wanyu/IteraTeR_v2 huggingface dataset:
Original: We present a  hybrid strategy based on deep learning to compute mean curvature in the level-set method.
Revised: We present a novel hybrid strategy based on deep learning to compute mean curvature in the level-set method.
Edit intent: clarity
Original: We present a  hybrid strategy based on deep learning to compute mean curvature in the level-set method.
Revised: We present a hybrid strategy based on machine learning to improve curvature estimation in the level-set method.
Edit intent: clarity
Original:  The proposed inference system combines a dictionary of improved regression models with standard numerical schemes to estimate curvature more accurately.
Revised:  The proposed inference system couples enhanced neural networks with standard numerical schemes to estimate curvature more accurately.
Edit intent: clarity
Original:  The proposed inference system combines a dictionary of improved regression models with standard numerical schemes to estimate curvature more accurately.
Revised:  The proposed inference system combines a dictionary of improved regression models with standard numerical schemes to compute curvature more accurately.
Edit intent: clarity
Original:  The core of our  framework is a switching mechanism that relies on well-established numerical techniques to gauge curvature.
Revised:  The core of our framework is a switching mechanism that relies on well established numerical techniques to gauge curvature.
Edit intent: fluency
Original:  Our networks are multi-layer perceptrons fitted to synthetic data sets composed of circular- and sinusoidal-interface samples at various configurations.
Revised:  Our networks are multilayer perceptrons fitted to synthetic data sets composed of circular- and sinusoidal-interface samples at various configurations.
Edit intent: fluency
Original:  Our networks are multi-layer perceptrons fitted to synthetic data sets composed of circular- and sinusoidal-interface samples at various configurations.
Revised:  Our networks are multi-layer perceptrons fitted to synthetic data sets composed of sinusoidal- and circular-interface samples at various configurations.
Edit intent: fluency
Original:  These savings result in compact networks able to outperform any of the system's numerical or neural component alone.
Revised:  These savings lead to a powerful inference system able to outperform any of the system's numerical or neural component alone.
Edit intent: clarity
Original:  These savings result in compact networks able to outperform any of the system's numerical or neural component alone.
Revised:  These savings result in compact networks able to outperform any of its numerical or neural component alone.
Edit intent: clarity
Original:  Experiments with static  interfaces show that our hybrid approach is suitable and notoriously superior to conventional numerical methods in under-resolved and steep, concave regions.
Revised:  Experiments with static interfaces show that our hybrid solver is notably superior to conventional numerical methods in under-resolved and steep, concave regions.
Edit intent: clarity
Original:  Compared to prior research, we have observed outstanding gains in precision after including training data pairs from more than a single interface type and other means of input preprocessing.
Revised:  Compared to prior research, we have observed outstanding gains in precision after training the regression model with data pairs from more than a single interface type and other means of input preprocessing.
Edit intent: clarity
Original:  Compared to prior research, we have observed outstanding gains in precision after including training data pairs from more than a single interface type and other means of input preprocessing.
Revised:  Compared to prior research, we have observed outstanding gains in precision after including training data pairs from more than a single interface type and transforming data with specialized input preprocessing.
Edit intent: clarity
Original:  In particular, our findings confirm that machine learning is a promising venue for devising viable solutions to the level-set method 's numerical shortcomings .
Revised:  In particular, our findings confirm that machine learning is a promising venue for devising viable solutions to the level-set method .
Edit intent: clarity
Original: In this paper we propose a novel observer to solve the problem of visual simultaneous localization and mapping , using the information of only the bearing vectors of landmarks observed from a single monocular camera and body-fixed velocities .
Revised: In this paper we propose a novel observer to solve the problem of visual simultaneous localization and mapping , using the information from a single monocular camera and body-fixed velocities .
Edit intent: clarity
Original: In this paper we propose a novel observer to solve the problem of visual simultaneous localization and mapping , using the information of only the bearing vectors of landmarks observed from a single monocular camera and body-fixed velocities .
Revised: In this paper we propose a novel observer to solve the problem of visual simultaneous localization and mapping , using the information of only the bearing vectors of landmarks observed from a single monocular camera and an inertial measurement unit (IMU) .
Edit intent: clarity
Original:  The system state evolves on the manifold SE(3)\times R^{3n}, on which we design dynamic extensions carefully in order to generate an invariant foliation, such that the problem is reformulated into online parameter identification.
Revised:  The system state evolves on the manifold SE(3)\times R^{3n}, on which we design dynamic extensions carefully in order to generate an invariant foliation, such that the problem is reformulated into online constant parameter identification.
Edit intent: clarity
Original:  Then, following the recently introduced parameter estimation-based observer  , we provide a novel and simple solutionto address the problem .
