wanyu/IteraTeR_v2 huggingface dataset:
Original:  Specifically, for each frame in a point cloud sequence, we first split it into cubes of fixed size as the processing unit, and treat cubes with holes inside as target cubes.
Revised:  Specifically, for each missing region in every frame of the point cloud sequence, we first split it into cubes of fixed size as the processing unit, and treat cubes with holes inside as target cubes.
Edit intent: clarity
Original:  Finally, we formulate dynamic point cloud inpainting as an optimization problem based on both intra- and inter-source cubes , which is regularized by the graph-signal smoothness prior.
Revised:  Finally, we formulate dynamic point cloud inpainting as an optimization problem based on the two types of references , which is regularized by the graph-signal smoothness prior.
Edit intent: clarity
Original:  Finally, we formulate dynamic point cloud inpainting as an optimization problem based on both intra- and inter-source cubes , which is regularized by the graph-signal smoothness prior.
Revised:  Finally, we formulate dynamic point cloud inpainting as an optimization problem based on both intra- and inter-source cubes , which is regularized by a graph-signal smoothness prior.
Edit intent: fluency
Original:  Experimental results show that the proposed approach outperforms three competing methods significantly, both in objective and subjective quality.
Revised:  Experimental results show the proposed approach outperforms three competing methods significantly, both in objective and subjective quality.
Edit intent: fluency
Original:  Any algorithm capable of computing an equilibrium of the corresponding game will inherit an approximation ratio that is, in the worst case, equal to the price-of-anarchy of the considered class of equilibria .
Revised:  Any algorithm capable of computing an equilibrium of the corresponding game inherits an approximation ratio that is, in the worst case, equal to the price-of-anarchy of the considered class of equilibria .
Edit intent: clarity
Original:  Any algorithm capable of computing an equilibrium of the corresponding game will inherit an approximation ratio that is, in the worst case, equal to the price-of-anarchy of the considered class of equilibria .
Revised:  Any algorithm capable of computing an equilibrium of the corresponding game will inherit an approximation ratio that is, in the worst case, equal to its price-of-anarchy of the considered class of equilibria .
Edit intent: clarity
Original:  Any algorithm capable of computing an equilibrium of the corresponding game will inherit an approximation ratio that is, in the worst case, equal to the price-of-anarchy of the considered class of equilibria .
Revised:  Any algorithm capable of computing an equilibrium of the corresponding game will inherit an approximation ratio that is, in the worst case, equal to the price-of-anarchy .
Edit intent: clarity
Original: As autonomous vehicles have benefited the society, understanding the dynamic change of human trust during human-autonomous vehicle interaction can help to improve the safety and performance of autonomous driving.
Revised: As autonomous vehicles have benefited the society, understanding the dynamic change of humans' trust during human-autonomous vehicle interaction can help to improve the safety and performance of autonomous driving.
Edit intent: clarity
Original:  We used analysis of variance (ANOVA) and Signal Temporal Logic (STL) techniques to analyze the experimental data.
Revised:  We used analysis of variance (ANOVA) and Signal Temporal Logic (STL) to analyze the experimental data.
Edit intent: clarity
Original: Data analysis is an inherently iterative process.
Revised: Knowledge discovery from data is an inherently iterative process.
Edit intent: clarity
Original:  That is, what we know about the data greatly determines our expectations, and hence, what result we would find the most interesting .
Revised:  That is, what we know about the data greatly determines our expectations, and therefore, what results we would find the most interesting .
Edit intent: coherence
Original:  With this in mind, we introduce a well-founded approach for succinctly summarizing data with a collection of itemsets;
Revised:  With this in mind, we introduce a well-founded approach for succinctly summarizing data with the most informative itemsets;
Edit intent: clarity
Original:  using a probabilistic maximum entropy model, we iteratively find the most interesting itemset , and in turn update our model of the data accordingly.
Revised:  using a probabilistic maximum entropy model, we iteratively find the most interesting itemset , and in turn update our model accordingly.
Edit intent: clarity
Original:  As we only include itemsets that are surprising with regard to the current model, the summary is guaranteed to be both descriptive and non-redundant.
Revised:  As we only include itemsets that are surprising with regard to the current model, the summaries we construct are guaranteed to be both descriptive and non-redundant.
Edit intent: clarity
Original:  The algorithm that we present can either mine the top-k most interesting itemsets, or use the Bayesian Information Criterion  to automatically identify the model containing only the itemsets most important for describing the data .
Revised:  The algorithm that we present can either mine the top-k most interesting itemsets, or we can employ either the Bayesian Information Criterion to automatically identify the model containing only the itemsets most important for describing the data .
