wanyu/IteraTeR_full_sent huggingface dataset:
Original:  For any constant c\geq 1, we propose two oracles: * The first oracle has size n^{2+1/c}(\log n/\epsilon)^{O(d)}\cdot \log W, answers a query in {\rm poly}(\log n,d^c,\log\log W,\epsilon^{-1}) time, and has stretch 1+\epsilon, for any constant \epsilon>0.
Revised:  For any constant c\geq 1, we propose two oracles: \bullet The first oracle has size n^{2+1/c}(\log n/\epsilon)^{O(d)}\cdot \log W, answers a query in {\rm poly}(\log n,d^c,\log\log W,\epsilon^{-1}) time, and has stretch 1+\epsilon, for any constant \epsilon>0.
Edit intent: meaning-changed
Original:  * The second oracle has size n^{2+1/c}{\rm poly}(\log (nW),d), answers a query in {\rm poly}(\log n,d^c,\log\log W) time, and has stretch {\rm poly}(\log n,d).
Revised:  \bullet The second oracle has size n^{2+1/c}{\rm poly}(\log (nW),d), answers a query in {\rm poly}(\log n,d^c,\log\log W) time, and has stretch {\rm poly}(\log n,d).
Edit intent: meaning-changed
Original:  In this paper, we present an approach to take the shape of the geometry into account when weighting its neighborhoods. This makes the obtained neighborhoods  more reliable in the sense that connectivity also depends on the orientation of the point set.
Revised:  In this paper, we present an approach to take the shape of the geometry , i.e. the normal information, into account. This causes the obtained neighborhoods more reliable in the sense that connectivity also depends on the orientation of the point set.
Edit intent: meaning-changed
Original:  For its evaluation , we turn to a Shannon entropy model for feature separation  . Based on this model, we apply our weight terms to a large scale of clean and to several real world models.
Revised:  For its evaluation , we turn to a Shannon entropy model for feature separation and rigorously prove its non-degeneracy for our family of weights . Based on this model, we apply our weight terms to a large scale of clean and to several real world models.
Edit intent: meaning-changed
Original:  The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy with {\it  memory reduce to the performance measures for the same policy without {\it  memory provided that the arrival rate is properly scaled.
Revised:  The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy {\it memory reduce to the performance measures for the same policy without {\it memory provided that the arrival rate is properly scaled.
Edit intent: fluency
Original:  The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy with {\it  memory reduce to the performance measures for the same policy without {\it  memory provided that the arrival rate is properly scaled.
Revised:  The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy with {\it with memory reduce to the performance measures for the same policy without {\it memory provided that the arrival rate is properly scaled.
Edit intent: meaning-changed
Original:  The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy with {\it  memory reduce to the performance measures for the same policy without {\it  memory provided that the arrival rate is properly scaled.
Revised:  The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy with {\it memory reduce to the performance measures for the same policy {\it memory provided that the arrival rate is properly scaled.
Edit intent: coherence
Original:  The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy with {\it  memory reduce to the performance measures for the same policy without {\it  memory provided that the arrival rate is properly scaled.
Revised:  The main insight provided is that the performance measures obtained via the cavity method for a load balancing policy with {\it memory reduce to the performance measures for the same policy without {\it without memory provided that the arrival rate is properly scaled.
Edit intent: meaning-changed
Original:  In particular this entails closed form solutions for joining the shortest or least loaded queue among d randomly selected queues with memory in case of exponential job sizes  .
Revised:  In particular this entails closed form solutions for joining the shortest or least loaded queue among d randomly selected queues with memory in case of exponential job sizes . Moreover, we obtain a simple closed form expression for the (scaled) expected waiting time as the system tends towards instability .
Edit intent: meaning-changed
Original:  The first is a blocked version of column pivoted Householder URLanized as a `` left-looking '' method to minimize the number of write operations (which are more expensive than read operations on a spinning disk drive).