Revised:  Then, following the recently introduced parameter estimation-based observer , we provide a new simple solution .
Edit intent: clarity
Original:  A notable merit is that the proposed observer guarantees almost global asymptotic stability requiring neither persistent excitation nor uniform complete observability, which, however, are widely adopted in the existing works .
Revised:  A notable merit is that the proposed observer guarantees almost global asymptotic stability requiring neither persistency of excitation nor uniform complete observability, which, however, are widely adopted in the existing works .
Edit intent: clarity
Original:  Meanwhile, 3D point cloud is an important form of data for obtaining environmental information , which is widely used in robotics, autonomous driving and other fields.
Revised:  Meanwhile, 3D point cloud , which is widely used in robotics, autonomous driving and other fields.
Edit intent: clarity
Original:  In order to work better , construction robots need to be able to understand their surroundings .
Revised:  In order to work better , construction robots can work better .
Edit intent: clarity
Original:  The  learning module is responsible for learning data features and evaluating the model, while the meta-learning module is responsible for updating the parameters of the model and improving the model generalization capability .
Revised:  The learning module is responsible for learning data features and evaluating the model, while the meta-learning module is responsible for updating the parameters of the model and improving the model generalization ability .
Edit intent: clarity
Original:  At the same time, experiments show that our method can allow the model to be quickly applied to new environments with a small number of samples.
Revised:  At the same time, experiments show that our method can allow the model to be quickly applied to new environments with a few samples.
Edit intent: clarity
Original:  Our proposed method has great application value .
Revised:  Our method has important applications .
Edit intent: clarity
Original:  We develop a model-based reinforcement learning framework with a disentangled universal value function , called GoalRec. Combining the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel model-based value function formalization is proposed.
Revised:  We develop a model-based reinforcement learning framework , called GoalRec. Combining the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel model-based value function formalization is proposed.
Edit intent: clarity
Original:  We develop a model-based reinforcement learning framework with a disentangled universal value function , called GoalRec. Combining the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel model-based value function formalization is proposed.
Revised:  We develop a model-based reinforcement learning framework with a disentangled universal value function , called GoalRec. Inspired by the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel model-based value function formalization is proposed.
Edit intent: clarity
Original:  We develop a model-based reinforcement learning framework with a disentangled universal value function , called GoalRec. Combining the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel model-based value function formalization is proposed.
Revised:  We develop a model-based reinforcement learning framework with a disentangled universal value function , called GoalRec. Combining the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel disentangled universal value function designed for item recommendation is proposed.
Edit intent: clarity
Original:  As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-irrelevant world model is trained to simulate complex environmental dynamics under a certain goal.
Revised:  As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal.
Edit intent: clarity
Original: We consider robust two-stage  optimization problems, which can be considered as a game between the decision maker and an adversary.
Revised: We consider two-stage optimization problems, which can be considered as a game between the decision maker and an adversary.
Edit intent: clarity
Original: We consider robust two-stage  optimization problems, which can be considered as a game between the decision maker and an adversary.
Revised: We consider robust two-stage optimization problems, which can be seen as games between a decision maker and an adversary.
Edit intent: clarity
Original:  In the former, we show that a wide range of robust combinatorial  problems can be decomposed into polynomially many subproblems, which in turn can often be solved in polynomial time  .
Revised:  For the former, we show that a wide range of robust combinatorial problems can be decomposed into polynomially many subproblems, which in turn can often be solved in polynomial time .
Edit intent: fluency
Original:  In the former, we show that a wide range of robust combinatorial  problems can be decomposed into polynomially many subproblems, which in turn can often be solved in polynomial time  .
Revised:  In the former, we show that a wide range of robust combinatorial optimization problems can be decomposed into polynomially many subproblems, which in turn can often be solved in polynomial time .
Edit intent: clarity
Original:  In the former, we show that a wide range of robust combinatorial  problems can be decomposed into polynomially many subproblems, which in turn can often be solved in polynomial time  .
Revised:  In the former, we show that a wide range of robust combinatorial problems can be decomposed into polynomially many subproblems, which can be solved in polynomial time .
Edit intent: clarity
Original:  The dataset and associated resources are publicly  URL
Revised:  The dataset and associated resources are publicly available. URL
Edit intent: clarity
Original:  Along with our main result, we further show that increasing the test-time  compute available to an agent can substitute for reduced train-time compute, and vice versa .
Revised:  Along with our main result, we further show that the test-time compute available to an agent can substitute for reduced train-time compute, and vice versa .
Edit intent: clarity
Original:  Along with our main result, we further show that increasing the test-time  compute available to an agent can substitute for reduced train-time compute, and vice versa .
Revised:  Along with our main result, we further show that increasing the test-time and train-time compute available to an agent can substitute for reduced train-time compute, and vice versa .
