wanyu/IteraTeR_full_sent huggingface dataset:
Original: State estimation problems that use relative observations routinely arise in navigation of unmanned aerial vehicles, autonomous ground vehicles, %DIFDELCMD < \etc %%%  whose proper operation relies on accurate state estimates and reliable covariances.
Revised: State estimation problems without absolute position measurements routinely arise in navigation of unmanned aerial vehicles, autonomous ground vehicles, %DIFDELCMD < \etc %%% whose proper operation relies on accurate state estimates and reliable covariances.
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Original: State estimation problems that use relative observations routinely arise in navigation of unmanned aerial vehicles, autonomous ground vehicles, %DIFDELCMD < \etc %%%  whose proper operation relies on accurate state estimates and reliable covariances.
Revised: State estimation problems that use relative observations routinely arise in navigation of unmanned aerial vehicles, autonomous ground vehicles, %DIFDELCMD < \etc %%% etc., whose proper operation relies on accurate state estimates and reliable covariances.
Edit intent: coherence
Original: Deep learning techniques achieve high accuracy in computer vision tasks. However, their accuracy suffers considerably when they face a domain change, i. e., as soon as they are used in a domain that differs from their training domain.
Revised: Traditional computer vision approaches, based on neural networks (NN), are typically trained on a large amount of image data. By minimizing the cross-entropy loss between a prediction and a given class label, the NN and its visual embedding space are learned to fulfill a given task. However, due to the sole dependence on the image data distribution of the training domain, these models tend to fail when applied to a target domain that differs from their training domain.
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Original:  However, their accuracy suffers considerably when they face a domain change, i. e., as soon as they are used in a domain that differs from their training domain. For example, a road sign recognition model trained to recognize road signs in Germany performs poorly in countries with different road sign standards like China. We propose ConTraKG , a neuro-symbolic approach that enables cross-domain transfer learning based on prior knowledge about the domain or context.
Revised:  However, their accuracy suffers considerably when they face a domain change, i. e., as soon as they are used in a domain that differs from their source domain. To learn a more robust NN to domain shifts, we propose the knowledge graph neural network (KG-NN) , a neuro-symbolic approach that enables cross-domain transfer learning based on prior knowledge about the domain or context.
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Original:  We propose ConTraKG , a neuro-symbolic approach that enables cross-domain transfer learning based on prior knowledge about the domain or context. A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space  according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
Revised:  We propose ConTraKG , a neuro-symbolic approach that supervises the training using image-data-invariant auxiliary knowledge. The auxiliary knowledge is first encoded in a knowledge graph with respective concepts and their relationships , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
Edit intent: meaning-changed
Original:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space  according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
Revised:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via an embedding method . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
Edit intent: clarity
Original:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space  according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
Revised:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a contrastive loss function, KG-NN learns to adapt its visual embedding space according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
Edit intent: clarity
Original:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space  according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
Revised:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space and thus its weights according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function.
Edit intent: meaning-changed
Original:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space  according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function. This allows the neural network to incorporate training data from different target domains that are already represented in the knowledge graph. We conduct a series of empirical evaluations to determine the accuracy of our approach.
Revised:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space according to the image-data invariant knowledge graph embedding space. We evaluate KG-NN on visual transfer learning tasks for classification using the mini-ImageNet dataset and its derivatives, as well as road sign recognition datasets from Germany and China.
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Original:  The results show that ConTraKG is significantly more accurate than the conventional approach for dealing with domain changes. In a transfer learning setup, where the network is trained on both domains, ConTraKG achieves 21\% higher accuracy when tested on the source domain and 15\% when tested on the target domain compared to the standard approach. Moreover, with only 10\% of the target data for training, it achieves the same accuracy as the cross-entropy-based model trained on the full target data .
Revised:  The results show that a visual model trained with a knowledge graph as a trainer outperforms a model trained with cross-entropy in all experiments, in particular when the domain gap increases. Besides better performance and stronger robustness to domain shifts, these KG-NN adapts to multiple datasets and classes without suffering heavily from catastrophic URLetting .
Edit intent: coherence
Original:  We provide three insights: First, if the number of misbehaving voters, denoted by #X , is high, both voting methods fail to deliver a positive outcome.
Revised:  We illustrate that vote delegation leads to quite different outcomes than conventional voting with abstention. In particular, we obtain three insights: First, if the number of misbehaving voters, denoted by #X , is high, both voting methods fail to deliver a positive outcome.
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Original:  Along with the model, this paper presents an evolution of the dataset SocNav1  which considers the movement of the robot and the humans, and an updated scenario-to-graph transformation which is tested using different Graph Neural Network blocks.
Revised:  Along with the model, this paper presents an evolution of the dataset SocNav1 25 which considers the movement of the robot and the humans, and an updated scenario-to-graph transformation which is tested using different Graph Neural Network blocks.
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Original:  In addition to its accuracy, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered in comparison with handcrafted models. 
