wanyu/IteraTeR_v2 huggingface dataset:
Original:  Various factors like the Radio Resource Control (RRC) states, the Mobile Network Operator (MNO) specific retransmission configurations, handovers triggered by the user mobility, the network load  etc. can cause high variability in these QoS metrics on 4G/LTE, and WiFi networks, which can be detrimental to the application QoE.
Revised:  Various factors like the Radio Resource Control (RRC) states, the Mobile Network Operator (MNO) specific retransmission configurations, handovers triggered by the user mobility, the network load , etc. can cause high variability in these QoS metrics on 4G/LTE, and WiFi networks, which can be detrimental to the application QoE.
Edit intent: fluency
Original:  Therefore, exposing  mobile application to realistic network QoS metrics is critical for testers attempting to predict its QoE.
Revised:  Therefore, exposing the mobile application to realistic network QoS metrics is critical for testers attempting to predict its QoE.
Edit intent: fluency
Original:  Therefore, exposing  mobile application to realistic network QoS metrics is critical for testers attempting to predict its QoE.
Revised:  Therefore, exposing mobile application to realistic network QoS metrics is critical for a tester attempting to predict its QoE.
Edit intent: fluency
Original:  The main challenge in generation of realisitc synthetic traces is the diversity of environments and  lack of wide scope of real traces to calibrate the generators.
Revised:  The main challenge in the generation of realistic synthetic traces is the diversity of environments and lack of wide scope of real traces to calibrate the generators.
Edit intent: fluency
Original:  The main challenge in generation of realisitc synthetic traces is the diversity of environments and  lack of wide scope of real traces to calibrate the generators.
Revised:  The main challenge in generation of realisitc synthetic traces is the diversity of environments and the lack of wide scope of real traces to calibrate the generators.
Edit intent: fluency
Original: While supervised learning has enabled great advances in many areas of music, labeled music datasets remain especially hard, expensive  and time-consuming to create.
Revised: While supervised learning has enabled great advances in many areas of music, labeled music datasets remain especially hard, expensive , and time-consuming to create.
Edit intent: fluency
Original:  In this work, we introduce SimCLR to the music domain and contribute a large chain of audio data augmentations , to form a simple framework for self-supervised learning of raw waveforms of music : CLMR. This approach requires no manual labeling and no preprocessing of music to learn useful representations.
Revised:  In this work, we introduce SimCLR to the music domain and contribute a large chain of audio data augmentations to form a simple framework for self-supervised learning of raw waveforms of music : CLMR. This approach requires no manual labeling and no preprocessing of music to learn useful representations.
Edit intent: fluency
Original:  We evaluate CLMR in the downstream task of music classification on the MagnaTagATune and Million Song datasets  . A linear classifier fine-tuned on representations from a pre-trained CLMR model achieves an average precision of 35.4\% on the MagnaTagATune dataset,
Revised:  We evaluate CLMR in the downstream task of music classification on the MagnaTagATune and Million Song datasets . A linear classifier trained on the proposed representations achieves a higher average precision than supervised models on the MagnaTagATune dataset,
Edit intent: clarity
Original:  superseding fully supervised models that currently achieve a score of 34.9\% . Moreover, we show that CLMR's representations are transferable using out-of-domain datasets, indicating that they capture important musical knowledge . Lastly, we show that self-supervised pre-training allows us to learn efficiently on smaller labeled datasets: we still achieve a score of 33.1\% despite using only 259 labeled songs during fine-tuning .
Revised:  superseding fully supervised models that currently achieve a score of 34.9\% . Moreover, we show that CLMR's representations are transferable using out-of-domain datasets, indicating that our method has strong generalisability in music classification . Lastly, we show that self-supervised pre-training allows us to learn efficiently on smaller labeled datasets: we still achieve a score of 33.1\% despite using only 259 labeled songs during fine-tuning .
Edit intent: clarity
Original:  superseding fully supervised models that currently achieve a score of 34.9\% . Moreover, we show that CLMR's representations are transferable using out-of-domain datasets, indicating that they capture important musical knowledge . Lastly, we show that self-supervised pre-training allows us to learn efficiently on smaller labeled datasets: we still achieve a score of 33.1\% despite using only 259 labeled songs during fine-tuning .
