wanyu/IteraTeR_v2 huggingface dataset:
Original:  For example, sparse random graphs typically have bounded average degree and good expansion properties, but they include vertices with unbounded degree, and therefore are excluded from the current polymer model framework.
Revised:  For example, sparse random graphs typically have bounded average degree and good expansion properties, but they include vertices with unbounded degree, and therefore are excluded from the current polymer-model framework.
Edit intent: fluency
Original:  OpTorch achieved the same accuracy as existing libraries on Cifar-10 and Cifar-100 datasets while reducing memory usage to approximately 50 %DIFDELCMD < \\%%% %DIF < . We also explore the effect of weights on total memory usage  in deep learning pipelines.
Revised:  OpTorch achieved the same accuracy as existing libraries on Cifar-10 and Cifar-100 datasets while reducing memory usage to approximately 50 %DIFDELCMD < \\%%% %DIF < . We also explore the effect of weights on total memory usage \%. We also explore the effect of weights on total memory usage in deep learning pipelines.
Edit intent: coherence
Original: Music is becoming an essential part of daily life .
Revised: With music becoming an essential part of daily life .
Edit intent: clarity
Original:  As the interactions between users and songs naturally construct a complex network, community detection approaches can be applied to reveal users' potential interests on songs by grouping relevant users \& songs to the same community.
Revised:  As the interactions between users and songs naturally construct a complex network, community detection approaches can be applied to reveal users' potential interests on songs by grouping relevant users songs to the same community.
Edit intent: fluency
Original:  However, as the types of interaction are diverse , it challenges conventional community detection methods which are designed originally for homogeneous networks.
Revised:  However, as the types of interaction could be heterogeneous , it challenges conventional community detection methods which are designed originally for homogeneous networks.
Edit intent: clarity
Original:  However, as the types of interaction are diverse , it challenges conventional community detection methods which are designed originally for homogeneous networks.
Revised:  However, as the types of interaction are diverse , it challenges conventional community detection methods designed originally for homogeneous networks.
Edit intent: coherence
Original:  Although there are existing works focusing on heterogeneous community detection, they are mostly task-driven approaches and not feasible for music retrieval and recommendationdirectly .
Revised:  Although there are existing works on heterogeneous community detection, they are mostly task-driven approaches and not feasible for music retrieval and recommendationdirectly .
Edit intent: clarity
Original:  Although there are existing works focusing on heterogeneous community detection, they are mostly task-driven approaches and not feasible for music retrieval and recommendationdirectly .
Revised:  Although there are existing works focusing on heterogeneous community detection, they are mostly task-driven approaches and not feasible for specific music recommendation .
Edit intent: clarity
Original:  Results show that for conventional methods, ETUD can help to detect communities significantly improving music recommendation accuracy while  reducing user searching cost simultaneously .
Revised:  Results show that for conventional methods, ETUD can help to detect communities significantly improving music recommendation accuracy while simultaneously reducing user searching cost simultaneously .
Edit intent: fluency
Original:  Results show that for conventional methods, ETUD can help to detect communities significantly improving music recommendation accuracy while  reducing user searching cost simultaneously .
Revised:  Results show that for conventional methods, ETUD can help to detect communities significantly improving music recommendation accuracy while reducing user searching cost .
Edit intent: clarity
Original:  Also the data exhibited grater rate of infection from a diagnosed case as compared to undetected infection.
Revised:  Also the data exhibited greater rate of infection from a diagnosed case as compared to undetected infection.
Edit intent: clarity
Original:  Finally, it is demonstrated that a very conservative estimate of undiagnosed cases is at least 75\\%DIF < of the total number of cases. %DIF > of the total number of cases.
Revised:  Finally, it is demonstrated that a very conservative estimate of undiagnosed cases is at least 75\\%DIF < of %DIF > of the total number of cases.
Edit intent: fluency
Original:  The ambient logic is a modal logic featuring modalities that are generically derived from the functor  .
