wanyu/IteraTeR_v2 huggingface dataset:
Original:  To address this issue, recent work jointly optimizes detection and data association and forms an integrated MOT approach , which has been shown to improve performance in both detection and tracking .
Revised:  To address this issue, recent work simultaneously optimizes detection and data association and forms an integrated MOT approach , which has been shown to improve performance in both detection and tracking .
Edit intent: clarity
Original:  To address this issue, recent work jointly optimizes detection and data association and forms an integrated MOT approach , which has been shown to improve performance in both detection and tracking .
Revised:  To address this issue, recent work jointly optimizes detection and data association modules under a joint MOT framework , which has been shown to improve performance in both detection and tracking .
Edit intent: clarity
Original:  To address this issue, recent work jointly optimizes detection and data association and forms an integrated MOT approach , which has been shown to improve performance in both detection and tracking .
Revised:  To address this issue, recent work jointly optimizes detection and data association and forms an integrated MOT approach , which has shown improved performance in both detection and tracking .
Edit intent: coherence
Original:  To address this issue, recent work jointly optimizes detection and data association and forms an integrated MOT approach , which has been shown to improve performance in both detection and tracking .
Revised:  To address this issue, recent work jointly optimizes detection and data association and forms an integrated MOT approach , which has been shown to improve performance in both modules .
Edit intent: clarity
Original:  In this work, we propose a new approach for joint MOT based on Graph Neural Networks (GNNs).
Revised:  In this work, we propose a new instance of joint MOT approach based on Graph Neural Networks (GNNs).
Edit intent: clarity
Original:  The key idea of our approach is that GNNs can explicitly model complex interactions between multiple objects in both the spatial and temporal domains, which is essential for learning discriminative features for detection and data association.
Revised:  The key idea is that GNNs can explicitly model complex interactions between multiple objects in both the spatial and temporal domains, which is essential for learning discriminative features for detection and data association.
Edit intent: clarity
Original:  The key idea of our approach is that GNNs can explicitly model complex interactions between multiple objects in both the spatial and temporal domains, which is essential for learning discriminative features for detection and data association.
Revised:  The key idea of our approach is that GNNs can model relations between variable-sized objects in both the spatial and temporal domains, which is essential for learning discriminative features for detection and data association.
Edit intent: clarity
Original:  Through extensive experiments on the MOT challenge dataset, we show that our proposed method achieves state-of-the-art performance on both object detection and MOT  .
Revised:  Through extensive experiments on the MOT challenge dataset, we show that our proposed method achieves state-of-the-art performance for both detection and MOT .
Edit intent: clarity
Original:  Through extensive experiments on the MOT challenge dataset, we show that our proposed method achieves state-of-the-art performance on both object detection and MOT  .
Revised:  Through extensive experiments on the MOT challenge dataset, we show that our proposed method achieves state-of-the-art performance on both object detection and MOT tasks .
Edit intent: clarity
Original:  Despite the fact that the two components are dependent on each other, prior work often designs detection and data association modules separately which are trained with different objectives.
Revised:  Despite the fact that the two components are dependent on each other, prior works often design detection and data association modules separately which are trained with different objectives.
Edit intent: fluency
Original:  Despite the fact that the two components are dependent on each other, prior work often designs detection and data association modules separately which are trained with different objectives.
Revised:  Despite the fact that the two components are dependent on each other, prior work often designs detection and data association modules separately which are trained with separate objectives.
Edit intent: fluency
Original:  As a result, we cannot back-propagate the gradients and optimize the entire MOT system, which leads to sub-optimal performance.
Revised:  As a result, one cannot back-propagate the gradients and optimize the entire MOT system, which leads to sub-optimal performance.
Edit intent: clarity
Original:  To address this issue, recent work simultaneously optimizes detection and data association modules under a joint MOT framework, which has shown improved performance in both modules.
Revised:  To address this issue, recent works simultaneously optimize detection and data association modules under a joint MOT framework, which has shown improved performance in both modules.
