wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Motivated by the recent success of BERT based pre-training technique for NLP and image-language tasks, VideoBERT and CBT are proposed to exploit BERT model for video and language pre-training using narrated instructional videos.
Revised:  Motivated by the recent success of BERT based pre-training technique for NLP and image-linguistic tasks, there are still few works on video-linguistic pre-training using narrated instructional videos.
Edit intent: clarity
Original:  Motivated by the recent success of BERT based pre-training technique for NLP and image-language tasks, VideoBERT and CBT are proposed to exploit BERT model for video and language pre-training using narrated instructional videos. Different from their works which only pre-train understanding task, we propose a unified video-language pre-training model for both understanding and generation tasks .
Revised:  Motivated by the recent success of BERT based pre-training technique for NLP and image-language tasks, VideoBERT and CBT are proposed to exploit BERT model for video and language pre-training . Besides, most of the existing multimodal models are pre-trained for understanding task, we propose a unified video-language pre-training model for both understanding and generation tasks .
Edit intent: coherence
Original:  Different from their works which only pre-train understanding task, we propose a unified video-language pre-training model for both understanding and generation tasks .
Revised:  Different from their works which only pre-train understanding task, which leads to a pretrain-finetune discrepency for generation tasks. In this paper, we propose UniViLM: a Unified Video and Language pre-training model for both understanding and generation tasks .
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Original: With the recent success of  pre-training technique for NLP and image-linguistic tasks, there are still few works on video-linguistic pre-training . Besides , most of the existing multimodal models are pre-trained for understanding task, which leads to a pretrain-finetune discrepency for generation tasks.
Revised: With the recent success of pre-training technique for NLP and image-linguistic tasks, there are still few works on video-linguistic pre-training works are gradually developed to improve video-text related downstream tasks. However , most of the existing multimodal models are pre-trained for understanding task, which leads to a pretrain-finetune discrepency for generation tasks.
Edit intent: meaning-changed
Original:  We first pre-train our model to learn the universal representation for both video and language on a large instructional video dataset.
Revised:  Five objectives, including video-text joint, conditioned masked language model (CMLM), conditioned masked frame model (CMFM), video-text alignment, and language reconstruction, are designed to train each of the components. We further develop two pre-training strategies, stage by stage pre-training (StagedP) and enhanced video representation (EnhancedV), to make the training process of the UniVL more effective. The pre-train our model to learn the universal representation for both video and language on a large instructional video dataset.
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Original:  We first pre-train our model to learn the universal representation for both video and language on a large instructional video dataset. Then we fine-tune the model on two multimodal tasks including understanding task (text-based video retrieval) and generation task (multimodal video captioning). Our extensive experiments show that our method can improve the performance of both understanding and generation tasks and achieves the state-of-the art results .
Revised:  We first pre-train is carried out on a sizeable instructional video dataset HowTo100M. Experimental results demonstrate that the state-of-the art results .
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Original:  In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step towards the ability to perform targeted manipulation of the brain's large-scale dynamical activity . In this paper, we investigate the role of community structure in the controllability of structural brain networks.
Revised:  In addition to offering insight into the structure-function relationship of networked systems, such an understanding is a critical step towards the ability to perform targeted manipulation of the brain's large-scale dynamical activity in a targeted manner. We investigate the role of community structure in the controllability of structural brain networks.
Edit intent: clarity
Original:  Additionally, we show that with SLA = 250ms, MDInference can increase the aggregate accuracy in 99.74\% of cases on faster university networks and 96.84\% of cases on residential networks.
Revised:  Additionally, we show that with a target latency of 250ms, MDInference can increase the aggregate accuracy in 99.74\% of cases on faster university networks and 96.84\% of cases on residential networks.
Edit intent: clarity
Original:  Additionally, we show that with SLA = 250ms, MDInference can increase the aggregate accuracy in 99.74\% of cases on faster university networks and 96.84\% of cases on residential networks.
Revised:  Additionally, we show that with SLA = 250ms, MDInference increased the aggregate accuracy in 99.74\% of cases on faster university networks and 96.84\% of cases on residential networks.
Edit intent: clarity
Original:  Additionally, we show that with SLA = 250ms, MDInference can increase the aggregate accuracy in 99.74\% of cases on faster university networks and 96.84\% of cases on residential networks.
Revised:  Additionally, we show that with SLA = 250ms, MDInference can increase the aggregate accuracy in 99.74\% cases on faster university networks and 96.84\% of cases on residential networks.
Edit intent: fluency
Original:  Additionally, we show that with SLA = 250ms, MDInference can increase the aggregate accuracy in 99.74\% of cases on faster university networks and 96.84\% of cases on residential networks.
Revised:  Additionally, we show that with SLA = 250ms, MDInference can increase the aggregate accuracy in 99.74\% of cases on faster university networks and 96.84\% cases on residential networks.
Edit intent: fluency
Original: The problem of nearest-neighbor condensation deals with finding a subset R from a set of labeled points P such that for every point p in R the nearest-neighbor of p in R has the same label as p.
