wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Instead, we perform sketching through leverage score sampling, crucially relying on the fact that the structure of the Khatri-Rao product allows sampling from overestimates of the leverage scores without forming the full product or the corresponding probabilities. Naive application of leverage score sampling is ineffective because we often have cases where a few scores are quite large, leading to repeatedly sampling the few entries with large scores. We improve the speed by combining repeated rows. Additionally, we propose a novel hybrid of deterministic and random leverage-score sampling which consistently yields improved fits . Numerical results on real-world large-scale tensors show the method is significantly faster than competing methods without sacrificing accuracy.
Revised:  Instead, we perform sketching through leverage score sampling, crucially relying on the fact that the structure of the Khatri-Rao product allows sampling from overestimates of the number of rows in the sketched system is independent of the number of nonzeros in the full tensor and the number of rows in the full system. Along the way, we provide a practical solution to the generic matrix sketching problem of sampling overabundance for high-leverage-score rows, proposing to include such rows deterministically and combine repeated samples in the sketched system; we conjecture that this can lead to improved theoretical bounds . Numerical results on real-world large-scale tensors show the method is significantly faster than competing methods without sacrificing accuracy.
Edit intent: meaning-changed
Original:  Additionally, we propose a novel hybrid of deterministic and random leverage-score sampling which consistently yields improved fits . Numerical results on real-world large-scale tensors show the method is significantly faster than competing methods without sacrificing accuracy.
Revised:  Additionally, we propose a novel hybrid of deterministic and random leverage-score sampling which consistently yields improved fits . Numerical results on real-world large-scale tensors show the method is significantly faster than deterministic methods without sacrificing accuracy.
Edit intent: clarity
Original:  We found that the recipients were not retaining most of the messages from the bulk emails though they opened a large number of them. The tools for designing and URLanizational bulk email for communicators were very limited. The assessments on bulk emails ' values and effectiveness were different between communicatorsand recipients. We discussed possible solutions that could URLanizational bulk emails ' designing and distributing mechanisms .
Revised:  We found that URLanizational bulk email system as a whole was strained, and communicators are caught in the middle of this multi-stakeholder problem. First, though the communicators had an interest in preserving the effectiveness of channels in reaching employees, they had high-level clients whose interests might outweigh judgment about whether a message deserves widespread circulation. Second, though communicators thought they were sending important information, recipients viewed most of URLanizational bulk emails as not relevant to them. Third, this disagreement was amplified by the success metric used by communicators. They viewed their bulk emails as successful if they had a high open rate. But recipients often opened and then rapidly discarded emails without reading the details. Last, while the communicators in general understood the challenge, they had a limited set of targeting and feedback tools to support their task .
Edit intent: meaning-changed
Original:  Modeling results show that a 50\\% adoption rate of Social distancing 2.0, with privacy-preserving contact tracing, would suffice to decrease the R_0 to less than 1 and prevent the resurgence of COVID-19 epidemic.
Revised:  Modelling results show that a 50\\% adoption rate of Social distancing 2.0, with privacy-preserving contact tracing, would suffice to decrease the R_0 to less than 1 and prevent the resurgence of COVID-19 epidemic.
Edit intent: clarity
Original:  We, therefore, replicated a study that reported benefits of haptic interaction between humans on individual motor improvement for tracking a target in a visuomotor rotation . Also , we tested the effect of more interaction time and  stronger haptic coupling between the partners on individual performance improvement in the same task.
Revised:  We, therefore, replicated a study that reported benefits of haptic interaction between humans on individual motor improvement for tracking a target in a visuomotor rotation perturbation. In addition , we tested the effect of more interaction time and stronger haptic coupling between the partners on individual performance improvement in the same task.
Edit intent: meaning-changed
Original:  We find that the associations between certain finger tapping characteristics (  number of taps of both hand of bi-inphase and bi-untiphase ) and TUG score , certain gait characteristics are relatively high.
Revised:  We find that the associations between certain finger tapping characteristics ( ' number of taps of both hand of bi-inphase and bi-untiphase ) and TUG score , certain gait characteristics are relatively high.
Edit intent: fluency
Original:  We find that the associations between certain finger tapping characteristics (  number of taps of both hand of bi-inphase and bi-untiphase ) and TUG score , certain gait characteristics are relatively high.
Revised:  We find that the associations between certain finger tapping characteristics ( number of taps ', 'average interval of tapping', 'frequency of tapping' of both hands of bimanual inphase and those of left hand of bimanual untiphase ) and TUG score , certain gait characteristics are relatively high.
Edit intent: meaning-changed
Original:  We find that the associations between certain finger tapping characteristics (  number of taps of both hand of bi-inphase and bi-untiphase ) and TUG score , certain gait characteristics are relatively high.
Revised:  We find that the associations between certain finger tapping characteristics ( number of taps of both hand of bi-inphase and bi-untiphase ) and TUG score are relatively high.
