meaning-changed
Original: This paper considers an initial market model , specified by the pair (S,\mathbb F) where S is its discounted assets' price process and \mathbb F its flow of information , and an arbitrary random time \tau.
Revised: This paper considers an initial market model , specified by the pair (S,\mathbb F) -specified by its assets' price process and \mathbb F its flow of information , and an arbitrary random time \tau.
Edit intent: clarity
Original: This paper considers an initial market model , specified by the pair (S,\mathbb F) where S is its discounted assets' price process and \mathbb F its flow of information , and an arbitrary random time \tau.
Revised: This paper considers an initial market model , specified by the pair (S,\mathbb F) where S is its discounted assets' price S and its flow of information , and an arbitrary random time \tau.
Edit intent: clarity
Original: This paper considers an initial market model , specified by the pair (S,\mathbb F) where S is its discounted assets' price process and \mathbb F its flow of information , and an arbitrary random time \tau.
Revised: This paper considers an initial market model , specified by the pair (S,\mathbb F) where S is its discounted assets' price process and \mathbb F its flow of information \mathbb F- is stopped at a random time \tau.
Edit intent: meaning-changed
Original:  For the  stopped model (S^{\tau},\mathbb G), we describe in different manners and as explicit as possible the num\'eraire portfolio, the log-optimal portfolio, the log-optimal deflator (which is the dual of the log-optimal portfolio), and we elaborate their duality without any further assumption .
Revised:  For the resulting stopped model (S^{\tau},\mathbb G), we describe in different manners and as explicit as possible the num\'eraire portfolio, the log-optimal portfolio, the log-optimal deflator (which is the dual of the log-optimal portfolio), and we elaborate their duality without any further assumption .
Edit intent: clarity
Original:  For the  stopped model (S^{\tau},\mathbb G), we describe in different manners and as explicit as possible the num\'eraire portfolio, the log-optimal portfolio, the log-optimal deflator (which is the dual of the log-optimal portfolio), and we elaborate their duality without any further assumption .
Revised:  For the stopped model (S^{\tau},\mathbb G), we study the two portfolios in different manners and as explicit as possible the num\'eraire portfolio, the log-optimal portfolio, the log-optimal deflator (which is the dual of the log-optimal portfolio), and we elaborate their duality without any further assumption .
Edit intent: meaning-changed
Original:  For the  stopped model (S^{\tau},\mathbb G), we describe in different manners and as explicit as possible the num\'eraire portfolio, the log-optimal portfolio, the log-optimal deflator (which is the dual of the log-optimal portfolio), and we elaborate their duality without any further assumption .
Revised:  For the stopped model (S^{\tau},\mathbb G), we describe in different manners , and describe their computations in terms of the \mathbb F-observable parameters of the pair (S, \tau) .
Edit intent: meaning-changed
Original:  It was found that the expected value of excess kurtosis divided by the sample size is finite for any value of the tail index and the sample estimate of kurtosis increase as a linear function of sample size and tail index  .
Revised:  It was found that the expected value of excess kurtosis divided by the sample size is finite for any value of the tail index and the sample estimate of kurtosis increases as a linear function of sample size and tail index .
Edit intent: fluency
Original:  It was found that the expected value of excess kurtosis divided by the sample size is finite for any value of the tail index and the sample estimate of kurtosis increase as a linear function of sample size and tail index  .
Revised:  It was found that the expected value of excess kurtosis divided by the sample size is finite for any value of the tail index and the sample estimate of kurtosis increase as a linear function of sample size and tail index . It is very sensitive to changes in the tail-index .
Edit intent: meaning-changed
Original: We propose a Active Walker Model for the movement of individual under the feedback of a time-dependent pheromone field . It is assumed that the sole feedback mechanism is tropotaxis: ants can sense the gradient of the pheromone concentration field and adjust their orientation accordingly.
Revised: We propose a Active Walker Model for the movement of individual under the feedback of a time-dependent pheromone field that feedbacks into the motion ants through tropotaxis: individuals can sense the gradient of the pheromone concentration field and adjust their orientation accordingly.
Edit intent: meaning-changed
Original:  We consider two types of pheromone fields: one emanating from the nest, and other actively produced by ants in their nest bound journey after finding a food source.
Revised:  The individual model takes the form of a Langevin system of equations in polar coordinates driven by two-dimensional Gaussian fluctuations and with orientation changes in response to two pheromone fields: one emanating from the nest, and other actively produced by ants in their nest bound journey after finding a food source.
