wanyu/IteraTeR_v2 huggingface dataset:
Original:  Nevertheless, to learn a good meta-model for within-task fast adaptation, a single agent alone has to learn over many tasks, inevitably leading to the cold-start problem.
Revised:  Nevertheless, to learn a good meta-model for within-task fast adaptation, a single agent alone has to learn over many tasks, and this is the so-called ' cold-start problem.
Edit intent: coherence
Original:  Seeing that in a multi-agent network the learning tasks across different agents often share some model similarity, a fundamental questionto ask is "Is it possible to accelerate the online meta-learning at each agent via limited communication and if yes how much benefit can be achieved?
Revised:  Seeing that in a multi-agent network the learning tasks across different agents often share some model similarity, we ask the following fundamental question: "Is it possible to accelerate the online meta-learning at each agent via limited communication and if yes how much benefit can be achieved?
Edit intent: clarity
Original:  Seeing that in a multi-agent network the learning tasks across different agents often share some model similarity, a fundamental questionto ask is "Is it possible to accelerate the online meta-learning at each agent via limited communication and if yes how much benefit can be achieved?
Revised:  Seeing that in a multi-agent network the learning tasks across different agents often share some model similarity, a fundamental questionto ask is "Is it possible to accelerate the online meta-learning across agents via limited communication and if yes how much benefit can be achieved?
Edit intent: clarity
Original:  " To answer this  , we propose a multi-agent online meta-learning framework and treat it as an equivalent two-level nested online convex optimization (OCO) problem.
Revised:  " To answer this question , we propose a multi-agent online meta-learning framework and treat it as an equivalent two-level nested online convex optimization (OCO) problem.
Edit intent: clarity
Original:  " To answer this  , we propose a multi-agent online meta-learning framework and treat it as an equivalent two-level nested online convex optimization (OCO) problem.
Revised:  " To answer this , we propose a multi-agent online meta-learning framework and cast it as an equivalent two-level nested online convex optimization (OCO) problem.
Edit intent: clarity
Original:  By characterizing the upper bound of the agent-task-averaged regret, we show that the performance ceiling of the multi-agent online meta-learning heavily depends on how much an agent can benefit from  distributed network-level OCO  via limited communication, which however remains unclear .
Revised:  By characterizing the upper bound of the agent-task-averaged regret, we show that the performance of multi-agent online meta-learning heavily depends on how much an agent can benefit from distributed network-level OCO via limited communication, which however remains unclear .
Edit intent: clarity
Original:  By characterizing the upper bound of the agent-task-averaged regret, we show that the performance ceiling of the multi-agent online meta-learning heavily depends on how much an agent can benefit from  distributed network-level OCO  via limited communication, which however remains unclear .
Revised:  By characterizing the upper bound of the agent-task-averaged regret, we show that the performance ceiling of the multi-agent online meta-learning depends heavily on how much an agent can benefit from distributed network-level OCO via limited communication, which however remains unclear .
Edit intent: fluency
Original:  By characterizing the upper bound of the agent-task-averaged regret, we show that the performance ceiling of the multi-agent online meta-learning heavily depends on how much an agent can benefit from  distributed network-level OCO  via limited communication, which however remains unclear .
Revised:  By characterizing the upper bound of the agent-task-averaged regret, we show that the performance ceiling of the multi-agent online meta-learning heavily depends on how much an agent can benefit from the distributed network-level OCO via limited communication, which however remains unclear .
Edit intent: fluency
Original:  By characterizing the upper bound of the agent-task-averaged regret, we show that the performance ceiling of the multi-agent online meta-learning heavily depends on how much an agent can benefit from  distributed network-level OCO  via limited communication, which however remains unclear .
Revised:  By characterizing the upper bound of the agent-task-averaged regret, we show that the performance ceiling of the multi-agent online meta-learning heavily depends on how much an agent can benefit from distributed network-level OCO via limited communication, which however is not well understood .
Edit intent: clarity
Original:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step  per iteration, and it results in  O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Revised:  To tackle this challenge, we devise a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step per iteration, and it results in O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Edit intent: clarity
Original:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step  per iteration, and it results in  O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Revised:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where each agent tracks the global gradient through only one communication step per iteration, and it results in O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Edit intent: clarity
Original:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step  per iteration, and it results in  O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Revised:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient using only one communication step per iteration, and it results in O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Edit intent: clarity
Original:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step  per iteration, and it results in  O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Revised:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step per iteration, and it results in an average regret O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Edit intent: fluency
Original:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step  per iteration, and it results in  O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Revised:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step per iteration, and it results in O(\sqrt{T/N}) per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Edit intent: clarity
Original:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step  per iteration, and it results in  O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Revised:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step per iteration, and it results in O(\sqrt{T/N}) for the average regret per agent, indicating that a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Edit intent: coherence
Original:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step  per iteration, and it results in  O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup compared with the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Revised:  To tackle this challenge, we further study a distributed online gradient descent algorithm with gradient tracking where agents collaboratively track the global gradient through only one communication step per iteration, and it results in O(\sqrt{T/N}) for the average regret per agent, i.e., a factor of \sqrt{1/N} speedup over the optimal single-agent regret O(\sqrt{T}) after T iterations, where N is the number of agents.
