wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In contrast to the fast marching method and the fast sweeping method for the Eikonal equation, our improved method does not need to compute the solution with any special ordering in either the remedy list or the active list so that it can be implemented in parallel.
Revised:  In contrast to the fast marching method and the fast sweeping method for the Eikonal equation, our improved method does not need to compute the solution with any special ordering in either the remedy list or the active list . Therefore, our algorithm can be implemented in parallel.
Edit intent: clarity
Original:  The novel approach of using generative transformer architectures for raw audio synthesis is, however, still far away from generating any meaningful music, without using latent codes/meta-data to aid the generation process.abstract 
Revised:  The novel approach of using generative transformer architectures for raw audio synthesis is, however, still far away from generating any meaningful music, without using latent codes/meta-data to aid the generation process.abstract \% on a similar dataset for predicting the next step. Using the attention mechanism, we enable the architecture to learn which audio samples are important for the prediction of the future sample. We show how causal transformer generative models can be used for raw waveform synthesis. We also show that this performance can be improved by another 2\% by conditioning samples over a wider context. The flexibility of the current model to synthesize audio from latent representations suggests a large number of potential applications. The novel approach of using generative transformer architectures for raw audio synthesis is, however, still far away from generating any meaningful music, without using latent codes/meta-data to aid the generation process.
Edit intent: meaning-changed
Original:  We proposed two models , EmoXNet which is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time.
Revised:  In this work, we propose two models named EmoXNet and EmoXNetLite. EmoXNet is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time.
Edit intent: clarity
Original:  We proposed two models , EmoXNet which is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time.
Revised:  We proposed two models , EmoXNet which is an ensemble learning technique for learning convoluted facial representations, whereas EmoXNetLite is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time.
Edit intent: coherence
Original:  We proposed two models , EmoXNet which is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time.
Revised:  We proposed two models , EmoXNet which is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time.
Edit intent: coherence
Original:  We proposed two models , EmoXNet which is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels for able to effectively detect expressions in real-time.
Revised:  We proposed two models , EmoXNet which is an ensemble learning technique for learning convoluted facial representations, and EmoXNetLite which is a distillation technique that is useful for transferring the knowledge from our ensemble model to an efficient deep neural network using label-smoothen soft labels to detect expressions effectively in real-time.
Edit intent: clarity
Original:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
Revised:  Both models attained better accuracy level in comparison to the models reported to date. The ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB .
Edit intent: clarity
Original:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
Revised:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) attained 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB .
Edit intent: clarity
Original:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
Revised:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER-2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB .
Edit intent: fluency
Original:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
Revised:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on Real-world Affective Faces Database ( RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB .
Edit intent: clarity
Original:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
Revised:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB ). Whereas , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB .
Edit intent: coherence
Original:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
Revised:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB . Moreover , the distilled model (EmoXNetLite) attained 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB .
Edit intent: clarity
Original:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
Revised:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER-2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB .
Edit intent: fluency
Original:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on  RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB  .
Revised:  Both of the techniques performed quite well, where the ensemble model (EmoXNet) helped to achieve 85.07\% test accuracy on FER2013 with FER+ annotations and 86.25\% test accuracy on RAF-DB . Moreover , the distilled model (EmoXNetLite) showed 82.07\% test accuracy on FER2013 with FER+ annotations and 81.78\% test accuracy on RAF-DB . Results show that our models seem to generalize well on new data and are learned to focus on relevant facial representations for expressions recognition .
Edit intent: meaning-changed
Original:  Achieving this mission is challenging in the absence of explicit coordination among agents . Additionally, existence of humans in mixed-autonomy environments create social dilemmas as they are known to be heterogeneous in social preference and their behavior is hard to predict by nature .
Revised:  Achieving this mission is challenging in the absence of explicit coordination among agents is challenging, mainly due to the difficulty of predicting the behavior of humans with heterogeneous preferences in mixed-autonomy environments create social dilemmas as they are known to be heterogeneous in social preference and their behavior is hard to predict by nature .
Edit intent: coherence
Original:  Formally, we model an AV's maneuver planning in mixed-autonomy traffic as a partially-observable stochastic game and attempt to derive optimal policies that lead to socially-desirable outcomes using our multi-agent reinforcement learning framework.
Revised:  Formally, we model an AV's maneuver planning in mixed-autonomy traffic as a partially-observable stochastic game and attempt to derive optimal policies that lead to socially-desirable outcomes using a multi-agent reinforcement learning framework.
Edit intent: fluency
Original:  As a case study, we compare egoistic AVs to our altruistic autonomous agents in a highway merging case study and demonstrate a significant improvement in the number of successful merges as well as the overall traffic flow and safety.
