wanyu/IteraTeR_v2 huggingface dataset:
Original:  To best of our knowledge, no prior approximation algorithm in full-information setting was known for the non-monotone continuous DR submodular functions even for thedown-closed convex domain.
Revised:  To best of our knowledge, no prior approximation algorithm in full-information setting was known for non-monotone continuous DR submodular functions even for thedown-closed convex domain.
Edit intent: fluency
Original:  To best of our knowledge, no prior approximation algorithm in full-information setting was known for the non-monotone continuous DR submodular functions even for thedown-closed convex domain.
Revised:  To best of our knowledge, no prior approximation algorithm in full-information setting was known for the non-monotone down-closed convex domain.
Edit intent: clarity
Original:  To best of our knowledge, no prior approximation algorithm in full-information setting was known for the non-monotone continuous DR submodular functions even for thedown-closed convex domain.
Revised:  To best of our knowledge, no prior approximation algorithm in full-information setting was known for the non-monotone continuous DR submodular functions even for the convex domain.
Edit intent: clarity
Original:  Second, we show that the online stochastic mirror ascent algorithm (in full information setting) achieves an improved approximation ratio of (1/4) for maximizing the non-monotone continuous DR-submodular functions over adown-closed convex domain.
Revised:  Second, we show that the online stochastic mirror ascent algorithm (in full information setting) achieves an improved approximation ratio of (1/4) for maximizing the non-monotone continuous DR-submodular down-closed convex domain.
Edit intent: clarity
Original:  Second, we show that the online stochastic mirror ascent algorithm (in full information setting) achieves an improved approximation ratio of (1/4) for maximizing the non-monotone continuous DR-submodular functions over adown-closed convex domain.
Revised:  Second, we show that the online stochastic mirror ascent algorithm (in full information setting) achieves an improved approximation ratio of (1/4) for maximizing the non-monotone continuous DR-submodular functions over a convex domain.
Edit intent: clarity
Original:  We recorded measurements inside of a hospital using an array of optically pumped magnetometers ( OPMs ) inside a portable shield that encompasses only the forearm and hand of the subject.
Revised:  We recorded measurements in a regular hospital room using an array of optically pumped magnetometers ( OPMs ) inside a portable shield that encompasses only the forearm and hand of the subject.
Edit intent: clarity
Original:  We recorded measurements inside of a hospital using an array of optically pumped magnetometers ( OPMs ) inside a portable shield that encompasses only the forearm and hand of the subject.
Revised:  We recorded measurements inside of a hospital using an array of optically pumped magnetometers ( OPM ) inside a portable shield that encompasses only the forearm and hand of the subject.
Edit intent: clarity
Original:  We present magnetomyograms (MMG)s of TMS-evoked movement in a human hand, together with a simultaneous surface electromyograph (EMG) and electroencephalograph (EEG) data.
Revised:  Main Results:
Edit intent: coherence
Original:  The biomagnetic signals recorded in the MMG provides detailed spatial and temporal information that is complementary to that of the electric signal channels.
Revised:  The biomagnetic signals recorded in the MMG provide detailed spatial and temporal information that is complementary to that of the electric signal channels.
Edit intent: fluency
Original:  Moreover, we identify features in the magnetic recording beyond that of the EMG , at time points which we further analyze in the EEG.
Revised:  Moreover, we identify features in the magnetic recording beyond those of the EMG , at time points which we further analyze in the EEG.
Edit intent: fluency
Original:  In fact, modern genomic insights have proven that more complex processes such as linkage, demography, and biased gene conversion further complicate our understanding of the roles of neutral versus selective processes in evolution.
Revised:  In fact, modern genomic insights have proved that complex processes such as linkage, demography, and biased gene conversion further complicate our understanding of the roles of neutral versus selective processes in evolution.
Edit intent: clarity
Original:  In fact, modern genomic insights have proven that more complex processes such as linkage, demography, and biased gene conversion further complicate our understanding of the roles of neutral versus selective processes in evolution.