Revised:  superseding fully supervised models that currently achieve a score of 34.9\% . Moreover, we show that CLMR's representations are transferable using out-of-domain datasets, indicating that they capture important musical knowledge . Lastly, we show that the proposed method allows data-efficient learning on smaller labeled datasets: we still achieve a score of 33.1\% despite using only 259 labeled songs during fine-tuning .
Edit intent: clarity
Original:  superseding fully supervised models that currently achieve a score of 34.9\% . Moreover, we show that CLMR's representations are transferable using out-of-domain datasets, indicating that they capture important musical knowledge . Lastly, we show that self-supervised pre-training allows us to learn efficiently on smaller labeled datasets: we still achieve a score of 33.1\% despite using only 259 labeled songs during fine-tuning .
Revised:  superseding fully supervised models that currently achieve a score of 34.9\% . Moreover, we show that CLMR's representations are transferable using out-of-domain datasets, indicating that they capture important musical knowledge . Lastly, we show that self-supervised pre-training allows us to learn efficiently on smaller labeled datasets: we achieve an average precision of 33.1\% despite using only 259 labeled songs during fine-tuning .
Edit intent: clarity
Original:  To foster reproducibility and future research on self-supervised learning in music, we publicly release the pre-trained models and the source code of all experiments of this paper on GitHub .
Revised:  To foster reproducibility and future research on self-supervised learning in music, we publicly release the pre-trained models and the source code of all experiments of this paper .
Edit intent: coherence
Original: Cell proliferation, apoptosis, and myosin-dependent contraction can generate elastic stress and strain in living tissues, which may be dissipated by tissue rearrangement through cell topological transition and cytoskeletal reorganization.
Revised: Cell proliferation, apoptosis, and myosin-dependent contraction can generate elastic stress and strain in living tissues, which may be dissipated by internal rearrangement through cell topological transition and cytoskeletal reorganization.
Edit intent: clarity
Original:   The present work demonstrates significant nonlinear effects in macroscopic tissue mechanics arising from the competition between force-generating and dissipating processes .
Revised:  The present work demonstrates the role of tissue compressibility and internal rearranging activities on its size and mechanics regulation in the context of differential growth induced by a field of growth-promoting chemical factors .
Edit intent: clarity
Original:  We develop a mathematical model  to describe the coupled dynamics of tissue activities and mechanics in the nonlinear regime.
Revised:  We develop a mathematical model to describe the coupled tissue growth and mechanics in the nonlinear regime.
Edit intent: clarity
Original:  We find that the elastic properties and rearrangement rate can regulate tissue size as a higher-order effect due to advection in tissue flow.
Revised:  We find that the tissue compressibility and fluidity influence their equilibrium size. By comparing the nonlinear simulations with the linear analytical solutions, we show the size change as a nonlinear effect due to advection in tissue flow.
Edit intent: coherence
Original:  The vulnerability of DNNs to adversarial attacks have been heavily investigated in the RGB image domain and more recently in the point cloud domain, but rarely in both domains simultaneously.
Revised:  The vulnerability of DNNs to adversarial attacks has been heavily investigated in the RGB image domain and more recently in the point cloud domain, but rarely in both domains simultaneously.
Edit intent: fluency
Original: For the large sparse generalized absolute value equation (GAVE) , based on the shift splitting for the coefficient matrix of linear part, we establish a new modified Newton-type (NMN) iteration method.
Revised: For the large sparse generalized absolute value equations (GAVEs) , the shift splitting for the coefficient matrix of linear part, we establish a new modified Newton-type (NMN) iteration method.
Edit intent: coherence
Original: For the large sparse generalized absolute value equation (GAVE) , based on the shift splitting for the coefficient matrix of linear part, we establish a new modified Newton-type (NMN) iteration method.
Revised: For the large sparse generalized absolute value equation (GAVE) , based on the shift splitting for the linear part's coefficient matrix is utilized to establish a new modified Newton-type (NMN) iteration method.