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Original:  The model yields, under certain assumptions on the parameters, that ants can successfully recruit others to food sources, and that a trail network pattern will emerge .
Revised:  The proposed tropotaxis model relating the pheromone field to the orientation changes is similar to Weber's law, but depends explicitly only on the gradient of the pheromone concentration. We identify ranges of values for the model parameters that yield the emergence of two key foraging patterns: successful recruitment to newly found sources, and that a trail network pattern will emerge .
Edit intent: meaning-changed
Original: This paper presents a new identification result for causal effects of group-level variables when agents select into groups. The model allows for group selection to be based on individual unobserved heterogeneity. This feature leads to correlation between group-level covariates and unobserved individual heterogeneity. Whereas many of the existing identification strategies rely on instrumental variables for group selection, I introduce alternative identifying conditions which involve individual-level covariates that "shift" the distribution of unobserved heterogeneity . I use these conditions to construct a valid control function .
Revised: This paper presents a new identification approach for partial effects in nonseparable models with endogeneity. I use a proxy variable for the unobserved heterogeneity correlated with the endogenous variable to construct a valid control function .
Edit intent: clarity
Original:  I use these conditions to construct a valid control function . The key identifying requirements on the observable "shifter" variables are likely to hold in settings where a rich array of individual characteristics are observed. The identification strategy is constructive and leads to a semiparametric, regression-based estimator of group-level causal effects, which I show to be consistent and asymptotically normal. A simulation study indicates good finite-sample properties of this estimator. I use my results to re-analyze the effects of school/neighborhood characteristics on student outcomes, following the work of Altonji and Mansfield (2018) .
Revised:  I use these conditions to construct a valid control function , where the definition of a proxy variable is the same as in the measurement error literature. The identifying assumptions are distinct from existing methods, in particular instrumental variables and selection on observables approaches, and I provide an alternative identification strategy in settings where existing approaches are not applicable. Building on the identification result, I consider three estimation approaches, ranging from nonparametric to flexible parametric methods, and characterize asymptotic properties of the proposed estimators .
Edit intent: coherence
Original:  In this study, we propose a novel methodology to predict drug-target binding affinity using only ligand SMILES information. We represent proteins using the word-embeddings of the SMILES representations of their strong binding ligands. Each SMILES is represented in the form of a set of chemical words and a protein is described by the set of chemical words with the highest Term Frequency- Inverse Document Frequency (TF-IDF) value. We then utilize the Support Vector Regression (SVR) algorithm to predict protein - drug binding affinities in the Davis and KIBA Kinase datasets. We also compared the performance of SMILES representation with the recently proposed DeepSMILES representation and found that using DeepSMILES yields better performance in the prediction task. Using only SMILESVec, which is a strictly string based representation of the proteins based on their interacting ligands, we were able to predict drug-target binding affinity as well as or better than the KronRLS or SimBoost models that utilize protein sequence .
Revised:  Proteins are generally represented by their structures or sequences. However, structures are available only for a small subset of biomolecules and sequence similarity is not always correlated with functional similarity. We propose ChemBoost, a chemical language based approach for affinity prediction using SMILES syntax. We hypothesize that SMILES is a codified language and ligands are documents composed of chemical words. These documents can be used to learn chemical word vectors that represent words in similar contexts with similar vectors. In ChemBoost, the ligands are represented via chemical word embeddings, while the proteins are represented through sequence-based features and/or chemical words of their ligands. Our aim is to process the patterns in SMILES as a language to predict protein-ligand affinity, even when we cannot infer the function from the sequence. We used eXtreme Gradient Boosting to predict protein-ligand affinities in KIBA and BindingDB data sets. ChemBoost was able to predict drug-target binding affinity as well as or better than the KronRLS or SimBoost models that utilize protein sequence .
Edit intent: coherence
Original:  Using only SMILESVec, which is a strictly string based representation of the proteins based on their interacting ligands, we were able to predict drug-target binding affinity as well as or better than the KronRLS or SimBoost models that utilize protein sequence .
Revised:  Using only SMILESVec, which is a strictly string based representation of the proteins based on their interacting ligands, we were able to predict drug-target binding affinity as well as or better than state-of-the-art machine learning systems. When powered with ligand-centric representations, ChemBoost was more robust to the changes in protein sequence similarity and successfully captured the interactions between a protein and a ligand, even if the protein has low sequence similarity to the known targets of the ligand .