Revised:  In fact, modern genomic insights have proven that more complex processes such as linkage, demography, and biased gene conversion complicate our understanding of the roles of neutral versus selective processes in evolution.
Edit intent: clarity
Original:  In fact, modern genomic insights have proven that more complex processes such as linkage, demography, and biased gene conversion further complicate our understanding of the roles of neutral versus selective processes in evolution.
Revised:  In fact, modern genomic insights have proven that more complex processes such as linkage, demography, and biased gene conversion further complicate our understanding of the role of neutral versus selective processes in evolution.
Edit intent: fluency
Original:  We highlight that these processes need to be taken into account to correctly identify the targets of selection and stress that a major  field-wide goal in the future should be to quantify the absolute importance of these mechanisms.
Revised:  We highlight that these processes need to be taken into account to correctly identify the targets of selection and stress that a major field-wide goal is to quantify the absolute importance of these mechanisms.
Edit intent: clarity
Original: This paper develop a deep mathematical-statistical approach to analyze a class of Flajolet-Martin algorithms (FMa), and provide a exact analytical confidence interval for the number F_0 of distinct elements in a stream, based on Chernoff bounds.
Revised: This paper develops a new mathematical-statistical approach to analyze a class of Flajolet-Martin algorithms (FMa), and provide a exact analytical confidence interval for the number F_0 of distinct elements in a stream, based on Chernoff bounds.
Edit intent: clarity
Original: This paper develop a deep mathematical-statistical approach to analyze a class of Flajolet-Martin algorithms (FMa), and provide a exact analytical confidence interval for the number F_0 of distinct elements in a stream, based on Chernoff bounds.
Revised: This paper develop a deep mathematical-statistical approach to analyze a class of Flajolet-Martin algorithms (FMa), and provides analytical confidence intervals for the number F_0 of distinct elements in a stream, based on Chernoff bounds.
Edit intent: clarity
Original: This paper develop a deep mathematical-statistical approach to analyze a class of Flajolet-Martin algorithms (FMa), and provide a exact analytical confidence interval for the number F_0 of distinct elements in a stream, based on Chernoff bounds.
Revised: This paper develop a deep mathematical-statistical approach to analyze a class of Flajolet-Martin algorithms (FMa), and provide a exact analytical confidence interval for the number F0 of distinct elements in a stream, based on Chernoff bounds.
Edit intent: fluency
Original:  The analysis provided here shows a deep connections with special mathematical functions and with extreme value theory.
Revised:  The analysis provided here shows deep connections with special mathematical functions and with extreme value theory.
Edit intent: fluency
Original:  The analysis provided here shows a deep connections with special mathematical functions and with extreme value theory.
Revised:  The analysis provided here shows a deep connections with mathematical special functions and with extreme value theory.
Edit intent: fluency
Original: We demonstrate that regularisation by projection and variational regularisation can be formulated in a purely data driven setting when the forward operator is given only through training data .
Revised: We demonstrate that regularisation by projection and variational regularization can be formulated in a purely data driven setting when the forward operator is given only through training data .
Edit intent: fluency
Original: We demonstrate that regularisation by projection and variational regularisation can be formulated in a purely data driven setting when the forward operator is given only through training data .
Revised: We demonstrate that regularisation by projection and variational regularisation can be formulated by using the training data only and without making use of the forward operator is given only through training data .
Edit intent: clarity
Original: We demonstrate that regularisation by projection and variational regularisation can be formulated in a purely data driven setting when the forward operator is given only through training data .
Revised: We demonstrate that regularisation by projection and variational regularisation can be formulated in a purely data driven setting when the forward operator .
Edit intent: clarity
Original:  Despite considerable efforts and successes witnessed in learning Boolean satisfiability (SAT), it remains an open question of learning GNN-based solvers for more complex predicate logic formulae.
