wanyu/IteraTeR_v2 huggingface dataset:
Original:  Unlike most other works, the GPP layer, instead of complex and heavy maps, uses road and intersections graphs obtained directly from internet applications like OpenStreetMaps (OSM).
Revised:  Unlike most other works, the GPP layer, instead of complex and heavy maps, uses road and intersections graphs obtained directly from OpenStreetMaps (OSM).
Edit intent: clarity
Original:  This map-free GPP frees us from the common area-size restrictions.
Edit intent: coherence
Original:  In the LPP layer, we use a novel Naive-Valley-Path method (NVP)  to generate a local path avoiding obstacles in the road in an extremely-low execution time period .
Revised:  In the LPP layer, we use a novel Naive-Valley-Path (NVP) to generate a local path avoiding obstacles in the road in an extremely-low execution time period .
Edit intent: clarity
Original:  In the LPP layer, we use a novel Naive-Valley-Path method (NVP)  to generate a local path avoiding obstacles in the road in an extremely-low execution time period .
Revised:  In the LPP layer, we use a novel Naive-Valley-Path method (NVP) method to generate a local path avoiding obstacles in the road in an extremely-low execution time period .
Edit intent: fluency
Original:  In the LPP layer, we use a novel Naive-Valley-Path method (NVP)  to generate a local path avoiding obstacles in the road in an extremely-low execution time period .
Revised:  In the LPP layer, we use a novel Naive-Valley-Path method (NVP) to generate a local path avoiding obstacles in the road in real-time .
Edit intent: clarity
Original:  We demonstrate the robustness of the system in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km in a 12.33 ha area.
Revised:  We demonstrate the system's robustness experimentally in our research platform BLUE, driving autonomously across the University of Alicante Scientific Park for more than 20 km in a 12.33 ha area.
Edit intent: clarity
Original: Federated Learning (FL) is a recent development in the field of machine learning that collaboratively trains models without the training data leaving client devices, in order to preserve data-privacy .
Revised: Federated Learning (FL) is a recent development in the field of machine learning that collaboratively trains models without the training data leaving client devices, to preserve data privacy .
Edit intent: coherence
Original:  In realistic  settings, the total training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, which has been shown extensively to harm FL convergence speed and final model performance.
Revised:  In realistic FL settings, the total training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, which has been shown extensively to harm FL convergence speed and final model performance.
Edit intent: fluency
Original:  In realistic  settings, the total training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, which has been shown extensively to harm FL convergence speed and final model performance.
Revised:  In realistic settings, the training set is distributed over clients in a highly non-Independent and Identically Distributed (non-IID) fashion, which has been shown extensively to harm FL convergence speed and final model performance.
Edit intent: clarity
Original:  We propose a novel, generalised approach for applying adaptive optimisation techniques to FL with the Federated Global Biased Optimiser (FedGBO) algorithm.
Revised:  We propose a novel, generalised approach for incorporating adaptive optimisation techniques to FL with the Federated Global Biased Optimiser (FedGBO) algorithm.
Edit intent: clarity
Original:  We propose a novel, generalised approach for applying adaptive optimisation techniques to FL with the Federated Global Biased Optimiser (FedGBO) algorithm.
Revised:  We propose a novel, generalised approach for applying adaptive optimisation techniques into FL with the Federated Global Biased Optimiser (FedGBO) algorithm.
Edit intent: fluency
Original:  FedGBO accelerates FL by applying a set of global biased optimiser values during the local training phaseof FL , which helps to reduce `client-drift' from non-IID data, whilst also benefiting from adaptive momentum/learning-rate methods .
Revised:  FedGBO accelerates FL by employing a set of global biased optimiser values during the local training phaseof FL , which helps to reduce `client-drift' from non-IID data, whilst also benefiting from adaptive momentum/learning-rate methods .
Edit intent: clarity
Original:  FedGBO accelerates FL by applying a set of global biased optimiser values during the local training phaseof FL , which helps to reduce `client-drift' from non-IID data, whilst also benefiting from adaptive momentum/learning-rate methods .
