wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We demonstrate AutoPerf's generality against three different types of performance anomalies : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance anomalies and 7 open source programs.
Revised:  We demonstrate AutoPerf's generality against three different types of performance regressions : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance anomalies and 7 open source programs.
Edit intent: clarity
Original:  We demonstrate AutoPerf's generality against three different types of performance anomalies : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance anomalies and 7 open source programs.
Revised:  We demonstrate AutoPerf's generality against three different types of performance anomalies : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance regressions and 7 open source programs.
Edit intent: clarity
Original: We present AutoPerf, a generalized software performance regression diagnosis system. AutoPerf uses autoencoders, an unsupervised learning technique, and hardware performance counters to learn the performance signatures of a program. It then uses this knowledge to identify when newer versions of the program suffer from performance regressions, while simultaneously providing root cause analysis to help programmers debug the program's performance. AutoPerf is the first zero-positive learning performance regression diagnosis system.
Revised: A performance regression is a degradation of software performance due to a change in code. A common way to identify such regressions is to manually create tests for them. This is known as regression testing. Although widely used, regression testing can be slow and error-prone due to its historically manual nature. Moreover, such tests' utility is usually constrained by the expertise of the developer creating them. Due to these limitations, researchers have turned their attention to the automatic generation of regression tests. In this paper, we present AutoPerf - a novel approach to automate regression testing that utilizes three core techniques: (i) zero-positive learning performance regression diagnosis system.
Edit intent: coherence
Original:  We demonstrate AutoPerf's generality against three different types of performance regressions : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance regressions and 7 open source programs.
Revised:  We demonstrate AutoPerf's generality and efficacy against 3 types of performance regressions : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance regressions and 7 open source programs.
Edit intent: clarity
Original:  We demonstrate AutoPerf's generality against three different types of performance regressions : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance regressions and 7 open source programs.
Revised:  We demonstrate AutoPerf's generality against three different types of performance regressions across 10 real performance bugs in 7 open source programs.
Edit intent: meaning-changed
Original:  We demonstrate AutoPerf's generality against three different types of performance regressions : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance regressions and 7 open source programs.
Revised:  We demonstrate AutoPerf's generality against three different types of performance regressions : (i) true sharing cache contention, (ii) false sharing cache contention, and (iii) NUMA latencies across 15 real world performance regressions and 7 benchmark and open-source programs.
Edit intent: clarity
Original:  In this paper, we  present AutoPerf - a novel approach to automate regression testing that utilizes three core techniques: (i) zero-positive learning, (ii) autoencoders, and (iii) hardware telemetry.
Revised:  In this paper, we apply MP to the automation of software performance regression testing. A performance regression is a software performance degradation caused by a code change. We present AutoPerf - a novel approach to automate regression testing that utilizes three core techniques: (i) zero-positive learning, (ii) autoencoders, and (iii) hardware telemetry.
Edit intent: meaning-changed
Original: Some researchers have proposed using non-Euclidean metrics for clustering data points . Generally, the metric should recognize that two points in the same cluster are close , even if their Euclidean distance is far.
Revised: We consider a simple graph-based metric on points in Euclidean space known as the edge-squared metric. This metric is defined by squaring the Euclidean distance between points, and taking the shortest paths on the resulting graph. This metric has been studied before in wireless networks and machine learning, and has the density sensitive property: distances between two points in the same cluster are close , even if their Euclidean distance is far.
Edit intent: meaning-changed
Original: Some researchers have proposed using non-Euclidean metrics for clustering data points . Generally, the metric should recognize that two points in the same cluster are close , even if their Euclidean distance is far.
Revised: Some researchers have proposed using non-Euclidean metrics for clustering data points . Generally, the metric should recognize that two points in the same cluster are short , even if their Euclidean distance is far.
Edit intent: meaning-changed
Original: Some researchers have proposed using non-Euclidean metrics for clustering data points . Generally, the metric should recognize that two points in the same cluster are close , even if their Euclidean distance is far. Multiple proposals have been suggested, including the Edge-Squared Metric (a specific example of a graph geodesic) and the Nearest Neighbor Metric, a geodesic metric .
Revised: Some researchers have proposed using non-Euclidean metrics for clustering data points . Generally, the metric should recognize that two points in the same cluster are close , even if their Euclidean distance is long. This property is desirable in machine learning .
