wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This paperproposes a novel method, called%DIFDELCMD < {\em %%%  deep manifold transformation (DMT) , to tackle this problem. As a multi-layer neural network, DMT can support a higher degree of nonlinearity than single-layer methods. Continuation strategy is applied during training to address the local minimum problem common in manifold learning algorithms. Such a learned DMT network can generalize to unseen data whereas traditional manifold learning methods only provide an embedding of the trainingdata .
Revised:  This paperproposes a novel method, called%DIFDELCMD < {\em %%% deep manifold transformation (DMT) for unsupervised NLDR and embedding learning. DMT enhances deep neural networks by using cross-layer local geometry-preserving (LGP) constraints. The LGP constraints constitute the loss for deep manifold learning and serve as geometric regularizers for NLDR network training .
Edit intent: coherence
Original:  Extensive experiments , comparisons, and ablation studies demonstrate that DMT can deliver results superior to UMAP and t-SNE, and other leading manifold-based NLDR methods  .
Revised:  Extensive experiments , comparisons, and ablation studies demonstrate that DMT can deliver results superior to UMAP and t-SNE, and other leading manifold-based NLDR methods in terms of preserving the structures of data .
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Original: We present a novel Deep Reinforcement Learning (DRL) based policy for mobile robot navigation in dynamic environments that computes dynamically feasible and spatially aware robot velocities . Our method addresses two primary issues associated with the Dynamic Window Approach (DWA) and DRL-based navigation policies and solves them by using the benefits of one method to fix the issues of the other.
Revised: We present a novel Deep Reinforcement Learning (DRL) based policy for mobile robot navigation in dynamic environments that computes dynamically feasible and spatially aware velocities for a robot navigating among mobile obstacles. Our approach combines the benefits of the Dynamic Window Approach (DWA) and DRL-based navigation policies and solves them by using the benefits of one method to fix the issues of the other.
Edit intent: clarity
Original:  Our method addresses two primary issues associated with the Dynamic Window Approach (DWA) and DRL-based navigation policies and solves them by using the benefits of one method to fix the issues of the other.
Revised:  Our method addresses two primary issues associated with the Dynamic Window Approach (DWA) in terms of satisfying the robot's dynamics constraints with state-of-the-art DRL-based navigation policies and solves them by using the benefits of one method to fix the issues of the other.
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Original:  Our method addresses two primary issues associated with the Dynamic Window Approach (DWA) and DRL-based navigation policies and solves them by using the benefits of one method to fix the issues of the other. The issues are: 1. DWA not utilizing the time evolution of the environment while choosing velocities from the dynamically feasible velocity set leading to sub-optimal dynamic collision avoidance behaviors, and 2. DRL-based navigation policies computing velocities that often violate the dynamics constraints such as the non-holonomic and acceleration constraints of the robot. Our DRL-based method generates velocities that are dynamically feasible while accounting for the motion of the obstacles in the environment. This is done by embedding the changes in the environment' s state in a novel observation space and a reward function formulation that reinforces spatially aware obstacleavoidance maneuvers .
Revised:  Our method addresses two primary issues associated with the Dynamic Window Approach (DWA) and DRL-based navigation methods that can handle moving obstacles and pedestrians well. Our formulation achieves these goals by embedding the changes in the environment' s state in a novel observation space and a reward function formulation that reinforces spatially aware obstacleavoidance maneuvers .
Edit intent: clarity
Original:  This is done by embedding the changes in the environment' s state in a novel observation space and a reward function formulation that reinforces spatially aware obstacleavoidance maneuvers .
Revised:  This is done by embedding the changes in the environment' s state in a novel low-dimensional observation space. It also uses a novel reward function to positively reinforce velocities that move the robot away from the obstacle's heading direction leading to significantly lower number of collisions .
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Original:  We evaluate our method in realistic 3-D simulation and on a real differential drive robot in challenging  indoor scenarios with crowds of varying densities. We make comparisons with traditional and current state-of-the-art collision avoidance methods and observe significant improvements in terms of collision rate , number of dynamics constraint violations and smoothness.
Revised:  We evaluate our method in realistic 3-D simulation and on a real differential drive robot in challenging indoor scenarios with several walking pedestrians. We compare our method with state-of-the-art collision avoidance methods and observe significant improvements in terms of collision rate , number of dynamics constraint violations and smoothness.
Edit intent: clarity
Original:  We make comparisons with traditional and current state-of-the-art collision avoidance methods and observe significant improvements in terms of collision rate , number of dynamics constraint violations and smoothness. We also conduct ablation studies to highlight the advantages and explain the rationale behind our observation space construction, reward structure and network architecture.
