wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our proposal uses a sub-optimal MAPF solver to obtain an initial solution quickly, then iterates the two procedures: 1)  select a subset of agents, 2)  use an optimal MAPF solver to refine paths of selected agents while keeping other paths unchanged.
Revised:  Our proposal uses a sub-optimal MAPF solver to obtain an initial solution quickly, then iterates the two procedures: 1) ~ select a subset of agents, 2) use an optimal MAPF solver to refine paths of selected agents while keeping other paths unchanged.
Edit intent: fluency
Original:  Our proposal uses a sub-optimal MAPF solver to obtain an initial solution quickly, then iterates the two procedures: 1)  select a subset of agents, 2)  use an optimal MAPF solver to refine paths of selected agents while keeping other paths unchanged.
Revised:  Our proposal uses a sub-optimal MAPF solver to obtain an initial solution quickly, then iterates the two procedures: 1) select a subset of agents, 2) ~ use an optimal MAPF solver to refine paths of selected agents while keeping other paths unchanged.
Edit intent: fluency
Original:  On standard language modeling benchmarks such as enwik8 and Wiki-103  datasets, our model obtains better perplexity and bits-per-character (bpc) while using 2.5x-10x less training time and cost compared to top-performing Transformer models.
Revised:  On standard language modeling benchmarks such as enwik8 , Wiki-103 datasets, our model obtains better perplexity and bits-per-character (bpc) while using 2.5x-10x less training time and cost compared to top-performing Transformer models.
Edit intent: fluency
Original:  On standard language modeling benchmarks such as enwik8 and Wiki-103  datasets, our model obtains better perplexity and bits-per-character (bpc) while using 2.5x-10x less training time and cost compared to top-performing Transformer models.
Revised:  On standard language modeling benchmarks such as enwik8 and Wiki-103 and Billion Word datasets, our model obtains better perplexity and bits-per-character (bpc) while using 2.5x-10x less training time and cost compared to top-performing Transformer models.
Edit intent: meaning-changed
Original:  On standard language modeling benchmarks such as enwik8 and Wiki-103  datasets, our model obtains better perplexity and bits-per-character (bpc) while using 2.5x-10x less training time and cost compared to top-performing Transformer models.
Revised:  On standard language modeling benchmarks such as enwik8 and Wiki-103 datasets, our model obtains better perplexity and bits-per-character (bpc) while using 3x-10x less training time and cost compared to top-performing Transformer models.
Edit intent: meaning-changed
Original:   Our results show that using MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.
Revised:  We refer to using MAP-Elites for blending as Blend-Elites. Our results show that using MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.
Edit intent: meaning-changed
Original:   Our results show that using MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.
Revised:  Our results show that MAP-Elites in conjunction with VAEs enables the generation of a diverse set of playable levels not just for each individual game but also for the blended domain while illuminating game-specific regions of the blended latent space.
Edit intent: coherence
Original:  Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photoface .
Revised:  Specifically, a new generator architecture is proposed to simultaneously transfer color/texture styles and transform local facial shapes into anime-like counterparts based on the style of a reference anime-face, while preserving the global structure of the source photo-face .
Edit intent: fluency
Original: This paper presents Learning-based Autonomous Guidance with Robustness, Optimality, and Safety guarantees (LAG-ROS), a real-time robust motion planning algorithm for safety-critical nonlinear systems perturbed by bounded disturbances.
Revised: This paper presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), a real-time robust motion planning algorithm for safety-critical nonlinear systems perturbed by bounded disturbances.