Revised: The problem of nearest-neighbor condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Edit intent: meaning-changed
Original:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and l _p metrics with p >= 2. \geq2 Finally, we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms, becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensation problem.
Revised:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and \ell _p metrics with p >= 2. \geq2 Finally, we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms, becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensation problem.
Edit intent: fluency
Original:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and l _p metrics with p >= 2. \geq2 Finally, we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms, becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensation problem.
Revised:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and l _p metrics with p \geq2 Finally, we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms, becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensation problem.
Edit intent: clarity
Original:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and l _p metrics with p >= 2. \geq2 Finally, we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms, becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensation problem.
Revised:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and l _p metrics with p >= 2. \geq2. Finally, we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms, becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensation problem.
Edit intent: fluency
Original: The problem of nearest-neighbor  condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Revised: Given a training set P of labeled points, the nearest-neighbor condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Edit intent: meaning-changed
Original: The problem of nearest-neighbor  condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Revised: The problem of nearest-neighbor rule predicts the class of an unlabeled query point as the label of its closest point in the set. To improve the time and space complexity of classification, a natural question is how to reduce the training set without significantly affecting the accuracy of the nearest-neighbor rule. Nearest-neighbor condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Edit intent: meaning-changed
Original: The problem of nearest-neighbor  condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Revised: The problem of nearest-neighbor condensation deals with finding a subset R \subseteq P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Edit intent: meaning-changed
Original: The problem of nearest-neighbor  condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P the nearest-neighbor of p in R has the same label as p.
Revised: The problem of nearest-neighbor condensation deals with finding a subset R from a set of labeled points P such that for every point p \in P , p's nearest-neighbor of p in R has the same label as p.
Edit intent: clarity
Original:  This is motivated by applications in classification, where the nearest-neighbor rule assigns to an unlabeled query point the label of its nearest-neighbor in the point set. In this context, condensation aims to reduce the size of the setneeded to classify new points .
Revised:  This relates to the concept of coresets, which can be broadly defined as subsets of the set, such that an exact result on the coreset corresponds to an approximate result on the original set .
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Original:  However, finding such subsets of minimum cardinality is NP-hard, and most research has focused on practical heuristics without performance guarantees. Additionally, the use of exact nearest-neighbors is always assumed, ignoring the effect of condensation in the classification accuracy when nearest-neighbors are computed approximately. In this paper, we address these shortcomings by proposing new approximation-sensitive criteria for the nearest-neighbor condensationproblem, along with practical algorithms with provable performance guarantees.
Revised:  However, the guarantees of a coreset hold for any query point, and not only for the points of the nearest-neighbor condensationproblem, along with practical algorithms with provable performance guarantees.
Edit intent: clarity
Original:  In this paper, we address these shortcomings by proposing new approximation-sensitive criteria for the nearest-neighbor condensationproblem, along with practical algorithms with provable performance guarantees. We characterize sufficient conditions to guarantee correct classification of unlabeled points using approximate nearest-neighbor queries on these subsets, which introduces the notion of coresets for classification with the nearest-neighbor rule.
Revised:  In this paper, we address these shortcomings by proposing new approximation-sensitive criteria for the training set. This paper introduces the concept of coresets for nearest-neighbor classification. We extend existing criteria used for condensation, and prove sufficient conditions to guarantee correct classification of unlabeled points using approximate nearest-neighbor queries on these subsets, which introduces the notion of coresets for classification with the nearest-neighbor rule.
Edit intent: clarity
Original:  We characterize sufficient conditions to guarantee correct classification of unlabeled points using approximate nearest-neighbor queries on these subsets, which introduces the notion of coresets for classification with the nearest-neighbor rule. Moreover , we prove that it is NP-hard to compute subsets with these characteristics, whose cardinality approximates that of the minimum cardinality subset.
Revised:  We characterize sufficient conditions to correctly classify any query point when using these subsets. Additionally , we prove that it is NP-hard to compute subsets with these characteristics, whose cardinality approximates that of the minimum cardinality subset.
Edit intent: clarity
Original:  Moreover , we prove that it is NP-hard to compute subsets with these characteristics, whose cardinality approximates that of the minimum cardinality subset. Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and \ell_p metrics with p%DIFDELCMD < \geq2%%% . Finally , we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms , becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensationproblem .
Revised:  Moreover , we prove that %DIFDELCMD < \geq2%%% . Finally , we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms , becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensationproblem .
Edit intent: coherence
Original:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and \ell_p metrics with p%DIFDELCMD < \geq2%%% . Finally , we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms , becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensationproblem .
Revised:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and \ell_p metrics with p%DIFDELCMD < \geq2%%% finding such subsets of minimum cardinality is NP-hard, and propose quadratic-time approximation algorithms with provable upper-bounds on the size of their selected subsets. Moreover , we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms , becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensationproblem .
