wanyu/IteraTeR_v2 huggingface dataset:
Original: In this paper  we develop the elements of the theory of algorithmic randomness in continuous-time Markov chains (CTMCs).
Revised: In this paper , we develop the elements of the theory of algorithmic randomness in continuous-time Markov chains (CTMCs).
Edit intent: fluency
Original:  As a preliminary application  we prove that, in any stochastic chemical reaction network, every random trajectory with bounded molecular counts has the non-Zeno property that infinitely many reactions do not occur in any finite interval of time.
Revised:  As a preliminary application , we prove that, in any stochastic chemical reaction network, every random trajectory with bounded molecular counts has the non-Zeno property that infinitely many reactions do not occur in any finite interval of time.
Edit intent: fluency
Original: Few-shot learning systems for sound event recognition gain interests since they require only a few examples to adapt to new target classes without fine-tuning.
Revised: Few-shot learning systems for sound event recognition have gained interests since they require only a few examples to adapt to new target classes without fine-tuning.
Edit intent: clarity
Original:  In this paper, we aim to achieve few-shot detection of rare sound events, from long query sequence that contain not only the target events but also the other events and background noise.
Revised:  In this paper, we aim to achieve few-shot detection of rare sound events, from query sequence that contain not only the target events but also the other events and background noise.
Edit intent: clarity
Original:  The contribution is to present the explicit inclusion of background noise as a independent class, a suitable loss function that emphasizes this additional class, and a corresponding sampling strategy that assists training.
Revised:  The contribution is to present the explicit inclusion of background noise as an independent class, a suitable loss function that emphasizes this additional class, and a corresponding sampling strategy that assists training.
Edit intent: fluency
Original: For mesh functions which satisfy a convexity condition at the discrete level, we associate the natural analogue of the Monge-Ampere measure.
Revised: To a mesh function we associate the natural analogue of the Monge-Ampere measure.
Edit intent: clarity
Original:  However, all existing works either focus on the tile prediction or focus on tile computing and delivering , overlooking the important fact that prediction, computing and communication have to share the same duration.
Revised:  However, all existing works either focus on the tile prediction or focus on tile delivering and computing , overlooking the important fact that prediction, computing and communication have to share the same duration.
Edit intent: clarity
Original:  However, all existing works either focus on the tile prediction or focus on tile computing and delivering , overlooking the important fact that prediction, computing and communication have to share the same duration.
Revised:  However, all existing works either focus on the tile prediction or focus on tile computing and delivering , overlooking the important fact that prediction, communication, and computing have to share the same duration.
Edit intent: fluency
Original:  Since the quality of experience (QoE) of proactive tile-based streaming depends on the worst performance of prediction, computing and communication, it is vital to match the prediction capability to the computing and communication capability.
Revised:  Moreover, the quality of experience (QoE) of proactive tile-based streaming depends on the worst performance of prediction, computing and communication, it is vital to match the prediction capability to the computing and communication capability.
Edit intent: coherence
Original:  Since the quality of experience (QoE) of proactive tile-based streaming depends on the worst performance of prediction, computing and communication, it is vital to match the prediction capability to the computing and communication capability.
Revised:  Since the quality of experience (QoE) of proactive tilebased streaming depends on the worst performance of prediction, computing and communication, it is vital to match the prediction capability to the computing and communication capability.
Edit intent: fluency
Original:  Since the quality of experience (QoE) of proactive tile-based streaming depends on the worst performance of prediction, computing and communication, it is vital to match the prediction capability to the computing and communication capability.
Revised:  Since the quality of experience (QoE) of proactive tile-based streaming depends on the worst performance of prediction, communication, and computing, thus it is vital to match the prediction capability to the computing and communication capability.
Edit intent: coherence
Original:  Since the quality of experience (QoE) of proactive tile-based streaming depends on the worst performance of prediction, computing and communication, it is vital to match the prediction capability to the computing and communication capability.
Revised:  Since the quality of experience (QoE) of proactive tile-based streaming depends on the worst performance of prediction, computing and communication, it is vital to match the prediction performance to the communication and computing capability.