Revised:  FedGBO accelerates FL by applying a set of global biased optimiser values during the client-training phase , which helps to reduce `client-drift' from non-IID data, whilst also benefiting from adaptive momentum/learning-rate methods .
Edit intent: clarity
Original:  FedGBO accelerates FL by applying a set of global biased optimiser values during the local training phaseof FL , which helps to reduce `client-drift' from non-IID data, whilst also benefiting from adaptive momentum/learning-rate methods .
Revised:  FedGBO accelerates FL by applying a set of global biased optimiser values during the local training phaseof FL , which helps to reduce `client-drift' from non-IID data, whilst also benefiting from adaptive optimisation .
Edit intent: clarity
Original:  We show that the FedGBO update with a generic optimiser can be viewed as a centralised update with biased gradients and optimiser update, and use this theoretical framework to prove the convergence of FedGBO using momentum-Stochastic Gradient Descent  .
Revised:  We show that the FedGBO update with a generic optimiser can be reformulated as centralised training using biased gradients and optimiser update, and use this theoretical framework to prove the convergence of FedGBO using momentum-Stochastic Gradient Descent .
Edit intent: clarity
Original:  We show that the FedGBO update with a generic optimiser can be viewed as a centralised update with biased gradients and optimiser update, and use this theoretical framework to prove the convergence of FedGBO using momentum-Stochastic Gradient Descent  .
Revised:  We show that the FedGBO update with a generic optimiser can be viewed as a centralised update with biased gradients and optimiser updates, and apply this theoretical framework to prove the convergence of FedGBO using momentum-Stochastic Gradient Descent .
Edit intent: clarity
Original:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers  to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Revised:  We also conduct extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Edit intent: fluency
Original:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers  to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Revised:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers to compare the performance of state-of-the-art adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Edit intent: clarity
Original:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers  to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Revised:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers to compare the performance of different adaptive-FL algorithms. The results demonstrate that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Edit intent: clarity
Original:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers  to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Revised:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance whilst achieving lower communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Edit intent: clarity
Original:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers  to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and providing highly practical insights for the use of adaptive optimisation in FL .
Revised:  We also perform extensive experiments using 4 realistic benchmark FL datasets and 3 popular adaptive optimisers to compare the performance of different adaptive-FL approaches, demonstrating that FedGBO has highly competitive performance considering its low communication and computation costs, and provide practical insights into the trade-offs associated with the different adaptive-FL algorithms and optimisers for real-world FL deployments .
Edit intent: clarity
Original:  Compared to a first attempt [11] in which the distance between two consecutive classes was 100 \mu m \mum , our proposal allows us to drastically reduce this distance to 1 \mu m \mum .
Revised:  Compared to a first attempt [11] in which the distance between two consecutive classes was 100 m \mum , our proposal allows us to drastically reduce this distance to 1 \mu m \mum .
Edit intent: fluency
Original:  Compared to a first attempt [11] in which the distance between two consecutive classes was 100 \mu m \mum , our proposal allows us to drastically reduce this distance to 1 \mu m \mum .
Revised:  Compared to a first attempt [11] in which the distance between two consecutive classes was 100 \mu \mum , our proposal allows us to drastically reduce this distance to 1 \mu m \mum .
Edit intent: fluency
Original:  Compared to a first attempt [11] in which the distance between two consecutive classes was 100 \mu m \mum , our proposal allows us to drastically reduce this distance to 1 \mu m \mum .
Revised:  Compared to a first attempt [11] in which the distance between two consecutive classes was 100 \mu m \mum , our proposal allows us to drastically reduce this distance to 1 m \mum .
Edit intent: fluency
Original:  Compared to a first attempt [11] in which the distance between two consecutive classes was 100 \mu m \mum , our proposal allows us to drastically reduce this distance to 1 \mu m \mum .
Revised:  Compared to a first attempt [11] in which the distance between two consecutive classes was 100 \mu m \mum , our proposal allows us to drastically reduce this distance to 1 \mu \mum .
