wanyu/IteraTeR_v2 huggingface dataset:
Original:  To improve the robustness of streaming and accelerate learning , we further exploit the partially known dynamics of the system  by integrating the concepts of safer layer, post-decision state, and virtual experience into the basic DDPG algorithm.
Revised:  To improve the robustness of streaming and accelerate learning , we further exploit the partially known dynamics of the system by integrating the concepts of safety layer, post-decision state, and virtual experience into the basic DDPG algorithm.
Edit intent: fluency
Original:  To improve the robustness of streaming and accelerate learning , we further exploit the partially known dynamics of the system  by integrating the concepts of safer layer, post-decision state, and virtual experience into the basic DDPG algorithm.
Revised:  To improve the robustness of streaming and accelerate learning , we further exploit the partially known dynamics of the system by integrating the concepts of safer layer, post-decision state, and virtual experiences into the basic DDPG algorithm.
Edit intent: fluency
Original:  Our simulation results show that the proposed policies converge to the optimal policy  derived based on perfect prediction of the future large-scale channel gains and outperforms the first-predict-then-optimize policy in the presence of prediction errors.
Revised:  Our simulation results show that the proposed policies converge to the optimal policy that is derived based on perfect prediction of the future large-scale channel gains and outperforms the first-predict-then-optimize policy in the presence of prediction errors.
Edit intent: clarity
Original:  Our simulation results show that the proposed policies converge to the optimal policy  derived based on perfect prediction of the future large-scale channel gains and outperforms the first-predict-then-optimize policy in the presence of prediction errors.
Revised:  Our simulation results show that the proposed policies converge to the optimal policy derived based on perfect large-scale channel gains and outperforms the first-predict-then-optimize policy in the presence of prediction errors.
Edit intent: clarity
Original:  Our simulation results show that the proposed policies converge to the optimal policy  derived based on perfect prediction of the future large-scale channel gains and outperforms the first-predict-then-optimize policy in the presence of prediction errors.
Revised:  Our simulation results show that the proposed policies converge to the optimal policy derived based on perfect prediction of the future large-scale channel prediction and outperform the first-predict-then-optimize policy in the presence of prediction errors.
Edit intent: clarity
Original:  By harnessing the partially known model of the system dynamics , the convergence speed can be dramatically improved.
Revised:  By harnessing the partially known model , the convergence speed can be dramatically improved.
Edit intent: clarity
Original: This letter expands the studies of the informational efficiency in the cryptocurrency market.
Revised: This paper studies of the informational efficiency in the cryptocurrency market.
Edit intent: clarity
Original: We propose a simple preconditioning technique that, if incorporated into algorithms for computing functions of triangular matrices, can make them more efficient.
Revised: We propose a simple technique that, if incorporated into algorithms for computing functions of triangular matrices, can make them more efficient.
Edit intent: clarity
Original: We propose a simple preconditioning technique that, if incorporated into algorithms for computing functions of triangular matrices, can make them more efficient.
Revised: We propose a simple preconditioning technique that, if combined with algorithms for computing functions of triangular matrices, can make them more efficient.
Edit intent: clarity
Original:  It can easily be extended to non triangular matrices, provided that it is combined with algorithms involving a prior Schur decomposition  . Special attention is devoted to particular algorithms like the inverse scaling and squaring to the matrix logarithm and the scaling and squaring to the matrix exponential.
Revised:  It can easily be extended to non-triangular matrices, provided that it is combined with algorithms involving a prior Schur decomposition . Special attention is devoted to particular algorithms like the inverse scaling and squaring to the matrix logarithm and the scaling and squaring to the matrix exponential.
Edit intent: fluency
Original:  Chances in basic reproduction number was estimated based on current in-patient numbers.
Revised:  Changes in basic reproduction number was estimated based on current in-patient numbers.
Edit intent: fluency
Original:  The first part is called stage recurrent neural network, which is proposed to effectively aggregate the deep feature dependencies across different stages with a memory mechanism and also remove the interference stageby-stage .
Revised:  The first part is called stage recurrent neural network, which is introduced to effectively aggregate the deep feature dependencies across different stages with a memory mechanism and also remove the interference stageby-stage .
Edit intent: clarity
Original:  The first part is called stage recurrent neural network, which is proposed to effectively aggregate the deep feature dependencies across different stages with a memory mechanism and also remove the interference stageby-stage .