Revised:  We make comparisons with traditional and current state-of-the-art collision avoidance methods and observe significant improvements in terms of 
Edit intent: coherence
Original: %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% \\%DIF > increase), number of dynamics constraint violations (up to 61\\% decrease), and smoothness.
Revised: %DIFDELCMD < \end{abstract} %DIFDELCMD < %%% success rate (up to 33\\%DIF > increase), number of dynamics constraint violations (up to 61\\% decrease), and smoothness.
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Original:  We model the buffering problem into an optimization problem . We use an efficient greedy-like algorithm , and our buffering method drops the frame that results in the least loss to the quality of the SLAM results.
Revised:  We model the buffering problem into an optimization problem by introducing a similarity metric between frames, and use an efficient greedy-like algorithm , and our buffering method drops the frame that results in the least loss to the quality of the SLAM results.
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Original:  Through an extensive evaluation on real-world scenarios and tens of gigabytes of datasets, we demonstrate that our ORBBuf method can be applied to different algorithms , different sensor data (both monocular images and stereo images), different scenes (both indoor and outdoor), and different network environments (both WiFi networks and 4G networks).
Revised:  Through an extensive evaluation on real-world scenarios and tens of gigabytes of datasets, we demonstrate that our ORBBuf method can be applied to different state-estimation algorithms (DSO and VINSFusion) , different sensor data (both monocular images and stereo images), different scenes (both indoor and outdoor), and different network environments (both WiFi networks and 4G networks).
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Original:  Experimental results show that the network interruptions indeed affect the SLAM results, and our ORBBuf method can reduce the RMSE up to 50 times  .
Revised:  Experimental results show that the network interruptions indeed affect the SLAM results, and our ORBBuf method can reduce the RMSE up to 50 times comparing with the Drop-Oldest and Random buffering methods .
Edit intent: meaning-changed
Original:  We model the buffering problem as an optimization problem by introducing a similarity metric between frames , and use an efficient greedy-like algorithm that drops the frame that results in the least loss to the quality of SLAM results.
Revised:  We model the buffering problem as an optimization problem by introducing a similarity metric between frames . To solve the buffering problem, we present an efficient greedy-like algorithm that drops the frame that results in the least loss to the quality of SLAM results.
Edit intent: coherence
Original:  We model the buffering problem as an optimization problem by introducing a similarity metric between frames , and use an efficient greedy-like algorithm that drops the frame that results in the least loss to the quality of SLAM results.
Revised:  We model the buffering problem as an optimization problem by introducing a similarity metric between frames , and use an efficient greedy-like algorithm to discard the frames that have the least impact on the quality of SLAM results.
Edit intent: clarity
Original:  Through an extensive evaluation on real-world scenarios and tens of gigabytes of datasets, we demonstrate that our ORBBuf method can be applied to different state-estimation algorithms (DSO and VINSFusion ), different sensor data (both monocular images and stereo images), different scenes (both indoor and outdoor), and different network environments (both WiFi networks and 4G networks).
Revised:  Through an extensive evaluation on real-world scenarios and tens of gigabytes of datasets, we demonstrate that our ORBBuf method can be applied to different state-estimation algorithms (DSO and VINS-Fusion ), different sensor data (both monocular images and stereo images), different scenes (both indoor and outdoor), and different network environments (both WiFi networks and 4G networks).
Edit intent: fluency
Original:  Awerbuch, Betke, Rivest and Singh [ ABRS 1999 ] considered graph exploration and treasure hunt for finite graphs in a restricted model where the agent has a fuel tank that can be replenished only at the starting node s.
Revised:  Awerbuch, Betke, Rivest and Singh [ 3 ] considered graph exploration and treasure hunt for finite graphs in a restricted model where the agent has a fuel tank that can be replenished only at the starting node s.
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Original:  Awerbuch, Betke, Rivest and Singh [ ABRS 1999 ] conjectured that it is impossible to find a treasure hidden in a node at distance at most d at cost nearly linear in e(d).
Revised:  Awerbuch, Betke, Rivest and Singh [ 3 ] conjectured that it is impossible to find a treasure hidden in a node at distance at most d at cost nearly linear in e(d).
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Original:  We first design a deterministic treasure hunt algorithm working in the model without any restrictions on the moves of the agent at cost O(e(d) \log d), and then show how to modify this algorithm to work in the model from [ ABRS 1999 ] with the same complexity.
Revised:  We first design a deterministic treasure hunt algorithm working in the model without any restrictions on the moves of the agent at cost O(e(d) \log d), and then show how to modify this algorithm to work in the model from [ 3 ] with the same complexity.
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Original:   Additionally, we showcase how our approach can be flexibly and effectively applied on diverse existing semantic architectures to deliver panoptic predictions.
