wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To address this issue, we design and implement SimilarityAtScale, the first communicationefficient distributed algorithm for computing the Jaccard Similarity. The key idea is to express the problem algebraically, as a sequence of matrix operations, and implement these operations with communication-avoiding distributed routines to minimize the amount of transferred data and ensure both high scalability and low latency. We then apply our algorithm to the problem of obtaining distances between whole-genome sequencing samples , a key part of modern metagenomics analysis and an evergrowing need due to the increasing availability of high-throughput DNA sequencing data.
Revised:  To address this issue, we design and implement SimilarityAtScale, the first communicationefficient distributed algorithm for computing the Jaccard similarity among pairs of large datasets. Our algorithm provides an efficient encoding of this problem into a multiplication of sparse matrices. Both the encoding and sparse matrix product are performed in a way that minimizes data movement in terms of communication and synchronization costs. We apply our algorithm to the problem of obtaining distances between whole-genome sequencing samples , a key part of modern metagenomics analysis and an evergrowing need due to the increasing availability of high-throughput DNA sequencing data.
Edit intent: clarity
Original:  We then apply our algorithm to the problem of obtaining distances between whole-genome sequencing samples , a key part of modern metagenomics analysis and an evergrowing need due to the increasing availability of high-throughput DNA sequencing data.
Revised:  We then apply our algorithm to obtain similarity among all pairs of a set of large samples of genomes. This task is a key part of modern metagenomics analysis and an evergrowing need due to the increasing availability of high-throughput DNA sequencing data.
Edit intent: clarity
Original:  The results demonstrate the feasibility of dynamical statistical analysis of RRI's to monitor excercise load in real time with wearable devices and without previous knowledge of external parameters such as the maximum heart rate of the individual.
Revised:  The results demonstrate the feasibility of dynamical statistical analysis of RRI's to monitor cardiovascular exercise conditions in real time with wearable devices and without previous knowledge of external parameters such as the maximum heart rate of the individual.
Edit intent: clarity
Original:  In particular, we introduce dynamical detrended fluctuation analysis and dynamical partial autocorrelation functions, which are able to detect real-time changes in the scaling and correlations of the RRI's .
Revised:  In particular, we introduce dynamical detrended fluctuation analysis and dynamical partial autocorrelation functions, which are able to detect real-time changes in the scaling and correlations of the RRIs as functions of the scale and the lag .
Edit intent: meaning-changed
Original:  We relate these changes to the exercise intensity quantified by the heart rate . Beyond a certain intensity threshold, RRI's show strong short-time anticorrelations on different scales, for which we propose a physiological interpretation in terms of hemodynamics. The results demonstrate the feasibility of dynamical statistical analysis of RRI's to monitor cardiovascular exercise conditions in real time with wearable devices and without previous knowledge of external parameters such as the maximum heart rate of the individual .
Revised:  We relate these changes to the exercise intensity quantified by the heart rate (HR). Beyond subject-specific HR thresholds the RRIs show multiscale anticorrelations with both universal and individual scale-dependent structure that is potentially affected by the stride frequency. These preliminary results are encouraging for future applications of the dynamical statistical analysis of RRI's to monitor cardiovascular exercise conditions in real time with wearable devices and without previous knowledge of external parameters such as the maximum heart rate of the individual .
Edit intent: clarity
Original: In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives.
Revised: We study identification of preferences in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives.
Edit intent: clarity
Original: In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives.
Revised: In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may be imperfectly informed about the utility generated by the available alternatives.
Edit intent: clarity
Original:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (  2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Revised:  We impose no restrictions on the information frictions the decision maker may face and impose weak assumptions on how the decision maker deals with the uncertainty induced by those frictions. We leverage on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris ( 2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Edit intent: meaning-changed
Original:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (  2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Revised:  By leveraging on the notion of one-player Bayes Correlated Equilibrium in Bergemann and Morris ( 2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Edit intent: fluency
Original:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (  2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Revised:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris ( 2013; 2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Edit intent: meaning-changed
Original:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (  2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Revised:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris ( 2016) to provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Edit intent: fluency
Original:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (  2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Revised:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris ( 2016) , we provide a tractable characterisation of the identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Edit intent: clarity
Original:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (  2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties.
Revised:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris ( 2016) , we provide a tractable characterisation of the sharp identified set and discuss inference . We use our methodology and data on the 2017 UK general election to estimate a spatial model of voting under weak assumptions on the rule with which the decision maker resolves ties.
Edit intent: meaning-changed
Original:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (  2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the rule with which the decision maker resolves ties. Simulations reveal that the obtained bounds on the preference parameters can be tight in several settings of empirical interest .