Edit intent: clarity
Original:  In this paper, we prove that the edge-squared and nearest-neighbor metrics are in fact equivalent . Previous best work showed that  the  edge-squared metric was a 3-approximation of the Nearest Neighbor metric.
Revised:  In this paper, we show that this metric is equal to previously studied geodesic-based metric defined on points in Euclidean space . Previous best work showed that the edge-squared metric was a 3-approximation of the Nearest Neighbor metric.
Edit intent: meaning-changed
Original:  In this paper, we prove that the edge-squared and nearest-neighbor metrics are in fact equivalent . Previous best work showed that  the  edge-squared metric was a 3-approximation of the Nearest Neighbor metric.
Revised:  In this paper, we prove that the edge-squared and nearest-neighbor metrics are in fact equivalent . Previous best work showed that these metrics are 3-approximations of each other. It was not known or suspected that these metrics were equal. We give fast algorithms to compute sparse spanners of this distance, a problem that can be seen as a generalization of both the Euclidean spanner and the edge-squared metric was a 3-approximation of the Nearest Neighbor metric.
Edit intent: meaning-changed
Original:  In this paper, we prove that the edge-squared and nearest-neighbor metrics are in fact equivalent . Previous best work showed that  the  edge-squared metric was a 3-approximation of the Nearest Neighbor metric.
Revised:  In this paper, we prove that the edge-squared and nearest-neighbor metrics are in fact equivalent . Previous best work showed that the Euclidean MST problem. Spanners of the edge-squared metric was a 3-approximation of the Nearest Neighbor metric.
Edit intent: meaning-changed
Original:  In this paper, we prove that the edge-squared and nearest-neighbor metrics are in fact equivalent . Previous best work showed that  the  edge-squared metric was a 3-approximation of the Nearest Neighbor metric. This paper represents one of the first proofs of equating a continuous metric with a discrete metric, using non-trivial discrete methods. Our proof uses the Kirszbraun theorem (also known as the Lipschitz Extension Theorem and Brehm's Extension Theorem), a notable theorem in functional analysis and computational geometry. The results of our paper, combined with the results of Hwang, Damelin, and Hero, tell us that the Nearest Neighbor distance on i. i.d samples of a density is a reasonable constant approximation of a natural density-based distance function .
Revised:  In this paper, we prove that the edge-squared and nearest-neighbor metrics are in fact equivalent . Previous best work showed that the edge-squared metric are faster to compute and sparser than the best known Euclidean spanners in a variety of settings .
Edit intent: meaning-changed
Original: We consider a simple graph-based metric on points in Euclidean space known as the edge-squared metric. This metric is defined by squaring the Euclidean distance between points, and taking the shortest paths on the resulting graph. This metrichas been studied before in wireless networks and machine learning, and has the density sensitive property: distances between two points in the same cluster are short, even if their Euclidean distance is long. This property is desirable in machine learning .
Revised: Data-sensitive metrics adapt distances locally based the density of data points with the goal of aligning distances and some notion of similarity. In this paper, we give the first exact algorithm for computing a data-sensitive metric called the nearest neighbor metric. In fact, we prove the surprising result that a previously published 3-approximation is an exact algorithm. The nearest neighbor metric can be viewed as a special case of a density-based distance used in machine learning .
Edit intent: meaning-changed
Original:  This property is desirable in machine learning . In this paper, we show that this metricis equal to previously studied geodesic-based metric defined on points in Euclidean space. Previous best work showed that these metrics are 3-approximations of each other. It was not known or suspected that these metrics were equal. We give fast algorithms to compute sparse spanners of this distance, a problem that can be seen as a generalization of both the Euclidean spanner and the Euclidean MST problem.
Revised:  This property is desirable in machine learning , or it can be seen as an example of a manifold metric. Previous computational research on such metrics despaired of computing exact distances on account of the apparent difficulty of minimizing over all continuous paths between a pair of points. We leverage the exact computation of the nearest neighbor metric to compute sparse spanners of this distance, a problem that can be seen as a generalization of both the Euclidean spanner and the Euclidean MST problem.
Edit intent: meaning-changed
Original:  We give fast algorithms to compute sparse spanners of this distance, a problem that can be seen as a generalization of both the Euclidean spanner and the Euclidean MST problem. Spanners of the edge-squared metric are faster to compute and sparser than the best known Euclidean spanners in a variety of settings .