Revised:  Despite considerable efforts and successes witnessed to solve Boolean satisfiability (SAT), it remains an open question of learning GNN-based solvers for more complex predicate logic formulae.
Edit intent: clarity
Original:  Despite considerable efforts and successes witnessed in learning Boolean satisfiability (SAT), it remains an open question of learning GNN-based solvers for more complex predicate logic formulae.
Revised:  Despite considerable efforts and successes witnessed in learning Boolean satisfiability (SAT), it remains a mystery of GNN-based solvers for more complex predicate logic formulae.
Edit intent: clarity
Original:  In this work, we conjectures with theoretically support discussion, that generally defined GNNs present some limitations in reasoning about a set of assignments and proving the unsatisfiability (UNSAT) in Boolean formulae.
Revised:  In this work, we conjectures with some evidences, that generally-defined GNNs present several limitations to certify the unsatisfiability (UNSAT) in Boolean formulae.
Edit intent: clarity
Original:  It implies that GNNs may probably fail in learning the logical reasoning tasks if they contain  UNSAT as the sub-problem , thus, included by most of predicate logic reasoning problems .
Revised:  It implies that GNNs may probably fail in learning the logical reasoning tasks if they contain proving UNSAT as the sub-problem , thus, included by most of predicate logic reasoning problems .
Edit intent: clarity
Original:  It implies that GNNs may probably fail in learning the logical reasoning tasks if they contain  UNSAT as the sub-problem , thus, included by most of predicate logic reasoning problems .
Revised:  It implies that GNNs may probably fail in learning the logical reasoning tasks if they contain UNSAT as the sub-problem included by most of predicate logic reasoning problems .
Edit intent: clarity
Original:  It implies that GNNs may probably fail in learning the logical reasoning tasks if they contain  UNSAT as the sub-problem , thus, included by most of predicate logic reasoning problems .
Revised:  It implies that GNNs may probably fail in learning the logical reasoning tasks if they contain UNSAT as the sub-problem , thus, included by most predicate logic formulae .
Edit intent: clarity
Original: In this paper, we present a framework to construct general stochastic Runge--Kutta Lawson schemes.
Revised: In this paper, we present a framework to construct general stochastic Runge-Kutta Lawson schemes.
Edit intent: fluency
Original:  We prove that the schemes inherit the consistency and convergence properties of the underlying Runge--Kutta scheme, and confirm this in some numerical experiments.
Revised:  We prove that the schemes inherit the consistency and convergence properties of the underlying Runge-Kutta scheme, and confirm this in some numerical experiments.
Edit intent: fluency
Original:  We investigate also the stability properties of the methods and show for some examples, that the new schemes have improved stability properties compared to the underlying schemes.
Revised:  We also investigate the stability properties of the methods and show for some examples, that the new schemes have improved stability properties compared to the underlying schemes.
Edit intent: fluency
Original:  Ma-chine learning techniques have recently been applied to solve such problems.
Revised:  Machine learning techniques have recently been applied to solve such problems.
Edit intent: clarity
Original:  We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based dis-criminator model to improve the diversity of generated molecules and at the same time steer the GA.
Revised:  We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA.
Edit intent: fluency
Original:  We show that our algorithm outperforms other generative mod-els in optimization tasks.
Revised:  We show that our algorithm outperforms other generative models in optimization tasks.
Edit intent: fluency
Original: The text classification is one of the most critical areas in machine learning and artificial intelligence research.
Revised: Text classification is one of the most critical areas in machine learning and artificial intelligence research.
Edit intent: fluency
Original:  One of the problems in supervised text classification models is that the models performance depend heavily on the quality of data labeling that are typically done by humans.
Revised:  One of the problems in supervised text classification models is that the models ' performance depends heavily on the quality of data labeling that are typically done by humans.
Edit intent: fluency
Original:  One of the problems in supervised text classification models is that the models performance depend heavily on the quality of data labeling that are typically done by humans.