Revised:  By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris ( 2016) , we provide a tractable characterisation of the sharp identified set and discuss inference under minimal assumptions on the amount of information processed by the decision maker and under no assumptions on the information that voters have about the returns to voting. We find that the assumptions on the information environment can drive the interpretation of voter preferences. Counterfactual exercises quantify the consequences of imperfect information in politics .
Edit intent: meaning-changed
Original:  It can achieve high throughput while obtaining very comparable (even slightly better) model quality as the synchronous counterpart . Experimental results show that XPipe outperforms other existing synchronous and asynchronous model parallelism approaches.
Revised:  Concretely, it can achieve very comparable (even slightly better) model quality as the synchronous counterpart . Experimental results show that XPipe outperforms other existing synchronous and asynchronous model parallelism approaches.
Edit intent: coherence
Original:  It can achieve high throughput while obtaining very comparable (even slightly better) model quality as the synchronous counterpart . Experimental results show that XPipe outperforms other existing synchronous and asynchronous model parallelism approaches.
Revised:  It can achieve high throughput while obtaining very comparable (even slightly better) model accuracy as its synchronous counterpart, while obtaining higher throughput than it . Experimental results show that XPipe outperforms other existing synchronous and asynchronous model parallelism approaches.
Edit intent: meaning-changed
Original:  It can achieve high throughput while obtaining very comparable (even slightly better) model quality as the synchronous counterpart . Experimental results show that XPipe outperforms other existing synchronous and asynchronous model parallelism approaches.
Revised:  It can achieve high throughput while obtaining very comparable (even slightly better) model quality as the synchronous counterpart . Experimental results show that XPipe outperforms other state-of-the-art synchronous and asynchronous model parallelism approaches.
Edit intent: clarity
Original:  To improve GPU utilization and achieve high throughput, it splits a mini-batch into a set of micro-batches and allows the overlapping of the pipelines of multiple micro-batches, including those belonging to different mini-batches.
Revised:  To improve GPU utilization and achieve high throughput, it splits a mini-batch into a set of micro-batches . It allows the overlapping of the pipelines of multiple micro-batches, including those belonging to different mini-batches.
Edit intent: coherence
Original:  We calculate a perturbation expansion of the frequency of the nonzero modes of the model , finding that approximating these modes with their unperturbed dynamics  as in a previous reduced model may not hold when the length of the DNA model increases.
Revised:  We calculate a perturbation expansion of the frequency of the model's nonzero modes , finding that approximating these modes with their unperturbed dynamics as in a previous reduced model may not hold when the length of the DNA model increases.
Edit intent: clarity
Original:  We calculate a perturbation expansion of the frequency of the nonzero modes of the model , finding that approximating these modes with their unperturbed dynamics  as in a previous reduced model may not hold when the length of the DNA model increases.
Revised:  We calculate a perturbation expansion of the frequency of the nonzero modes of the model , finding that approximating these modes with their unperturbed dynamics , as in a previous reduced model may not hold when the length of the DNA model increases.
Edit intent: fluency
Original:  We calculate a perturbation expansion of the frequency of the nonzero modes of the model , finding that approximating these modes with their unperturbed dynamics  as in a previous reduced model may not hold when the length of the DNA model increases.
Revised:  We calculate a perturbation expansion of the frequency of the nonzero modes of the model , finding that approximating these modes with their unperturbed dynamics as in a previous reduced order model, may not hold when the length of the DNA model increases.
Edit intent: clarity
Original:  We investigate the conformational change to local perturbation in models of varying lengths, finding that for chosen input and parameters, there are two regions of DNA length in the model, first where the minimum energy required to undergo the conformational change increases with DNA length  and second, where it is almost independent of the length of the DNA model.
Revised:  We investigate the conformational change to local perturbation in models of varying lengths, finding that for chosen input and parameters, there are two regions of DNA length in the model, first where the minimum energy required to undergo the conformational change increases with DNA length ; and second, where it is almost independent of the length of the DNA model.
Edit intent: fluency
Original:  We analyzed the conformational change in these models by adding randomness to the local perturbation, finding that the robustness of the conformational states of the model to random perturbation decreases with an increase in the DNA length.
Revised:  We analyze the conformational change in these models by adding randomness to the local perturbation, finding that the robustness of the conformational states of the model to random perturbation decreases with an increase in the DNA length.
Edit intent: fluency
Original:  We analyzed the conformational change in these models by adding randomness to the local perturbation, finding that the robustness of the conformational states of the model to random perturbation decreases with an increase in the DNA length.
Revised:  We analyzed the conformational change in these models by adding randomness to the local perturbation, finding that the tendency of the system to remain in a stable conformation against random perturbation decreases with an increase in the DNA length.
Edit intent: clarity
Original:  Our key findings include: Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular featuresDataset size is directly correlated to performance of prediction : single-task deep learning models only outperform shallow learners if there is enough data.