Edit intent: fluency
Original:  Through extensive experiments on the MOT15/16/17/20 datasets, we demonstrate the effectiveness of our GNN-based joint MOT approach and show the state-of-the-art performance for both detection and MOT tasks. 
Revised:  Through extensive experiments on the MOT15/16/17/20 datasets, we demonstrate the effectiveness of our GNN-based joint MOT approach and show state-of-the-art performance for both detection and MOT tasks. 
Edit intent: clarity
Original:  Analog multiplication is then carried out in the synapse circuits, while the results are accumulated on the neurons' membrane capacitors.
Revised:  Analog multiplication is carried out in the synapse circuits, while the results are accumulated on the neurons' membrane capacitors.
Edit intent: clarity
Original:  Further limitations are imposed by the digital resolution of the input values ( 5-bit ), matrix weights ( 6-bit ) and resulting neuron activations ( 8-bit ).
Revised:  Further limitations are imposed by the digital resolution of the input values ( 5 bit ), matrix weights ( 6-bit ) and resulting neuron activations ( 8-bit ).
Edit intent: fluency
Original:  Further limitations are imposed by the digital resolution of the input values ( 5-bit ), matrix weights ( 6-bit ) and resulting neuron activations ( 8-bit ).
Revised:  Further limitations are imposed by the digital resolution of the input values ( 5-bit ), matrix weights ( 6 bit ) and resulting neuron activations ( 8-bit ).
Edit intent: fluency
Original:  Further limitations are imposed by the digital resolution of the input values ( 5-bit ), matrix weights ( 6-bit ) and resulting neuron activations ( 8-bit ).
Revised:  Further limitations are imposed by the digital resolution of the input values ( 5-bit ), matrix weights ( 6-bit ) and resulting neuron activations ( 8 bit ).
Edit intent: fluency
Original:  Thus, outliers and wrong correspondences are  down-weighted to enable robust tracking.
Revised:  Thus, outliers and wrong correspondences are automatically down-weighted to enable robust tracking.
Edit intent: clarity
Original:  Thus, outliers and wrong correspondences are  down-weighted to enable robust tracking.
Revised:  Thus, outliers and wrong correspondences are down-weighted to enable effective tracking.
Edit intent: clarity
Original:  For internal tumors, this is currently achieved by means of an antenna array equipped with a proper cooling system (the water bolus) to avoid overheating of the skin.
Revised:  For internal tumors, this can be achieved by means of an antenna array equipped with a proper cooling system (the water bolus) to avoid overheating of the skin.
Edit intent: clarity
Original:  The planningof the administered heating is usually tackled by finding the antenna feedings that maximize the specific absorption rate (SAR) inside the tumor .
Revised:  In patient-specific treatment planning, antenna feedings are optimized to maximize the specific absorption rate (SAR) inside the tumor .
Edit intent: clarity
Original:  Application of this strategy to a tumor in the 3D head and neck region is presented to show the effectiveness of the approach .
Revised:  Application of this method to the 3D head and neck region is presented to show the effectiveness of the approach .
Edit intent: clarity
Original:  Several numerical experiments based on typical benchmark problems illustrate the effectiveness of the new matrix-oriented setting, also for coupled systems of nonlinear matrix differential equations .
Revised:  Several numerical experiments based on typical benchmark problems illustrate the effectiveness of the new setting .
Edit intent: clarity
Original:  Despite a number of recent results that show the promise of transfer from proxy datasets, a comprehensive evaluation of different NAS methods studying the impact of different source datasets and training protocols has not yet been addressed.
Revised:  Despite a number of recent results that show the promise of transfer from proxy datasets, a comprehensive evaluation of different NAS methods studying the impact of different source datasets has not yet been addressed.
Edit intent: clarity
Original:  (ii) While the different NAS methods show similar performance on a source dataset (e.g., CIFAR10), they significantly differ on the transfer performance to a large dataset (e.g., ImageNet1K).
Revised:  (ii) While different NAS methods show similar performance on a source dataset (e.g., CIFAR10), they significantly differ on the transfer performance to a large dataset (e.g., ImageNet1K).