Edit intent: clarity
Original: With the breakthrough of recent state-of-the-art  sequencing technology, genomics data sets have become ubiquitous.
Revised: Due to recent breakthroughs in state-of-the-art sequencing technology, genomics data sets have become ubiquitous.
Edit intent: clarity
Original:  The emergence of large-scale data sets provides great opportunities for better understanding of genomics and one fundamental task is to understand gene regulation.
Revised:  The emergence of large-scale data sets provides great opportunities for better understanding of genomics , especially gene regulation.
Edit intent: clarity
Original:  There are two important factors that control the expression level of each gene: (1) Gene regulation such as histone modification can directly regulate gene expression.
Revised:  There are two important factors that control the expression level of each gene: (1) Gene regulation such as histone modifications can directly regulate gene expression.
Edit intent: fluency
Original:  (2) Neighbor genes that are functional related or interact to each other can effect the gene expression level.
Revised:  (2) Neighboring genes that are functional related or interact to each other can effect the gene expression level.
Edit intent: fluency
Original:  (2) Neighbor genes that are functional related or interact to each other can effect the gene expression level.
Revised:  (2) Neighbor genes that are functionally related to or interact with each other that can also affect gene expression level.
Edit intent: clarity
Original:  Previous efforts  tried to address the former using Attention-based model.
Revised:  Previous efforts have tried to address the former using Attention-based model.
Edit intent: clarity
Original:  To address the second problem , it requires incorporation of all potential related gene information into the model.
Revised:  However, addressing the second problem , it requires incorporation of all potential related gene information into the model.
Edit intent: coherence
Original:  To address the second problem , it requires incorporation of all potential related gene information into the model.
Revised:  To address the second problem requires the incorporation of all potential related gene information into the model.
Edit intent: coherence
Original:  To address the second problem , it requires incorporation of all potential related gene information into the model.
Revised:  To address the second problem , it requires incorporation of all potentially related gene information into the model.
Edit intent: fluency
Original:  Though modern machine learning and deep learning models were able to capture  signals when applied to moderately sized data, they  struggled to recover the underlying signals of the data due to  the data's higher dimensionality.
Revised:  Though modern machine learning and deep learning models have been able to capture signals when applied to moderately sized data, they struggled to recover the underlying signals of the data due to the data's higher dimensionality.
Edit intent: clarity
Original:  Though modern machine learning and deep learning models were able to capture  signals when applied to moderately sized data, they  struggled to recover the underlying signals of the data due to  the data's higher dimensionality.
Revised:  Though modern machine learning and deep learning models were able to capture signals when applied to moderately sized data, they have struggled to recover the underlying signals of the data due to the data's higher dimensionality.
Edit intent: fluency
Original:  Though modern machine learning and deep learning models were able to capture  signals when applied to moderately sized data, they  struggled to recover the underlying signals of the data due to  the data's higher dimensionality.
Revised:  Though modern machine learning and deep learning models were able to capture signals when applied to moderately sized data, they struggled to recover the underlying signals of the data due to the nature of the data's higher dimensionality.
Edit intent: clarity
Original: In order to meet the requirements for safety and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge , calling for edge intelligence.
Revised: In order to meet the requirements for performance, safety, and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge , calling for edge intelligence.
Edit intent: clarity
Original:  To facilitate fast edge learning, this work advocates a platform-aided federated meta-learning architecture , where a set of edge nodes joint force to learn a meta-model (i.e., model initialization for adaptation in a new learning task) by exploiting the similarity among edge nodes as well as the cloud knowledge transfer .
Revised:  To facilitate fast edge learning, we propose a platform-aided federated meta-learning architecture , where a set of edge nodes joint force to learn a meta-model (i.e., model initialization for adaptation in a new learning task) by exploiting the similarity among edge nodes as well as the cloud knowledge transfer .
Edit intent: clarity
Original:  To facilitate fast edge learning, this work advocates a platform-aided federated meta-learning architecture , where a set of edge nodes joint force to learn a meta-model (i.e., model initialization for adaptation in a new learning task) by exploiting the similarity among edge nodes as well as the cloud knowledge transfer .
Revised:  To facilitate fast edge learning, this work advocates a platform-aided federated meta-learning architecture where edge nodes collaboratively learn a meta-model (i.e., model initialization for adaptation in a new learning task) by exploiting the similarity among edge nodes as well as the cloud knowledge transfer .
Edit intent: clarity
Original:  We then devise an inexact alternating direction method of multiplier (ADMM ) based Hessian-free federated meta-learning algorithm, called ADMM-FedMeta, with inexact Hessian estimation .
Revised:  We then devise an inexact alternating direction method of multiplier (ADMM ) based Hessian-free federated meta-learning algorithm, namely ADMM-FedMeta, with inexact Hessian estimation .
Edit intent: clarity
Original:  Further, we analyze the convergence properties and the rapid adaptation performance of ADMM-FedMeta for the general non-convex case.
Revised:  Further, we analyze the convergence properties , the rapid adaptation performance of ADMM-FedMeta for the general non-convex case.