Revised:  The first is a blocked version of column pivoted Householder URLanized as a " left-looking '' method to minimize the number of write operations (which are more expensive than read operations on a spinning disk drive).
Edit intent: fluency
Original:  The first is a blocked version of column pivoted Householder URLanized as a `` left-looking '' method to minimize the number of write operations (which are more expensive than read operations on a spinning disk drive).
Revised:  The first is a blocked version of column pivoted Householder URLanized as a `` left-looking " method to minimize the number of write operations (which are more expensive than read operations on a spinning disk drive).
Edit intent: fluency
Original:  By utilizing  matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models , while being able to meaningfully model local information ( degrees) as well as global information (  clustering coefficient, assortativity, etc.) if desired.
Revised:  By utilizing fast matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models , while being able to meaningfully model local information ( degrees) as well as global information ( clustering coefficient, assortativity, etc.) if desired.
Edit intent: meaning-changed
Original:  By utilizing  matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models , while being able to meaningfully model local information ( degrees) as well as global information (  clustering coefficient, assortativity, etc.) if desired.
Revised:  By utilizing matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions , while being able to meaningfully model local information ( degrees) as well as global information ( clustering coefficient, assortativity, etc.) if desired.
Edit intent: clarity
Original:  By utilizing  matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models , while being able to meaningfully model local information ( degrees) as well as global information (  clustering coefficient, assortativity, etc.) if desired.
Revised:  By utilizing matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models , while being able to meaningfully model both local information of the graph (e.g., degrees) as well as global information ( clustering coefficient, assortativity, etc.) if desired.
Edit intent: clarity
Original:  By utilizing  matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models , while being able to meaningfully model local information ( degrees) as well as global information (  clustering coefficient, assortativity, etc.) if desired.
Revised:  By utilizing matrix block-approximation techniques, we propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) models , while being able to meaningfully model local information ( degrees) as well as global information ( e.g., clustering coefficient, assortativity, etc.) if desired.
Edit intent: clarity
Original:  we then employ a mathematical model of infectious disease spreading to calculate some key indicators of the epidemic control, evaluate the effectiveness of some epidemic prevention and control measures, and finally, advise on public policies and living behaviors of the general public, in the mist of this historic event .
Revised:  we then employ a mathematical model of disease transmission dynamics to evaluate the effectiveness of some epidemic prevention and control measures, and finally, advise on public policies and living behaviors of the general public, in the mist of this historic event .
Edit intent: clarity
Original:  we then employ a mathematical model of infectious disease spreading to calculate some key indicators of the epidemic control, evaluate the effectiveness of some epidemic prevention and control measures, and finally, advise on public policies and living behaviors of the general public, in the mist of this historic event .
Revised:  we then employ a mathematical model of infectious disease spreading to calculate some key indicators of the epidemic control, evaluate the effectiveness of some epidemic control measures, and finally, advise on public policies and living behaviors of the general public, in the mist of this historic event .
Edit intent: clarity
Original:  we then employ a mathematical model of infectious disease spreading to calculate some key indicators of the epidemic control, evaluate the effectiveness of some epidemic prevention and control measures, and finally, advise on public policies and living behaviors of the general public, in the mist of this historic event .
Revised:  we then employ a mathematical model of infectious disease spreading to calculate some key indicators of the epidemic control, evaluate the effectiveness of some epidemic prevention and control measures, and more importantly, to offer a few tips on preventive measures .
Edit intent: clarity
Original:  In the paper , we propose a new pricing theory with several rules to solve the paradoxand state that the fair pricing should be judged by the buyer and the seller independently. The seller is concerned about costs, and the buyer is concerned about the realistic prospect of returns.
Revised:  In the paper , we propose a new pricing theory with several rules to solve the paradoxand state that the fair pricing should be judged by the seller and the buyer independently. Reasonable pricing for the seller may not be appropriate for the buyer. The seller cares about costs, and the buyer is concerned about the realistic prospect of returns.
Edit intent: meaning-changed
Original:  This pricing theory can be applied to financial market to solve the confusion with fat tail distribution .