Edit intent: clarity
Original:  (iii) Even on large datasets, the randomly sampled architecture baseline is very competitive and significantly outperforms many representative NAS methods.
Revised:  (iii) Even on large datasets, random sampling baseline is very competitive and significantly outperforms many representative NAS methods.
Edit intent: clarity
Original:  We believe that our NASTransfer benchmark will be key to designing future NAS strategies that consistently show superior transfer performance on large scale datasets .
Revised:  We believe that our extensive empirical analysis will prove useful for future design of NAS algorithms .
Edit intent: clarity
Original:  However, the question of how personal that information is and how it can be used to identify people has not been explored.
Revised:  However, the question of how personal that information is and how it can be used to identify individuals has not been explored.
Edit intent: clarity
Original:  Using published data and statistical modeling, we tested its ability to identify individuals with great accuracy, including identical twins, by computing false positive and false negative rates < 10^{-6} using 10,000 cells.
Revised:  Using published data and statistical modeling, we tested its ability to identify individuals with great accuracy, including identical twins, by computing false positive and false negative rates < 10^{-6} from samples composed of 10,000 cells.
Edit intent: clarity
Original:  To the best of our knowledge, the only known upper bound was n \log_\phi n \approx 1.441n\log_2 n, where \phi is the golden ratio, mentioned by Fraenkel and Simpson [TCS 1999 based on a theorem by Crochemore and Rytter Algorithmica 1995 ] obtained via the Three Squares Lemma.
Revised:  To the best of our knowledge, the only known upper bound was n \log_\phi n \approx 1.441n\log_2 n, where \phi is the golden ratio, reported by Fraenkel and Simpson [TCS 1999 based on a theorem by Crochemore and Rytter Algorithmica 1995 ] obtained via the Three Squares Lemma.
Edit intent: clarity
Original:  To the best of our knowledge, the only known upper bound was n \log_\phi n \approx 1.441n\log_2 n, where \phi is the golden ratio, mentioned by Fraenkel and Simpson [TCS 1999 based on a theorem by Crochemore and Rytter Algorithmica 1995 ] obtained via the Three Squares Lemma.
Revised:  To the best of our knowledge, the only known upper bound was n \log_\phi n \approx 1.441n\log_2 n, where \phi is the golden ratio, mentioned by Fraenkel and Simpson [TCS 1999 Algorithmica 1995 ] obtained via the Three Squares Lemma.
Edit intent: coherence
Original:  To the best of our knowledge, the only known upper bound was n \log_\phi n \approx 1.441n\log_2 n, where \phi is the golden ratio, mentioned by Fraenkel and Simpson [TCS 1999 based on a theorem by Crochemore and Rytter Algorithmica 1995 ] obtained via the Three Squares Lemma.
Revised:  To the best of our knowledge, the only known upper bound was n \log_\phi n \approx 1.441n\log_2 n, where \phi is the golden ratio, mentioned by Fraenkel and Simpson [TCS 1999 based on a theorem by Crochemore and Rytter ] obtained via the Three Squares Lemma.
Edit intent: coherence
Original: We present a new version of ReLoC: a relational  logic for proving refinements of programs in a language with higher-order state, fine-grained concurrency, polymorphism and recursive types.
Revised: We present a new version of ReLoC: a relational logic for proving refinements of programs with higher-order state, fine-grained concurrency, polymorphism and recursive types.
Edit intent: clarity
Original:  The core of ReLoC is the refinement judgment e \precsim e' : \tau, which expresses that a program e refines a program e' at type \tau.
Revised:  The core of ReLoC is its refinement judgment e \precsim e' : \tau, which expresses that a program e refines a program e' at type \tau.
Edit intent: fluency
Original:  The core of ReLoC is the refinement judgment e \precsim e' : \tau, which expresses that a program e refines a program e' at type \tau.
Revised:  The core of ReLoC is the refinement judgment e \precsim e' : \tau, which states that a program e refines a program e' at type \tau.
Edit intent: clarity
Original:  In contrast to earlier work on refinements for languages with higher-order state and concurrency, ReLoC provides type-directed structural rules and symbolic execution rules for manipulating this judgment, whereas previously , such proofs were carried out by unfolding the judgment into its definition in the model.