Edit intent: meaning-changed
Original:   Using the Selection problem as an example , we show that it is possible to produce instances which are ten times as hard to solve on average for a state-of-the-art mixed-integer programming solver  .
Revised:  Our approach does not make any assumptions on the problem structure and can thus be applied to any combinatorial problem. Using the Selection problem as an example , we show that it is possible to produce instances which are ten times as hard to solve on average for a state-of-the-art mixed-integer programming solver .
Edit intent: meaning-changed
Original:   Using the Selection problem as an example , we show that it is possible to produce instances which are ten times as hard to solve on average for a state-of-the-art mixed-integer programming solver  .
Revised:  Using the Selection and Traveling Salesman problems as examples , we show that it is possible to produce instances which are ten times as hard to solve on average for a state-of-the-art mixed-integer programming solver .
Edit intent: meaning-changed
Original:   Using the Selection problem as an example , we show that it is possible to produce instances which are ten times as hard to solve on average for a state-of-the-art mixed-integer programming solver  .
Revised:  Using the Selection problem as an example , we show that it is possible to produce instances which are ten times as hard to solve on average for a state-of-the-art mixed-integer programming solver than the current state-of-the-art instances .
Edit intent: meaning-changed
Original:  By focussing on the properties that martingales and compensators show on infinitesimally small intervals, the classical martingale concept is extended and corresponding martingale representations are developed. The extended representations make it possible to generalize Thiele's equation in insurance mathematics to situations where information restrictions apply .
Revised:  By focussing on the properties that martingales and compensators show on infinitesimally short intervals. Infinitesimal martingale representations are derived that extend classical martingale representations to non-monotone information. While the classical representations describe innovations only, the extended representations have an additional symmetric counterpart that quantifies the effect of information loss .
Edit intent: meaning-changed
Original:   The central idea is to focus only on those properties that martingales and compensators show on infinitesimally short intervals.
Revised:  A fundamental tool for calculating and managing risks in finance and insurance are martingale representations. We present a general theory that extends classical martingale representations to non-monotone information generated by marked point processes. The central idea is to focus only on those properties that martingales and compensators show on infinitesimally short intervals.
Edit intent: meaning-changed
Original:  Infinitesimal martingale representations are derived that extend classical martingale representations to non-monotone information. While the classical representations describe innovations only, the extended representations have an additional symmetric counterpart that quantifies the effect of information loss  .
Revised:  Infinitesimal martingale representations are derived that extend classical martingale representations describe innovations only, the extended representations have an additional symmetric counterpart that quantifies the effect of information loss .
Edit intent: clarity
Original:  We show that a modification of May's symmetry assumption allows for a far richer set of rules that still treat voters equally , but have minimal winning coalitions comprising a vanishing fraction of the population . We conclude that procedural fairness can coexist with the empowerment of a small minority of individuals .
Revised:  We show that a modification of May's symmetry assumption allows for a far richer set of rules that still treat voters equally , but have minimal winning coalitions comprising a vanishing fraction of the population , but not less than the square root of the population size .
Edit intent: meaning-changed
Original:  This novel method was verified on fish (European bass, Dicentrarchus labrax and Tiger Barbs , Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes.
Revised:  This novel method was verified on fish (European bass, Dicentrarchus labrax , and tiger barbs , Puntius tetrazona) swimming in a reasonably small tank, which forced them to exhibit a large variety of shapes.
Edit intent: fluency
Original:  The correct number of objects was determined for 88\% of overlaps and the mean Dice-S \o%DIFDELCMD < }%%% rensen coefficient was 0:84, implying that this method is feasible in real-life applications such as toxicity testing.
Revised:  Compared with manual detection, the correct number of objects was determined for 88\% of overlaps and the mean Dice-S \o%DIFDELCMD < }%%% rensen coefficient was 0:84, implying that this method is feasible in real-life applications such as toxicity testing.
Edit intent: meaning-changed
Original:  The correct number of objects was determined for 88\% of overlaps and the mean Dice-S \o%DIFDELCMD < }%%% rensen coefficient was 0:84, implying that this method is feasible in real-life applications such as toxicity testing.
Revised:  The correct number of objects was determined for \o%DIFDELCMD < }%%% rensen coefficient was 0:84, implying that this method is feasible in real-life applications such as toxicity testing.
Edit intent: coherence
Original:  The correct number of objects was determined for 88\% of overlaps and the mean Dice-S \o%DIFDELCMD < }%%% rensen coefficient was 0:84, implying that this method is feasible in real-life applications such as toxicity testing.