Revised:  One of the problems in supervised text classification models is that the models performance depend heavily on the quality of data labeling that is typically done by humans.
Edit intent: fluency
Original:  We train and test Support vector machine and Random forest models on both the human labeled data and network community detection labeled data.
Revised:  We train and test Support Vector Machine and Random Forest models on both the human labeled data and network community detection labeled data.
Edit intent: fluency
Original:  We train and test Support vector machine and Random forest models on both the human labeled data and network community detection labeled data.
Revised:  We train and test Support vector machine and Random forest models on both the human-labeled data and network community detection labeled data.
Edit intent: fluency
Original:  Our method may help development of a more accurate conversational intelligence system and other text classification systems.
Revised:  Our method may help developments of more accurate conversational intelligence system and other text classification systems.
Edit intent: clarity
Original:  Our method may help development of a more accurate conversational intelligence system and other text classification systems.
Revised:  Our method may help development of a more accurate conversational intelligence and other text classification systems.
Edit intent: fluency
Original:  Specifically, we build a network with sentences as the network nodes and pairwise cosine similarities between TFIDF vector representations of the sentences as the network link weights.
Revised:  Specifically, we build networks with sentences as the network nodes and pairwise cosine similarities between TFIDF vector representations of the sentences as the network link weights.
Edit intent: fluency
Original:  We use the Louvain method to detect the communities in the sentence network .
Revised:  We use the Louvain method to detect the communities in the sentence networks .
Edit intent: fluency
Original:  We train and test  Support Vector Machine and  Random Forest models on both the human-labeled data and network community detection labeled data.
Revised:  We train and test the Support Vector Machine and Random Forest models on both the human-labeled data and network community detection labeled data.
Edit intent: fluency
Original:  We train and test  Support Vector Machine and  Random Forest models on both the human-labeled data and network community detection labeled data.
Revised:  We train and test Support Vector Machine and the Random Forest models on both the human-labeled data and network community detection labeled data.
Edit intent: fluency
Original:  Results showed that models with the data labeled by  network community detection outperformed the models with the human-labeled data by 2.68-3.75\% of classification accuracy.
Revised:  Results showed that models with the data labeled by the network community detection outperformed the models with the human-labeled data by 2.68-3.75\% of classification accuracy.
Edit intent: fluency
Original: In this paper, we propose a phase shift deep neural network (PhaseDNN)  which provides a  wideband convergence in approximating high frequency  solutions of wave equations.
Revised: In this paper, we propose a phase shift deep neural network (PhaseDNN) , which provides a wideband convergence in approximating high frequency solutions of wave equations.
Edit intent: fluency
Original:  The PhaseDNN accounts for the fact that many DNN achieves convergence in the low frequency range first,  a series of moderately-sized of DNNs are constructed and trained for selected high frequency ranges.
Revised:  The PhaseDNN makes use of the fact that many DNN achieves convergence in the low frequency range first, a series of moderately-sized of DNNs are constructed and trained for selected high frequency ranges.
Edit intent: clarity
Original:  The PhaseDNN accounts for the fact that many DNN achieves convergence in the low frequency range first,  a series of moderately-sized of DNNs are constructed and trained for selected high frequency ranges.
Revised:  The PhaseDNN accounts for the fact that common DNNs often achieve convergence in the low frequency range first, a series of moderately-sized of DNNs are constructed and trained for selected high frequency ranges.
Edit intent: clarity
Original:  The PhaseDNN accounts for the fact that many DNN achieves convergence in the low frequency range first,  a series of moderately-sized of DNNs are constructed and trained for selected high frequency ranges.
Revised:  The PhaseDNN accounts for the fact that many DNN achieves convergence in the low frequency range first, and a series of moderately-sized of DNNs are constructed and trained for selected high frequency ranges.
Edit intent: coherence
Original:  The PhaseDNN accounts for the fact that many DNN achieves convergence in the low frequency range first,  a series of moderately-sized of DNNs are constructed and trained for selected high frequency ranges.