Revised:  The ambient logic is a modal logic featuring modalities that are generically extracted from the functor .
Edit intent: fluency
Original:  From the simulation, we confirmed that the proposed method  reduces the lateral offset error by about 30\% , compared with other controllers based on Linear Time-Invariant (LTI) system.
Revised:  From the simulation, we confirmed that the proposed method reduces the lateral offset error , compared with other controllers based on Linear Time-Invariant (LTI) system.
Edit intent: clarity
Original: Recent advances in neural architectures, such as the Transformer, coupled with the emergence of large-scale pre-trained models such as BERT, have revolutionized the field of Natural Language Processing (NLP), pushing the state-of-the-art for a number of NLP tasks.
Revised: Recent advances in neural architectures, such as the Transformer, coupled with the emergence of large-scale pre-trained models such as BERT, have revolutionized the field of Natural Language Processing (NLP), pushing the state of the art for a number of NLP tasks.
Edit intent: fluency
Original:  Thus, here we aim to shed some light on some important theoretical limitations of pre-trained BERT-style models that are inherent in the general Transformer architecture.
Revised:  Thus, here we aim to shed light on some important theoretical limitations of pre-trained BERT-style models that are inherent in the general Transformer architecture.
Edit intent: fluency
Original:  First, we demonstrate in practice on two general types of tasks -- segmentation and segment labeling -- and  four datasets that these limitations are indeed harmful and that addressing them, even in some very simple and naive ways, can yield sizable improvements over vanilla RoBERTa and XLNet  .
Revised:  First, we demonstrate in practice on two general types of tasks -- segmentation and segment labeling -- and on four datasets that these limitations are indeed harmful and that addressing them, even in some very simple and naive ways, can yield sizable improvements over vanilla RoBERTa and XLNet .
Edit intent: fluency
Original:  First, we demonstrate in practice on two general types of tasks -- segmentation and segment labeling -- and  four datasets that these limitations are indeed harmful and that addressing them, even in some very simple and naive ways, can yield sizable improvements over vanilla RoBERTa and XLNet  .
Revised:  First, we demonstrate in practice on two general types of tasks -- segmentation and segment labeling -- and four datasets that these limitations are indeed harmful and that addressing them, even in some very simple and naive ways, can yield sizable improvements over vanilla RoBERTa and XLNet models .
Edit intent: clarity
Original:  Unfortunately, not all information online is true, and thus a number of fact-checking initiatives have been launched, both manual and automatic  .
Revised:  Unfortunately, not all information online is true, and thus a number of fact-checking initiatives have been launched, both manual and automatic , to deal with the problem .
Edit intent: clarity
Original:  Here, we present our contribution in this regard: WhatTheWikiFact , a system for automatic claim verification using Wikipedia.
Revised:  Here, we present our contribution in this regard: WhatTheWikiFact , a system for automatic claim verification using Wikipedia.
Edit intent: fluency
Original:  The system predicts the veracity of an input claim, and it further shows the evidence it has retrieved as part of the verification process.
Revised:  The system can predict the veracity of an input claim, and it further shows the evidence it has retrieved as part of the verification process.
Edit intent: clarity
Original:  It shows confidence scores and a list of relevant Wikipedia articles, together with detailed information about each article, including the phrase used to retrieve it, the most relevant sentences it contains, and their stances with respect to the input claim, with associated probabilities .
Revised:  It shows confidence scores and a list of relevant Wikipedia articles, together with detailed information about each article, including the phrase used to retrieve it, the most relevant sentences extracted from it and their stance with respect to the input claim, with associated probabilities .
Edit intent: clarity
Original:  We also obtain high confidence annotation performance with limited computation cost 
Revised:  We also obtain high confidence annotation performance with limited computation cost .
Edit intent: fluency
Original:  Evaluated with Microsoft production data, the splicing data adaptation method improves the baseline and adaption with the text to speech method by 58.03\% and 15.25\% relative word error rate reduction, respectively.