Revised:  Without any post-processing, Panoster reached state-of-the-art results among published approaches on the challenging SemanticKITTI benchmark, and further increased its lead by exploiting heuristic techniques. Additionally, we showcase how our approach can be flexibly and effectively applied on diverse existing semantic architectures to deliver panoptic predictions.
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Original:  Building on these recent developments, and with an aim of improving the quality of generated captions, the contribution of our work in this paper is two-fold: First, we propose a generic multimodal model fusion framework for caption generation as well as emendation where we utilize different fusion strategies to integrate a pretrained Auxiliary Language Model (AuxLM) within the traditional encoder-decoder visual captioning frameworks.
Revised:  Building on these recent developments, and with the aim of improving the quality of generated captions, the contribution of our work in this paper is two-fold: First, we propose a generic multimodal model fusion framework for caption generation as well as emendation where we utilize different fusion strategies to integrate a pretrained Auxiliary Language Model (AuxLM) within the traditional encoder-decoder visual captioning frameworks.
Edit intent: fluency
Original:  While the restrictions imposed by some states inhibited a significant number of virus deaths, we find that more than two-thirds of U.S. COVID-19 deaths could have been prevented by late September 2020 had the federal government imposed federal mandates as early as some of the earliest states did.
Revised:  While the restrictions imposed by some states inhibited a significant number of virus deaths, we find that more than two-thirds of U.S. COVID-19 deaths could have been prevented by late November 2020 had the federal government imposed federal mandates as early as some of the earliest states did.
Edit intent: meaning-changed
Original:  While the restrictions imposed by some states inhibited a significant number of virus deaths, we find that more than two-thirds of U.S. COVID-19 deaths could have been prevented by late September 2020 had the federal government imposed federal mandates as early as some of the earliest states did.
Revised:  While the restrictions imposed by some states inhibited a significant number of virus deaths, we find that more than two-thirds of U.S. COVID-19 deaths could have been prevented by late September 2020 had the federal government enforced federal mandates as early as some of the earliest states did.
Edit intent: clarity
Original:  The main problem we focus on in this paper is the frequent label permutation switching of permutation invariant training (PIT) . For N-speaker separation, there would be N! possible label permutations. How to stably select correct label permutations is a long-standing problem .
Revised:  The main problem we focus on in this paper is the frequent label permutation switching of permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired .
Edit intent: clarity
Original:  In this paper, we utilize self-supervised pre-training to stabilize the label permutations. Among several types of self-supervised tasks, speech enhancement based pre-training tasks show significant effectiveness in our experiments.
Revised:  In this paper, we utilize self-supervised pre-training to stabilize the label assignment in training the speech separation model. Experiments over several types of self-supervised tasks, speech enhancement based pre-training tasks show significant effectiveness in our experiments.
Edit intent: clarity
Original:  Among several types of self-supervised tasks, speech enhancement based pre-training tasks show significant effectiveness in our experiments. When using off-the-shelf pre-trained models , training duration could be shortened to one-third to two-thirds. Furthermore, even taking pre-training time into account, the entire training process could still be shorter without a performance drop when using a larger batch size .
Revised:  Among several types of self-supervised approaches, several typical speech separation models and two different datasets showed that very good improvements are achievable if a proper self-supervised approach is chosen .
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Original:  In this work, we propose a novel approximation scheme of the Laplace--Beltrami operator on point clouds and utilize it for developing a free-boundary conformal parameterization method for disk-type point clouds  .
Revised:  In this work, we propose a novel approximation scheme of the Laplace--Beltrami operator on point clouds and utilize it for developing a free-boundary conformal parameterization method for disk-type point clouds , which involves a novel approximation scheme of the point cloud Laplacian with accumulated cotangent weights together with a special treatment at the boundary points .
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Original:  Results show that this approach can effectively exploit an N-best list of pseudo-labels with associated scores,  outperforming standard pseudo-labeling by a large margin , with ASR results close to an oracle experiment in which the best hypotheses of the N-best lists are selected manually.
Revised:  Results show that this approach can effectively exploit an N-best list of pseudo-labels with associated scores, considerably outperforming standard pseudo-labeling by a large margin , with ASR results close to an oracle experiment in which the best hypotheses of the N-best lists are selected manually.
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Original:  Results show that this approach can effectively exploit an N-best list of pseudo-labels with associated scores,  outperforming standard pseudo-labeling by a large margin , with ASR results close to an oracle experiment in which the best hypotheses of the N-best lists are selected manually.
Revised:  Results show that this approach can effectively exploit an N-best list of pseudo-labels with associated scores, outperforming standard pseudo-labeling , with ASR results close to an oracle experiment in which the best hypotheses of the N-best lists are selected manually.