Edit intent: clarity
Original:  The convergence conditions of the NMN iteration method are discussed in depth.
Revised:  The conditions for the convergence of the NMN iteration method are discussed in depth.
Edit intent: clarity
Original:  Furthermore, some specific sufficient convergence conditions are derived when the coefficient matrix is a symmetric positive definite matrix and an H_{+}-matrix.
Revised:  Furthermore, certain sufficient convergence conditions are derived when the coefficient matrix is a symmetric positive definite matrix and an H_{+}-matrix.
Edit intent: clarity
Original:  Furthermore, some specific sufficient convergence conditions are derived when the coefficient matrix is a symmetric positive definite matrix and an H_{+}-matrix.
Revised:  Furthermore, some specific sufficient convergence conditions are obtained when the coefficient matrix is a symmetric positive definite matrix and an H_{+}-matrix.
Edit intent: clarity
Original:  Furthermore, some specific sufficient convergence conditions are derived when the coefficient matrix is a symmetric positive definite matrix and an H_{+}-matrix.
Revised:  Furthermore, some specific sufficient convergence conditions are derived when the coefficient matrix is a symmetric positive definite matrix or an H_{+}-matrix.
Edit intent: coherence
Original:  Both two numerical examples indicate that the NMN iteration method is an effective approach to solve the GAVE , especially when the coefficient matrix is indefinite.
Revised:  According to both two numerical examples indicate that the NMN iteration method is an effective approach to solve the GAVE , especially when the coefficient matrix is indefinite.
Edit intent: clarity
Original:  Both two numerical examples indicate that the NMN iteration method is an effective approach to solve the GAVE , especially when the coefficient matrix is indefinite.
Revised:  Both two numerical examples , the NMN iteration method is an effective approach to solve the GAVE , especially when the coefficient matrix is indefinite.
Edit intent: clarity
Original:  Both two numerical examples indicate that the NMN iteration method is an effective approach to solve the GAVE , especially when the coefficient matrix is indefinite.
Revised:  Both two numerical examples indicate that the NMN iteration method is an effective approach to solve the GAVEs , especially when the coefficient matrix is indefinite.
Edit intent: fluency
Original: For the large sparse generalized absolute value equations (GAVEs) , the shift splitting for the linear part's coefficient matrix is utilized to establish a new modified Newton-type ( NMN ) iteration method  .
Revised: For the large sparse generalized absolute value equations (GAVEs) , the shift splitting for the linear part's coefficient matrix is utilized to establish a new modified Newton-type ( MN ) iteration method .
Edit intent: fluency
Original:  The conditions for the convergence of the NMN iteration method are discussed in depth .
Revised:  The conditions for the convergence of the proposed method are discussed in depth .
Edit intent: clarity
Original:  Furthermore, certain sufficient convergence conditions are obtained when the coefficient matrix is a  symmetric positive definite matrix or an H_{+}-matrix.
Revised:  Furthermore, certain sufficient convergence conditions are obtained when the coefficient matrix is a symmetric positive definite matrix , and an H_{+}-matrix.
Edit intent: coherence
Original:  According to both two numerical examples, the NMN iteration method is an effective approach to solve the GAVEs, especially when the coefficient matrix is indefinite.
Revised:  Through two numerical examples, the NMN iteration method is an effective approach to solve the GAVEs, especially when the coefficient matrix is indefinite.
Edit intent: coherence
Original:  To overcome this weakness , we propose STYLER, a novel expressive text-to-speech model with parallelized architecture.
Revised:  To overcome this weakness , we propose STYLER, an expressive and controllable text-to-speech model with parallelized architecture.
Edit intent: clarity
Original:  Expelling autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust even with high style transfer performance .
Revised:  Expelling autoregressive decoding and introducing speech decomposition for encoding enables speech synthesis more robust on long, unseen data. Disentangled style factor modeling under supervision enlarges the controllability of synthesizing speech with fruitful expressivity .
Edit intent: clarity
Original:  Moreover, our novel noise modeling approach from audio using domain adversarial training and Residual Decoding enabled style transferwithout transferring noise .