Revised:  The correct number of objects was determined for 88\% of overlaps and the mean Dice-S %DIFDELCMD < }%%% rensen coefficient was 0:84, implying that this method is feasible in real-life applications such as toxicity testing.
Edit intent: fluency
Original:  The correct number of objects was determined for 88\% of overlaps and the mean Dice-S \o%DIFDELCMD < }%%% rensen coefficient was 0:84, implying that this method is feasible in real-life applications such as toxicity testing.
Revised:  The correct number of objects was determined for 88\% of overlaps and the mean Dice-S \o%DIFDELCMD < }%%% up to almost 90
Edit intent: meaning-changed
Original:  Our results for the 2012 and 2016 U.S. races are largely in agreement with those of popular pollsters , and we use our new model to explore how subjective choices about uncertainty impact results.
Revised:  Our results for the 2012 and 2016 U.S. races are largely in agreement with those of popular forecasters , and we use our new model to explore how subjective choices about uncertainty impact results.
Edit intent: clarity
Original:  Our goals are to open up new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate a fascinating example of data-driven forecasting using dynamical systems.
Revised:  Our goals are to open new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate a fascinating example of data-driven forecasting using dynamical systems.
Edit intent: fluency
Original:  Our goals are to open up new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate a fascinating example of data-driven forecasting using dynamical systems.
Revised:  Our goals are to open up new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate the value of data-driven forecasting using dynamical systems.
Edit intent: clarity
Original: To forecast political elections , popular pollsters and forecasters gather polls and combine information from them with fundamental data such as historical trends, the national economy, and incumbency.
Revised: Forecasting elections --- a challenging, high-stakes problem --- is the subject of much uncertainty, subjectivity, and media scrutiny. To shed light on this process , popular pollsters and forecasters gather polls and combine information from them with fundamental data such as historical trends, the national economy, and incumbency.
Edit intent: clarity
Original: To forecast political elections , popular pollsters and forecasters gather polls and combine information from them with fundamental data such as historical trends, the national economy, and incumbency. This process is complicated, and it includes many subjective choices (e. g., when identifying likely voters, estimating turnout, and quantifying other sources of uncertainty), leading to forecasts that differ between sources even when they use the same underlying polling data. With the goal of shedding light on election forecasts (using the United States as an example), we develop a theory for forecasting elections from the perspective of dynamical systems.
Revised: To forecast political elections , we develop a theory for forecasting elections from the perspective of dynamical systems.
Edit intent: coherence
Original:  Through a simple approach that borrows ideas from epidemiology, we show how to combine a compartmental model of disease spreading with public polling data to forecast gubernatorial, senatorial, and presidential elections at the state level. Our results for the 2012 and 2016 U.S. races are largely in agreement with those of popular forecasters, and we use our new model to explore how subjective choices about uncertainty impact results.
Revised:  Through a simple approach that borrows ideas from epidemiology, and we use polling data from United States elections to determine its parameters. Surprisingly, our general model performs as well as popular forecasters for the 2012 and 2016 U.S. races are largely in agreement with those of popular forecasters, and we use our new model to explore how subjective choices about uncertainty impact results.
Edit intent: meaning-changed
Original:  Our results for the 2012 and 2016 U.S. races are largely in agreement with those of popular forecasters, and we use our new model to explore how subjective choices about uncertainty impact results. Our goals are to open new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate the value of data-driven forecasting using dynamical systems  .
Revised:  Our results for the 2012 and 2016 U.S. races for president, Senate, and governor. Although contagion and voting dynamics differ, our work suggests a new approach to elucidate how elections are related across states. It also illustrates the effect of accounting for uncertainty in different ways, provides an illuminating example of data-driven forecasting using dynamical systems .
Edit intent: meaning-changed
Original:  Our goals are to open new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate the value of data-driven forecasting using dynamical systems  .
Revised:  Our goals are to open new avenues for improving how elections are forecast, to increase understanding of the results that are reported by popular news sources, and to illustrate the value of data-driven forecasting using dynamical systems , and suggests avenues for future research on political elections .
Edit intent: meaning-changed
Original:  We conclude with our forecast of the senatorial and gubernatorial races in the 2018 U.S. midterm elections of 6 November , which we posted online as a preprint on 5 November.
Revised:  We conclude with our forecast of the senatorial and gubernatorial races in the 2018 U.S. midterm elections of 6 ~November 2018 , which we posted online as a preprint on 5 November.
Edit intent: clarity