Edit intent: clarity
Original:  Results show that this approach can effectively exploit an N-best list of pseudo-labels with associated scores,  outperforming standard pseudo-labeling by a large margin , with ASR results close to an oracle experiment in which the best hypotheses of the N-best lists are selected manually.
Revised:  Results show that this approach can effectively exploit an N-best list of pseudo-labels with associated scores, outperforming standard pseudo-labeling by a large margin , with ASR results approaching an oracle experiment in which the best hypotheses of the N-best lists are selected manually.
Edit intent: clarity
Original: Second-order differential equations define smooth system behavior. In general, there is no guarantee that a system will behave well when forced by a potential function, but in some cases they do and may exhibit smooth optimization properties such as convergence to a local minimum of the potential. Such a property is desirable and inherently linked to asymptotic stability. This paper presents a comprehensive theory of optimization fabrics which are second-order differential equations that encode nominal behaviors on a space and are guaranteed to optimize when forced by a potential function.
Revised: A common approach to the provably stable design of reactive behavior, exemplified by operational space control, is to reduce the problem to the design of virtual classical mechanical systems (energy shaping). This framework is widely used, and through it we gain stability, but at the price of expressivity. This work presents a comprehensive theory of optimization fabrics which are second-order differential equations that encode nominal behaviors on a space and are guaranteed to optimize when forced by a potential function.
Edit intent: meaning-changed
Original:  This paper presents a comprehensive theory of optimization fabrics which are second-order differential equations that encode nominal behaviors on a space and are guaranteed to optimize when forced by a potential function.
Revised:  This paper presents a comprehensive theoretical framework expanding this approach showing that there is a much larger class of differential equations generalizing classical mechanical systems (and the broader class of Lagrangian systems) and greatly expanding their expressivity while maintaining the same governing stability principles. At the core of our framework is a class of differential equations we call fabrics which constitute a behavioral medium across which we can optimize a potential function.
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Original:  Optimization fabrics , or fabrics for short, can encode commonalities among optimization problems that reflect the structure of the space itself, enabling smooth optimization processes to intelligently navigate each problem even when the potential function is simple and relatively naive. Importantly, optimization over a fabric is asymptotically stable, so optimization fabrics constitute a  building block for stable system design.
Revised:  These fabrics shape the system's behavior during optimization but still always provably converge to a building block for stable system design.
Edit intent: clarity
Original:  Importantly, optimization over a fabric is asymptotically stable, so optimization fabrics constitute a  building block for stable system design.
Revised:  Importantly, optimization over a fabric is asymptotically stable, so optimization fabrics constitute a building block of stable behavioral design. We build the theoretical foundations of our framework here and provide a simple empirical demonstration of a practical class of geometric fabrics, which additionally exhibit a natural geometric path consistency making them convenient for flexible and intuitive behavioral design.
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Original:  The method is applicable to div/curl-free vector fields in \mathbb{R \R ^2 and tangential fields on two-dimensional surfaces, such as the sphere, and the curl-free method can be generalized to vector fields in \mathbb{R \R ^d.
Revised:  The method is applicable to div/curl-free vector fields in \R ^2 and tangential fields on two-dimensional surfaces, such as the sphere, and the curl-free method can be generalized to vector fields in \mathbb{R \R ^d.
Edit intent: fluency
Original:  The method is applicable to div/curl-free vector fields in \mathbb{R \R ^2 and tangential fields on two-dimensional surfaces, such as the sphere, and the curl-free method can be generalized to vector fields in \mathbb{R \R ^d.
Revised:  The method is applicable to div/curl-free vector fields in \mathbb{R \R ^2 and tangential fields on two-dimensional surfaces, such as the sphere, and the curl-free method can be generalized to vector fields in \R ^d.
Edit intent: fluency
Original:  As a result of the analysis, model parameters for 3 November were calculated as -0.213423 for Democrats and 0.0455818 for Republicans.
Revised:  As a result of the analysis, consistent with the results of the polls, Biden wins the election with a difference of 9.22\% according to our method of estimating votes based on positive and negative tweet numbers, which are the current approaches in the literature. However, by using our multifactor model, the parameters for 3 November were calculated as -0.213423 for Democrats and 0.0455818 for Republicans.
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Original: We propose and study an algorithm for computing a nearest passive system to a given non-passive linear time-invariant system (with much freedom in the choice of the metric defining `nearest'  ), and also a closely related algorithm for computing the structured distance of a given passive system to non-passivity.
Revised: We propose and study an algorithm for computing a nearest passive system to a given non-passive linear time-invariant system (with much freedom in the choice of the metric defining `nearest' , which may be restricted to structured perturbations ), and also a closely related algorithm for computing the structured distance of a given passive system to non-passivity.
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