Edit intent: clarity
Original:  We show that this is possible using a recently developed theory that relates parameters of such a distribution to parameters of the evolutionary dynamics. This extends classical results on the Quasi-Linkage Equilibrium (QLE) regime first obtained by Kimura , and more recently studied by Neher and Shraiman.
Revised:  In the recent literature this has been done by applying the Quasi-Linkage Equilibrium (QLE) regime first obtained by Kimura , and more recently studied by Neher and Shraiman.
Edit intent: clarity
Original:  This extends classical results on the Quasi-Linkage Equilibrium (QLE) regime first obtained by Kimura , and more recently studied by Neher and Shraiman. In particular, the new theory outperforms the Kimura-Neher-Shraiman theory in the interesting regime where the effects of mutations are comparable to or larger than recombination.
Revised:  This extends classical results on the Quasi-Linkage Equilibrium (QLE) regime first obtained by Kimura in the limit of high recombination. Here we show that the approach also works in the interesting regime where the effects of mutations are comparable to or larger than recombination.
Edit intent: meaning-changed
Original: A central problem in cognitive science and behavioural neuroscience as well as in machine learning and artificial intelligence research is to ascertain whether two or more decision makers ( e.g. brains or algorithms) use the same strategy.
Revised: A central problem in cognitive science and behavioural neuroscience as well as in machine learning and artificial intelligence research is to ascertain whether two or more decision makers ( be they brains or algorithms) use the same strategy.
Edit intent: clarity
Original:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone--indicating that humans and CNNs are likely implementing very different strategies  (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a  ResNet-50 in our analysis--that is, just like a standard feedforward network .
Revised:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone---indicating that humans and CNNs are likely implementing very different strategies (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a ResNet-50 in our analysis--that is, just like a standard feedforward network .
Edit intent: fluency
Original:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone--indicating that humans and CNNs are likely implementing very different strategies  (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a  ResNet-50 in our analysis--that is, just like a standard feedforward network .
Revised:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone--indicating that humans and CNNs are likely implementing very different strategies . (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a ResNet-50 in our analysis--that is, just like a standard feedforward network .
Edit intent: fluency
Original:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone--indicating that humans and CNNs are likely implementing very different strategies  (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a  ResNet-50 in our analysis--that is, just like a standard feedforward network .
Revised:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone--indicating that humans and CNNs are likely implementing very different strategies (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a standard purely feedforward ResNet-50 in our analysis--that is, just like a standard feedforward network .
Edit intent: meaning-changed
Original:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone--indicating that humans and CNNs are likely implementing very different strategies  (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a  ResNet-50 in our analysis--that is, just like a standard feedforward network .
Revised:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone--indicating that humans and CNNs are likely implementing very different strategies (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a ResNet-50 in our analysis .
Edit intent: meaning-changed
Original:  Taken together, error consistency analysis suggests that the strategies used by human and machine vision are still very different--but we envision our general-purpose error consistency analysis to serve as a fruitful tool for quantifying future progress.
Revised:  Taken together, error consistency analysis suggests that the strategies used by human and machine vision are still very different---but we envision our general-purpose error consistency analysis to serve as a fruitful tool for quantifying future progress.
Edit intent: fluency
Original:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone---indicating that humans and CNNs are likely implementing very different strategies. (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a standard purely feedforward ResNet-50 in our analysis.
Revised:  The consistency between CNNs and human observers, however, is little above what can be expected by chance alone -- indicating that humans and CNNs are likely implementing very different strategies. (3.) CORnet-S, a recurrent model termed the "current best model of the primate ventral visual stream", fails to capture essential characteristics of human behavioural data and behaves essentially like a standard purely feedforward ResNet-50 in our analysis.
Edit intent: fluency
Original:  Taken together, error consistency analysis suggests that the strategies used by human and machine vision are still very different---but we envision our general-purpose error consistency analysis to serve as a fruitful tool for quantifying future progress.
Revised:  Taken together, error consistency analysis suggests that the strategies used by human and machine vision are still very different -- but we envision our general-purpose error consistency analysis to serve as a fruitful tool for quantifying future progress.
Edit intent: fluency
Original:  Afterwards, we  derive almost optimal upper bounds for ELU-neural networks in Sobolev norms up to second-order .
Revised:  Afterwards, we provide a unifying framework for the construction of approximate partitions of unity by neural networks with fairly general activation functions. This allows us to approximate localized Taylor polynomials by neural networks and make use of the Bramble-Hilbert Lemma. Based on our framework, we derive almost optimal upper bounds for ELU-neural networks in Sobolev norms up to second-order .
Edit intent: meaning-changed
Original:  We show here that the arc deletion, arc completion and arc editing problems for BMGs are NP-complete and that they can be formulated and solved as integer linear programs  .
Revised:  We show here that the arc deletion, arc completion and arc editing problems for BMGs are NP-complete and that they can be formulated and solved as integer linear programs . To this end, we provide a novel characterization of BMGs in terms of triples (binary trees on three leaves) and a characterization of BMGs with two colors in terms of forbidden subgraphs .