Revised:  We give fast algorithms to compute sparse spanners of this distance, a problem that can be seen as a generalization of both the Euclidean spanner and the metric built from point sets drawn from an underlying distribution and consider the more general case of inputs that are finite collections of path-connected compact sets. The main results connect several classical theories such as the conformal change of Riemannian metrics, the theory of positive definite functions of Schoenberg, and screw function theory of Schoenberg and Von Neumann. We develop novel proof techniques based on the combination of screw functions and Lipschitz extensions that may be of independent interest .
Edit intent: meaning-changed
Original:  We derive a general hyperparameter optimisation technique for X-LSTMs, allowing us to significantly improve on the LSTM , as well as on a prior state-of-the-art cross-modal approach, using a comparable number of parameters.
Revised:  We present a general hyperparameter optimisation technique for X-LSTMs, allowing us to significantly improve on the LSTM , as well as on a prior state-of-the-art cross-modal approach, using a comparable number of parameters.
Edit intent: clarity
Original:  We derive a general hyperparameter optimisation technique for X-LSTMs, allowing us to significantly improve on the LSTM , as well as on a prior state-of-the-art cross-modal approach, using a comparable number of parameters.
Revised:  We derive a general hyperparameter optimisation technique for X-LSTMs, which allows us to significantly improve on the LSTM , as well as on a prior state-of-the-art cross-modal approach, using a comparable number of parameters.
Edit intent: coherence
Original:  We derive a general hyperparameter optimisation technique for X-LSTMs, allowing us to significantly improve on the LSTM , as well as on a prior state-of-the-art cross-modal approach, using a comparable number of parameters.
Revised:  We derive a general hyperparameter optimisation technique for X-LSTMs, allowing us to significantly improve on the LSTM and a prior state-of-the-art cross-modal approach, using a comparable number of parameters.
Edit intent: clarity
Original:  Although CCLs are designed to help institutions mitigate counterparty risk by selective diversification of their exposures , their implementation restricts the liquidity that institutions can access in an otherwise centralized pool. We address the question of how this mechanism impacts trade prices and volatility, both empirically and via a new model of trading with CCLs.
Revised:  Although CCLs are designed to help institutions mitigate counterparty risk by selective diversification of their exposures . In this paper, we analyze how CCLs impact the prices that institutions pay for their trades during everyday trading. We study a high-quality data set from a large electronic trading platform in the foreign exchange spot market, which enables institutions to apply CCLs.
Edit intent: clarity
Original:  We find empirically that CCLs cause little impact on trade. However, our model highlights that in extreme situations, CCLs could serve to destabilize prices and thereby influence systemic risk .
Revised:  We find empirically that CCLs cause little impact on the vast majority of trades in this data. We also study the impact of CCLs using a new model of trading. By simulating our model with different underlying CCL networks, we highlight that CCLs can have a major impact in some situations .
Edit intent: meaning-changed
Original: Regression shrinkage and variable selection are important concepts in high-dimensional statistics that allow the inference of robust models from large data sets. Bayesian methods achieve this by subjecting the model parameters to a prior distribution whose mass is centred around zero. In particular, the lasso and elastic net linear regression models employ a double-exponential distribution in their prior , which results in some maximum-likelihood regression coefficients being identically zero.
Revised: The lasso and elastic net linear regression models employ a double-exponential distribution in their prior , which results in some maximum-likelihood regression coefficients being identically zero.
Edit intent: coherence
Original:  In particular, the lasso and elastic net linear regression models employ a double-exponential distribution in their prior , which results in some maximum-likelihood regression coefficients being identically zero. Because of their ability to simultaneously perform parameter estimation and variable selection, these models have become enormously popular .
Revised:  In particular, the lasso and elastic net linear regression models employ a double-exponential prior distribution on the model parameters to achieve regression shrinkage and variable selection, these models have become enormously popular .
Edit intent: clarity
Original:  However, there has been limited success in moving beyond maximum-likelihood estimation and deriving estimates for the  posterior distribution of regression coefficients  , due to a need for computationally expensive Gibbs sampling approaches to evaluate analytically intractable partition function integrals.
Revised:  However, there has been limited success in deriving estimates for the posterior distribution of regression coefficients , due to a need for computationally expensive Gibbs sampling approaches to evaluate analytically intractable partition function integrals.
Edit intent: coherence
Original:  However, there has been limited success in moving beyond maximum-likelihood estimation and deriving estimates for the  posterior distribution of regression coefficients  , due to a need for computationally expensive Gibbs sampling approaches to evaluate analytically intractable partition function integrals.