Revised:  ReLoC provides type-directed structural rules and symbolic execution rules for manipulating this judgment, whereas previously , such proofs were carried out by unfolding the judgment into its definition in the model.
Edit intent: coherence
Original:  In contrast to earlier work on refinements for languages with higher-order state and concurrency, ReLoC provides type-directed structural rules and symbolic execution rules for manipulating this judgment, whereas previously , such proofs were carried out by unfolding the judgment into its definition in the model.
Revised:  In contrast to earlier work on refinements for languages with higher-order state and concurrency, ReLoC provides type-directed structural rules and symbolic execution rules for manipulating this judgment, whereas in prior work on refinements for languages with higher-order state and concurrency , such proofs were carried out by unfolding the judgment into its definition in the model.
Edit intent: clarity
Original:  These more abstract proof rules make it simpler to carry out refinement proofs, and enable us to generalize the notion of logically atomic specifications to the relational case, which we call logically atomic relational specifications.
Revised:  ReLoC's abstract proof rules make it simpler to carry out refinement proofs, and enable us to generalize the notion of logically atomic specifications to the relational case, which we call logically atomic relational specifications.
Edit intent: clarity
Original:  We implement tactics for interactive refinements proofs , allowing us to mechanize several case studies  , and thereby demonstrate the practicality of ReLoC.
Revised:  We implement tactics for interactive proofs in ReLoC , allowing us to mechanize several case studies , and thereby demonstrate the practicality of ReLoC.
Edit intent: clarity
Original:  ReLoC Reloaded extends our ReLoC logic (LICS'18) with various technical improvements, a new Coq mechanization, and support for Iris's prophecy variables.
Revised:  ReLoC Reloaded extends ReLoC (LICS'18) with various technical improvements, a new Coq mechanization, and support for Iris's prophecy variables.
Edit intent: clarity
Original:  Three different regression models are compared, including radial basis function (RBF) regression, Gaussian process regression (GPR)  and artificial neural networks (ANNs).
Revised:  Three different regression models : Radial basis function (RBF) regression, Gaussian process regression (GPR) and artificial neural networks (ANNs).
Edit intent: clarity
Original:  Three different regression models are compared, including radial basis function (RBF) regression, Gaussian process regression (GPR)  and artificial neural networks (ANNs).
Revised:  Three different regression models are compared, including radial basis function (RBF) regression, Gaussian process regression (GPR) , and artificial neural networks (ANNs).
Edit intent: fluency
Original:  We find that GPR can offer several advantages over an ANN, constituting a viable and computationally inexpensive non-intrusive RBM .
Revised:  We find that GPR offers several advantages over an ANN, constituting a viable and computationally inexpensive non-intrusive RBM .
Edit intent: clarity
Original:  Our method achieves state-of-the-art results, outperforming Screened Poisson Surface Reconstruction and modern neural network based techniques . Because our approach is based on a simple kernel formulation, it is fast to run and easy to analyze  .
Revised:  Our method achieves state-of-the-art results, outperforming Screened Poisson Surface Reconstruction and modern neural network based techniques . Because our approach is based on a simple kernel formulation, it is easy to analyze .
Edit intent: coherence
Original: In this paper, we focus on structure-preserving model order reduction (SPMOR) of the second-order system using the Iterative Rational Krylov Algorithm (IRKA).
Revised: In this paper, we focus on structure-preserving model order reduction (SPMOR) of the second-order systems using the Iterative Rational Krylov Algorithm (IRKA).
Edit intent: fluency
Original:  In general, the standard IRKA can be used to the second-order structure system by converting the system into an equivalent first-order form  .
Revised:  In general, the classical IRKA can be used to the second-order structure system by converting the system into an equivalent first-order form .
Edit intent: clarity
Original:  In general, the standard IRKA can be used to the second-order structure system by converting the system into an equivalent first-order form  .
Revised:  In general, the standard IRKA can be applied to the second-order structure system by converting the system into an equivalent first-order form .