Revised:  The first part is called stage recurrent neural network, which is proposed to effectively aggregate the deep feature dependencies across different stages with a memory mechanism and also remove the interference stage by stage .
Edit intent: fluency
Original:  Recursive learning is adopted to significantly improve the parameter efficiency and therefore, the number of trainable parameters is effectively reduced without sacrificing its performance.
Revised:  Recursive learning is adopted to improve the parameter efficiency and therefore, the number of trainable parameters is effectively reduced without sacrificing its performance.
Edit intent: clarity
Original:  The experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network achieves consistently better performance in  both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Revised:  Numerous experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network achieves consistently better performance in both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Edit intent: fluency
Original:  The experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network achieves consistently better performance in  both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Revised:  The experiments are conducted on TIMIT corpus and experimental results demonstrate that the proposed network achieves consistently better performance in both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Edit intent: coherence
Original:  The experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network achieves consistently better performance in  both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Revised:  The experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network can achieve consistently better performance in both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Edit intent: clarity
Original:  The experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network achieves consistently better performance in  both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Revised:  The experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network achieves consistently better performance in terms of both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Edit intent: clarity
Original:  The experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network achieves consistently better performance in  both PESQ and STOI scores than two advanced time domain-based baselines in different conditions.
Revised:  The experiments are conducted on TIMIT corpus . Experimental results demonstrate that the proposed network achieves consistently better performance in both PESQ and STOI scores than two state-of-the-art time domain-based baselines in different conditions.
Edit intent: clarity
Original: In this paper, we propose a type of neural network with recursive learning in the time domain called RTNet for monaural speech enhancement, where the proposed network consists of three principal components.
Revised: In this paper, we propose a type of neural network with feedback learning in the time domain called RTNet for monaural speech enhancement, where the proposed network consists of three principal components.
Edit intent: clarity
Original:  Recursive learning is adopted to improve the parameter efficiency and therefore, the number of trainable parameters is effectively reduced without sacrificing its performance.
Revised:  Feedback learning is adopted to improve the parameter efficiency and therefore, the number of trainable parameters is effectively reduced without sacrificing its performance.
Edit intent: clarity
Original: The goal of this work is to learn implicit 3D shape representation with 2D supervision (i. e., a collection of images).
Revised: In this work we address the challenging problem of multiview 3D shape representation with 2D supervision (i. e., a collection of images).
Edit intent: clarity
Original:  To that end we introduce the Universal Differentiable Renderer (UDR) a neural network architecture that can provably approximate reflected light from an implicit neural representation of a 3D surface, under a wide set of reflectance properties and lighting conditions . Experimenting with the task of multiview 3D reconstruction, we find our model to improve upon the baselines in the accuracy of the reconstructed 3D geometry and rendering from unseen viewing directions .
Revised:  To that end we introduce the Universal Differentiable Renderer (UDR) a neural network architecture that can provably approximate reflected light from an implicit neural representation of a 3D surface, under a wide set of reflectance properties and lighting conditions . Experimenting with the task of multiview 3D reconstruction, we find our model to produce state of the art 3D geometry and rendering from unseen viewing directions .
Edit intent: clarity
Original:  To that end we introduce the Universal Differentiable Renderer (UDR) a neural network architecture that can provably approximate reflected light from an implicit neural representation of a 3D surface, under a wide set of reflectance properties and lighting conditions . Experimenting with the task of multiview 3D reconstruction, we find our model to improve upon the baselines in the accuracy of the reconstructed 3D geometry and rendering from unseen viewing directions .
Revised:  To that end we introduce the Universal Differentiable Renderer (UDR) a neural network architecture that can provably approximate reflected light from an implicit neural representation of a 3D surface, under a wide set of reflectance properties and lighting conditions . Experimenting with the task of multiview 3D reconstruction, we find our model to improve upon the baselines in the accuracy of the reconstructed 3D surface reconstructions with high fidelity, resolution and detail .
Edit intent: clarity
Original:  Far more generally, we establish a lower bound on the running time of any 2QCFA or o(\log n)-space QTM that recognizes any language L in terms of a natural `` hardness measure" of L. This allows us to exhibit a large family of languages for which we have asymptotically matching lower and upper bounds on the running time of any such 2QCFA or QTM recognizer.