Edit intent: clarity
Original: This paper presents Learning-based Autonomous Guidance with Robustness, Optimality, and Safety guarantees (LAG-ROS), a real-time robust motion planning algorithm for safety-critical nonlinear systems perturbed by bounded disturbances. The LAG-ROS method consists of three phases: 1) Control Lyapunov Function (CLF) construction via contraction theory;
Revised: This paper presents Learning-based Autonomous Guidance with Robustness, Optimality, and Safety guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with formal robustness and stability guarantees, by designing a differential Lyapunov function using contraction theory. LAG-ROS method consists of three phases: 1) Control Lyapunov Function (CLF) construction via contraction theory;
Edit intent: meaning-changed
Original:  The LAG-ROS method consists of three phases: 1) Control Lyapunov Function (CLF) construction via contraction theory; 2) imitation learning of the CLF-based robust feedback motion planner; and 3) its real-time and decentralized implementation with a learning-based model predictive safety filter. For the CLF, we exploit a neural-network-based method of Neural Contraction Metrics (NCMs), which provides a differential Lyapunov function to minimize an upper bound of the steady-state Euclidean distance between perturbed and unperturbed system trajectories .
Revised:  The LAG-ROS utilizes a neural network to model a robust tracking controller independently of a target trajectory, for which we show that the Euclidean distance between perturbed and unperturbed system trajectories .
Edit intent: clarity
Original:  For the CLF, we exploit a neural-network-based method of Neural Contraction Metrics (NCMs), which provides a differential Lyapunov function to minimize an upper bound of the steady-state Euclidean distance between perturbed and unperturbed system trajectories . The NCM ensures the perturbed state to stay in bounded error tubes around given desired trajectories, where we sample training data for imitation learning of the NCM-CLF-based robust centralized motion planner. Using local observations in trainingalso enables its decentralized implementation. Simulation results for perturbed nonlinear systems show that the LAG-ROS achieves higher control performance and task success rate with faster execution speed for real-time computation, when compared with the existing real-time robust MPC and learning-based feedforward motion planners.
Revised:  For the CLF, we exploit a neural-network-based method of Neural Contraction Metrics (NCMs), which provides a differential Lyapunov function to minimize an upper bound of the steady-state Euclidean distance between the target and controlled trajectories is exponentially bounded linearly in the learning error, even under the existence of bounded external disturbances. We also present a convex optimization approach that minimizes the steady-state bound of the tracking error to construct the robust control law for neural network training. In numerical simulations, it is demonstrated that the proposed method indeed possesses superior properties of robustness and nonlinear stability resulting from contraction theory, whilst retaining the computational efficiency of existing learning-based feedforward motion planners.
Edit intent: meaning-changed
Original:  Simulation results for perturbed nonlinear systems show that the LAG-ROS achieves higher control performance and task success rate with faster execution speed for real-time computation, when compared with the existing real-time robust MPC and learning-based feedforward motion planners.
Revised:  Simulation results for perturbed nonlinear systems show that the LAG-ROS achieves higher control performance and task success rate with faster execution speed for real-time computation, when compared with the existing real-time robust MPC and learning-based motion planners.
Edit intent: clarity
Original:  It can detect complete, partial, and imperfect ellipses in extreme weather and lighting conditions and is lightweight enough to be used on robots' resource-limited onboard computers  .
Revised:  It can detect complete, partial, and imperfect ellipses in extreme weather and lighting conditions and is lightweight enough to be used on robots' resource-limited onboard computers , can deal with lighting variations and detect the pattern even when the view is partial .
Edit intent: meaning-changed
Original: Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several learning-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems.
Revised: Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems.
Edit intent: clarity
Original: We introduce a compact pangenome representation based on an optimal segmentation concept that aims to reconstruct founder sequences from a multiple sequence alignment (MSA). Such founder sequences have the feature that each row of the MSA is a recombination of the founders. Several linear time dynamic programming algorithms have been previously devised to optimize segmentations that induce founder blocks that then can be concatenated into a set of founder sequences. All possible concatenation orders can be expressed as a founder graph . We observe a key property of such graphs: if the node labels (founder segments)do not repeat in the paths of the graph, such graphs can be indexed for efficient string matching. We call such graphs repeat-free founder graphs when constructed from a gapless MSA and repeat-free elastic founder graphswhen constructed from a general MSA with gaps .
