wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show through extensive simulations that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival (MA) , Match to the Highest (MH) and Match to the Earliest Deadline First (MED) .
Revised:  We show through extensive simulations that the learning algorithm learns an effective matching policy for different generation-consumption profiles and exhibits better performance compared to standard online matching heuristics such as Match on Arrival (MA) , Match to the Highest (MH) and Match to the Earliest Deadline policies .
Edit intent: clarity
Original: In this paper, we propose an end-to-end Mandarin tone classification method from continuous speech utterances utilizing both the spectrogram and the short term context information as the inputs.
Revised: In this paper, we propose an end-to-end Mandarin tone classification method from continuous speech utterances utilizing both the spectrogram and the short-term context information as the inputs.
Edit intent: fluency
Original:  Then we extract the short term segment features to capture the context information across multiple syllables.
Revised:  Then we extract the short-term segment features to capture the context information across multiple syllables.
Edit intent: fluency
Original:  Feeding both the Mel-spectrogram and the short term context segment features into an end-to-end model could significantly improve the performance.
Revised:  Feeding both the spectrogram and the short-term context segment features into an end-to-end model could significantly improve the performance.
Edit intent: clarity
Original:  Experiments are performed on a large scale open source Mandarin speech dataset to evaluate the proposed method.
Revised:  Experiments are performed on a large-scale open-source Mandarin speech dataset to evaluate the proposed method.
Edit intent: fluency
Original:  Results show that the this method improves the classification accuracy from 79.5 %DIFDELCMD < \\%%% %DIF < to 88.7\\% on the AISHELL3 database.abstract 
Revised:  Results show that this method improves the classification accuracy from 79.5 %DIFDELCMD < \\%%% %DIF < to 88.7\\% on the AISHELL3 database.abstract 
Edit intent: fluency
Original:  As we show, the semi-infinite case arises in several applications, such  supervised learning and function approximation, and allows for novel interpretations of existing algorithms.
Revised:  As we show, the semi-infinite case arises in several applications, such as supervised learning and function approximation, and allows for novel interpretations of existing algorithms.
Edit intent: fluency
Original:  To that end, we give a formal framework for working with quasimatrices, and generalize several algorithms designed for  finite problem to the infinite case.
Revised:  To that end, we give a formal framework for working with quasimatrices, and generalize several algorithms designed for the finite problem to the infinite case.
Edit intent: fluency
Original: Procedural events can often be thought of as a high level goal composed of a sequence of steps .
Revised: Understanding what sequence of steps .
Edit intent: clarity
Original:  We propose the Visual Goal-Step Inference (VGSI) task  where a model is given a textual goal and must choose  a plausible step towards that goal from among four candidate images.
Revised:  We propose the Visual Goal-Step Inference (VGSI) task , where a model is given a textual goal and must choose a plausible step towards that goal from among four candidate images.
Edit intent: fluency
Original:  We propose the Visual Goal-Step Inference (VGSI) task  where a model is given a textual goal and must choose  a plausible step towards that goal from among four candidate images.
Revised:  We propose the Visual Goal-Step Inference (VGSI) task where a model is given a textual goal and must choose which of four images represents a plausible step towards that goal from among four candidate images.
Edit intent: clarity
Original:  We introduce a novel dataset harvested from wikiHow that consists of 772, 294 images representing human actions .
Revised:  We introduce a novel dataset harvested from wikiHow consisting of 772, 294 images representing human actions .
Edit intent: coherence
Original:  We show that the knowledge learned from our data can effectively transfer to other datasets like HowTo100M , increasing the multiple-choice accuracy by 15 \% to 20\%.
Revised:  We show that the knowledge learned from our data can be effectively transferred to other datasets like HowTo100M , increasing the multiple-choice accuracy by 15 \% to 20\%.
Edit intent: fluency
Original:  We show that the knowledge learned from our data can effectively transfer to other datasets like HowTo100M , increasing the multiple-choice accuracy by 15 \% to 20\%.
Revised:  We show that the knowledge learned from our data can effectively transfer to other datasets like HowTo100m , increasing the multiple-choice accuracy by 15 \% to 20\%.
Edit intent: fluency
Original:  We show that the knowledge learned from our data can effectively transfer to other datasets like HowTo100M , increasing the multiple-choice accuracy by 15 \% to 20\%.
