wanyu/IteraTeR_v2 huggingface dataset:
Original:  Second, demand-side shocks  are becoming increasingly important in creating the short-run connectedness.
Revised:  Second, demand-side shocks are becoming increasingly important in creating short-run connectedness.
Edit intent: fluency
Original:  Third, the supply-side shocks  resonating in both long-run and short-run are important sources of connectedness.
Revised:  Third, the supply-side shocks resonating in both the long run and short run are important sources of connectedness.
Edit intent: fluency
Original:  At last, based on the Ekeland's variational principle for a proper lower semicontinuous- L ^{0 function, we show that \epsilon-subdifferentials can be approximated by subdifferentials.
Revised:  At last, based on the Ekeland's variational principle for a proper lower semicontinuous L ^{0 function, we show that \epsilon-subdifferentials can be approximated by subdifferentials.
Edit intent: fluency
Original:  We would like to emphasize that the success of this paper lies in simultaneously considering the ( \epsilon , \lambda) -topology and the locally L ^{0 topology for a random locally convex module.
Revised:  We would like to emphasize that the success of this paper lies in simultaneously considering the ( \epsilon , \lambda) --topology and the locally L ^{0 topology for a random locally convex module.
Edit intent: fluency
Original:  We show that there exist one-dimensional  pre-expansive CAs which are not (positively ) expansive and they can be chosen reversible  .
Revised:  We show that there exist one-dimensional pre-expansive CAs which are not positively expansive and they can be chosen reversible .
Edit intent: coherence
Original:  The winning condition is inclusion in a regular language for the terminal words derived in maximal plays .
Revised:  The winning condition is inclusion of the derived terminal word in the language of a finite automaton .
Edit intent: clarity
Original:  It is compatible with recent antichain- and subsumption-based optimizations, and  admits a lazy evaluation strategy  .
Revised:  It is compatible with recent antichain- and subsumption-based optimizations, and that it admits a lazy evaluation strategy .
Edit intent: coherence
Original:  This system has the capability to process key machine learning algorithms such as deep neural network , autoencoder, and k-means clustering .
Revised:  This system has the capability to process key machine learning algorithms such as deep neural network based processing .
Edit intent: clarity
Original:  The proposed system could be used for classification, unsupervised clustering, dimensionality reduction, feature extraction, and anomaly detection applications.
Revised:  The proposed system could be used for classification, dimensionality reduction, feature extraction, and anomaly detection applications.
Edit intent: clarity
Original:  Our experimental evaluations show that the proposed architecture can provide four to six orders of magnitude more energy efficiency over GPGPUs for big data processing.
Revised:  Our experimental evaluations show that the proposed architecture can provide four to six orders of magnitude more energy efficiency over GPGPUs for deep neural network processing.
Edit intent: clarity
Original:  Due to their vital importance in a large number of research fields, various studies have offered explanations on how complex networks evolve, but the full underlying dynamics of complex networks are not completely understood.
Revised:  Such networks are vitally important, but their full underlying dynamics of complex networks are not completely understood.
Edit intent: clarity
Original:  Due to their vital importance in a large number of research fields, various studies have offered explanations on how complex networks evolve, but the full underlying dynamics of complex networks are not completely understood.
Revised:  Due to their vital importance in a large number of research fields, various studies have offered explanations on how complex networks evolve, but the full underlying dynamics are not completely understood.
Edit intent: clarity
Original:  Many of the barriers to better understanding the evolution process of these networks can be removed with the emergence of new data sources .
Revised:  Utilizing new data sources, however, can unveil the evolution process of these networks can be removed with the emergence of new data sources .
Edit intent: clarity
Original:  Many of the barriers to better understanding the evolution process of these networks can be removed with the emergence of new data sources .
Revised:  Many of the barriers to better understanding the evolution process of these networks .
Edit intent: clarity
Original:  This study utilizes the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus , which contains detailed information on the evolution process of 11,965 social networks .
Revised:  This study uses the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus , which contains detailed information on the evolution process of 11,965 social networks .