Revised:  Moreover, our novel noise modeling pipeline using domain adversarial training and Residual Decoding enabled style transferwithout transferring noise .
Edit intent: clarity
Original: Previous works on neural text-to-speech (TTS) have been tackled on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability.
Revised: Previous works on neural text-to-speech (TTS) have been addressed on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability.
Edit intent: clarity
Original:  Although several approaches resolve some limitations, none of them has resolved all weaknesses at once.
Revised:  Although several approaches resolve some limitations, there has been no attempt to solve all weaknesses at once.
Edit intent: clarity
Original:  Excluding autoregressive decoding and introducing a novel audio-text aligning method called Mel Calibrator leads speech synthesis more robust on long, unseen data.
Revised:  Excluding autoregressive decoding and introducing a novel audio-text aligning method called Mel Calibrator and excluding autoregressive decoding enable rapid training and inference and robust synthesis on unseen data.
Edit intent: clarity
Original:  Disentangled style factor modeling under supervision enlarges the controllability of synthesizing speech with fruitful expressivity.
Revised:  Also, disentangled style factor modeling under supervision enlarges the controllability of synthesizing speech with fruitful expressivity.
Edit intent: coherence
Original:  Moreover, our novel noise modeling pipeline using domain adversarial training and Residual Decoding enables noise-robust style transfer, decomposing the noise without any additional label.
Revised:  Moreover, our novel noise modeling pipeline using domain adversarial training and Residual Decoding empowers noise-robust style transfer, decomposing the noise without any additional label.
Edit intent: clarity
Original:  The restriction to suitable subspaces of the exponential search spaceproposed in this paper ensures computational tractability while yielding{\sc   improvements by factors of ten and higher over standard approaches based on second-order tangent and adjoint algorithmic differentiation .
Revised:  We propose a dynamic programming formulation for the solution of{\sc improvements by factors of ten and higher over standard approaches based on second-order tangent and adjoint algorithmic differentiation .
Edit intent: clarity
Original:  The restriction to suitable subspaces of the exponential search spaceproposed in this paper ensures computational tractability while yielding{\sc   improvements by factors of ten and higher over standard approaches based on second-order tangent and adjoint algorithmic differentiation .
Revised:  The restriction to suitable subspaces of the exponential search spaceproposed in this paper ensures computational tractability while yielding{\sc improvements by factors of ten and higher over the state of the art based on second-order tangent and adjoint algorithmic differentiation .
Edit intent: clarity
Original:  In this work, we focus  on evaluating the PINN potential to replace or accelerate traditional approaches for solving linear systems.
Revised:  In this work, we focus first on evaluating the PINN potential to replace or accelerate traditional approaches for solving linear systems.
Edit intent: clarity
Original:  We solve the Poisson equation, one of the most critical and computational-intensive tasks in scientific computing , with different source terms.
Revised:  We solve the Poisson equation, an omnipresent equation in scientific computing , with different source terms.
Edit intent: clarity
Original: An additive +\beta spanner of a graph G is a subgraph which preserves shortest paths up to an additive +\beta error.
Revised: An additive +\beta spanner of a graph G is a subgraph which preserves distances up to an additive +\beta error.
Edit intent: clarity
Original:  for each vertex pair (s,t), where W(s, t) is the maximum edge weight along the shortest s--t path in G. These include pairwise +(2+\eps)W(\cdot,\cdot) and +(6+\eps) W(\cdot, \cdot) spanners over vertex pairs \Pc \subseteq V \times V on O_{\eps}(n|\Pc|^{1/3}) and O_{\eps}(n|\Pc|^{1/4}) edges for all \eps > 0, which extend previously known unweighted results up to \eps dependence, as well as an all-pairs +4W(\cdot,\cdot) spanner on O (n^{7/5}) edges.