Revised:  Far more generally, we establish a lower bound on the running time of any 2QCFA or o(\log n)-space QTM that recognizes any language L in terms of a natural " hardness measure" of L. This allows us to exhibit a large family of languages for which we have asymptotically matching lower and upper bounds on the running time of any such 2QCFA or QTM recognizer.
Edit intent: fluency
Original:  We show that the local information cost  presents a natural lower bound on the communication complexity of distributed algorithms.
Revised:  We show that the local information cost ( presents a natural lower bound on the communication complexity of distributed algorithms.
Edit intent: fluency
Original:  We show that the local information cost  presents a natural lower bound on the communication complexity of distributed algorithms.
Revised:  We show that the local information cost ) presents a natural lower bound on the communication complexity of distributed algorithms.
Edit intent: fluency
Original:  A consequence of our lower bound is that achieving both time- and communication-optimality is impossible when designing spanner algorithms for this setting .
Revised:  A consequence of our lower bound is that achieving both time- and communication-optimality is impossible when designing a distributed spanner algorithm .
Edit intent: clarity
Original: Graph representation learning is a fundamental task of various applications , aiming to learn low-dimensional embeddings for nodes which can preserve graph topology information.
Revised: Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes which can preserve graph topology information.
Edit intent: clarity
Original: Graph representation learning is a fundamental task of various applications , aiming to learn low-dimensional embeddings for nodes which can preserve graph topology information.
Revised: Graph representation learning is a fundamental task of various applications , aiming to learn low-dimensional embeddings for nodes that can preserve graph topology information.
Edit intent: fluency
Original:  However, many existing methods focus on static graphs while ignoring graph evolving patterns.
Revised:  However, many existing methods focus on static graphs while ignoring evolving graph patterns.
Edit intent: fluency
Original:  In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity in a unified framework while simultaneously capturing graph dynamics.
Revised:  In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity while simultaneously capturing graph dynamics.
Edit intent: clarity
Original:  However, treatment of these communities in the framework of the classical  stochastic block model ( SBM) does not take into account  linear connectivity of the chromatin  .
Revised:  However, treatment of these communities in the framework of the classical stochastic block model , SBM) does not take into account linear connectivity of the chromatin .
Edit intent: fluency
Original:  Here we propose the "polymer" variant of the SBM , paving the way for spectral community detection in polymer networks.
Revised:  Here we propose the "polymer" block model , paving the way for spectral community detection in polymer networks.
Edit intent: clarity
Original:  Here we propose the "polymer" variant of the SBM , paving the way for spectral community detection in polymer networks.
Revised:  Here we propose the "polymer" variant of the SBM , paving the way for community detection in polymer networks.
Edit intent: fluency
Original:  The benchmark analyses demonstrates that the spectrum of the polymer non-backtracking allows to resolve the true compartmental structure up to the  detectability threshold, while traditionally used operators fail above it.
Revised:  The benchmark analyses demonstrates that the spectrum of the polymer non-backtracking operator resolves the true compartmental structure up to the detectability threshold, while traditionally used operators fail above it.
Edit intent: clarity
Original:  The benchmark analyses demonstrates that the spectrum of the polymer non-backtracking allows to resolve the true compartmental structure up to the  detectability threshold, while traditionally used operators fail above it.
Revised:  The benchmark analyses demonstrates that the spectrum of the polymer non-backtracking allows to resolve the true compartmental structure up to the detectability threshold, while all commonly used operators fail above it.
Edit intent: clarity
Original: In this paper, a scalable iterative projection-type algorithm for solving non-stationary sys-tems of linear inequalities is considered.
Revised: In this paper, a scalable iterative projection-type algorithm for solving non-stationary systems of linear inequalities is considered.
Edit intent: fluency
Original:  An analytical esti-mation of the algorithm scalability boundary is obtained on the base of the BSF cost met-ric .
Revised:  An analytical estimation of the algorithm scalability boundary is obtained on the base of the BSF cost met-ric .
Edit intent: fluency
Original:  An analytical esti-mation of the algorithm scalability boundary is obtained on the base of the BSF cost met-ric .
Revised:  An analytical esti-mation of the algorithm scalability boundary is obtained on the base of the BSF cost metric .
Edit intent: fluency
Original: There is growing concern about the possibility for tacit collusion using algorithmic pricing, and regulators need tools to help detect the possibility of such collusion.
