wanyu/IteraTeR_full_sent huggingface dataset:
Original: We present the design and optimization of a solver for efficient and high-throughput computation of the marginalized graph kernel on General Purpose GPUs. The graph kernel is computed using the conjugate gradient method to solve a generalized Laplacian of the tensor product between a pair of graphs.
Revised: We present the design and optimization of a solver for efficient and high-throughput computation of the marginalized graph kernel between pairs of labeled graphs. The solver implements a preconditioned conjugate gradient (PCG) method to compute the solution to a generalized Laplacian of the tensor product between a pair of graphs.
Edit intent: meaning-changed
Original:  To cope with the large gap between the instruction throughput and the memory bandwidth of the GPUs, our solver forms the graph tensor product on-the-fly without storing it in memory . This is achieved by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse.
Revised:  To cope with the large gap between the instruction throughput and the memory bandwidth of the GPUs, our solver forms the graph tensor product on-the-fly without storing it in memory when performing matrix-vector dot product operations in PCG. Such on-the-fly computation is accomplished by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse.
Edit intent: meaning-changed
Original:  This is achieved by using threads in a warp cooperatively to stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse.
Revised:  This is achieved by using threads in a warp to cooperatively stream the adjacency and edge label matrices of individual graphs by small square matrix blocks called tiles, which are then staged in registers and the shared memory for later reuse.
Edit intent: fluency
Original: The regression discontinuity design (RDD) has become the "gold standard" for causal inference with observational data. Local average treatment effects (LATE) for RDDs are often estimated using local linear regressions with pre-treatment covariates typically added to increase the efficiencyof treatment effect estimates, but their inclusion can have large impacts on LATE point estimates and standard errors, particularly in small samples. In this paper , I propose a principled , efficiency-maximizing approach for covariate adjustment of LATE in RDDs . This approach allows researchers to combine context-specific, substantive insights with automated model selection via a novel adaptive lasso algorithm.
Revised: Regression discontinuity designs are frequently used to estimate the causal effect of election outcomes and policy interventions. In these contexts, treatment effects are typically estimated with covariates included to improve efficiency. While including covariates improves precision asymptotically, in practice, treatment effects are estimated with a small number of observations, resulting in considerable fluctuations in treatment effect magnitude and precision depending upon the covariates chosen. This practice thus incentivizes researchers to select covariates which maximize treatment effect statistical significance rather than precision. Here , I propose a principled , efficiency-maximizing approach for covariate adjustment of LATE in RDDs . This approach allows researchers to combine context-specific, substantive insights with automated model selection via a novel adaptive lasso algorithm.
Edit intent: coherence
Original:  In this paper , I propose a principled , efficiency-maximizing approach for covariate adjustment of LATE in RDDs . This approach allows researchers to combine context-specific, substantive insights with automated model selection via a novel adaptive lasso algorithm. When combined with currently existing robust estimation methods, this approach improves the efficiency of LATE RDD with pre-treatment covariates . The approach will be implemented in a forthcoming R package , AdaptiveRDD which can be used to estimate and compare treatment effects generated by this approach with extant approaches .
Revised:  In this paper , I propose a principled approach for estimating RDDs which provides a means of improving precision with covariates while minimizing adverse incentives. This is accomplished by integrating the adaptive LASSO, a machine learning method, into RDD estimation using an R package developed for this purpose, adaptiveRDD. Using simulations, I show that this method significantly improves treatment effect precision, particularly when estimating treatment effects with fewer than 200 observations .
Edit intent: clarity
Original:  Our estimation strategy is related to a measure of the contemporaneous correlation between financial returns and their volatility increments .
Revised:  Our estimation strategy is related to a measure of the contemporaneous correlation between financial returns and their related volatility increments, in a stochastic volatility model set-up. We define a novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002). We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination. When applying our methodology to real data, our results provide evidence of the presence of the stochastic leverage effect .
Edit intent: meaning-changed
Original:  We do not prior assume that the aforementioned correlation is constant, as mainly done in the literature. We instead consider it as a stochastic process. In this framework, we show that the Fourier estimator is asymptotically unbiased but its mean squared error diverges when noisy high-frequency data are in use. This drawback of the estimator is further analyzed in a simulation study where a feasible estimation strategy is developed to tackle this problem. The paper concludes with an empirical study on the leverage effect patterns estimated using high-frequency data for the S P 500 futures between January 2007 and December 2008.
Revised:  P 500 futures between January 2007 and December 2008.
Edit intent: coherence
Original:  We define a novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002).
Revised:  A novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002).
Edit intent: coherence
Original:  We define a novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002).
Revised:  We define a novel estimator of the effect is defined using a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002).
Edit intent: clarity
Original:  We define a novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and of the volatility process as defined in Malliavin and Mancino (2002). We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination.