Revised:  As a case study, we compare egoistic AVs to our altruistic autonomous agents in a highway merging setting and demonstrate the emerging behaviors that lead to a noticeable improvement in the number of successful merges as well as the overall traffic flow and safety.
Edit intent: clarity
Original:  We use  neural vocoder to re-synthesize audio and find that the difference between the ASV scores for the original and re-synthesized audio is a good indicator to distinguish genuine and adversarial samples.
Revised:  We use the neural vocoder to re-synthesize audio and find that the difference between the ASV scores for the original and re-synthesized audio is a good indicator to distinguish genuine and adversarial samples.
Edit intent: fluency
Original:  We use  neural vocoder to re-synthesize audio and find that the difference between the ASV scores for the original and re-synthesized audio is a good indicator to distinguish genuine and adversarial samples.
Revised:  We use neural vocoder to re-synthesize audio and find that the difference between the ASV scores for the original and re-synthesized audio is a good indicator for discrimination between genuine and adversarial samples.
Edit intent: clarity
Original:  In this paper, we ask whether lack of shift invariance is a problem in sound event classification , and whether there are benefits in addressing it . Specifically, we evaluate two pooling methods to improve shift invariance in CNNs, based on low-pass filtering and adaptive sampling of incoming feature maps.
Revised:  In this paper, we analyze the benefits of addressing lack of shift invariance is a problem in sound event classification , and whether there are benefits in addressing it . Specifically, we evaluate two pooling methods to improve shift invariance in CNNs, based on low-pass filtering and adaptive sampling of incoming feature maps.
Edit intent: clarity
Original:  In this paper, we ask whether lack of shift invariance is a problem in sound event classification , and whether there are benefits in addressing it . Specifically, we evaluate two pooling methods to improve shift invariance in CNNs, based on low-pass filtering and adaptive sampling of incoming feature maps.
Revised:  In this paper, we ask whether lack of shift invariance in CNN-based sound event classification , and whether there are benefits in addressing it . Specifically, we evaluate two pooling methods to improve shift invariance in CNNs, based on low-pass filtering and adaptive sampling of incoming feature maps.
Edit intent: clarity
Original:  In this paper, we ask whether lack of shift invariance is a problem in sound event classification , and whether there are benefits in addressing it . Specifically, we evaluate two pooling methods to improve shift invariance in CNNs, based on low-pass filtering and adaptive sampling of incoming feature maps.
Revised:  In this paper, we ask whether lack of shift invariance is a problem in sound event classification . Specifically, we evaluate two pooling methods to improve shift invariance in CNNs, based on low-pass filtering and adaptive sampling of incoming feature maps.
Edit intent: clarity
Original:  We show that these modifications consistently improve sound event classification in all cases considered , without adding any (or adding very few) trainable parameters, which makes them an appealing alternative to conventional pooling layers.
Revised:  We show that these modifications consistently improve sound event classification in all cases considered . We also demonstrate empirically that the proposed pooling methods increase shift invariance in the network, making it more robust against time/frequency shifts in input spectrograms. This is achieved by adding a negligible amount of trainable parameters, which makes them an appealing alternative to conventional pooling layers.
Edit intent: meaning-changed
Original: In this paper we present an experience report for the RMQ\-FMU , a plug and play tool, that enables feeding data to/from an FMI2-based co-simulation environment based on the AMQP protocol.
Revised: In this paper we present an experience report for the RMQFMU , a plug and play tool, that enables feeding data to/from an FMI2-based co-simulation environment based on the AMQP protocol.
Edit intent: fluency
Original:  On the development set, the achieved CCC is 0.410 for valence and 0.661 for arousal, which significantly outperforms the baseline method with the corresponding CCC of 0.210 and 0.230 for valence and arousal, respectively.
Revised:  On the development set, the achieved CCC is 0.469 for valence and 0.661 for arousal, which significantly outperforms the baseline method with the corresponding CCC of 0.210 and 0.230 for valence and arousal, respectively.
Edit intent: meaning-changed
Original:  On the development set, the achieved CCC is 0.410 for valence and 0.661 for arousal, which significantly outperforms the baseline method with the corresponding CCC of 0.210 and 0.230 for valence and arousal, respectively.
Revised:  On the development set, the achieved CCC is 0.410 for valence and 0.649 for arousal, which significantly outperforms the baseline method with the corresponding CCC of 0.210 and 0.230 for valence and arousal, respectively.