Revised:  We show that the knowledge learned from our data can effectively transfer to other datasets like HowTo100M , increasing the multiple-choice accuracy by 15 - 20\%.
Edit intent: coherence
Original:  Our task will facilitate multi-modal reasoning about procedural events.
Revised:  Our task will facilitate multimodal reasoning about procedural events.
Edit intent: fluency
Original: This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical models for applications in machine learning and system identification .
Revised: This paper introduces recurrent equilibrium networks (RENs), a new class of nonlinear dynamical models for applications in machine learning , system identification and control .
Edit intent: clarity
Original:  The new model class has "built in" guarantees of stability and robustness: all models in the class are contracting -- a strong form of nonlinear stability -- and models can have prescribed Lipschitz bounds . RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks  , all deep feedforward neural networks, and all stable Wiener/Hammerstein models.
Revised:  The new model class has ``built in'' guarantees of stability and robustness: all models in the class are contracting -- a strong form of nonlinear stability -- and models can have prescribed Lipschitz bounds . RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks , all deep feedforward neural networks, and all stable Wiener/Hammerstein models.
Edit intent: fluency
Original:  The new model class has "built in" guarantees of stability and robustness: all models in the class are contracting -- a strong form of nonlinear stability -- and models can have prescribed Lipschitz bounds . RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks  , all deep feedforward neural networks, and all stable Wiener/Hammerstein models.
Revised:  The new model class has "built in" guarantees of stability and robustness: all models in the class are contracting - a strong form of nonlinear stability -- and models can have prescribed Lipschitz bounds . RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks , all deep feedforward neural networks, and all stable Wiener/Hammerstein models.
Edit intent: fluency
Original:  The new model class has "built in" guarantees of stability and robustness: all models in the class are contracting -- a strong form of nonlinear stability -- and models can have prescribed Lipschitz bounds . RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks  , all deep feedforward neural networks, and all stable Wiener/Hammerstein models.
Revised:  The new model class has "built in" guarantees of stability and robustness: all models in the class are contracting -- a strong form of nonlinear stability - and models can have prescribed Lipschitz bounds . RENs are otherwise very flexible: they can represent all stable linear systems, all previously-known sets of contracting recurrent neural networks , all deep feedforward neural networks, and all stable Wiener/Hammerstein models.
Edit intent: fluency
Original:  The performance of the robustness of the new model set is evaluated on benchmark nonlinear system identification problems  .
Revised:  The performance and robustness of the new model set is evaluated on benchmark nonlinear system identification problems .
Edit intent: clarity
Original:  Considering a small set of training and development data, we also employ transfer learning on several well-known emotional detection datasets, i.e. RAVDESS, CREMA-D, to learn advanced emotional representations that can be applied to the  escalation detection task.
Revised:  Considering a small set of training and development data, we also employ transfer learning on several wellknown emotional detection datasets, i.e. RAVDESS, CREMA-D, to learn advanced emotional representations that can be applied to the escalation detection task.
Edit intent: fluency
Original:  Considering a small set of training and development data, we also employ transfer learning on several well-known emotional detection datasets, i.e. RAVDESS, CREMA-D, to learn advanced emotional representations that can be applied to the  escalation detection task.
Revised:  Considering a small set of training and development data, we also employ transfer learning on several well-known emotional detection datasets, i.e. RAVDESS, CREMA-D, to learn advanced emotional representations that is then applied to the escalation detection task.
Edit intent: clarity
Original:  We discuss three issues with a proposed solution to extract aleatoric and epistemic model uncertainty from regression-based neural networks (NN) . The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparemters of the evidential distribution.
Revised:  We discuss three issues with a proposed solution to extract aleatoric and epistemic uncertainties from regression-based neural networks (NN) . The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparemters of the evidential distribution.
Edit intent: clarity
Original:  We discuss three issues with a proposed solution to extract aleatoric and epistemic model uncertainty from regression-based neural networks (NN) . The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparemters of the evidential distribution.
Revised:  We discuss three issues with a proposed solution to extract aleatoric and epistemic model uncertainty from regression-based neural networks . The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparemters of the evidential distribution.
Edit intent: clarity
Original:  We discuss three issues with a proposed solution to extract aleatoric and epistemic model uncertainty from regression-based neural networks (NN) . The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparemters of the evidential distribution.