Revised: We study the problem of matching a string in a labeled graph. Previous research has shown that unless the Orthogonal Vectors Hypothesis (OVH) is false, one cannot solve this problem in strongly sub-quadratic time, nor index the graph in polynomial time to answer queries efficiently (Equi et al. ICALP 2019, SOFSEM 2021). These conditional lower-bounds cover even deterministic graphs with binary alphabet, but there naturally exist also graph classes that are easy to index: E.g. Wheeler graphs (Gagie et al. Theor. Comp. Sci. 2017) cover graphs admitting a Burrows-Wheeler transform -based indexing scheme. However, it is NP-complete to recognize if a graph is a Wheeler graph (Gibney, Thankachan, ESA 2019). We propose an approach to alleviate the construction bottleneck of Wheeler graphs. Rather than starting from an arbitrary graph, we study graphs induced from multiple sequence alignments (MSAs). Elastic degenerate strings (Bernadini et al. SPIRE 2017, ICALP 2019) can be seen as such graphs, and we introduce here their generalization: elastic founder graphs. We first prove that even such induced graphs are hard to index under OVH. Then we introduce two subclasses, repeat-free founder graphs when constructed from a gapless MSA and repeat-free elastic founder graphswhen constructed from a general MSA with gaps .
Edit intent: meaning-changed
Original:  We give a linear time algorithm and a parameterized near linear time algorithm to construct a repeat-free founder graph and a repeat-free elastic founder graph, respectively.
Revised:  We give a linear time algorithm and a parameterized near linear time algorithm to construct a repeat-free non-elastic founder graph from a gapless MSA, and (parameterized) near-linear time algorithms to construct semi-repeat-free ( repeat-free elastic founder graph, respectively.
Edit intent: meaning-changed
Original:  We give a linear time algorithm and a parameterized near linear time algorithm to construct a repeat-free founder graph and a repeat-free elastic founder graph, respectively. We derive a tailored succinct index structure to support queries of arbitrary length in the paths of a repeat-free (elastic) founder graph. In addition , we show how to turn a repeat-free (elastic ) founder graph into a Wheeler graph in polynomial time .
Revised:  We give a linear time algorithm and a parameterized near linear time algorithm to construct a repeat-free founder graph and a repeat-free , respectively) elastic founder graphs from general MSAs. Finally , we show how to turn a repeat-free (elastic ) founder graph into a Wheeler graph in polynomial time .
Edit intent: meaning-changed
Original:  In addition , we show how to turn a repeat-free (elastic ) founder graph into a Wheeler graph in polynomial time . Furthermore, we show that a property such as repeat-freeness is essential for indexability. In particular, we show that unless the Strong Exponential Time Hypothesis (SETH) fails, one cannot build an index on an elastic founder graph in polynomial time to support fast queries .
Revised:  In addition , we show how to turn a repeat-free (elastic ) founder graph into a Wheeler graph in polynomial time .
Edit intent: coherence
Original: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Revised: Many algorithms for maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Edit intent: clarity
Original: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Revised: The problem of maximizing a monotone submodular function subject to a knapsack constraint rely on the natural greedy heuristic. We present a novel refined analysis of this greedy heuristic which enables us to: (1) reduce the enumeration in the tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Edit intent: meaning-changed
Original: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Revised: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation of [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Edit intent: clarity
Original: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Revised: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko 04 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Edit intent: fluency
Original: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Revised: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] from subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Edit intent: coherence
Original: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size at most twoand still retain a tight (1-e^{-1 of the greedy heuristic.
Revised: The problem of maximizing a monotone submodular function subject to a knapsack constraint admits a tight (1-e^{-1})-approximation : exhaustively enumerate over all subsets of size at most three and extend each using the greedy heuristic [Sviridenko , 2004 ] . We prove it suffices to enumerate only over all subsets of size three to two; (2) present an improved upper bound of 0.42945 for the classic algorithm which returns the better between a single element and the output of the greedy heuristic.