Edit intent: clarity
Original:  Along with our main result, we further show that increasing the test-time  compute available to an agent can substitute for reduced train-time compute, and vice versa .
Revised:  Along with our main result, we further show that increasing the test-time compute available to an agent can be traded off while maintaining performance .
Edit intent: clarity
Original:  Information and communication technology along with the internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge.
Revised:  Information and communication technology along with internet of things enables data collection and with the help of artificial intelligence (AI) situation awareness can be obtained to feed the SCC actors with enriched knowledge.
Edit intent: fluency
Original:  There is an urgent need to make robots understand human expectations to proactively reduce unnecessary human engagements and further reduce human cognitive loads.
Revised:  There is an urgent need to proactively reduce unnecessary human engagements and further reduce human cognitive loads.
Edit intent: coherence
Original: We analyze the popular kernel polynomial method (KPM) for approximating the spectral density (eigenvalue distribution) of a real symmetric (or Hermitian ) matrix A\in R^{n\times n . We prove that a simple and practical variant of the KPM algorithm can approximate the spectral density to \epsilon accuracy in the Wasserstein-1 distance with roughly O({1}/{\epsilon}) matrix-vector multiplications with A.
Revised: We analyze the popular kernel polynomial method (KPM) for approximating the spectral density (eigenvalue distribution) of an n\times n Hermitian matrix A . We prove that a simple and practical variant of the KPM algorithm can approximate the spectral density to \epsilon accuracy in the Wasserstein-1 distance with roughly O({1}/{\epsilon}) matrix-vector multiplications with A.
Edit intent: clarity
Original: Many real-world IoT systems comprising various internet-connected sensory devices generate substantial amounts of multivariate time series data.
Revised: Many real-world IoT systems , which include a variety of internet-connected sensory devices generate substantial amounts of multivariate time series data.
Edit intent: clarity
Original: Many real-world IoT systems comprising various internet-connected sensory devices generate substantial amounts of multivariate time series data.
Revised: Many real-world IoT systems comprising various internet-connected sensory devices , produce substantial amounts of multivariate time series data.
Edit intent: fluency
Original:  Meanwhile, those critical IoT infrastructures , such as smart power grids and water distribution networks , are often targets of cyber-attacks, making anomaly detection of high research value.
Revised:  Meanwhile, vital IoT infrastructures like smart power grids and water distribution networks , are often targets of cyber-attacks, making anomaly detection of high research value.
Edit intent: clarity
Original:  Meanwhile, those critical IoT infrastructures , such as smart power grids and water distribution networks , are often targets of cyber-attacks, making anomaly detection of high research value.
Revised:  Meanwhile, those critical IoT infrastructures , such as smart power grids and water distribution networks are frequently targeted by cyber-attacks, making anomaly detection of high research value.
Edit intent: style
Original:  However, considering the complex topological and nonlinear dependencies that are initially unknown among sensors, modeling such relatedness is inevitable for any efficient and accurate anomaly detection system .
Revised:  However, considering the complex topological and nonlinear dependencies that are initially unknown among sensors, modeling such relatedness is , nevertheless, unavoidable for any efficient and accurate anomaly detection system .
Edit intent: clarity
Original:  However, considering the complex topological and nonlinear dependencies that are initially unknown among sensors, modeling such relatedness is inevitable for any efficient and accurate anomaly detection system .
Revised:  However, considering the complex topological and nonlinear dependencies that are initially unknown among sensors, modeling such relatedness is inevitable for any efficient and effective anomaly detection system .
Edit intent: clarity
Original:  This work proposed a novel framework, namely GTA, for multivariate time series anomaly detection by automatically learning a graph structure followed by the graph convolutionand modeling the temporal dependency through a Transformer-based architecture.
Revised:  This work proposed a novel framework, namely GTA, for multivariate time series anomaly detection that involves automatically learning a graph structure followed by the graph convolutionand modeling the temporal dependency through a Transformer-based architecture.
Edit intent: clarity
Original:  This work proposed a novel framework, namely GTA, for multivariate time series anomaly detection by automatically learning a graph structure followed by the graph convolutionand modeling the temporal dependency through a Transformer-based architecture.
Revised:  This work proposed a novel framework, namely GTA, for multivariate time series anomaly detection by automatically learning a graph structure , graph convolution, and modeling temporal dependency using a Transformer-based architecture.
Edit intent: coherence
Original:  The core idea of learning graph structure is called the connection learning policy  based on the Gumbel-softmax sampling strategy to learn bi-directed associations among sensors directly .
Revised:  The connection learning policy based on the Gumbel-softmax sampling strategy to learn bi-directed associations among sensors directly .