Revised:  We discuss three issues with a proposed solution to extract aleatoric and epistemic model uncertainty from regression-based neural networks (NN) . The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the neural network to infer the hyperparemters of the evidential distribution.
Edit intent: fluency
Original:  We describe our issues in detail, give a possible solution  and generalize the technique for the multivariate case.
Revised:  We describe the outstanding issues in detail, give a possible solution and generalize the technique for the multivariate case.
Edit intent: clarity
Original:  We describe our issues in detail, give a possible solution  and generalize the technique for the multivariate case.
Revised:  We describe our issues in detail, provide a possible solution and generalize the technique for the multivariate case.
Edit intent: fluency
Original:  We describe our issues in detail, give a possible solution  and generalize the technique for the multivariate case.
Revised:  We describe our issues in detail, give a possible solution , and generalize the technique for the multivariate case.
Edit intent: fluency
Original:  The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the neural network to infer the hyperparemters of the evidential distribution.
Revised:  The approach derives a technique by placing evidential priors over the original Gaussian likelihood function and training the neural network to infer the hyperparemters of the evidential distribution.
Edit intent: clarity
Original:  The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the neural network to infer the hyperparemters of the evidential distribution.
Revised:  The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparemters of the evidential distribution.
Edit intent: clarity
Original:  The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the neural network to infer the hyperparemters of the evidential distribution.
Revised:  The aforementioned proposal derives a technique by placing evidential priors over the original Gaussian likelihood function and training the neural network to infer the hyperparameters of the evidential distribution.
Edit intent: fluency
Original: Recently, we have shaken the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status in caching enabled Internet of Things (IoT) networks [1].
Revised: Recently, we have struck the balance between the information freshness, in terms of age of information (AoI), experienced by users and energy consumed by sensors, by appropriately activating sensors to update their current status in caching enabled Internet of Things (IoT) networks [1].
Edit intent: clarity
Original:  Moreover, to circumvent the curse of dimensionality, we have established a methodology for designing deep reinforcement learning (DRL) algorithms to maximize (resp. minimize) the average reward (resp. cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather  the average one.
Revised:  Moreover, to circumvent the curse of dimensionality, we have established a methodology for designing deep reinforcement learning (DRL) algorithms to maximize (resp. minimize) the average reward (resp. cost), by integrating R-learning, a tabular reinforcement learning (RL) algorithm tailored for maximizing the long-term average reward, and traditional DRL algorithms, initially developed to optimize the discounted long-term cumulative reward rather than the average one.
Edit intent: fluency
Original: This paperpresents the results of a measurement campaign to investigate and analyze the performance of a  5G mmWave cell  .
Revised: MmWave communication is one of the cornerstones of future generations of mobile networks. It is recognized by its multi-gigabit transmission capacity, high propagation loss, and sensitivity to blockage. The high rate provided by mmWave links can potentially unlock emerging applications such as online ultra-high-definition video streaming, augmented and virtual reality, and instant file sharing for 5G networks. While the performance of mmWave links has been thoroughly investigated either by simulations or testbeds, the behavior of this technology in real-world commercial setups has not yet been thoroughly documented. In this paper, we address this gap and present the results of a measurement campaign to investigate and analyze the performance of a 5G mmWave cell .
Edit intent: coherence
Original: This paperpresents the results of a measurement campaign to investigate and analyze the performance of a  5G mmWave cell  .
Revised: This paperpresents the results of an empirical study to determine the actual performance of a 5G mmWave cell .
Edit intent: clarity
Original:  This measurement analysis provides useful information for researchers and 5G verticals to fully understand and appropriately model the mmWave signals propagation in a real-world and operational deployment .
Revised:  This measurement analysis provides valuable information for researchers and 5G verticals to fully understand and appropriately model the mmWave signals propagation in a real-world and operational deployment .
Edit intent: clarity
Original:  This measurement analysis provides useful information for researchers and 5G verticals to fully understand and appropriately model the mmWave signals propagation in a real-world and operational deployment .
Revised:  This measurement analysis provides useful information for researchers and 5G verticals to fully understand how a 5G mmWave access network operates in real-world and operational deployment .
Edit intent: clarity
Original: In this paper , we consider distributed control of microgrids composed of storages, generators, renewable energy sources, critical and controllable loads.
Revised: This paper deals with distributed control of microgrids composed of storages, generators, renewable energy sources, critical and controllable loads.