Edit intent: meaning-changed
Original:  To this end, we pose the training of a DNN for binary classification as a constrained optimization problem and introduce a novel constraint that can be used with existing loss functions to enforce maximal area under the ROC curve (AUC)  .
Revised:  To this end, we pose the training of a DNN for binary classification as a constrained optimization problem and introduce a novel constraint that can be used with existing loss functions to enforce maximal area under the ROC curve (AUC) through prioritizing FPR reduction at high TPR .
Edit intent: meaning-changed
Original:  We solve the resulting constrained optimization problem using an Augmented Lagrangian method (ALM) , where the constraint emphasizes reduction of FPR at high TPR . We present experimental results for image-based  classification applications using the CIFAR10 and an in-house medical imaging dataset  .
Revised:  We solve the resulting constrained optimization problem using an Augmented Lagrangian method (ALM) . Going beyond binary, we also propose two possible extensions of the proposed constraint for multi-class classification problems . We present experimental results for image-based classification applications using the CIFAR10 and an in-house medical imaging dataset .
Edit intent: meaning-changed
Original:  We solve the resulting constrained optimization problem using an Augmented Lagrangian method (ALM) , where the constraint emphasizes reduction of FPR at high TPR . We present experimental results for image-based  classification applications using the CIFAR10 and an in-house medical imaging dataset  .
Revised:  We solve the resulting constrained optimization problem using an Augmented Lagrangian method (ALM) , where the constraint emphasizes reduction of FPR at high TPR . We present experimental results for image-based binary and multi-class classification applications using the CIFAR10 and an in-house medical imaging dataset .
Edit intent: meaning-changed
Original:  We solve the resulting constrained optimization problem using an Augmented Lagrangian method (ALM) , where the constraint emphasizes reduction of FPR at high TPR . We present experimental results for image-based  classification applications using the CIFAR10 and an in-house medical imaging dataset  .
Revised:  We solve the resulting constrained optimization problem using an Augmented Lagrangian method (ALM) , where the constraint emphasizes reduction of FPR at high TPR . We present experimental results for image-based classification applications using an in-house medical imaging dataset .
Edit intent: coherence
Original:  We solve the resulting constrained optimization problem using an Augmented Lagrangian method (ALM) , where the constraint emphasizes reduction of FPR at high TPR . We present experimental results for image-based  classification applications using the CIFAR10 and an in-house medical imaging dataset  .
Revised:  We solve the resulting constrained optimization problem using an Augmented Lagrangian method (ALM) , where the constraint emphasizes reduction of FPR at high TPR . We present experimental results for image-based classification applications using the CIFAR10 and an in-house medical imaging dataset , CIFAR10, and CIFAR100 .
Edit intent: meaning-changed
Original:  The error representation using the straight difference of two vectors in the inertial navigation system may not be reasonable as it does not take the direction difference into consideration.
Revised: This paper proposes an SE_2(3) based extended Kalman filtering (EKF) framework for the inertial-integrated state estimation problem. The error representation using the straight difference of two vectors in the inertial navigation system may not be reasonable as it does not take the direction difference into consideration.
Edit intent: meaning-changed
Original:  With the new velocity and position error definition, we leverage the group affine dynamics with the autonomous error properties and derive the error state differential equation for the inertial-integrated navigation on the north-east-down local-level navigation frame and the earth-centered earth-fixed  frame, respectively, the corresponding extending Kalman filter (EKF) , terms as SE_2(3) -EKF has also been derived.
Revised:  With the new velocity and position error definition, we leverage the group affine dynamics with the autonomous error properties and derive the error state differential equation for the inertial-integrated navigation on the north-east-down (NED) navigation frame and the earth-centered earth-fixed frame, respectively, the corresponding extending Kalman filter (EKF) , terms as SE_2(3) -EKF has also been derived.
Edit intent: meaning-changed