Edit intent: fluency
Original:  In this paper, we jointly optimize the duration of the observation window for tile predictionand the duration used for computingand communication, to maximize the QoE of watching a VR video.
Revised:  In this paper, utilizing optimizing duration allocation for prediction, communication, and computing, we match the prediction performance to the communication and computing capability to maximize the QoE of watching a VR video.
Edit intent: clarity
Original:  From the optimal solution  we find two regions where tile prediction and computing and communication capabilities respectively play the dominant role, and reveal the tradeoff between the performance of tile prediction and the capability of computing and communication . Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration of observation window .
Revised:  From the optimal solution , we find two regions where tile prediction and computing and communication capabilities respectively play the dominant role, and reveal the tradeoff between the performance of tile prediction and the capability of computing and communication . Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration of observation window .
Edit intent: fluency
Original:  From the optimal solution  we find two regions where tile prediction and computing and communication capabilities respectively play the dominant role, and reveal the tradeoff between the performance of tile prediction and the capability of computing and communication . Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration of observation window .
Revised:  From the optimal solution we find two regions where tile prediction and communication and computing capability respectively play the dominant role, and reveal the tradeoff between the performance of tile prediction and the capability of computing and communication . Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration of observation window .
Edit intent: clarity
Original:  From the optimal solution  we find two regions where tile prediction and computing and communication capabilities respectively play the dominant role, and reveal the tradeoff between the performance of tile prediction and the capability of computing and communication . Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration of observation window .
Revised:  From the optimal solution we find two regions where tile prediction and computing and communication capabilities respectively play the dominant role, and reveal the tradeoff between the prediction performance and the communication and computing capability . Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration of observation window .
Edit intent: clarity
Original:  From the optimal solution  we find two regions where tile prediction and computing and communication capabilities respectively play the dominant role, and reveal the tradeoff between the performance of tile prediction and the capability of computing and communication . Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration of observation window .
Revised:  From the optimal solution we find two regions where tile prediction and computing and communication capabilities respectively play the dominant role, and reveal the tradeoff between the performance of tile prediction and the capability of computing and communication . Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration in prediction, communication, and computing .
Edit intent: clarity
Original: Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles in a segment to be requested before playback.
Revised: Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles to be requested before playback.
Edit intent: clarity
Original:  However, all existing works either focus on the  tile prediction or focus on tile delivering and computing , overlooking the important fact that prediction, communication, and computing have to share the same duration .
Revised:  All existing works either focus on the tile prediction or focus on tile delivering and computing , overlooking the important fact that prediction, communication, and computing have to share the same duration .
Edit intent: coherence
Original:  However, all existing works either focus on the  tile prediction or focus on tile delivering and computing , overlooking the important fact that prediction, communication, and computing have to share the same duration .
Revised:  However, all existing works either focus on the task of tile prediction or focus on tile delivering and computing , overlooking the important fact that prediction, communication, and computing have to share the same duration .
Edit intent: clarity
Original:  However, all existing works either focus on the  tile prediction or focus on tile delivering and computing , overlooking the important fact that prediction, communication, and computing have to share the same duration .
Revised:  However, all existing works either focus on the tile prediction or on the tasks of computing and communications , overlooking the important fact that prediction, communication, and computing have to share the same duration .
Edit intent: clarity
Original:  However, all existing works either focus on the  tile prediction or focus on tile delivering and computing , overlooking the important fact that prediction, communication, and computing have to share the same duration .
Revised:  However, all existing works either focus on the tile prediction or focus on tile delivering and computing , overlooking the facts that these successively executed tasks have to share the same duration .
Edit intent: clarity
Original:  Moreover, the quality of experience (QoE) of proactive tilebased streaming depends on the worst performance of prediction, communication, and computing, thus it is vital to match the prediction performance to the communication and computing capability .
Revised:  Moreover, the quality of experience (QoE) of proactive VR streaming depends on the worst performance of prediction, communication, and computing, thus it is vital to match the prediction performance to the communication and computing capability .
Edit intent: clarity
Original:  Moreover, the quality of experience (QoE) of proactive tilebased streaming depends on the worst performance of prediction, communication, and computing, thus it is vital to match the prediction performance to the communication and computing capability .