Revised:  We define a novel estimator of the effect which is based on a pre-estimation of the Fourier coefficients of the return and the volatility processes. The consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination.
Edit intent: coherence
Original:  We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise contamination. When applying our methodology to real data, our results provide evidence of the presence of the stochastic leverage effect  .
Revised:  We establish the consistency of the estimator and we analyse its finite sample performance in the presence of microstructure noise effects. The Fourier methodology is applied to S\&P500 futures prices to investigate the magnitude of the stochastic leverage effect .
Edit intent: meaning-changed
Original: This paper suggests a factor-based imputation procedure that uses the factors estimated from a TALL block along with the re-rotated loadings estimated from a WIDE block to impute missing values in a panel of data.
Revised: This paper suggests a factor-based imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a WIDE block to impute missing values in a panel of data.
Edit intent: fluency
Original: This paper suggests a factor-based imputation procedure that uses the factors estimated from a TALL block along with the re-rotated loadings estimated from a WIDE block to impute missing values in a panel of data.
Revised: This paper suggests a factor-based imputation procedure that uses the factors estimated from a TALL block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data.
Edit intent: fluency
Original:  Under a strong factor assumption , it is shown that the common component can be consistently estimated but there will be four different convergence rates . Re-estimation of the factors from the imputed data matrix can accelerate convergence.
Revised:  Assuming that a strong factor assumption , it is shown that the common component can be consistently estimated but there will be four different convergence rates . Re-estimation of the factors from the imputed data matrix can accelerate convergence.
Edit intent: fluency
Original:  Under a strong factor assumption , it is shown that the common component can be consistently estimated but there will be four different convergence rates . Re-estimation of the factors from the imputed data matrix can accelerate convergence.
Revised:  Under a strong factor structure holds for the full panel of data and its sub-blocks , it is shown that the common component can be consistently estimated but there will be four different convergence rates . Re-estimation of the factors from the imputed data matrix can accelerate convergence.
Edit intent: meaning-changed
Original:  Under a strong factor assumption , it is shown that the common component can be consistently estimated but there will be four different convergence rates . Re-estimation of the factors from the imputed data matrix can accelerate convergence.
Revised:  Under a strong factor assumption , it is shown that the common component can be consistently estimated at four different rates of convergence . Re-estimation of the factors from the imputed data matrix can accelerate convergence.
Edit intent: clarity
Original:  Under a strong factor assumption , it is shown that the common component can be consistently estimated but there will be four different convergence rates . Re-estimation of the factors from the imputed data matrix can accelerate convergence. A complete characterization of the sampling error is obtained without requiring regularization or imposing the missing at random assumption.
Revised:  Under a strong factor assumption , it is shown that the common component can be consistently estimated but there will be four different convergence rates . An asymptotic analysis of the estimation error is obtained without requiring regularization or imposing the missing at random assumption.
Edit intent: clarity
Original:  A complete characterization of the sampling error is obtained without requiring regularization or imposing the missing at random assumption. Under the assumption that potential outcome has a factor structure , we provide a distribution theory for the estimated average and individual treatment effects on the treated .
Revised:  A complete characterization of the sampling error is obtained without requiring regularization or imposing the missing at random . We also consider estimation of average and individual treatment effects on the treated under the assumption that potential outcome has a factor structure , we provide a distribution theory for the estimated average and individual treatment effects on the treated .
Edit intent: meaning-changed
Original:  Information is acquired continuously--where the agent chooses both which sources to sample from, and also how to allocate resources across them--until an endogenously chosen time, at which point a decision is taken.
Revised:  Information is acquired continuously\textemdash where the agent chooses both which sources to sample from, and also how to allocate resources across them--until an endogenously chosen time, at which point a decision is taken.
Edit intent: meaning-changed
Original:  Information is acquired continuously--where the agent chooses both which sources to sample from, and also how to allocate resources across them--until an endogenously chosen time, at which point a decision is taken.
Revised:  Information is acquired continuously--where the agent chooses both which sources to sample from, and also how to allocate attention across them\textemdash until an endogenously chosen time, at which point a decision is taken.
Edit intent: clarity
Original:  We show that the optimal information acquisition strategy proceeds in stages, where resource allocation is constant over a fixed set of providers during each stage, and at each stage a new provider is added to the set. We additionally apply this characterization to derive  results regarding: (1) endogenous information acquisition in a binary choiceproblem , and (2) equilibrium information provision by competing news sources.
Revised:  We show that the optimal information acquisition strategy for settings where the attributes are not too strongly correlated. We then apply this characterization to derive results regarding: (1) endogenous information acquisition in a binary choiceproblem , and (2) equilibrium information provision by competing news sources.