Revised:  The PhaseDNN accounts for the fact that many DNN achieves convergence in the low frequency range first, a series of moderately-sized DNNs are constructed and trained for selected high frequency ranges.
Edit intent: fluency
Original:  With the help of phase shifts in the frequency domain, each DNN will be trained to approximate the target solution 's higher frequency content over a specific range .
Revised:  With the help of phase shifts in the frequency domain, each of the DNNs will be trained to approximate the target solution 's higher frequency content over a specific range .
Edit intent: clarity
Original:  With the help of phase shifts in the frequency domain, each DNN will be trained to approximate the target solution 's higher frequency content over a specific range .
Revised:  With the help of phase shifts in the frequency domain, each DNN will be trained to approximate the function 's higher frequency content over a specific range .
Edit intent: clarity
Original:  As a result, the proposed PhaseDNN is able to convert high frequency learning to low frequency learning, thus allowing a uniform learning to wideband high frequency functions.
Revised:  As a result, the proposed PhaseDNN is able to convert high frequency learning to low frequency one, allowing a uniform learning to wideband high frequency functions.
Edit intent: clarity
Original:  As a result, the proposed PhaseDNN is able to convert high frequency learning to low frequency learning, thus allowing a uniform learning to wideband high frequency functions.
Revised:  As a result, the proposed PhaseDNN is able to convert high frequency learning to low frequency learning, thus allowing a uniform learning to wideband functions.
Edit intent: clarity
Original:  Numerical results have demonstrated the capability of  PhaseDNN in learning high frequency functions and oscillatory solutions of  Helmholtz equations.
Revised:  Numerical results have demonstrated the capability of the PhaseDNN in learning high frequency functions and oscillatory solutions of Helmholtz equations.
Edit intent: fluency
Original: Local differential privacy (LDP) enables private data sharing and analytics without the need of a trusted data collector.
Revised: Local differential privacy (LDP) enables private data sharing and analytics without the need for a trusted data collector.
Edit intent: fluency
Original:  In this paper, we investigate a policy-aware%DIFDELCMD < } %%% extension of eps-LDP\eps , where a policy is customizable and defines heterogeneous privacy guarantees for different pairs of private data values .
Revised:  In this paper, we policy-aware%DIFDELCMD < } %%% extension of eps-LDP\eps , where a policy is customizable and defines heterogeneous privacy guarantees for different pairs of private data values .
Edit intent: clarity
Original:  In this paper, we investigate a policy-aware%DIFDELCMD < } %%% extension of eps-LDP\eps , where a policy is customizable and defines heterogeneous privacy guarantees for different pairs of private data values .
Revised:  In this paper, we investigate a %DIFDELCMD < } %%% extension of eps-LDP\eps , where a policy is customizable and defines heterogeneous privacy guarantees for different pairs of private data values .
Edit intent: clarity
Original:  In this paper, we investigate a policy-aware%DIFDELCMD < } %%% extension of eps-LDP\eps , where a policy is customizable and defines heterogeneous privacy guarantees for different pairs of private data values .
Revised:  In this paper, we investigate a policy-aware%DIFDELCMD < } %%% utilize an extension of\eps , where a policy is customizable and defines heterogeneous privacy guarantees for different pairs of private data values .
Edit intent: clarity
Original:  In this paper, we investigate a policy-aware%DIFDELCMD < } %%% extension of eps-LDP\eps , where a policy is customizable and defines heterogeneous privacy guarantees for different pairs of private data values .
Revised:  In this paper, we investigate a policy-aware%DIFDELCMD < } %%% extension of eps-LDP\eps , where a is customizable and defines heterogeneous privacy guarantees for different pairs of private data values .
Edit intent: fluency
Original:  The policy provides another knob besides eps to\eps  tune utility-privacy trade-offs in analytical workloads .
Revised:  The policy provides another knob besides eps to\eps tune utility-privacy trade-offs .
Edit intent: clarity