Revised: There is growing concern about tacit collusion using algorithmic pricing, and regulators need tools to help detect the possibility of such collusion.
Edit intent: fluency
Original:  We demonstrate our method with computational experiments of the famous Bertrand competition game (with and without collusion) and show how our method performs in this environment .
Revised:  We demonstrate our method with computational experiments of the Bertrand competition game (with and without collusion) and show how our method performs in this environment .
Edit intent: clarity
Original:  We demonstrate our method with computational experiments of the famous Bertrand competition game (with and without collusion) and show how our method performs in this environment .
Revised:  We demonstrate our method with computational experiments of the famous Bertrand competition game (with and without collusion) and show how our method performs .
Edit intent: clarity
Original:  This paper explains a formalization (the first one to the best of our knowledge) in the Coq proof assistant of a monad equipped with both choices: the geometrically convex monad.
Revised:  This paper explains a formalization in the Coq proof assistant of a monad equipped with both choices: the geometrically convex monad.
Edit intent: clarity
Original: Interacting, self-propelled particles such as motile epithelial cells can dynamically self-organize into large-scale patterns.
Revised: Interacting, self-propelled particles such as epithelial cells can dynamically self-organize into large-scale patterns.
Edit intent: clarity
Original:  For a given particle configuration , topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology) , and can compare different particle configurations based on the "cost" of reorganizing one configuration into another.
Revised:  For a given particle configuration , topological data analysis (TDA) determines the stability of spatial connectivity at varying length scales (i.e. persistent homology) and can compare different particle configurations based on the "cost" of reorganizing one configuration into another.
Edit intent: fluency
Original:   We show that TDA can accurately map out phase diagrams for interacting particles with varying adhesion and self-propulsion , at constant population size as well as when proliferation is permitted.
Revised:  We show that TDA can accurately map out phase diagrams for simulated particles with varying adhesion and self-propulsion , at constant population size as well as when proliferation is permitted.
Edit intent: clarity
Original:   We show that TDA can accurately map out phase diagrams for interacting particles with varying adhesion and self-propulsion , at constant population size as well as when proliferation is permitted.
Revised:  We show that TDA can accurately map out phase diagrams for interacting particles with varying adhesion and propulsion , at constant population size as well as when proliferation is permitted.
Edit intent: clarity
Original:  Finally, we characterize the statistical robustness of this approach over repeated simulations and with random particle removal .
Revised:  Finally, we characterize the robustness of this approach over repeated simulations and with random particle removal .
Edit intent: clarity
Original: Recently sequence-to-sequence models have started to achieve state-of-the art performance on standard speech recognition tasks when processing audio data in batch mode, i.e., the complete audio data is available when starting processing.
Revised: Recently sequence-to-sequence models have started to achieve state-of-the-art performance on standard speech recognition tasks when processing audio data in batch mode, i.e., the complete audio data is available when starting processing.
Edit intent: fluency
Original:  However, when it comes to perform run-on recognition on an input stream of audio data while producing recognition results in real-time and with a low word-based latency, these models face several challenges.
Revised:  However, when it comes to performing run-on recognition on an input stream of audio data while producing recognition results in real-time and with a low word-based latency, these models face several challenges.
Edit intent: fluency
Original:  However, when it comes to perform run-on recognition on an input stream of audio data while producing recognition results in real-time and with a low word-based latency, these models face several challenges.
Revised:  However, when it comes to perform run-on recognition on an input stream of audio data while producing recognition results in real-time and with low word-based latency, these models face several challenges.
Edit intent: fluency
Original:  For many techniques, the whole audio sequence to be decoded needs to be available at the start of the processing, e.g., for the attention mechanism or for the bidirectional LSTM (BLSTM).
Revised:  For many techniques, the whole audio sequence to be decoded needs to be available at the start of the processing, e.g., for the attention mechanism or the bidirectional LSTM (BLSTM).
Edit intent: fluency
Original:  In this paper  we propose several techniques to mitigate these problems.
Revised:  In this paper , we propose several techniques to mitigate these problems.
Edit intent: fluency
Original:  Our experiments show that with the right combination of these techniques  it is possible to perform run-on speech recognition with a low word-based latency without sacrificing performance in terms of word error rate  .
Revised:  Our experiments show that with the right combination of these techniques , it is possible to perform run-on speech recognition with a low word-based latency without sacrificing performance in terms of word error rate .
Edit intent: fluency