Revised:  Evaluated with Microsoft production data, the splicing data adaptation method improves the baseline and adaptation with the text to speech method by 58.03\% and 15.25\% relative word error rate reduction, respectively.
Edit intent: fluency
Original: The world is dynamic and changes over time, thus any optimization problems used to model real life problems must address this dynamic nature .
Revised: The world is dynamic and changes over time, thus any optimization problem used to model real life problems must address this dynamic nature .
Edit intent: fluency
Original:  In the multistage model we are given a series of instance of an optimization problem,  and a solution is provided for each instance.
Revised:  In this model we are given a series of instance of an optimization problem, and a solution is provided for each instance.
Edit intent: clarity
Original:  In the multistage model we are given a series of instance of an optimization problem,  and a solution is provided for each instance.
Revised:  In the multistage model we are given a series of instances of an optimization problem, and a solution is provided for each instance.
Edit intent: fluency
Original:  The strive for continuous and similar solutions over time are quantified and integrated into the objective function.
Revised:  The strive for obtaining near-optimal solutions for each instance on one hand, while maintaining similar solutions for consecutive time units on the other hand, is quantified and integrated into the objective function.
Edit intent: clarity
Original:  In this paper we consider the Generalized Multistage d-Knapsack problem, a generalization of the multistage variants of the Multiple Knapsack problem  as well as the d-Dimensional Knapsack problem.
Revised:  In this paper we consider the Generalized Multistage d-Knapsack problem, a generalization of the multistage variants of the Multiple Knapsack problem , as well as the d-Dimensional Knapsack problem.
Edit intent: fluency
Original:  Learning how to interact with such devices in an autonomous manner is  key for interoperability and automatic verification of their capabilities.
Revised:  Learning how to interact with such devices in an autonomous manner is the key for interoperability and automatic verification of their capabilities.
Edit intent: fluency
Original:  In this paper, we propose RL-IoT -- a system that explores how to automatically interact with possibly unknown IoT devices.
Revised:  In this paper, we propose RL-IoT , a system that explores how to automatically interact with possibly unknown IoT devices.
Edit intent: fluency
Original:  We leverage reinforcement learning (RL) to understand the semantics of protocol messages and to control the device to reach a given goal, while minimizing the number of interactions.
Revised:  We leverage reinforcement learning (RL) to recover the semantics of protocol messages and to control the device to reach a given goal, while minimizing the number of interactions.
Edit intent: clarity
Original:  We leverage reinforcement learning (RL) to understand the semantics of protocol messages and to control the device to reach a given goal, while minimizing the number of interactions.
Revised:  We leverage reinforcement learning (RL) to understand the semantics of protocol messages and to take control of the device to reach a given goal, while minimizing the number of interactions.
Edit intent: clarity
Original:  We assume only to know a database of possible IoT protocol messages, whose semantics are however unknown.
Revised:  We assume to know only a database of possible IoT protocol messages, whose semantics are however unknown.
Edit intent: fluency
Original:  RL-IoT opens the opportunity to use RL to automatically explore how to interact with IoT protocols with limited information, and paving the road for interoperable systems.
Revised:  RL-IoT paves the road for automatic interactions with poorly documented IoT protocols, thus enabling interoperable systems.
Edit intent: clarity
Original: High-Frequency (HF) signal are ubiquitous in the industrial world and are of great use for the monitoring of industrial assets.
Revised: High-Frequency (HF) signals are ubiquitous in the industrial world and are of great use for the monitoring of industrial assets.
Edit intent: fluency
Original: High-Frequency (HF) signal are ubiquitous in the industrial world and are of great use for the monitoring of industrial assets.
Revised: High-Frequency (HF) signal are ubiquitous in the industrial world and are of great use for monitoring of industrial assets.
Edit intent: fluency
Original:  In this paper, we propose a fully unsupervised deep learning framework that is able to extract  meaningful and sparse representation of raw HF signals.
Revised:  In this paper, we propose a fully unsupervised deep learning framework that is able to extract a meaningful and sparse representation of raw HF signals.