Edit intent: clarity
Original:  In general, the standard IRKA can be used to the second-order structure system by converting the system into an equivalent first-order form  .
Revised:  In general, the standard IRKA can be used to the second-order system by converting the system into an equivalent first-order form .
Edit intent: clarity
Original:  In general, the standard IRKA can be used to the second-order structure system by converting the system into an equivalent first-order form  .
Revised:  In general, the standard IRKA can be used to the second-order structure system by converting it into an equivalent first-order form .
Edit intent: fluency
Original:  In this case, the reduced model can not preserve the structure of the second-order form which is however necessary for further manipulation.
Revised:  In this case, the reduced model can not preserve the structure of the second-order system which is however necessary for further manipulation.
Edit intent: clarity
Original:  Here we develop IRKA based algorithm which enables us to generate a reduced second-order system without explicitly converting the system into a first-order form.
Revised:  Here we develop IRKA based algorithms that enable us to generate a reduced second-order system without explicitly converting the system into a first-order form.
Edit intent: fluency
Original:  Here we develop IRKA based algorithm which enables us to generate a reduced second-order system without explicitly converting the system into a first-order form.
Revised:  Here we develop IRKA based algorithm which enables us to generate approximate reduced second-order system without explicitly converting the system into a first-order form.
Edit intent: clarity
Original:  Here we develop IRKA based algorithm which enables us to generate a reduced second-order system without explicitly converting the system into a first-order form.
Revised:  Here we develop IRKA based algorithm which enables us to generate a reduced second-order systems without explicitly converting the system into a first-order form.
Edit intent: fluency
Original:  Here we develop IRKA based algorithm which enables us to generate a reduced second-order system without explicitly converting the system into a first-order form.
Revised:  Here we develop IRKA based algorithm which enables us to generate a reduced second-order system without explicitly converting the systems into a first-order form.
Edit intent: fluency
Original:  The theoretical results are experimented by applying them to several sets of data of large-scale dynamical systems.
Revised:  The efficiency and applicability of the proposed techniques are validated by applying them to several sets of data of large-scale dynamical systems.
Edit intent: clarity
Original:  The theoretical results are experimented by applying them to several sets of data of large-scale dynamical systems.
Revised:  The theoretical results are experimented by applying them to several large-scale dynamical systems.
Edit intent: clarity
Original:  Numerical results are discussed to show the applicability and efficiency of the proposed techniques .
Revised:  The computation is done numerically and the achieved results are discussed to show the applicability and efficiency of the proposed techniques .
Edit intent: clarity
Original:  In general , the classical IRKA can be applied to the second-order system by converting it into an equivalent first-order form and get the reduced model in a first-order form.
Revised:  To get the reduced models of the second-order systems , the classical IRKA can be applied to the second-order system by converting it into an equivalent first-order form and get the reduced model in a first-order form.
Edit intent: clarity
Original:  In general , the classical IRKA can be applied to the second-order system by converting it into an equivalent first-order form and get the reduced model in a first-order form.
Revised:  In general , the classical IRKA deals with the equivalent first-order form and get the reduced model in a first-order form.
Edit intent: clarity
Original:  Here we develop IRKA based algorithms that enable us to generate approximate reduced second-order systems without explicitly converting the systems into a first-order form .
Revised:  Here we develop IRKA based algorithms that enable us to approximate the second-order systems without explicitly converting the systems into a first-order form .
Edit intent: clarity
Original:  Here we develop IRKA based algorithms that enable us to generate approximate reduced second-order systems without explicitly converting the systems into a first-order form .
Revised:  Here we develop IRKA based algorithms that enable us to generate approximate reduced second-order systems without explicitly converting the systems into a first-order forms .
Edit intent: fluency
Original:  On the other hand, there are very challenging tasks to find reduced-order form of a large-scale system with a minimized H_2 error norm and attain the rapid rate of convergence.
Revised:  On the other hand, there are very challenging tasks to the Model Order Reduction (MOR) of the large-scale system with a minimized H_2 error norm and attain the rapid rate of convergence.
Edit intent: clarity