Revised:  As a result of these comprehensive experiments, we have found that physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular featuresDataset size is directly correlated to performance of prediction : single-task deep learning models only outperform shallow learners if there is enough data.
Edit intent: clarity
Original:  Our key findings include: Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular featuresDataset size is directly correlated to performance of prediction : single-task deep learning models only outperform shallow learners if there is enough data.
Revised:  Our key findings include: Physicochemical descriptors and deep learning-based graph representations significantly outperform traditional fingerprints in the characterization of molecular features. We have also found that dataset size is directly correlated to performance of prediction : single-task deep learning models only outperform shallow learners if there is enough data.
Edit intent: clarity
Original:  Our key findings include: Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular featuresDataset size is directly correlated to performance of prediction : single-task deep learning models only outperform shallow learners if there is enough data.
Revised:  Our key findings include: Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular featuresDataset size is directly correlated to prediction performance, and that single-task deep learning models only outperform shallow learners if there is enough data.
Edit intent: clarity
Original:  Our key findings include: Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular featuresDataset size is directly correlated to performance of prediction : single-task deep learning models only outperform shallow learners if there is enough data.
Revised:  Our key findings include: Physicochemical descriptors and deep learning-based graph representations are significantly better than traditional fingerprints to characterize molecular featuresDataset size is directly correlated to performance of prediction : single-task deep learning models only outperform shallow learners if there is sufficient data.
Edit intent: clarity
Original:  We find that certain examples , which we term pruning identified exemplars (PIEs) , and classes are systematically more impacted by the introduction of sparsity.
Revised:  We find that models with radically different numbers of weights have comparable top-line performance metrics but diverge considerably in behavior on a narrow subset of the dataset. This small subset of data points , which we term pruning identified exemplars (PIEs) , and classes are systematically more impacted by the introduction of sparsity.
Edit intent: meaning-changed
Original:  Removing PIE images from the test-set greatly improves top-1 accuracy for both pruned and non-pruned models. These hard-to-generalize-to images tend to be mislabelled, of lower image quality, depict multiple objects or require fine-grained classification. These findings shed light on previously unknown trade-offs , and suggest that a high degree of caution should be exercised before pruning is used in sensitive domains .
Revised:  Compression disproportionately impacts model performance on the underrepresented long-tail of the data distribution. PIEs over-index on atypical or noisy images that are far more challenging for both humans and algorithms to classify. Our work provides intuition into the role of capacity in deep neural networks and the trade-offs , and suggest that a high degree of caution should be exercised before pruning is used in sensitive domains .
Edit intent: meaning-changed
Original: Online portfolio selection is a sequential decision-making problem in financial engineering, aiming to construct a portfolio by optimizing the allocation of wealth across a set of assets to achieve the highest return .
Revised: As the cornerstone of modern portfolio theory, Markowitz's mean-variance optimization is considered a major model adopted in portfolio management. However, due to the difficulty of estimating its parameters, it cannot be applied to all periods. In some cases, naive strategies such as Equally-weighted and Value-weighted portfolios can even get better performance. Under these circumstances, we can use multiple classic strategies as multiple strategic arms in multi-armed bandit to naturally establish a connection with the portfolio selection problem. This can also help to maximize the rewards in the bandit algorithm by the trade-off between exploration and exploitation .
Edit intent: meaning-changed
Original:  In this paper, we present a novel context-aware dynamic assets selection problem and propose two innovative online portfolio selection methods named Exp4.ONSE and Exp4.EGE respectively based on contextual bandit algorithms to adapt to the dynamic assets selection scenario. We also provide regret upper bounds for both methods which achieve sublinear regrets. In addition, we analyze the computational complexity of the proposed algorithms, which implies that the computational complexity can be significantly reduced to polynomial time. Extensive experiments demonstrate that our algorithms have marked superiority across representative real-world market datasets in term of efficiency and effectiveness .
Revised:  In this paper, we present a portfolio bandit strategy through Thompson sampling which aims to make online portfolio choices by effectively exploiting the performances among multiple arms. Also, by constructing multiple strategic arms, we can obtain the optimal investment portfolio to adapt different investment periods. Moreover, we devise a novel reward function based on users' different investment risk preferences, which can be adaptive to various investment styles. Our experimental results demonstrate that our algorithms have marked superiority across representative real-world market datasets in term of efficiency and effectiveness .
Edit intent: clarity
Original: URLanized bistability  is the counterpart of URLanized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Revised: URLanized bistability (SOB) is the counterpart of URLanized criticality " (SOC), for systems tuning themselves to the edge of bistability of a discontinuous /first-order phase transition, rather than to the critical point of a continuous /second order one.
Edit intent: meaning-changed