Revised:  Moreover, the quality of experience (QoE) of proactive tilebased streaming depends on the worst performance of the three tasks .
Edit intent: clarity
Original:  In this paper, utilizing optimizing duration allocation for prediction, communication, and computing, we match the prediction performance to the communication and computing capability to maximize the QoE of watching a VR video.
Revised:  In this paper, we jointly optimize the duration of the observation window for predicting tiles and the durations for computing and transmitting the predicted tiles to maximize the QoE of watching a VR video.
Edit intent: clarity
Original:  We find the global optimal solution with closed-form expression by decomposing the original problem equivalently into two matching subproblems.
Revised:  We find the global optimal solution with closed-form expression by decomposing the formulated problem equivalently into two matching subproblems.
Edit intent: clarity
Original:  Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration in prediction, communication, and computing .
Revised:  Simulation results using three existing tile predictors with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration in prediction, communication, and computing .
Edit intent: clarity
Original:  Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized duration in prediction, communication, and computing .
Revised:  Simulation results using two existing tile prediction methods with a real dataset demonstrate the gain of the optimized duration over the non-optimized counterparts .
Edit intent: clarity
Original:  Finally, we show that the mutual information between the cells peaks at this critical point , and can remain large far from the critical point if the cells assume the roles of producer/consumer .
Revised:  Finally, we show that the mutual information between the cells peaks at this critical point . Information can remain large far from the critical point if the cells assume the roles of producer/consumer .
Edit intent: clarity
Original:  Our results also apply to a more general family of random matrices with continuous i.i.d . entries, assuming that m \leq O(n/n).
Revised:  Our results also apply to a more general family of random matrices with continuous i.i.d entries, assuming that m \leq O(n/n).
Edit intent: fluency
Original:  Despite this trade off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature for understanding the trade off and addressing it appropriately.
Revised:  Despite this trade off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature regarding providing an understanding of the trade off and addressing it appropriately.
Edit intent: clarity
Original:  Despite this trade off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature for understanding the trade off and addressing it appropriately.
Revised:  Despite this trade off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature for understanding the trade off and how to address it appropriately.
Edit intent: clarity
Original:  In order to provide an understanding  , we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of noise reduction each technique is targeting.
Revised:  In order to provide an understanding , we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of accuracy improvement each technique/approach is targeting.
Edit intent: clarity
Original:  Both smooth and finitely smooth kernels are covered, with stronger results available in the univariate case.
Revised:  Both smooth and finitely smooth kernels are covered, with stronger results available in the finite smoothness case.
Edit intent: clarity
Original:  Building upon that, in this paper  we complete our results by providing another angle to multiagent network dynamics from a duality perspective  which allows us to view the network structure as dual variables of a constrained convex program.
Revised:  Building upon that, in this paper , we complete our results by providing another angle to multiagent network dynamics from a duality perspective which allows us to view the network structure as dual variables of a constrained convex program.
Edit intent: fluency
Original:  Building upon that, in this paper  we complete our results by providing another angle to multiagent network dynamics from a duality perspective  which allows us to view the network structure as dual variables of a constrained convex program.
Revised:  Building upon that, in this paper we complete our results by providing another angle to multiagent network dynamics from a duality perspective , which allows us to view the network structure as dual variables of a constrained convex program.
Edit intent: fluency
Original:  This  bridges the Lyapunov stability of state-dependent network dynamics and frequently used optimization techniques such as block coordinated descent, mirror descent, Newton method, and subgradient method.
Revised:  This view bridges the Lyapunov stability of state-dependent network dynamics and frequently used optimization techniques such as block coordinated descent, mirror descent, Newton method, and subgradient method.
Edit intent: clarity
Original:  As a result, we develop a systematic framework to analyze the Lyapunov stability of state-dependent network dynamics using well-known techniques from nonlinear optimization  .
Revised:  As a result, we develop a systematic framework to analyze the Lyapunov stability of state-dependent network dynamics using well-developed techniques from nonlinear optimization .