Edit intent: clarity
Original:  Or, in other words, it will `tell you what you need to know' . Experiments on synthetic and benchmarkdata show that the discovered summaries are succinct, and correctly identify the key patterns in the data .
Revised:  Or, in other words, our method will `tell you what you need to know' . Experiments on synthetic and benchmarkdata show that the discovered summaries are succinct, and correctly identify the key patterns in the data .
Edit intent: clarity
Original:  The immediate neighbors are divided into the required stencil  and an existing WENO limiting strategy is used .
Revised:  The immediate neighbors are divided into the required stencil for WENO reconstruction and an existing WENO limiting strategy is used .
Edit intent: clarity
Original:  At the molecular level, finite-size effects, pronounced nonlinearities, and ballistic processes conspire to produce behavior that diverges from the macroscale.
Revised:  At the molecular level, finite-size effects, pronounced nonlinearities, and ballistic processes produce behavior that diverges from the macroscale.
Edit intent: clarity
Original:  Here, we show that transient thermal transport reflects macromolecular energy landscape architecture through both (i) the topological characteristics of the conformational ensemble and (ii) the nonlinear processes that mediate dynamics.
Revised:  Here, we show that transient thermal transport reflects macromolecular energy landscape architecture through both (i) the topological characteristics of molecular contacts and the nonlinear processes that mediate dynamics.
Edit intent: clarity
Original: We consider the problem of super-replication (hedging without risk) for the Arbitrage Pricing Theory.
Revised: We consider infinite dimensional optimization problems motivated by the financial model called Arbitrage Pricing Theory.
Edit intent: clarity
Original:  The dual characterization of  super-replication cost is provided.
Revised:  The dual characterization of the super-replication cost is provided.
Edit intent: fluency
Original:  It is shown that the reservation prices of investors converge to this cost as their respective risk-aversion tends to infinity.
Revised:  It is shown that the reservation prices of investors converge to this cost as their risk-aversion tends to infinity.
Edit intent: clarity
Original:  We investigate approaches to improving the accuracy of lower-precision arithmetic types, using examples in an important domain for numerical computation in neuroscience: the solution of Ordinary Differential Equations (ODEs).
Revised:  We investigate approaches to improving the accuracy of fixed-point arithmetic types, using examples in an important domain for numerical computation in neuroscience: the solution of Ordinary Differential Equations (ODEs).
Edit intent: clarity
Original:  In particular,  stochastic rounding consistently results in smaller errors compared to single-precision floatingpoint and fixed-point arithmetic with round-tonearest across a range of neuron behaviours and ODE solvers.
Revised:  In particular, fixed-point arithmetic with stochastic rounding consistently results in smaller errors compared to single-precision floatingpoint and fixed-point arithmetic with round-tonearest across a range of neuron behaviours and ODE solvers.
Edit intent: fluency
Original:  In particular,  stochastic rounding consistently results in smaller errors compared to single-precision floatingpoint and fixed-point arithmetic with round-tonearest across a range of neuron behaviours and ODE solvers.
Revised:  In particular, stochastic rounding consistently results in smaller errors compared to single-precision floating-point and fixed-point arithmetic with round-tonearest across a range of neuron behaviours and ODE solvers.
Edit intent: fluency
Original:  In particular,  stochastic rounding consistently results in smaller errors compared to single-precision floatingpoint and fixed-point arithmetic with round-tonearest across a range of neuron behaviours and ODE solvers.
Revised:  In particular, stochastic rounding consistently results in smaller errors compared to single-precision floatingpoint and fixed-point arithmetic with round-to-nearest across a range of neuron behaviours and ODE solvers.
Edit intent: clarity
Original:  A computationally much cheaper alternative is also investigated, inspired by the concept of dither that is a widely understood mechanism for providing resolution below the LSB ( east significant bit) in digital signal processing.
Revised:  A computationally much cheaper alternative is also investigated, inspired by the concept of dither that is a widely understood mechanism for providing resolution below the LSB ( least significant bit) in digital signal processing.
Edit intent: fluency
Original:  In particular, fixed-point arithmetic with stochastic rounding consistently results in smaller errors compared to single-precision floating-point and fixed-point arithmetic with round-to-nearest across a range of neuron behaviours and ODE solvers.
Revised:  In particular, fixed-point arithmetic with stochastic rounding consistently results in smaller errors compared to single precision floating-point and fixed-point arithmetic with round-to-nearest across a range of neuron behaviours and ODE solvers.
Edit intent: fluency
Original:  A computationally much cheaper alternative is also investigated, inspired by the concept of dither that is a widely understood mechanism for providing resolution below the LSB ( least significant bit  ) in digital signal processing.
Revised:  A computationally much cheaper alternative is also investigated, inspired by the concept of dither that is a widely understood mechanism for providing resolution below the least significant bit ) in digital signal processing.