Edit intent: fluency
Original:  This study utilizes the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus , which contains detailed information on the evolution process of 11,965 social networks .
Revised:  This study utilizes the recently published Reddit dataset, containing over 1.65 billion comments, to construct the largest publicly available social network corpus to date .
Edit intent: clarity
Original:  that is, different arrival patterns create different topological properties.
Revised:  i.e., different user-join patterns create different topological properties.
Edit intent: clarity
Original: Graphlets are small induced subgraphs and have been frequently applied to characterize the local and global structure of graphs across various domains, e.g., online social networks (OSNs) and biological networks.
Revised: Graphlets are induced subgraph patterns and have been frequently applied to characterize the local and global structure of graphs across various domains, e.g., online social networks (OSNs) and biological networks.
Edit intent: clarity
Original:  In this work, we propose a general and novel framework to estimate graphlet statistics of any size .
Revised:  In this work, we propose a general and novel framework to estimate graphlet statistics of "any size" .
Edit intent: fluency
Original:  Our framework is based on collecting samples through consecutive steps of random walk .
Revised:  Our framework is based on collecting samples through consecutive steps of random walks .
Edit intent: fluency
Original:  We derive analytic bound on the needed sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator.
Revised:  We derive an analytical bound on the needed sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator.
Edit intent: fluency
Original:  We derive analytic bound on the needed sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator.
Revised:  We derive analytic bound on the sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator.
Edit intent: clarity
Original:  Experimental evaluations demonstrate that our methods outperform the state-of-the-art method up to two orders of magnitude both in terms of accuracy and time cost.
Revised:  Experimental evaluations demonstrate that our methods outperform the state-of-the-art method up to an order of magnitude both in terms of accuracy and time cost.
Edit intent: clarity
Original:  We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity which are usable by general ecologists, but which are grounded in well-developed statistical practice and are efficient to fit to large data sets.
Revised:  We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity that are usable by general ecologists, but which are grounded in well-developed statistical practice and are efficient to fit to large data sets.
Edit intent: fluency
Original:  We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity which are usable by general ecologists, but which are grounded in well-developed statistical practice and are efficient to fit to large data sets.
Revised:  We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity which are usable by general ecologists, but grounded in well-developed statistical practice and are efficient to fit to large data sets.
Edit intent: fluency
Original:  We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity which are usable by general ecologists, but which are grounded in well-developed statistical practice and are efficient to fit to large data sets.
Revised:  We contend that there is a need for an appropriate balance between the extremes of either being overly complex or being overly simplistic, whereby the discipline relies on models of intermediate complexity which are usable by general ecologists, but which are grounded in well-developed statistical practice and efficient to fit to large data sets.
Edit intent: fluency
Original:  New approaches of ever greater complexity are constantly being added to the literature.
Revised:  New approaches of ever greater complexity are continue to be added to the literature.
Edit intent: clarity
Original:  Corresponding approaches, e.g. based on Levy walk-type models, continue to be popular despite having been largely discredited.
Revised:  Corresponding approaches, e.g. based on L\'evy walk-type models, continue to be popular despite having been largely discredited.
Edit intent: fluency
Original:  We skip the fault-tolerance layer, as it is not necessary in this work.
Revised:  We skip the fault-tolerance layer, as it is not within the scope of this work.
Edit intent: clarity
Original:  Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are  associated.
Revised:  Neural networks take one event as input and compute a conditional probability of the other event to model how likely these two events are to be associated.
Edit intent: fluency
Original:  In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks ( DNNs) and relation modulated neural nets ( RMNNs ), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multirelational knowledge bases and common-sense reasoning.
Revised:  In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks ( DNN) and relation-modulated neural nets ( RMNNs ), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multirelational knowledge bases and common-sense reasoning.
Edit intent: fluency
Original:  In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks ( DNNs) and relation modulated neural nets ( RMNNs ), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multirelational knowledge bases and common-sense reasoning.