Edit intent: meaning-changed
Original:  The most extreme quantization is a 1-bit representation of parameters such that they have only two possible values, typically -1(0) or + 1. Models that constrain the weights to binary values enable efficient implementation of the ubiquitous dot product by additions only without requiring floating point multiplications which is beneficial for resources constrained inference .
Revised:  The most extreme quantization is a 1-bit representation of parameters such that they have only two possible values, typically -1(0) or + 1, enabling efficient implementation of the ubiquitous dot product by additions only without requiring floating point multiplications which is beneficial for resources constrained inference .
Edit intent: coherence
Original:  The most extreme quantization is a 1-bit representation of parameters such that they have only two possible values, typically -1(0) or + 1. Models that constrain the weights to binary values enable efficient implementation of the ubiquitous dot product by additions only without requiring floating point multiplications which is beneficial for resources constrained inference .
Revised:  The most extreme quantization is a 1-bit representation of parameters such that they have only two possible values, typically -1(0) or + 1. Models that constrain the weights to binary values enable efficient implementation of the ubiquitous dot product using only additions .
Edit intent: clarity
Original:  The proposed method adds little overhead in training, can be readily applied without any substantial modifications to the original architecture, does not introduce additional saturating non-linearities or auxiliary losses, and does not prohibit applying other methods for binarizing the activations.
Revised:  The proposed method adds little overhead in training, can be readily applied without any substantial modifications to the original architecture, does not introduce additional saturating nonlinearities or auxiliary losses, and does not prohibit applying other methods for binarizing the activations.
Edit intent: fluency
Original:  It is demonstrated that contrary to common assertions made in the literature,  binary weighted networks can train well with the same standard optimization techniques and similar hyperparameters settings as their full-precision counterparts, namely momentum SGD with large learning rates and L_2 regularization.
Revised:  Contrary to common assertions made in the literature, binary weighted networks can train well with the same standard optimization techniques and similar hyperparameters settings as their full-precision counterparts, namely momentum SGD with large learning rates and L_2 regularization.
Edit intent: coherence
Original:  It is demonstrated that contrary to common assertions made in the literature,  binary weighted networks can train well with the same standard optimization techniques and similar hyperparameters settings as their full-precision counterparts, namely momentum SGD with large learning rates and L_2 regularization.
Revised:  It is demonstrated that contrary to common assertions made in the literature, it is demonstrated that binary weighted networks can train well with the same standard optimization techniques and similar hyperparameters settings as their full-precision counterparts, namely momentum SGD with large learning rates and L_2 regularization.
Edit intent: coherence
Original:  It is demonstrated that contrary to common assertions made in the literature,  binary weighted networks can train well with the same standard optimization techniques and similar hyperparameters settings as their full-precision counterparts, namely momentum SGD with large learning rates and L_2 regularization.
Revised:  It is demonstrated that contrary to common assertions made in the literature, binary weighted networks can train well with the same standard optimization techniques and similar hyperparameter settings as their full-precision counterparts, namely momentum SGD with large learning rates and L_2 regularization.
Edit intent: fluency
Original:  It is demonstrated that contrary to common assertions made in the literature,  binary weighted networks can train well with the same standard optimization techniques and similar hyperparameters settings as their full-precision counterparts, namely momentum SGD with large learning rates and L_2 regularization.
Revised:  It is demonstrated that contrary to common assertions made in the literature, binary weighted networks can train well with the same standard optimization techniques and similar hyperparameters settings as their full-precision counterparts, specifically momentum SGD with large learning rates and L_2 regularization.
Edit intent: fluency
Original: We define a persistent cohomology invariant called persistent cup-length which is able to extract non trivial information about the evolution of the cohomology ring structure across a filtration.
Revised: Cohomological ideas have recently been injected into persistent homology and have been utilized for both enriching and accelerating the calculation of persistence diagrams. For instance, the software Ripser fundamentally exploits the computational advantages offered by cohomological ideas. The cup product operation which is available at cohomology level gives rise to a graded ring structure which extends the natural vector space structure and is therefore able to extract non trivial information about the evolution of the cohomology ring structure across a filtration.
Edit intent: meaning-changed
Original: We define a persistent cohomology invariant called persistent cup-length which is able to extract non trivial information about the evolution of the cohomology ring structure across a filtration.
Revised: We define a persistent cohomology invariant called persistent cup-length which is able to extract and encode additional rich information. The maximum number of cocycles having non-zero cup product yields an invariant, the Cup-Length, which is efficient at discriminating spaces. In this paper, we lift the cup-length into the Persistent Cup-Length invariant for the purpose of extracting non-trivial information about the evolution of the cohomology ring structure across a filtration.
Edit intent: meaning-changed