Revised:  In addition to its accuracy, the main advantage of the approach is its scalability in terms of the number of social factors that can be considered in comparison with handcrafted models. The dataset and the model are available in a public repository URL
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Original:  The quantitative and qualitative results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.
Revised:  Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks.
Edit intent: clarity
Original: URLanized criticality (SOC) refers to the ability of complex systems to evolve towards a second-order phase transition at which interactions between system components lead to scale-invariant events beneficial for system performance.
Revised: URLanized criticality (SOC) refers to the ability of complex systems to evolve towards a 2nd-order phase transition at which interactions between system components lead to scale-invariant events beneficial for system performance.
Edit intent: fluency
Original:  Here we review experimental findings of isolated, layered cortex preparations to URLanize towards the four dynamical motifs of up-states, oscillations, neuronal avalanches, and coherence potentials.
Revised:  Here we review experimental findings of isolated, layered cortex preparations to URLanize towards four dynamical motifs of up-states, oscillations, neuronal avalanches, and coherence potentials.
Edit intent: fluency
Original:  Here we review experimental findings of isolated, layered cortex preparations to URLanize towards the four dynamical motifs of up-states, oscillations, neuronal avalanches, and coherence potentials.
Revised:  Here we review experimental findings of isolated, layered cortex preparations to URLanize towards the four dynamical motifs identified in the cortex in vivo: up-states, oscillations, neuronal avalanches, and coherence potentials.
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Original: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP)  , emerges autonomously in superficial layers URLanotypic cortex cultures  , is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies.
Revised: 3/ 2 and a critical branching parameter of 1. This dynamical coordination, tracked in the local field potential (nLFP) , emerges autonomously in superficial layers URLanotypic cortex cultures , is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies.
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Original: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP)  , emerges autonomously in superficial layers URLanotypic cortex cultures  , is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies.
Revised: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP) and pyramidal neuron activity using 2-photon imaging , emerges autonomously in superficial layers URLanotypic cortex cultures , is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies.
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Original: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP)  , emerges autonomously in superficial layers URLanotypic cortex cultures  , is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies.
Revised: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP) , emerges autonomously in superficial layers URLanotypic cortex cultures and acute cortex slices , is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies.
Edit intent: meaning-changed
Original: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP)  , emerges autonomously in superficial layers URLanotypic cortex cultures  , is homeostatically regulated, exhibits separation of time scales, and reveals unique size vs. quiet time dependencies.
Revised: 3/ 2. This precise dynamical coordinationcan be tracked in negative transients of the local field potential (nLFP) , emerges autonomously in superficial layers URLanotypic cortex cultures , is homeostatically regulated, displays separation of time scales, and reveals unique size vs. quiet time dependencies.
Edit intent: clarity
Original:  While blockchain technology is leveraged to ensure data integrity in a distributed manner, Hyperledger Fabric (HLF) attracts attention with its distinctive strong point without requiring the power-consuming consensus protocol, that is , proof-of-work (PoW) . However, even though such security concerns can be mitigated using HLF , the additional processing time spent in HLF may emerge as another issue because most IoT devices handle real-time and latency critical jobs.
Revised:  While blockchain technology is leveraged to ensure data integrity in a distributed manner, Hyperledger Fabric (HLF) attracts attention with its distinctive strong point without requiring the power-consuming consensus protocol, that is , which is a private blockchain platform, has been leveraged for blockchain-enabled IoT (BC-IoT) networks. From the perspective of an IoT application, however , the additional processing time spent in HLF may emerge as another issue because most IoT devices handle real-time and latency critical jobs.
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Original:  However, even though such security concerns can be mitigated using HLF , the additional processing time spent in HLF may emerge as another issue because most IoT devices handle real-time and latency critical jobs.
Revised:  However, even though such security concerns can be mitigated using HLF , the additional processing time spent in HLF may emerge as another problem because many IoT applications handle real-time and latency critical jobs.
Edit intent: clarity
Original:  However, even though such security concerns can be mitigated using HLF , the additional processing time spent in HLF may emerge as another issue because most IoT devices handle real-time and latency critical jobs. This problem still remains unresolved because of the absence of a HLF latency model and a parameter setup guideline to reducing the mean latency .
Revised:  However, even though such security concerns can be mitigated using HLF , the additional processing time spent in HLF may emerge as another issue because most IoT devices handle real-time and latency-critical jobs. In other words, it is necessary to estimate the HLF latency before practical deployment of BC-IoT networks. However, this problem has still remained unresolved because the distribution of the HLF latency has not been revealed until the present .
Edit intent: coherence
Original:  In this paper, therefore, we develop a HLF latency model for HLF-based IoT networks based on probability distribution fitting , by which mean latency prediction is facilitated once probable configuration environments are determined, in terms of the block size, block-generation timeout, and transaction generation rate parameters .
Revised:  In this paper, therefore, we develop an HLF latency distribution model using probability distribution fitting , by which mean latency prediction is facilitated once probable configuration environments are determined, in terms of the block size, block-generation timeout, and transaction generation rate parameters .