Revised:  In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks ( DNNs) and relation modulated neural nets ( RMNN ), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multirelational knowledge bases and common-sense reasoning.
Edit intent: fluency
Original:  In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks ( DNNs) and relation modulated neural nets ( RMNNs ), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multirelational knowledge bases and common-sense reasoning.
Revised:  In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks ( DNNs) and relation modulated neural nets ( RMNNs ), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multi-relational knowledge bases and common-sense reasoning.
Edit intent: fluency
Original:  In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks ( DNNs) and relation modulated neural nets ( RMNNs ), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multirelational knowledge bases and common-sense reasoning.
Revised:  In this work, as two case studies, we have investigated two NAM structures, namely deep neural networks ( DNNs) and relation modulated neural nets ( RMNNs ), on several probabilistic reasoning tasks in AI, including recognizing textual entailment, triple classification in multirelational knowledge bases and commonsense reasoning.
Edit intent: fluency
Original:  Experimental results on several popular data sets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNNs and RMNNs perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks.
Revised:  Experimental results on several popular datasets derived from WordNet, FreeBase and ConceptNet have all demonstrated that both DNNs and RMNNs perform equally well and they can significantly outperform the conventional methods available for these reasoning tasks.
Edit intent: clarity
Original:  Moreover, comparing with DNNs, RMNNs are superior in knowledge transfer, where a pre-trained model can be quickly extended to an unseen relation after observing only a few training samples  .
Revised:  Moreover, compared with DNNs, RMNNs are superior in knowledge transfer, where a pre-trained model can be quickly extended to an unseen relation after observing only a few training samples .
Edit intent: fluency
Original:  Furthemore , we give a sufficient machine characterization of compact languages.
Revised:  Furthermore , we give a sufficient machine characterization of compact languages.
Edit intent: coherence
Original:  We demonstrate the utility of this machine characterization by obtaining  lower bounds in streaming models  and upper bounds on extension complexities of several polytopes.
Revised:  We demonstrate the utility of this machine characterization by obtaining lower bounds in streaming models ; and upper bounds on extension complexities of several polytopes.
Edit intent: fluency
Original: We describe here the package {\tt subdivision _solver \\ } for the mathematical software {\tt SageMath}.
Revised: We describe here the package {\tt subdivision \\ } for the mathematical software {\tt SageMath}.
Edit intent: clarity
Original:  Recent works in end-to-end dialogue systems have adopted metrics from machine translation and text summarization to compare a model's generated response to a single target response.
Revised:  Recent works in response generation have adopted metrics from machine translation and text summarization to compare a model's generated response to a single target response.
Edit intent: clarity
Original:  Recent works in end-to-end dialogue systems have adopted metrics from machine translation and text summarization to compare a model's generated response to a single target response.
Revised:  Recent works in end-to-end dialogue systems have adopted metrics from machine translation to compare a model's generated response to a single target response.
Edit intent: clarity
Original:  We show that these metrics correlate very weakly or not at all with human judgements of the response quality in both technical and non-technical domains .
Revised:  We show that these metrics correlate very weakly with human judgements of the response quality in both technical and non-technical domains .
Edit intent: clarity
Original:  We show that these metrics correlate very weakly or not at all with human judgements of the response quality in both technical and non-technical domains .
Revised:  We show that these metrics correlate very weakly or not at all with human judgements in the non-technical domains .
Edit intent: clarity
Original:  We introduce a family of normalized polynomial kernels of degree p that interpolates between the Kendall (degree one) and Mallows  (infinite degree) kernels , and show that for d-dimensional permutations, the p-th degree kernel is characteristic when p is greater or equal than d - 1, unlike the Euclidean case in which no finite-degree polynomial kernel is characteristic .
Revised:  We introduce a family of normalized polynomial kernels of degree p that interpolates between the Kendall (degree one) and Mallows ' (infinite degree) kernels , and show that for d-dimensional permutations, the p-th degree kernel is characteristic when p is greater or equal than d - 1, unlike the Euclidean case in which no finite-degree polynomial kernel is characteristic .