Edit intent: fluency
Original:  A computationally much cheaper alternative is also investigated, inspired by the concept of dither that is a widely understood mechanism for providing resolution below the LSB ( least significant bit  ) in digital signal processing.
Revised:  A computationally much cheaper alternative is also investigated, inspired by the concept of dither that is a widely understood mechanism for providing resolution below the LSB ( least significant bit (LSB ) in digital signal processing.
Edit intent: clarity
Original:  In this paper, we propose a class of growth models, named Fibonacci trees F(t), with respect to the intrinsic character of Fibonacci sequence \{F_{t}\}.
Revised:  In this paper, we propose a class of growth models, named Fibonacci trees F(t), with respect to the intrinsic advantage of Fibonacci sequence \{F_{t}\}.
Edit intent: clarity
Original:  First, we turn out model F(t) to be scale-free because its degree distribution satisfies the power law with power-law exponent \gamma greater than 3.
Revised:  First, we turn out model F(t) to have power-law degree distribution with exponent \gamma greater than 3.
Edit intent: clarity
Original:  We provide a deterministic algorithm for computing OMFPT and then obtain a closed-form expression of OMFPT . Meanwhile, an approximation algorithm and a deterministic algorithm are introduced, respectively, to capture a valid solution to MFPT.
Revised:  We obtain a closed-form expression of OMFPT . Meanwhile, an approximation algorithm and a deterministic algorithm are introduced, respectively, to capture a valid solution to MFPT.
Edit intent: coherence
Original:  Especially, we capture a nontrivial result that the MFPT reported by deterministic algorithm is no longer correlated linearly with the order of model F(t) . Extensive analytical results are in better agreement with our statements .
Revised:  Especially, we capture a nontrivial result that the MFPT reported by deterministic algorithm is no longer correlated linearly with the order of model F(t) .
Edit intent: clarity
Original: We consider continuous-time Mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) setting .
Revised: We approach the continuous-time Mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) setting .
Edit intent: clarity
Original: We consider continuous-time Mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) setting .
Revised: We consider continuous-time mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) setting .
Edit intent: fluency
Original: We consider continuous-time Mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) setting .
Revised: We consider continuous-time Mean-variance (MV) portfolio selection with reinforcement learning (RL) setting .
Edit intent: clarity
Original: We consider continuous-time Mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) setting .
Revised: We consider continuous-time Mean-variance (MV) portfolio optimization problem in the Reinforcement Learning (RL) .
Edit intent: clarity
Original:  The problem falls into the entropy-regularized  relaxed stochastic control framework recently introduced in Wang et al. (2019).
Revised:  The problem falls into the entropy-regularized , relaxed stochastic control framework recently introduced in Wang et al. (2019).
Edit intent: fluency
Original:  Close connections between the entropy-regularized MV and the classical MV are also discussed , including the solvability equivalence and the convergence as exploration decays .
Revised:  We then establish connections between the entropy-regularized MV and the classical MV are also discussed , including the solvability equivalence and the convergence as exploration decays .
Edit intent: clarity
Original:  Close connections between the entropy-regularized MV and the classical MV are also discussed , including the solvability equivalence and the convergence as exploration decays .
Revised:  Close connections between the entropy-regularized MV and the classical MV , including the solvability equivalence and the convergence as exploration decays .
Edit intent: coherence
Original:  Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.
Revised:  Our algorithm outperforms an adaptive control based method and a deep neural networks for continuous control problems by a large margin in nearly all simulations.
Edit intent: clarity
Original: Terahertz communication is emerging as a future technology to support Terabits per second link (Tbps) with highlighting features as high throughput and negligible latency.
Revised: Terahertz communication is emerging as a future technology to support Terabits per second link with highlighting features as high throughput and negligible latency.
Edit intent: clarity
Original:  However, the unique features of  Terahertz band such as high path loss, scattering and reflection pose new challenges and results in short communication distance.
Revised:  However, the unique features of the Terahertz band such as high path loss, scattering and reflection pose new challenges and results in short communication distance.
Edit intent: fluency
Original:  Some open challenges and future research directions on Terahertz MAC protocols are  highlighted.
Revised:  The open challenges and future research directions on Terahertz MAC protocols are highlighted.
Edit intent: fluency
Original:  Some open challenges and future research directions on Terahertz MAC protocols are  highlighted.
Revised:  Some open challenges and future research directions on Terahertz MAC protocols are also highlighted.
Edit intent: clarity
Original:  When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire systems may be conducted in a progressive way due to limited repair resources.
Revised:  When such advanced network systems face a massive failure by natural disasters or attacks, the recovery of the entire system may be conducted in a progressive way due to limited repair resources.