Edit intent: meaning-changed
Original:  The development of a multivariate ordered probit model drawing on U.S. survey data provides well-needed insights into individuals preparedness to adopt multiple active modes as a function of personal, neighborhood and psychosocial factors.
Revised:  Multivariate ordered probit modeling drawing on U.S. survey data provides well-needed insights into individuals preparedness to adopt multiple active modes as a function of personal, neighborhood and psychosocial factors.
Edit intent: clarity
Original:  The strongest positive association is found for walking and cycling adoption , whereas other joint model effects are less evident. Identifying strongly with active mobility  , experiences with multimodal travel, possessing better navigational skills, along with supportive local community norms are the factors that appear to drive the joint adoption decisions.
Revised:  The strongest positive association is found for walking and cycling adoption . 2) Each mode has a distinctive adoption path with either three or four separate stages. We discuss the implications of derived stage-thresholds and plot adoption contours for selected scenarios. 3) Psychological and neighborhood variables generate more coupling among active modes than individual and household factors. Specifically, identifying strongly with active mobility , experiences with multimodal travel, possessing better navigational skills, along with supportive local community norms are the factors that appear to drive the joint adoption decisions.
Edit intent: meaning-changed
Original:  Moreover, they may be relevant alternatives even for settings in which the number of treated units is usually considered as large enough to rely on clustered robust standard errors .
Revised:  Moreover, they may be relevant alternatives even for settings in which the number of treated units is usually considered as large enough to rely on cluster-robust standard errors. We also present an empirical application that highlights some common misunderstandings in the use of randomization inference in DID applications, and illustrates how our results can be used to provide proper inference .
Edit intent: meaning-changed
Original:  With this chapter we aim to give modelers a helping hand by describing the main types of anatomical data that may be useful for informing neuronal network models , aspects of the underlying experimental techniques relevant to their interpretation , particularly comprehensive datasets, and methods for filling in the gaps in the data.
Revised:  With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models , aspects of the underlying experimental techniques relevant to their interpretation , particularly comprehensive datasets, and methods for filling in the gaps in the data.
Edit intent: clarity
Original:  With this chapter we aim to give modelers a helping hand by describing the main types of anatomical data that may be useful for informing neuronal network models , aspects of the underlying experimental techniques relevant to their interpretation , particularly comprehensive datasets, and methods for filling in the gaps in the data.
Revised:  With this chapter we aim to give modelers a helping hand by describing the main types of anatomical data that may be useful for informing neuronal network models . We further discuss aspects of the underlying experimental techniques relevant to their interpretation , particularly comprehensive datasets, and methods for filling in the gaps in the data.
Edit intent: meaning-changed
Original:  With this chapter we aim to give modelers a helping hand by describing the main types of anatomical data that may be useful for informing neuronal network models , aspects of the underlying experimental techniques relevant to their interpretation , particularly comprehensive datasets, and methods for filling in the gaps in the data.
Revised:  With this chapter we aim to give modelers a helping hand by describing the main types of anatomical data that may be useful for informing neuronal network models , aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the data.
Edit intent: clarity
Original:  With this chapter we aim to give modelers a helping hand by describing the main types of anatomical data that may be useful for informing neuronal network models , aspects of the underlying experimental techniques relevant to their interpretation , particularly comprehensive datasets, and methods for filling in the gaps in the data. Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on  mammalian cerebral cortex.
Revised:  With this chapter we aim to give modelers a helping hand by describing the main types of anatomical data that may be useful for informing neuronal network models , aspects of the underlying experimental techniques relevant to their interpretation , particularly comprehensive datasets, and methods for filling in the gaps in the experimental data. Such methods of `predictive connectomics' estimate connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on mammalian cerebral cortex.
Edit intent: clarity
Original:  Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on  mammalian cerebral cortex.
Revised:  Estimating connectivity where the data are lacking based on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on mammalian cerebral cortex.
Edit intent: coherence
Original:  Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on  mammalian cerebral cortex.
Revised:  Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities . It is instructive, and in certain cases necessary, to URLanizational principles that link the plethora of data within a unifying framework where regularities of brain structure can be exploited to inform computational models . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on mammalian cerebral cortex.
Edit intent: meaning-changed
Original:  Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on  mammalian cerebral cortex.
Revised:  Estimating connectivity where the data are lacking generally relies on statistical relationships with known quantities and may be referred to as `predictive connectomics' . In addition, we touch upon the most prominent features of URLanization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex.
Edit intent: fluency
Original:  We demonstrate that it is nevertheless possible to achieve an appreciably high throughput of the computing kernels of such a scheme. We discuss the algorithmic design that allows a SIMD vectorization of the compute kernel, analyze the node-level performance  and report excellent weak and strong scaling of a hybrid thread/MPI parallelization.
Revised:  We develop an algorithmic design that allows a SIMD vectorization of the compute kernel, analyze the node-level performance and report excellent weak and strong scaling of a hybrid thread/MPI parallelization.
Edit intent: clarity