Edit intent: clarity
Original: Despite significant progress for stability analysis of conventional multiagent networked systems with weakly coupled state-network dynamics, most of the existing results have shortcomings to address multiagent systems with highly coupled state-network dynamics.
Revised: Despite significant progress on stability analysis of conventional multiagent networked systems with weakly coupled state-network dynamics, most of the existing results have shortcomings to address multiagent systems with highly coupled state-network dynamics.
Edit intent: fluency
Original: Despite significant progress for stability analysis of conventional multiagent networked systems with weakly coupled state-network dynamics, most of the existing results have shortcomings to address multiagent systems with highly coupled state-network dynamics.
Revised: Despite significant progress for stability analysis of conventional multiagent networked systems with weakly coupled state-network dynamics, most of the existing results have shortcomings in addressing multiagent systems with highly coupled state-network dynamics.
Edit intent: fluency
Original:  Motivated by numerous applications of such dynamics, in our previous work [1], we initiated a new direction for stability analysis of such systems using a sequential optimization framework.
Revised:  Motivated by numerous applications of such dynamics, in our previous work [1], we initiated a new direction for stability analysis of such systems that uses a sequential optimization framework.
Edit intent: coherence
Original:  Building upon that, in this paper, we complete our results by providing another angle to multiagent network dynamics from a duality perspective, which allows us to view the network structure as dual variables of a constrained nonlinear program.
Revised:  Building upon that, in this paper, we extend our results by providing another angle to multiagent network dynamics from a duality perspective, which allows us to view the network structure as dual variables of a constrained nonlinear program.
Edit intent: clarity
Original:  Building upon that, in this paper, we complete our results by providing another angle to multiagent network dynamics from a duality perspective, which allows us to view the network structure as dual variables of a constrained nonlinear program.
Revised:  Building upon that, in this paper, we complete our results by providing another angle on multiagent network dynamics from a duality perspective, which allows us to view the network structure as dual variables of a constrained nonlinear program.
Edit intent: fluency
Original:  Leveraging this idea, we show that the evolution of the coupled state-network multiagent dynamics can be viewed as iterates of a primal-dual algorithm to a static constrained optimization/saddle-point problem.
Revised:  Leveraging that idea, we show that the evolution of the coupled state-network multiagent dynamics can be viewed as iterates of a primal-dual algorithm to a static constrained optimization/saddle-point problem.
Edit intent: fluency
Original:  Leveraging this idea, we show that the evolution of the coupled state-network multiagent dynamics can be viewed as iterates of a primal-dual algorithm to a static constrained optimization/saddle-point problem.
Revised:  Leveraging this idea, we show that the evolution of the coupled state-network multiagent dynamics can be viewed as iterates of a primal-dual algorithm for a static constrained optimization/saddle-point problem.
Edit intent: fluency
Original:  This view bridges the Lyapunov stability of state-dependent network dynamics and frequently used optimization techniques such as block coordinated descent, mirror descent,  Newton method, and  subgradient method.
Revised:  This view bridges the Lyapunov stability of state-dependent network dynamics and frequently used optimization techniques such as block coordinated descent, mirror descent, the Newton method, and subgradient method.
Edit intent: clarity
Original:  This view bridges the Lyapunov stability of state-dependent network dynamics and frequently used optimization techniques such as block coordinated descent, mirror descent,  Newton method, and  subgradient method.
Revised:  This view bridges the Lyapunov stability of state-dependent network dynamics and frequently used optimization techniques such as block coordinated descent, mirror descent, Newton method, and the subgradient method.
Edit intent: fluency
Original:  As a result, we develop a systematic framework to analyze the Lyapunov stability of state-dependent network dynamics using well-developed techniques from nonlinear optimization.
Revised:  As a result, we develop a systematic framework for analyzing the Lyapunov stability of state-dependent network dynamics using well-developed techniques from nonlinear optimization.
Edit intent: fluency
Original:  As a result, we develop a systematic framework to analyze the Lyapunov stability of state-dependent network dynamics using well-developed techniques from nonlinear optimization.
Revised:  As a result, we develop a systematic framework to analyze the Lyapunov stability of state-dependent network dynamics using techniques from nonlinear optimization.
Edit intent: clarity