Edit intent: fluency
Original:  Two deep learning (DL) architectures are compared: Convolutional Neural Network (CNN) and Visual transformer (ViT).
Revised:  Two deep learning (DL) architectures are compared: Convolutional Neural Network (CNN) and Vision Transformer (ViT).
Edit intent: fluency
Original:  In this paper, DNDFN, namely, Dual-Neighborhood Deep Fusion Network  , is proposed to deal with this problem.
Revised:  In this paper, DNDFN, namely, Dual-Neighborhood Deep Fusion Network (DNDFN) , is proposed to deal with this problem.
Edit intent: clarity
Original:  A trainable neighborhood learning meth-od called TN-Learning is proposed, which can capture the global neighborhood.
Revised:  A trainable neighborhood learning method called TN-Learning is proposed, which can capture the global neighborhood.
Edit intent: fluency
Original:  A trainable neighborhood learning meth-od called TN-Learning is proposed, which can capture the global neighborhood.
Revised:  A trainable neighborhood learning meth-od called TN-Learning to capture the global neighborhood.
Edit intent: clarity
Original:  In particular, we show that  the capacity is given respectively by {(1+1/N+\dots+1/N^{K-D})^{-1}} or {(1+1/N+\dots+1/N^{\lceil K/D\rceil-1})^{-1}} when joint or individual privacy is required\pmod  .
Revised:  In particular, we show that when joint privacy is required the capacity is given respectively by {(1+1/N+\dots+1/N^{K-D})^{-1}} or {(1+1/N+\dots+1/N^{\lceil K/D\rceil-1})^{-1}} when joint or individual privacy is required\pmod .
Edit intent: fluency
Original:  In particular, we show that  the capacity is given respectively by {(1+1/N+\dots+1/N^{K-D})^{-1}} or {(1+1/N+\dots+1/N^{\lceil K/D\rceil-1})^{-1}} when joint or individual privacy is required\pmod  .
Revised:  In particular, we show that the capacity is given by {(1+1/N+\dots+1/N^{K-D})^{-1}} or {(1+1/N+\dots+1/N^{\lceil K/D\rceil-1})^{-1}} when joint or individual privacy is required\pmod .
Edit intent: clarity
Original:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by (1+1/N+%DIFDELCMD < \dots%%% +1/N^{(K-D)/L and%DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Revised:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations (1+1/N+%DIFDELCMD < \dots%%% +1/N^{(K-D)/L and%DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Edit intent: coherence
Original:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by (1+1/N+%DIFDELCMD < \dots%%% +1/N^{(K-D)/L and%DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Revised:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by %DIFDELCMD < \dots%%% +1/N^{(K-D)/L and%DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Edit intent: fluency
Original:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by (1+1/N+%DIFDELCMD < \dots%%% +1/N^{(K-D)/L and%DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Revised:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by (1+1/N+%DIFDELCMD < \dots%%% and%DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Edit intent: coherence
Original:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by (1+1/N+%DIFDELCMD < \dots%%% +1/N^{(K-D)/L and%DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Revised:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by (1+1/N+%DIFDELCMD < \dots%%% +1/N^{(K-D)/L %DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Edit intent: fluency
Original:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by (1+1/N+%DIFDELCMD < \dots%%% +1/N^{(K-D)/L and%DIFDELCMD < {%%% (1+1/N+%DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Revised:  Using similar proof techniques, we also establish upper and lower bounds on the capacity for the cases in which the user wants to compute L (potentially more than one) linear combinations . Specifically, we show that the capacity is upper and lower bounded respectively by (1+1/N+%DIFDELCMD < \dots%%% +1/N^{(K-D)/L and%DIFDELCMD < {%%% %DIFDELCMD < \dots%%% +1/N^{K-D+L-1 , when joint privacy is required .
Edit intent: fluency
Original: Contrastive representation learning is an effective unsupervised method to alleviate the demand for expensive annotated data in medical image processing.