Edit intent: clarity
Original:  The resulting algorithm is able to compute high-quality feasible solutions to the two-stage stochastic  problem, for which we  provide a theoretical upper bound on the constraint violation.
Revised:  The resulting algorithm is able to compute high-quality feasible solutions to a two-stage stochastic problem, for which we provide a theoretical upper bound on the constraint violation.
Edit intent: fluency
Original:  we design a series of serial and parallel blocks to realize the co-scale attention mechanism.
Revised:  we design a series of serial and parallel blocks to realize the co-scale mechanism.
Edit intent: clarity
Original:  On ImageNet, relatively small CoaT models attain superior classification results compared with the similar-sized convolutional neural networks and image/vision Transformers.
Revised:  On ImageNet, relatively small CoaT models attain superior classification results compared with similar-sized convolutional neural networks and image/vision Transformers.
Edit intent: fluency
Original:  The effectiveness of CoaT's backbone is also illustrated on object detection and instance segmentation, demonstrating its applicability to the downstream computer vision tasks.
Revised:  The effectiveness of CoaT's backbone is also illustrated on object detection and instance segmentation, demonstrating its applicability to downstream computer vision tasks.
Edit intent: fluency
Original: We propose temporally abstract soft actor-critic ( TASAC ), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic (SAC) framework in a simple manner.
Revised: We propose temporally abstract soft actor-critic ( TASAC ), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the soft actor-critic framework in a simple manner.
Edit intent: clarity
Original:  TASAC adds a second-stage binary policy to choose between the previous action and the action output by an SAC actor.
Revised:  TAAC adds a second-stage binary policy to choose between the previous action and the action output by an SAC actor.
Edit intent: fluency
Original:  It has two benefits compared to traditional off-policy RL algorithms : persistent exploration and an unbiased multi-step Q operator for TD learning .
Revised:  It has two benefits compared to traditional off-policy RL algorithms : persistent exploration and an unbiased multi-step Q operator for multi-step TD backup .
Edit intent: clarity
Original: We propose temporally abstract actor-critic (TAAC), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework in a simple manner . TAAC adds a second-stage binary policy to choose between the previous action and a new action output by an actor.
Revised: We present temporally abstract actor-critic (TAAC), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework in a simple manner . TAAC adds a second-stage binary policy to choose between the previous action and a new action output by an actor.
Edit intent: clarity
Original: We propose temporally abstract actor-critic (TAAC), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework in a simple manner . TAAC adds a second-stage binary policy to choose between the previous action and a new action output by an actor.
Revised: We propose temporally abstract actor-critic (TAAC), an off-policy RL algorithm that incorporates closed-loop temporal abstraction into the actor-critic framework . TAAC adds a second-stage binary policy to choose between the previous action and a new action output by an actor.
Edit intent: clarity
Original:  Crucially, its  act-or-repeat  decision hinges on the actually sampled action instead of the expected behavior of the actor.
Revised:  Crucially, its " act-or-repeat decision hinges on the actually sampled action instead of the expected behavior of the actor.
Edit intent: fluency
Original:  Crucially, its  act-or-repeat  decision hinges on the actually sampled action instead of the expected behavior of the actor.
Revised:  Crucially, its act-or-repeat " decision hinges on the actually sampled action instead of the expected behavior of the actor.
Edit intent: fluency
Original:  TAAC has two important features: persistent exploration and a new compare-through Q operator for multi-step TD backup  .
Revised:  TAAC has two important features: a) persistent exploration, and b) a new compare-through Q operator for multi-step TD backup .
Edit intent: coherence
Original: We present a second order ensemble method based on a blended three-step BDF timestepping scheme to compute an ensemble of Navier-Stokes equations.
Revised: We present a second-order ensemble method based on a blended three-step BDF timestepping scheme to compute an ensemble of Navier-Stokes equations.
Edit intent: fluency
Original: The Steiner tree enumeration problem is a well known problem that asks for enumerating Steiner tree .
Revised: The Steiner tree enumeration problem is a well known problem that asks for enumerating Steiner trees .
Edit intent: fluency
Original:  Numerous theoretical works propose algorithms for the problem and analyze their complexity, but there are no practical algorithms and experimental studies.
Revised:  Numerous theoretical works proposed algorithms for the problem and analyze their complexity, but there are no practical algorithms and experimental studies.
Edit intent: fluency