Edit intent: fluency
Original:  Our simulation results indicate that DeepPR can obtain 98.4\% of the theoretical optimum in certain networks .
Revised:  Our simulation results indicate that DeepPR can achieve the near-optimal solutions in certain networks .
Edit intent: clarity
Original:  Second, we evaluate the Stieltjes transform of such a distribution at real values exceeding the boundary.
Revised:  Second, we evaluate the Stieltjes transform of the spectral distribution at real values exceeding the boundary.
Edit intent: clarity
Original:  We prove the correctness of the algorithms from a detailed analysis of the master equations that characterize the Stieltjes transform .
Revised:  We prove the correctness of the algorithms from a detailed analysis of the master equations characterizing the Stieltjes transform .
Edit intent: fluency
Original:  In certain signal-plus-noise models of data, such as the classical spiked covariance model and its variants, there are closed formulas for the spectral signal detection threshold (the largest sample eigenvalue attributable solely to noise) in the isotropic noise setting .
Revised:  In certain signal-plus-noise models of data, such as the classical spiked covariance model and its variants, there are closed formulas for the spectral signal detection threshold (the largest sample eigenvalue attributable solely to noise) for isotropic noise in the limit of infinitely large data matrices .
Edit intent: clarity
Original:  However, existing methods for numerically evaluating the threshold for more general noise models remain unsatisfactory .
Revised:  However, existing methods for numerically evaluating the threshold .
Edit intent: clarity
Original: The explosive growth of fake news and its erosion of democracy , justice, and public trust has significantly increased the demand for accurate fake news detection.
Revised: Massive dissemination of fake news and its erosion of democracy , justice, and public trust has significantly increased the demand for accurate fake news detection.
Edit intent: clarity
Original: The explosive growth of fake news and its erosion of democracy , justice, and public trust has significantly increased the demand for accurate fake news detection.
Revised: The explosive growth of fake news and its potential to erode democracy has increased the demand for accurate fake news detection.
Edit intent: clarity
Original:  hence, motivating the need to develop approaches that can detect fake news by focusing mainly on news content.
Revised:  hence, motivating the need to develop approaches that can detect fake news by focusing on news content.
Edit intent: clarity
Original:  Experiments conducted on two real-world datasets indicate that the proposed method can outperform the state-of-the-art and enable fake news early detection , even when there is limited content information.
Revised:  Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection , even when there is limited content information.
Edit intent: fluency
Original:  Experiments conducted on two real-world datasets indicate that the proposed method can outperform the state-of-the-art and enable fake news early detection , even when there is limited content information.
Revised:  Experiments conducted on two real-world datasets indicate that the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.
Edit intent: coherence
Original:  Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks  tend to assume linear, current-based superposition of post-synaptic currents.
Revised:  Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks tend to assume a linear superposition of post-synaptic currents.
Edit intent: clarity
Original:  We extend the theory underlying the Neural Engineering Framework to systematically exploit nonlinear interactions between the local membrane potential and conductance-based synaptic channels as a computational resource.
Revised:  In this paper, we present a series of extensions to the Neural Engineering Framework to systematically exploit nonlinear interactions between the local membrane potential and conductance-based synaptic channels as a computational resource.
Edit intent: clarity
Original:  However, the brain poses a rich architecture organized in multiple scales linked to one another.
Revised:  However, the brain possesses a rich architecture organized in multiple scales linked to one another.
Edit intent: clarity
Original:  We explored the multiscale organization of the human connectome using a dataset of healthy subjects reconstructed at five different resolutions.
Revised:  We explored the multiscale organization of the human connectome using datasets of healthy subjects reconstructed at five different resolutions.
Edit intent: fluency
Original:  We find that the structure of the human brain remains self-similar when the resolution length is progressively decreased by hierarchical coarse-graining of the anatomical regions.
Revised:  We found that the structure of the human brain remains self-similar when the resolution length is progressively decreased by hierarchical coarse-graining of the anatomical regions.
Edit intent: fluency
Original:  Strikingly, geometric renormalization of connectome maps in hyperbolic space, which decreases the resolution by coarse-graining and averaging over short similarity distances , predicts the properties of connectomes including self-similarity .
Revised:  Strikingly, geometric renormalization of connectome maps in hyperbolic space, which decreases the resolution by coarse-graining and averaging over short similarity distances .
Edit intent: clarity
Original:  Our results suggest that the same principles regulate connectivity between brain regions at different length scales and that the multiscale self-similarity of brain connectomes may offer an advantageous architecture for  navigation purposes.
Revised:  Our results suggest that the same principles regulate connectivity between brain regions at different length scales . Moreover, the multiscale self-similarity of brain connectomes may offer an advantageous architecture for navigation purposes.
Edit intent: coherence