Edit intent: fluency
Original: This paper explains why the CVC Verilog hardware description language (HDL) optimized flow graph compiled simulator is fast.
Revised: This paper explains how to develop Verilog hardware description language (HDL) optimized flow graph compiled simulator is fast.
Edit intent: clarity
Original:  CVC development history from a slow interpreter into a fast flow graph based machine code compiled simulator is described.
Revised:  Development history from a slow interpreter into a fast flow graph based machine code compiled simulator is described.
Edit intent: clarity
Original:  Finally, we also find that the LV value is mostly uncorrelated with conventional centrality measures of nodes in the aggregate network, suggesting that this type of analysis reveals a new dimension of social networks, associated to their temporal properties.
Edit intent: coherence
Original: The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP systems, including both traditional count based models and deep learning models.
Revised: The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP systems, including both traditional count based and deep learning models.
Edit intent: fluency
Original: The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP systems, including both traditional count based and deep learning models.
Revised: The problem of rare and unknown words is an important issue that can potentially influence the performance of many NLP systems, including both traditional count based and deep learning models.
Edit intent: clarity
Original: The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP systems, including both traditional count based and deep learning models.
Revised: The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP systems, including both the traditional count-based and the deep learning models.
Edit intent: fluency
Original:  We propose a novel way to deal with the rare and unseen words for the neural network models with attention.
Revised:  We propose a novel way to deal with the rare and unseen words for the neural network models using attention.
Edit intent: fluency
Original:  Our model uses two softmax layers in order to predict the next word in conditional language models: one of the softmax layers predicts the location of a word in the source sentence, and the other softmax layer predicts a word in the shortlist vocabulary.
Revised:  Our model uses two softmax layers in order to predict the next word in conditional language models: one predicts the location of a word in the source sentence, and the other softmax layer predicts a word in the shortlist vocabulary.
Edit intent: clarity
Original:  Our model uses two softmax layers in order to predict the next word in conditional language models: one of the softmax layers predicts the location of a word in the source sentence, and the other softmax layer predicts a word in the shortlist vocabulary.
Revised:  Our model uses two softmax layers in order to predict the next word in conditional language models: one of the softmax layers predicts the location of a word in the source sentence, and the other predicts a word in the shortlist vocabulary.
Edit intent: clarity
Original:  The decision of which softmax layer to use at each timestep is adaptively made by an MLP which is conditioned on the context.
Revised:  At each time-step, the decision of which softmax layer to use at each timestep is adaptively made by an MLP which is conditioned on the context.
Edit intent: clarity
Original:  The decision of which softmax layer to use at each timestep is adaptively made by an MLP which is conditioned on the context.
Revised:  The decision of which softmax layer to use choose adaptively made by an MLP which is conditioned on the context.
Edit intent: clarity
Original:  We motivate this work from a psychological evidence that humans naturally have a tendency to point towards objects in the context or the environment when the name of an object is not known.
Revised:  ~We motivate our work from a psychological evidence that humans naturally have a tendency to point towards objects in the context or the environment when the name of an object is not known.
Edit intent: clarity
Original:  Using our proposed model, we observe improvements in two tasks, neural machine translation on the Europarl English to French parallel corpora and text summarization on the Gigaword dataset  .
Revised:  ~We observe improvements on two tasks, neural machine translation on the Europarl English to French parallel corpora and text summarization on the Gigaword dataset .
Edit intent: clarity
Original:  approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1100 and 2300 frames per second and using between 25 and 325 mW (effectively > 5000 frames / sec / W) and iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning.
Revised:  approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1100 and 2300 frames per second and using between 25 and 325 mW (effectively > 6000 frames / sec / W) and iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning.
Edit intent: fluency
Original: The so-called great divergence in the income per capita is described in the Unified Growth Theory as one of the mind-boggling and unresolved mysteries about the growth process.
Revised: The so-called great divergence in the income per capita is described in the Unified Growth Theory as the mind-boggling and unresolved mysteries about the growth process.
Edit intent: clarity