Revised:  This pricing theory can be applied to financial market to solve the confusion that financial asset return with fat tails distribution will cause the option pricing formula to fail, thus making up the theoretical defects of quantitative financial pricing theory. We want to pay tribute to Edward O. Thorp with this article, a man for all markets, for the author entered the financial field just because getting read his story .
Edit intent: meaning-changed
Original: The pricing theory we proposed can be applied to financial market to solve the confusion that financial asset return with fat tails distribution will cause the option pricing formula to fail, thus making up the theoretical defects of quantitative financial pricing theory .
Revised: The pricing theory we proposed can be applied to financial markets to solve the confusion that financial asset return with fat tails distribution will cause the option pricing formula to fail, thus making up the theoretical defects of quantitative financial pricing theory .
Edit intent: fluency
Original: The pricing theory we proposed can be applied to financial market to solve the confusion that financial asset return with fat tails distribution will cause the option pricing formula to fail, thus making up the theoretical defects of quantitative financial pricing theory . We want to pay tribute to Edward O. Thorp with this article, a man for all markets, for the author entered the financial field just because getting read his story .
Revised: The pricing theory we proposed can be applied to financial market to solve the confusion that financial asset return with fat tails distribution will cause the option pricing formula to fail, thus making up the theoretical defects of quantitative financial pricing theory .
Edit intent: coherence
Original:   For a technical reason, the +6 unweighted spanner becomes a +8W weighted spanner;
Revised:  Specifically, we show that a weighted graph G contains all-pairs (pairwise) +2W and +4W weighted spanners of size O(n^{3/2 For a technical reason, the +6 unweighted spanner becomes a +8W weighted spanner;
Edit intent: meaning-changed
Original: Individuals' output often depends not just on their ability and actions, but also on external factors or fundamentals, whose effect they cannot separately identify . At the same time, many individuals have incorrect beliefs about their own ability .
Revised: Individuals' output often depends not just on their ability and actions, but also on external factors or fundamentals, whose effect they cannot separately identify the effects of their ability, actions, and the parameter on their output .
Edit intent: meaning-changed
Original:  Heidhues et al. (2018) characterise overconfident and underconfident individuals' equilibrium beliefs and learning process in these situations. They argue overconfident individuals will act sub-optimally because of how they learn. We carry out the first experimental test of their theory. Subjects take incorrectly marked tests, and we measure how they learn about the marker's accuracy over time.
Revised:  Heidhues et al. (2018) argue that learning makes overconfident individuals worse off as their beliefs about the fundamental get less accurate, causing them to take worse actions. In our experiment, subjects take incorrectly-marked tests, and we measure how they learn about the marker's accuracy over time.
Edit intent: clarity
Original:  Overconfident subjects have lower beliefs about the fundamental, as Heidhues et al. predict, and thus would make sub-optimal decisions. But we find no evidence it is because of how they learn .
Revised:  Overconfident subjects have lower beliefs about the marker's accuracy got worse, as they learnt. Beliefs about the proportion of correct answers marked as correct fell by 0.05 over the experiment. We find no effect in underconfident subjects .
Edit intent: meaning-changed
Original: We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes . We formulate the problem as a linear state space Markov process with a high dimensional state and propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments  .
Revised: We consider the estimation of treatment effects in settings when multiple treatments are assigned over time and treatments can have a causal effect on future outcomes or the state of the treated unit. We propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments .
Edit intent: clarity
Original:  We formulate the problem as a linear state space Markov process with a high dimensional state and propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments  .
Revised:  We formulate the problem as a linear state space Markov process with a high dimensional state and propose an extension of the double/debiased machine learning framework to estimate the dynamic effects of treatments , which can be viewed as a Neyman orthogonal (locally robust) cross-fitted version of g-estimation in the dynamic treatment regime .