Edit intent: fluency
Original:  The theoretical results show that under mild conditions, ADMM-FedMeta converges to an \epsilon-approximate first-order stationary point after at most O(1/\epsilon^2) communication rounds.
Edit intent: coherence
Original:  Extensive experimental studies on benchmark datasets demonstrate the effectiveness and efficiency of ADMM-FedMeta, and showcase that ADMM-FedMeta outperforms the existing baselines.
Revised:  Extensive experimental studies demonstrate the effectiveness and efficiency of ADMM-FedMeta, and showcase that ADMM-FedMeta outperforms the existing baselines.
Edit intent: clarity
Original:  In this paper, we provide an approach through which we can apply the method of manufactured solutions to isolate and verify the solution discretization error.
Revised:  In this paper, we provide an approach through which we can apply the method of manufactured solutions to isolate and verify the solution-discretization error.
Edit intent: fluency
Original:  In order to construct a finite element method in four-dimensional space , it is necessary to create a numerical foundation, or equivalently a numerical infrastructure.
Revised:  In order to develop a finite element method in four-dimensional space , it is necessary to create a numerical foundation, or equivalently a numerical infrastructure.
Edit intent: clarity
Original:  In order to construct a finite element method in four-dimensional space , it is necessary to create a numerical foundation, or equivalently a numerical infrastructure.
Revised:  In order to construct a finite element method in this setting , it is necessary to create a numerical foundation, or equivalently a numerical infrastructure.
Edit intent: clarity
Original: Over the past five years, deep generative models have achieved a qualitative new level of performance.
Revised: Over the past five years, deep generative models have achieved a qualitatively new level of performance.
Edit intent: fluency
Original:  Experimental results show that our method fulfills key properties of a fingerprinting mechanism and achieves effectiveness in deep fake detection and attribution.
Revised:  Experiments show that our method fulfills key properties of a fingerprinting mechanism and achieves effectiveness in deep fake detection and attribution.
Edit intent: clarity
Original: Amidst the current COVID-19 pandemic, quantifying the effect of strategies that mitigate the spread of infectious diseases is more important than ever .
Revised: Amidst the current COVID-19 pandemic, quantifying the effects of strategies that mitigate the spread of infectious diseases is more important than ever .
Edit intent: fluency
Original: Amidst the current COVID-19 pandemic, quantifying the effect of strategies that mitigate the spread of infectious diseases is more important than ever .
Revised: Amidst the current COVID-19 pandemic, quantifying the effect of strategies that mitigate the spread of infectious diseases is critical .
Edit intent: clarity
Original:  We propose and examine two different models -- a branching model and an individual-based model-- both of which capture the  heterogeneous nature of interactions that occur within a community.
Revised:  We propose a branching model and an individual-based model-- both of which capture the heterogeneous nature of interactions that occur within a community.
Edit intent: coherence
Original:  We propose and examine two different models -- a branching model and an individual-based model-- both of which capture the  heterogeneous nature of interactions that occur within a community.
Revised:  We propose and examine two different models -- a branching model and an individual-based model-- both of which capture the heterogeneous nature of interactions within a community.
Edit intent: clarity
Original:  The most important and perhaps surprising result from this analytical exercise is that a community's stability to disease outbreaks is independent of its underlying network structure .
Revised:  The most important and perhaps surprising result from this analytical exercise is that a community's resilience to disease outbreaks is independent of its underlying network structure .
Edit intent: clarity
Original: A central topic in recent artificial intelligence technologies is deep learning, which can be regarded as a  multilayer feedforward neural network.
Revised: Deep learning is the backbone of artificial intelligence technologies is deep learning, which can be regarded as a multilayer feedforward neural network.
Edit intent: clarity
Original: A central topic in recent artificial intelligence technologies is deep learning, which can be regarded as a  multilayer feedforward neural network.
Revised: A central topic in recent artificial intelligence technologies , and it can be regarded as a multilayer feedforward neural network.
Edit intent: coherence
Original: A central topic in recent artificial intelligence technologies is deep learning, which can be regarded as a  multilayer feedforward neural network.
Revised: A central topic in recent artificial intelligence technologies is deep learning, which can be regarded as a kind of multilayer feedforward neural network.
Edit intent: clarity
Original:  An essence of deep learning is the information propagation through the layers, suggesting a connection between deep neural networks and dynamical systems , in the sense that the information propagation is explicitly modeled by the time-evolution of dynamical systems.
Revised:  An essence of deep learning is information propagation through the layers, suggesting a connection between deep neural networks and dynamical systems , in the sense that the information propagation is explicitly modeled by the time-evolution of dynamical systems.
Edit intent: fluency
Original:  An essence of deep learning is the information propagation through the layers, suggesting a connection between deep neural networks and dynamical systems , in the sense that the information propagation is explicitly modeled by the time-evolution of dynamical systems.
Revised:  An essence of deep learning is the information propagation through layers. This suggests that there is a connection between deep neural networks and dynamical systems , in the sense that the information propagation is explicitly modeled by the time-evolution of dynamical systems.
Edit intent: clarity