Edit intent: fluency
Original:  We embed in our architecture important properties of the fast discrete wavelet transformation (FDWT) such as (1) the cascade algorithm, (2) the quadrature mirror filter property that relates together the wavelet, the scaling and transposed filter functions, and (3) the coefficient denoising.
Revised:  We embed in our architecture important properties of the fast discrete wavelet transformation (FDWT) such as (1) the cascade algorithm, (2) the quadrature mirror filter property that links together the wavelet, the scaling and transposed filter functions, and (3) the coefficient denoising.
Edit intent: fluency
Original:  To achieve this objective, we introduce a new activation function that performs a learnable hard-thresholding of the wavelet coefficients.
Revised:  To achieve this objective, we propose a new activation function that performs a learnable hard-thresholding of the wavelet coefficients.
Edit intent: clarity
Original:  We demonstrate the benefit of embedding all these properties on three machine-learning tasks performed on open source sound datasets.
Revised:  We demonstrate the benefits of embedding all these properties on three machine-learning tasks performed on open source sound datasets.
Edit intent: fluency
Original:  We achieve results well above baseline and we perform an ablation study of the impact of each property on the performance of the architecture  .
Revised:  We perform an ablation study of the impact of each property on the performance of the architecture .
Edit intent: coherence
Original:  We focus on recovering the generative parameters of BA graphs by replacing their beta-VAE decoder with a sequential one.
Revised:  We focus on recovering the generative parameters of BA graphs by replacing their \beta-VAE decoder with a sequential one.
Edit intent: fluency
Original:  Next, we train a beta-VAE model, combining the GNN encoder from the first stage with an LSTM-based decoder with a customized loss.
Revised:  Next, we train a \beta-VAE model, combining the GNN encoder from the first stage with an LSTM-based decoder with a customized loss.
Edit intent: fluency
Original: Process analytics is the field focusing on predictions for individual process instances or overall process models.
Revised: Process analytics is an umbrella of data-driven techniques which includes making predictions for individual process instances or overall process models.
Edit intent: clarity
Original:  The derivation of the formulas involves the dual process of a stationary processthat has the symbol as spectral density .
Revised:  The derivation of the formulas involves the dual process of the stationary process .
Edit intent: clarity
Original:  The significance of the closed-form formulas is that they provide us with a linear-time algorithm to compute the solutions of corresponding  Toeplitz systems.
Revised:  The significance of the latter is that they provide us with a linear-time algorithm to compute the solutions of corresponding Toeplitz systems.
Edit intent: clarity
Original:  We have recently proposed to address this challenge in the simplifying replica-mean-field (RMF) limit.
Revised:  We propose to address this challenge in the simplifying replica-mean-field (RMF) limit.
Edit intent: clarity
Original:  Unfortunately, realistic metastable dynamics typically unfold in networks with mixed inhibition and excitation.
Revised:  However, metastable dynamics typically unfold in networks with mixed inhibition and excitation.
Edit intent: clarity
Original:  To address this point , we extend the RMF computational framework to neural networks modeled by point processes with exponential stochastic intensities  .
Revised:  Here , we extend the RMF computational framework to neural networks modeled by point processes with exponential stochastic intensities .
Edit intent: coherence
Original:  To address this point , we extend the RMF computational framework to neural networks modeled by point processes with exponential stochastic intensities  .
Revised:  To address this point , we extend the RMF computational framework to point-process-based neural network models with exponential stochastic intensities .
Edit intent: clarity
Original:  Within this framework , we show that metastable finite-size networks admit multistable RMF limits, which are fully characterized by stationary firing rates.
Revised:  Within this setting , we show that metastable finite-size networks admit multistable RMF limits, which are fully characterized by stationary firing rates.
Edit intent: clarity
Original:  We solve this original singular-perturbation problem by adapting the resolvent formalism to our delayed setting .
Revised:  We solve this original problem by combining the resolvent formalism to our delayed setting .