Edit intent: meaning-changed
Original:  Information is acquired continuously \textemdash where the agent chooses both which sources to sample from, and also how to allocate attention across them \textemdash until an endogenously chosen time, at which point a decision is taken.
Revised:  Information is acquired continuously -- where the agent chooses both which sources to sample from, and also how to allocate attention across them \textemdash until an endogenously chosen time, at which point a decision is taken.
Edit intent: fluency
Original:  Information is acquired continuously \textemdash where the agent chooses both which sources to sample from, and also how to allocate attention across them \textemdash until an endogenously chosen time, at which point a decision is taken.
Revised:  Information is acquired continuously \textemdash where the agent chooses both which sources to sample from, and also how to allocate attention across them -- until an endogenously chosen time, at which point a decision is taken.
Edit intent: coherence
Original: This paper proposes a foundation for heterogeneous beliefs in games, in which disagreement arises not because players observe different information, but because they learn from common information in different ways. Players may be misspecified, and may moreover be misspecified about how others learn. The key assumption is that players nevertheless have some common understanding of how to interpret the data; formally, players have common certainty in the predictions of a class of learning rules.
Revised: Players are statistical learners who learn about payoffs from data. They may interpret the same data differently, but have common knowledge of a class of learning rules.
Edit intent: clarity
Original:  The main results characterize which rationalizable actions and Nash equilibria can be predicted when agents observe a finite quantity of data , and how much data is needed to predict various solutions. This number of observations needed depends on the degree of strictness of the solution and speed of common learning .
Revised:  The main results characterize which rationalizable actions and Nash equilibria can be predicted when agents observe a finite quantity of data grows large, and provide bounds for small datasets. The approach generates new predictions, e.g. that speculative trade is more likely given high-dimensional data, and that coordination is less likely given noisy data .
Edit intent: meaning-changed
Original:  This leads to a deterministic hierarchical construction method that yields an efficient topological way to control both the number of connected pieces (T) and the total degrees of freedom (DoF). We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Revised:  This leads to a deterministic hierarchical construction method that yields an efficient topological way to control both the number of connected pieces (T) and the total degrees of freedom . A statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Edit intent: coherence
Original:  We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Revised:  We then turn to a statistical approach to the control of rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Edit intent: clarity
Original:  We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Revised:  We then turn to a statistical approach to the question by studying the rigidity and connectivity in kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Edit intent: clarity
Original:  We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Revised:  We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts complements the deterministic pathway, and shows that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Edit intent: meaning-changed
Original:  We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Revised:  We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the number of connected pieces and the degrees of freedom show percolation transitions as a function of the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity.
Edit intent: clarity
Original:  We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) in the neighborhood of percolation transitions in the connectivity and rigidity. All together, our work provides a general framework for the topological and statistical control of rigidity and connectivity in planar kirigami.
Revised:  We then turn to a statistical approach to the question by studying the rigidity and connectivity of kirigami with random cuts , and find that both the T and DoF can be exquisitely controlled by the density of cuts (links) . Together this provides a general framework for the topological and statistical control of rigidity and connectivity in planar kirigami.
Edit intent: clarity
Original:  To address this issue, we introduce a general audio-to-spiking conversion procedure and provide two novel spike-based classification datasets . The datasets are free and require no additional preprocessing, which renders them broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks  .
Revised:  To address this issue, we introduce a general audio-to-spiking conversion procedure and provide two novel spike-based classification datasets , broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks .
Edit intent: coherence
Original:  The datasets are free and require no additional preprocessing, which renders them broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks  .
Revised:  The datasets are free and require no additional preprocessing, which renders them broadly applicable to benchmark both software and neuromorphic hardware implementations of spiking neural networks . To accomplish this, we developed a general audio-to-spiking conversion procedure inspired by neurophysiology. Further, we applied this conversion to an existing and a novel speech dataset. The latter is the free, high-fidelity, and word-level aligned Heidelberg digit dataset that we created specifically for this study .
Edit intent: meaning-changed
Original: We study an optimization problem for a portfolio with a risk-free, a liquid risky , and an illiquid asset which is sold in an exogenous random moment of time with a prescribed liquidation time distribution.
Revised: We study an optimization problem for a portfolio with a risk-free, a liquid risky , and an illiquid risky asset. The illiquid risky asset is sold in an exogenous random moment of time with a prescribed liquidation time distribution.
Edit intent: clarity
Original:  We study the optimization problem with a utility function of a CARA type, i.e. with negative and positive exponential utility functions (EXPn and EXPp) .
Revised:  We study the optimization problem with a utility function of a CARA type, i.e. with negative and positive exponential utility function. We prove that both cases are connected by a one-to-one analytical substitution and are identical from the economic, analytical, or Lie algebraic points of view .
Edit intent: meaning-changed