Revised:  for each vertex pair (s,t), where W(s, t) is the maximum edge weight along the shortest s--t path in G. These include pairwise +(2+\eps)W(\cdot,\cdot) and +(6+\eps) W(\cdot, \cdot) spanners over vertex pairs \Pc \subseteq V \times V on O_{\eps}(n|\Pc|^{1/3}) and O_{\eps}(n|\Pc|^{1/4}) edges for all \eps > 0, which extend previously known unweighted results up to \eps dependence, as well as an all-pairs +4W(\cdot,\cdot) spanner on (n^{7/5}) edges.
Edit intent: fluency
Original: Partition functions are an important research object in combinatorics and mathematical physics [ Bavinok , 2016].
Revised: Partition functions are an important research object in combinatorics and mathematical physics [ Barvinok , 2016].
Edit intent: fluency
Original:  In this paper, we propose an overflow/underflow-free bit-width optimization method for fixed-point digital circuit of OS-ELM.
Revised:  In this paper, we propose an overflow/underflow-free bit-width optimization method for fixed-point digital circuits of OS-ELM.
Edit intent: fluency
Original:  Experimental results show that our method realizes overflow/underflow-free OS-ELM digital circuits with 1.0x - 1.5x more area cost compared to an ordinary simulation-based optimization method where overflow or underflow can happen.
Revised:  Experimental results show that our method realizes overflow/underflow-free OS-ELM digital circuits with 1.0x - 1.5x more area cost compared to the baseline simulation method where overflow or underflow can happen.
Edit intent: clarity
Original: We consider the numerical approximation of the inertial Landau-Lifshitz-Gilbert  (iLLG) equation , which describes the dynamics of the magnetization in ferromagnetic materials at subpicosecond time scales.
Revised: We consider the numerical approximation of the inertial Landau-Lifshitz-Gilbert equation (iLLG) equation , which describes the dynamics of the magnetization in ferromagnetic materials at subpicosecond time scales.
Edit intent: fluency
Original: We consider the numerical approximation of the inertial Landau-Lifshitz-Gilbert  (iLLG) equation , which describes the dynamics of the magnetization in ferromagnetic materials at subpicosecond time scales.
Revised: We consider the numerical approximation of the inertial Landau-Lifshitz-Gilbert (iLLG) , which describes the dynamics of the magnetization in ferromagnetic materials at subpicosecond time scales.
Edit intent: clarity
Original:  We propose and analyze two fully discrete numerical schemes based on two different approaches : The first method is based on a reformulation of the problem as a linear constrained variational formulation for the time derivative of the magnetization .
Revised:  We propose and analyze two fully discrete numerical schemes : The first method is based on a reformulation of the problem as a linear constrained variational formulation for the time derivative of the magnetization .
Edit intent: clarity
Original:  Both schemes are implicit, based on first-order finite elements, and the constructed numerical approximations satisfy the inherent unit-length constraint of iLLG at the vertices of the underlying mesh.
Revised:  Both schemes are implicit, based on first-order finite elements, and generate approximations satisfying the unit-length constraint of iLLG at the vertices of the underlying mesh.
Edit intent: clarity
Original:  For both schemes, we establish a discrete energy law and prove convergence of the approximations towards a weak solution of the problem.
Revised:  For both methods, we prove convergence of the approximations towards a weak solution of the problem.
Edit intent: clarity
Original:  The proposed numerical forward scattering transform computes the solution of the AKNS system that  valid on the entire real axis and thereby computes a reflection coefficient at a point by solving a single linear system.
Revised:  The proposed numerical forward scattering transform computes the solution of the AKNS system that is valid on the entire real axis and thereby computes a reflection coefficient at a point by solving a single linear system.
Edit intent: clarity
Original:  The later development enables access to high-precision computations and this is demonstrated on the inverse scattering transform for the one-dimensional Schr\"odinger operator with a sech^2 potential.
Revised:  The latter development enables access to high-precision computations and this is demonstrated on the inverse scattering transform for the one-dimensional Schr\"odinger operator with a sech^2 potential.
Edit intent: fluency
Original:  The results show that the best reward signal rewards velocity along, and punishes the lateral deviation from a precalculated, optimal reference trajectory .
Revised:  The results indicate that a reward based on the distance and velocity relative to a minimum curvature trajectory produces the fastest lap times .
Edit intent: clarity