Edit intent: clarity
Original:  In this paper, therefore, we develop a HLF latency model for HLF-based IoT networks based on probability distribution fitting , by which mean latency prediction is facilitated once probable configuration environments are determined, in terms of the block size, block-generation timeout, and transaction generation rate parameters .
Revised:  In this paper, therefore, we develop a HLF latency model for HLF-based IoT networks based on probability distribution fitting and show the latency can be modeled with the gamma distribution .
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Original:  Furthermore, we conclude by analyzing the impacts of influential HLF parameters on the mean latency, in order to provide insights not only on optimizing the mean latency, but also on coping with long mean latency.
Revised:  Furthermore, we define three influential HLF parameters on the mean latency, in order to provide insights not only on optimizing the mean latency, but also on coping with long mean latency.
Edit intent: clarity
Original:  Furthermore, we conclude by analyzing the impacts of influential HLF parameters on the mean latency, in order to provide insights not only on optimizing the mean latency, but also on coping with long mean latency.
Revised:  Furthermore, we conclude by analyzing the impacts of influential HLF parameters on the HLF latency. Their impacts on the HLF latency are analyzed to provide strategies for decreasing and minimizing the mean HLF latency.
Edit intent: clarity
Original:  SpeedyFeed is also a model-agnostic framework, thus being potentially applicable to a wide spectrum of content-based recommender systems . We've made the source code open to the public so as to facilitate research and applications in related areas.
Revised:  SpeedyFeed is also a model-agnostic framework, thus being potentially applicable to a wide spectrum of content-based recommender systems ; therefore, the whole framework is open-sourced to facilitate the progress in related areas.
Edit intent: clarity
Original: Video of nematodes/roundworms was analyzed using persistent homology to study locomotion and behavior. In each frame, URLanism's body posture was represented by a high-dimensional vector. By concatenating points in fixed-duration segments of this time series, we created a sliding window embedding (sometimes called a time delay embedding)where each point corresponds to a sequence of postures of URLanism. Persistent homology on the points in this time series detected behaviors and comparisons of these persistent homologycomputations detected variation in their corresponding behaviors. We used average persistence landscapes and machine learning techniques to study changes in locomotion and behavior in varying environments .
Revised: We apply topological data analysis to video of C. elegans, a widely-studied URLanism in biology. We demonstrate that each of the bars in the resulting barcode may be visualized as a synthetic video of a characteristic behavior. Our main goal is to develop a quantitative summary of complex behavior that may be applied to high-throughput data. Our method is able to distinguish and classify videos from various environmental conditions. We use sliding window embeddings (time delay embeddings), persistent homology, average persistence landscapes and machine learning techniques to study changes in locomotion and behavior in varying environments .
Edit intent: coherence
Original:  Our method is able to distinguish and classify videos from various environmental conditions . We use sliding window embeddings (time delay embeddings), persistent homology, average persistence landscapes, and support vector machines .
Revised:  Our method is able to distinguish and classify videos from various environmental conditions and we analyze the trade-off between accuracy and interpretability. Furthermore, we present a novel technique for visualizing the outputs of our analysis in terms of the input. Specifically, we use representative cycles of persistent homology to produce synthetic videos of stereotypical behaviors .
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Original:  Our approach: Given an arbitrary classifier, we propose a simple autoencoder and gradient update (Latent Shift) that can transform the latent representation of an input image to exaggerate or curtail the features used for prediction.
Revised:  Our approach: Given an arbitrary classifier, we propose a simple autoencoder and gradient update (Latent Shift) that can transform the latent representation of a specific input image to exaggerate or curtail the features used for prediction.
Edit intent: clarity
Original:  This paper describes how Facebook determines if a given piece of text - anything from a hashtag to a post - belongs to a narrow topic such as COVID-19.
Revised:  This paper introduces infrastructure and methodologies for text classifiers based on large-scale regular expressions. In particular, we describe how Facebook determines if a given piece of text - anything from a hashtag to a post - belongs to a narrow topic such as COVID-19.
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Original:  However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either  as solvers or as helper functions.
Revised:  However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either directly as solvers or as helper functions.
Edit intent: meaning-changed
Original:  However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either  as solvers or as helper functions. GNNs are an inductive bias that effectively encodes combinatorial and relational input due to their permutation-invariance and sparsity awareness .
Revised:  However, recent years have seen a surge of interest in using machine learning, especially graph neural networks (GNNs), as a key building block for combinatorial tasks, either as solvers or by enhancing exact solvers. The inductive bias of GNNs effectively encodes combinatorial and relational input due to their permutation-invariance and sparsity awareness .
Edit intent: clarity
Original:  Our novel approach achieves state-of-the-art  in extracting information from documents and answering questions , demanding layout understanding (DocVQA, CORD, WikiOps, SROIE).
Revised:  Our novel approach achieves state-of-the-art results in extracting information from documents and answering questions , demanding layout understanding (DocVQA, CORD, WikiOps, SROIE).
Edit intent: clarity