Revised:  However, there has been limited success in moving beyond maximum-likelihood estimation and deriving estimates for the full posterior distribution of regression coefficients , due to a need for computationally expensive Gibbs sampling approaches to evaluate analytically intractable partition function integrals.
Edit intent: clarity
Original:  However, there has been limited success in moving beyond maximum-likelihood estimation and deriving estimates for the  posterior distribution of regression coefficients  , due to a need for computationally expensive Gibbs sampling approaches to evaluate analytically intractable partition function integrals.
Revised:  However, there has been limited success in moving beyond maximum-likelihood estimation and deriving estimates for the posterior distribution of regression coefficients in these models , due to a need for computationally expensive Gibbs sampling approaches to evaluate analytically intractable partition function integrals.
Edit intent: clarity
Original:  However, there has been limited success in moving beyond maximum-likelihood estimation and deriving estimates for the  posterior distribution of regression coefficients  , due to a need for computationally expensive Gibbs sampling approaches to evaluate analytically intractable partition function integrals.
Revised:  However, there has been limited success in moving beyond maximum-likelihood estimation and deriving estimates for the posterior distribution of regression coefficients , due to a need to evaluate analytically intractable partition function integrals.
Edit intent: coherence
Original:  This results in an analytic expansion and stationary phase approximation for the partition functions of the Bayesian lasso and elastic net, where the non-differentiability of the double-exponential prior distribution has so far eluded such an approach.
Revised:  This results in an analytic expansion and stationary phase approximation for the partition functions of the Bayesian lasso and elastic net, where the non-differentiability of the double-exponential prior has so far eluded such an approach.
Edit intent: clarity
Original:  Results are presented for the out-of-sample and the in-sample estimator of the free energy , the relative estimation error, the density of the assets eliminated from the portfolio by the regularizer, and the distribution of the optimal portfolio weights.
Revised:  Results are presented for the out-of-sample and the in-sample estimator of the regularized variance , the relative estimation error, the density of the assets eliminated from the portfolio by the regularizer, and the distribution of the optimal portfolio weights.
Edit intent: meaning-changed
Original:  It is found that regularization extends the interval where the optimization can be carried out, and suppresses the infinitely large sample fluctuations, but the performance of L1 regularization is disappointing: if the sample size is large relative to the dimension, i.e. r is small, the regularizer does not play any role, while for r's where the regularizer starts to be felt the estimation error is already so large as to make the whole optimization exercise pointless.
Revised:  Regularization extends the interval where the optimization can be carried out, and suppresses the infinitely large sample fluctuations, but the performance of L1 regularization is disappointing: if the sample size is large relative to the dimension, i.e. r is small, the regularizer does not play any role, while for r's where the regularizer starts to be felt the estimation error is already so large as to make the whole optimization exercise pointless.
Edit intent: coherence
Original:  It is found that regularization extends the interval where the optimization can be carried out, and suppresses the infinitely large sample fluctuations, but the performance of L1 regularization is disappointing: if the sample size is large relative to the dimension, i.e. r is small, the regularizer does not play any role, while for r's where the regularizer starts to be felt the estimation error is already so large as to make the whole optimization exercise pointless.
Revised:  It is found that regularization extends the interval where the optimization can be carried out, and suppresses the large sample fluctuations, but the performance of L1 regularization is disappointing: if the sample size is large relative to the dimension, i.e. r is small, the regularizer does not play any role, while for r's where the regularizer starts to be felt the estimation error is already so large as to make the whole optimization exercise pointless.
Edit intent: clarity
Original:  It is found that regularization extends the interval where the optimization can be carried out, and suppresses the infinitely large sample fluctuations, but the performance of L1 regularization is disappointing: if the sample size is large relative to the dimension, i.e. r is small, the regularizer does not play any role, while for r's where the regularizer starts to be felt the estimation error is already so large as to make the whole optimization exercise pointless.
Revised:  It is found that regularization extends the interval where the optimization can be carried out, and suppresses the infinitely large sample fluctuations, but the performance of \ell_1 regularization is rather disappointing: if the sample size is large relative to the dimension, i.e. r is small, the regularizer does not play any role, while for r's where the regularizer starts to be felt the estimation error is already so large as to make the whole optimization exercise pointless.
Edit intent: clarity