Revised: Contrastive learning has been proven effective to alleviate the demand for expensive annotated data in medical image processing.
Edit intent: clarity
Original:  Recent work mainly based on instance-wise discrimination to learn global features, while neglect local details, which limit their application in processing tiny anatomical structures, tissues and lesions.
Revised:  Recent work mainly based on instance-wise discrimination and learn global discriminative features; however, they cannot assist clinicians to deal with tiny anatomical structures, tissues and lesions.
Edit intent: clarity
Original:  The finer contrastive rule makes the learnt representation more generalized for segmentation tasks and outperform extensive state-of-the-art methods by wining 11 out of all 12 downstream tasks in color fundus and chest X-ray.
Revised:  The finer contrastive rule makes the learnt representation more generalized for segmentation tasks and outperform extensive state-of-the-art methods and wins 11 out of all 12 downstream tasks in color fundus and chest X-ray.
Edit intent: coherence
Original:  Finally, we enhance the center-sensitive ability of patch discrimination by introducing center-sensitive averaging to realize one-shot landmark localization , this is an effective application for patch discrimination .
Revised:  Finally, we enhance the center-sensitive one-shot landmark localization , this is an effective application for patch discrimination .
Edit intent: clarity
Original:  Finally, we enhance the center-sensitive ability of patch discrimination by introducing center-sensitive averaging to realize one-shot landmark localization , this is an effective application for patch discrimination .
Revised:  Finally, we enhance the center-sensitive ability of patch discrimination by introducing center-sensitive averaging to realize one-shot landmark localization algorithm .
Edit intent: clarity
Original: Contrastive learning has been proven effective to alleviate the high demand of expensive annotations in medical images analysis , which can capture general patterns in images and naturally be used as initial feature extractors for various tasks . Recent works are mainly based on instance-wise discrimination and learn global discriminative features;
Revised: Contrastive learning has been proven effective to alleviate the high demand of expensive annotations in medical images analysis . Recent works are mainly based on instance-wise discrimination and learn global discriminative features;
Edit intent: coherence
Original: In recent years, visible-spectrum face verification systems have been shown to match expert forensic examiner recognition performance .
Revised: In recent years, visible-spectrum face verification systems have been shown to match the performance of experienced forensic examiners .
Edit intent: clarity
Original:  Due to the increased cost and difficulty of obtaining diverse, paired thermal and visible spectrum datasets,  algorithms and large-scale benchmarks for low-light recognition are limited .
Revised:  Due to the increased cost and difficulty of obtaining diverse, paired thermal and visible spectrum datasets, algorithms and large-scale benchmarks for low-light recognition are available .
Edit intent: clarity
Original:  We design a framework to extract, filter, and encode knowledge atop a standard multimodal transformer for vision language understanding tasks.
Revised:  We design a framework to extract, validate, and reason with knowledge using a standard multimodal transformer for vision language understanding tasks.
Edit intent: clarity
Original:  Using the idea of infinite descent , we prove that (iii) for any even set of points in \mathbb R^d , there exists a perfect matching that is an open Tverberg graph;
Revised:  Also , we prove that (iii) for any even set of points in \mathbb R^d , there exists a perfect matching that is an open Tverberg graph;
Edit intent: coherence
Original: Digital computers store information in the form of bits that can take on one of two values 0 and 1, while quantum computers are based on qubits that are described by a complex wavefunction  whose squared magnitude gives the probability of measuring either a 0 or a 1. Here  we make the case for a probabilistic computer based on p-bits which take on values 0 and 1 with controlled probabilities and can be implemented with specialized compact energy-efficient hardware.
Revised: Digital computers store information in the form of bits that can take on one of two values 0 and 1, while quantum computers are based on qubits that are described by a complex wavefunction , whose squared magnitude gives the probability of measuring either a 0 or a 1. Here we make the case for a probabilistic computer based on p-bits which take on values 0 and 1 with controlled probabilities and can be implemented with specialized compact energy-efficient hardware.
Edit intent: fluency