Edit intent: coherence
Original:  The core idea of learning graph structure is called the connection learning policy  based on the Gumbel-softmax sampling strategy to learn bi-directed associations among sensors directly .
Revised:  The core idea of learning graph structure is called the connection learning policy , which is based on the Gumbel-softmax sampling strategy to learn bi-directed associations among sensors directly .
Edit intent: coherence
Original:  The core idea of learning graph structure is called the connection learning policy  based on the Gumbel-softmax sampling strategy to learn bi-directed associations among sensors directly .
Revised:  The core idea of learning graph structure is called the connection learning policy based on the Gumbel-softmax sampling approach to learn bi-directed associations among sensors directly .
Edit intent: clarity
Original:  The core idea of learning graph structure is called the connection learning policy  based on the Gumbel-softmax sampling strategy to learn bi-directed associations among sensors directly .
Revised:  The core idea of learning graph structure is called the connection learning policy based on the Gumbel-softmax sampling strategy to learn bi-directed links among sensors directly .
Edit intent: clarity
Original:  Moreover, we proposed a multi-branch attention mechanism to substitute for original multi-head self-attention to overcome the quadratic complexity challenge.
Revised:  Moreover, we proposed a multi-branch attention mechanism to replace the original multi-head self-attention to overcome the quadratic complexity challenge.
Edit intent: fluency
Original:  The extensive experiments on four public anomaly detection benchmarks further demonstrate our approach's superiority over other state-of-the-arts.
Revised:  The extensive experiments on four publicly available anomaly detection benchmarks further demonstrate our approach's superiority over other state-of-the-arts.
Edit intent: clarity
Original:  The extensive experiments on four public anomaly detection benchmarks further demonstrate our approach's superiority over other state-of-the-arts.
Revised:  The extensive experiments on four public anomaly detection benchmarks further demonstrate the superiority of our approach over alternative state-of-the-arts.
Edit intent: clarity
Original: We present a new open source C library msolve dedicated to solving multivariate polynomial systems of dimension zero through computer algebra methods.
Revised: We present a new open source C library msolve dedicated to solving multivariate polynomial systems of dimension zero through computer algebra methods.
Edit intent: fluency
Original:  The core algorithmic framework of msolve relies on Gr \"o bner bases and linear algebra based algorithms for polynomial system solving.
Revised:  The core algorithmic framework of msolve relies on Gr \"o bner bases and linear algebra based algorithms for polynomial system solving.
Edit intent: fluency
Original:  The core algorithmic framework of msolve relies on Gr \"o bner bases and linear algebra based algorithms for polynomial system solving.
Revised:  The core algorithmic framework of msolve relies on Gr bner bases and linear algebra based algorithms for polynomial system solving.
Edit intent: fluency
Original:  The core algorithmic framework of msolve relies on Gr \"o bner bases and linear algebra based algorithms for polynomial system solving.
Revised:  The core algorithmic framework of msolve relies on Gr \"o \''obner bases and linear algebra based algorithms for polynomial system solving.
Edit intent: fluency
Original:  It relies on Gr \"o bner basis computation w.r.t.  the degree reverse lexicographical order, Gr \"o bner conversion to a lexicographical Gr \"o bner basis and real solving of univariate polynomials.
Revised:  It relies on Gr bner basis computation w.r.t. the degree reverse lexicographical order, Gr \"o bner conversion to a lexicographical Gr \"o bner basis and real solving of univariate polynomials.
Edit intent: fluency
Original:  It relies on Gr \"o bner basis computation w.r.t.  the degree reverse lexicographical order, Gr \"o bner conversion to a lexicographical Gr \"o bner basis and real solving of univariate polynomials.
Revised:  It relies on Gr \"o \''obner basis computation w.r.t. the degree reverse lexicographical order, Gr \"o bner conversion to a lexicographical Gr \"o bner basis and real solving of univariate polynomials.
Edit intent: fluency
Original:  It relies on Gr \"o bner basis computation w.r.t.  the degree reverse lexicographical order, Gr \"o bner conversion to a lexicographical Gr \"o bner basis and real solving of univariate polynomials.
Revised:  It relies on Gr \"o bner basis computation w.r.t. \ the degree reverse lexicographical order, Gr \"o bner conversion to a lexicographical Gr \"o bner basis and real solving of univariate polynomials.
Edit intent: fluency
Original:  It relies on Gr \"o bner basis computation w.r.t.  the degree reverse lexicographical order, Gr \"o bner conversion to a lexicographical Gr \"o bner basis and real solving of univariate polynomials.
Revised:  It relies on Gr \"o bner basis computation w.r.t. the degree reverse lexicographical order, Gr bner conversion to a lexicographical Gr \"o bner basis and real solving of univariate polynomials.
Edit intent: fluency