Edit intent: meaning-changed
Original:  Our method  allows the use of arbitrary machine learning methods to control for the high dimensional state  , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Revised:  Our method applies to a general class of non-linear dynamic treatment models known as Structural Nested Mean Models and allows the use of arbitrary machine learning methods to control for the high dimensional state , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Edit intent: meaning-changed
Original:  Our method  allows the use of arbitrary machine learning methods to control for the high dimensional state  , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Revised:  Our method allows the use of machine learning methods to control for the high dimensional state , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Edit intent: clarity
Original:  Our method  allows the use of arbitrary machine learning methods to control for the high dimensional state  , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Revised:  Our method allows the use of arbitrary machine learning methods to control for potentially high dimensional state , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Edit intent: clarity
Original:  Our method  allows the use of arbitrary machine learning methods to control for the high dimensional state  , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Revised:  Our method allows the use of arbitrary machine learning methods to control for the high dimensional state variables , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Edit intent: meaning-changed
Original:  Our method  allows the use of arbitrary machine learning methods to control for the high dimensional state  , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the dynamic treatment effect parameters of interest.
Revised:  Our method allows the use of arbitrary machine learning methods to control for the high dimensional state , subject to a mean square error guarantee, while still allowing parametric estimation and construction of confidence intervals for the structural parameters of interest.
Edit intent: meaning-changed
Original:  Our method is based on a sequential regression peeling process, which we show can be equivalently interpreted as a Neyman orthogonal moment estimator.
Revised:  These structural parameters can be used for off-policy evaluation of any target dynamic policy at parametric rates, subject to semi-parametric restrictions on the data generating process. Our work is based on a sequential regression peeling process, which we show can be equivalently interpreted as a Neyman orthogonal moment estimator.
Edit intent: meaning-changed
Original:  Our method is based on a sequential regression peeling process, which we show can be equivalently interpreted as a Neyman orthogonal moment estimator. This allows us to show root-n asymptotic normality of the estimated causal effects .
Revised:  Our method is based on a sequential regression peeling process, typical in g-estimation, and formulates a strongly convex objective at each stage, which allows us to show root-n asymptotic normality of the estimated causal effects .
Edit intent: meaning-changed
Original:  This allows us to show root-n asymptotic normality of the estimated causal effects .
Revised:  This allows us to extend the g-estimation framework in multiple directions: i) to provide finite sample guarantees, ii) to estimate non-linear effect heterogeneity with respect to fixed unit characteristics, within arbitrary function spaces, enabling a dynamic analogue of the RLearner algorithm for heterogeneous effects, iii) to allow for high-dimensional sparse parameterizations of the target structural functions, enabling automated model selection via a recursive lasso algorithm. We also provide guarantees for data stemming from a single treated unit over a long horizon and under stationarity conditions .
Edit intent: meaning-changed
Original:  We present space-efficient algorithms for sorting, dense ranking and (competitive) ranking S on the word RAM model with word size \Omega(\log N)  . Our algorithms run in O(k + N{\log N}) time and use O(N) bits.
Revised:  We present space-efficient algorithms for sorting, dense ranking and (competitive) ranking S on the word RAM model with word size \Omega(\log N) bits . Our algorithms run in O(k + N{\log N}) time and use O(N) bits.
Edit intent: fluency
Original:  The sorting algorithm returns the given numbers in sorted order  stored within a bit-vector of N bits  whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees  .
Revised:  The sorting algorithm returns the given numbers in sorted order , stored within a bit-vector of N bits whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees .
Edit intent: fluency
Original:  The sorting algorithm returns the given numbers in sorted order  stored within a bit-vector of N bits  whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees  .
Revised:  The sorting algorithm returns the given numbers in sorted order stored within a bit-vector of N bits , whereas our ranking algorithms construct data structures that allows us subsequently to return the (dense) rank of each number x in S in constant time if the position of x in S is given together with x. As an application of our algorithms we give an algorithm for tree isomorphism that runs in O(n) time and uses O(n) bits on n-node trees .
Edit intent: fluency