Edit intent: clarity
Original:  We also perform the analysis of the stochastic bifurcationof a bistable neural network in terms of static phase transition of its RMF limits.
Revised:  We also discuss the emergence of metastability as a stochastic bifurcation, which can also be interpreted as a static phase transition of its RMF limits.
Edit intent: clarity
Original:  We also perform the analysis of the stochastic bifurcationof a bistable neural network in terms of static phase transition of its RMF limits.
Revised:  We also perform the analysis of the stochastic bifurcationof a bistable neural network in terms of static phase transition in the RMF limits.
Edit intent: clarity
Original:  In turn, we expect to leverage the static picture of RMF limit to infer purely dynamical features of metastable , finite-size networks, such as transition rates .
Revised:  In turn, we expect to leverage the static picture of RMF limits to infer purely dynamical features of metastable , finite-size networks, such as transition rates .
Edit intent: fluency
Original:  In turn, we expect to leverage the static picture of RMF limit to infer purely dynamical features of metastable , finite-size networks, such as transition rates .
Revised:  In turn, we expect to leverage the static picture of RMF limit to infer purely dynamical features of metastable finite-size networks, such as transition rates .
Edit intent: fluency
Original:  Here we construct a Markov chain that uses the Gibbs measure and realistic open channel configurations to show that spark activation is described as a system transition from a metastable state to an absorbing state .
Revised:  Here we construct a Markov chain that uses the Gibbs measure and realistic open channel configurations to demonstrate that spark activation is described as a system transition from a metastable state to an absorbing state .
Edit intent: coherence
Original:  Here we construct a Markov chain that uses the Gibbs measure and realistic open channel configurations to show that spark activation is described as a system transition from a metastable state to an absorbing state .
Revised:  Here we construct a Markov chain that uses the Gibbs measure and realistic open channel configurations to show that spark activation is described as a system transition from a metastable to an absorbing state .
Edit intent: fluency
Original:  This vision of the release channel system yields similar results for the lumenal Ca threshold for spark activation as previous experimental results, providing a mechanism of the spark initiation threshold .
Revised:  This vision of the release channel system yields similar results for the lumenal Ca threshold for spark activation as previous experimental results, providing a mechanistic explanation of the spark initiation threshold .
Edit intent: clarity
Original: To unfold the tremendous amount of audiovisual data uploaded daily to social media platforms, effective topic modelling techniques are needed.
Revised: To unfold the tremendous amount of multimedia data uploaded daily to social media platforms, effective topic modelling techniques are needed.
Edit intent: clarity
Original: To unfold the tremendous amount of audiovisual data uploaded daily to social media platforms, effective topic modelling techniques are needed.
Revised: To unfold the tremendous amount of audiovisual data uploaded daily to social media platforms, effective topic modeling techniques are needed.
Edit intent: fluency
Original:  Existing work tends to apply variants of topic models on text data sets .
Revised:  Existing work tends to apply topic models on text data sets .
Edit intent: clarity
Original:  Existing work tends to apply variants of topic models on text data sets .
Revised:  Existing work tends to apply variants of topic models on written text datasets .
Edit intent: clarity
Original:  In this paper, we aim at developing a topic extractor on video transcriptions.
Revised:  In this paper, we propose a topic extractor on video transcriptions.
Edit intent: clarity
Original:  The model improves coherence by exploiting neural word embeddings through a graph-based clustering method.
Revised:  The model improves coherence by exploiting neural word embeddings through graph-based clustering method.
Edit intent: fluency
Original:  Unlike typical topic models, this approach works without knowing the true number of topics  . Experimental results on the real-life multimodal data set MuSe-CaR demonstrates that our approach  extracts coherent and meaningful topics , outperforming baseline methods.
Revised:  Unlike typical topic models, this approach works without knowing the true number of topics . Experimental results on the real-life multimodal dataset MuSe-CaR demonstrates that our approach extracts coherent and meaningful topics , outperforming baseline methods.
Edit intent: clarity