Edit intent: meaning-changed
Original:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and \ell_p metrics with p%DIFDELCMD < \geq2%%% . Finally , we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms , becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensationproblem .
Revised:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and \ell_p metrics with p%DIFDELCMD < \geq2%%% . Finally , we show how to improve one of these algorithms , becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensationproblem .
Edit intent: clarity
Original:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and \ell_p metrics with p%DIFDELCMD < \geq2%%% . Finally , we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms , becoming the first truly subquadratic approximation algorithm for the nearest-neighbor condensationproblem .
Revised:  Additionally, we propose new algorithms for computing such subsets, with tight approximation factors in general metrics, and improved factors for doubling metrics and \ell_p metrics with p%DIFDELCMD < \geq2%%% . Finally , we show an alternative implementation scheme that reduces the worst-case time complexity of one of these algorithms to have subquadratic runtime, being the first of this kind for condensation .
Edit intent: clarity
Original:  Currently, a general method to reliably infer inertial dynamics from realistic experimental trajectories -- sampled at discrete times and subject to measurement noise -- is lacking. Here, we derive a principled framework to infer the deterministic and stochastic fields from realistic trajectories of inertial systems with multiplicative noise. Our framework yields an operational method, termed Stochastic Inertial Force Inference , which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems  including flocks with Viscek-like alignment interactions.  .
Revised:  Currently, a general method to reliably infer inertial dynamics from realistic experimental trajectories -- sampled at discrete times and subject to measurement errors. This framework yields an operational method, termed Stochastic Inertial Force Inference , which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems including flocks with Viscek-like alignment interactions. .
Edit intent: clarity
Original:  Our framework yields an operational method, termed Stochastic Inertial Force Inference , which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems  including flocks with Viscek-like alignment interactions.  .
Revised:  Our framework yields an operational method, Underdamped Langevin Inference (ULI) , which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems including flocks with Viscek-like alignment interactions. .
Edit intent: meaning-changed
Original:  Our framework yields an operational method, termed Stochastic Inertial Force Inference , which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems  including flocks with Viscek-like alignment interactions.  .
Revised:  Our framework yields an operational method, termed Stochastic Inertial Force Inference , which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems , including flocks with Viscek-like alignment interactions. .
Edit intent: fluency
Original:  Our framework yields an operational method, termed Stochastic Inertial Force Inference , which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems  including flocks with Viscek-like alignment interactions.  .
Revised:  Our framework yields an operational method, termed Stochastic Inertial Force Inference , which performs well on experimental trajectories of single migrating cells and in complex high-dimensional systems including flocks with Viscek-like alignment interactions. Our method is robust to experimental measurement errors, and includes a self-consistent estimate of the inference error .
Edit intent: meaning-changed
Original: Adhesive interactions between elastic structures such as graphene sheets, carbon nanotubes, and microtubule axonemes exhibit hysteresis due to irrecoverable energy loss associated with bond breakage, even in static (rate-independent) experiments.
Revised: Adhesive interactions between elastic structures such as graphene sheets, carbon nanotubes, and microtubules have been shown to exhibit hysteresis due to irrecoverable energy loss associated with bond breakage, even in static (rate-independent) experiments.
Edit intent: clarity
Original:  Here we provide a simple theory that explains how bond breaking and elastic relaxation can drive static adhesion hysteresis over a debonding / rebonding cycle.
Revised:  To understand this phenomenon, we start with a minimal theory for the peeling of a thin sheet from a substrate, coupling the local event of bond breaking to the nonlocal elastic relaxation of the sheet and show that this can drive static adhesion hysteresis over a debonding / rebonding cycle.
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Original:  Here we provide a simple theory that explains how bond breaking and elastic relaxation can drive static adhesion hysteresis over a debonding / rebonding cycle. We show that energy loss emerges from the coupling of a local event (bond breaking) to a nonlocal event (overall elastic relaxation), in a manner similar to the Lake-Thomas effect in polymers. Experiments with adherent microtubules allow us to quantify the hysteresis as a function of adhesion and elasticity parameters .
Revised:  Here we provide a simple theory that explains how bond breaking and elastic relaxation can drive static adhesion hysteresis over a debonding / debonding cycle. Using this model we quantify hysteresis in terms of the adhesion and elasticity parameters .
Edit intent: clarity
Original:  Experiments with adherent microtubules allow us to quantify the hysteresis as a function of adhesion and elasticity parameters . Our model also helps guide the derivation of a self-consistent continuum boundary condition at the interface of adherent mechanical systems .
Revised:  Experiments with adherent microtubules allow us to quantify the hysteresis as a function of adhesion and elasticity parameters of the system. This allows us to derive a scaling relation that preserves hysteresis at different levels of granularity while resolving a seeming paradox of lattice trapping in the continuum limit of a discrete fracture process. Finally, to verify our theory, we use new experiments to demonstrate and measure adhesion hysteresis in bundled microtubules .
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