wanyu/IteraTeR_full_sent huggingface dataset:
Original: Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of  activity on the anatomical, or structural network of synaptic connections , which possess a URLanization.
Revised: Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of neural activity on the anatomical, or structural network of synaptic connections , which possess a URLanization.
Edit intent: clarity
Original: Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of  activity on the anatomical, or structural network of synaptic connections , which possess a URLanization.
Revised: Functional networks provide a topological description of activity patterns in the brain, as they stem from the propagation of activity on the anatomical, or structural network of synaptic connections . This last is well known to URLanized in hierarchical and modular way.
Edit intent: meaning-changed
Original:  We show that hierarchical connectivity is persistent in the quasi-critical regime associated with optimal processing capabilities and normal brain function (Griffiths phase) and breaks down in states deviating from this regime , often associated with pathological conditions.
Revised:  We show that hierarchical connectivity appears in functional networks in a persistent way if the dynamics is set to be in the quasi-critical regime associated with optimal processing capabilities and normal brain function (Griffiths phase) and breaks down in states deviating from this regime , often associated with pathological conditions.
Edit intent: clarity
Original:  We show that hierarchical connectivity is persistent in the quasi-critical regime associated with optimal processing capabilities and normal brain function (Griffiths phase) and breaks down in states deviating from this regime , often associated with pathological conditions.
Revised:  We show that hierarchical connectivity is persistent in the quasi-critical regime associated with optimal processing capabilities and normal brain function , while it breaks down in states deviating from this regime , often associated with pathological conditions.
Edit intent: coherence
Original:  We show that hierarchical connectivity is persistent in the quasi-critical regime associated with optimal processing capabilities and normal brain function (Griffiths phase) and breaks down in states deviating from this regime , often associated with pathological conditions.
Revised:  We show that hierarchical connectivity is persistent in the quasi-critical regime associated with optimal processing capabilities and normal brain function (Griffiths phase) and breaks down in other (supercritical) dynamical regimes , often associated with pathological conditions.
Edit intent: meaning-changed
Original:  Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of tuning patterns of activity and information flow , and show how the hierarchical topology and its quasi-critical functional counterpart provide an effective balance between local specialized processing and global integration  .
Revised:  Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of controlling patterns of activity and information flow , and show how the hierarchical topology and its quasi-critical functional counterpart provide an effective balance between local specialized processing and global integration .
Edit intent: clarity
Original:  Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of tuning patterns of activity and information flow , and show how the hierarchical topology and its quasi-critical functional counterpart provide an effective balance between local specialized processing and global integration  .
Revised:  Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of tuning patterns of activity and information flow . We conclude that functional connectivity patterns achieve optimal balance between local specialized processing and global integration .
Edit intent: coherence
Original:  Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of tuning patterns of activity and information flow , and show how the hierarchical topology and its quasi-critical functional counterpart provide an effective balance between local specialized processing and global integration  .
Revised:  Our results offer important clues for the study of optimal neurocomputing architectures and processes, which are capable of tuning patterns of activity and information flow , and show how the hierarchical topology and its quasi-critical functional counterpart provide an effective balance between local specialized processing and global integration , by inheriting the URLanization of the underlying structural architecture .
Edit intent: meaning-changed
Original: Spitzoid lesions may be largely categorized into Spitz Nevus  , Atypical Spitz Tumors  , and Spitz Melanomas . Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz nevus and malignant Spitz melanomas.
Revised: Spitzoid lesions broadly categorized into Spitz Nevus , Atypical Spitz Tumors , and Spitz Melanomas . Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz nevus and malignant Spitz melanomas.
Edit intent: clarity
Original: Spitzoid lesions may be largely categorized into Spitz Nevus  , Atypical Spitz Tumors  , and Spitz Melanomas . Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz nevus and malignant Spitz melanomas.
Revised: Spitzoid lesions may be largely categorized into Spitz Nevus (SN) , Atypical Spitz Tumors , and Spitz Melanomas . Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz nevus and malignant Spitz melanomas.
Edit intent: meaning-changed
Original: Spitzoid lesions may be largely categorized into Spitz Nevus  , Atypical Spitz Tumors  , and Spitz Melanomas . Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz nevus and malignant Spitz melanomas.
Revised: Spitzoid lesions may be largely categorized into Spitz Nevus , Atypical Spitz Tumors (AST) , and Spitz Melanomas . Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz nevus and malignant Spitz melanomas.
Edit intent: clarity
Original: Spitzoid lesions may be largely categorized into Spitz Nevus  , Atypical Spitz Tumors  , and Spitz Melanomas . Classifying a lesion precisely as Atypical Spitz Tumors or AST is challenging and often requires the integration of clinical, histological, and immunohistochemical features to differentiate AST from regular Spitz nevus and malignant Spitz melanomas. Specifically, this paper aims to test several artificial intelligence techniques so as to build a computer aided diagnosis system. A proposed three-phase approach is being implemented. In Phase I, collected data are preprocessed with an effective Synthetic Minority Oversampling TEchnique or SMOTE-based method being implemented to treat the imbalance data problem .
Revised: Spitzoid lesions may be largely categorized into Spitz Nevus , Atypical Spitz Tumors , and Spitz Melanomas (SM). The accurate diagnosis of these lesions is one of the most challenges for dermapathologists; this is due to the high similarities between them. Data mining techniques are successfully applied to situations like these where complexity exists. This study aims to develop an artificial intelligence model to support the diagnosis of Spitzoid lesions. A private spitzoid lesions dataset have been used to evaluate the system proposed in this study. The proposed system has three stages. In the first stage, SMOTE method applied to solve the imbalance data problem .
Edit intent: coherence
Original:  In Phase I, collected data are preprocessed with an effective Synthetic Minority Oversampling TEchnique or SMOTE-based method being implemented to treat the imbalance data problem . Then, a feature selection mechanism using genetic algorithm (GA) is applied in Phase II. Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Revised:  In Phase I, collected data are preprocessed with an effective Synthetic Minority Oversampling TEchnique or SMOTE-based method being implemented to treat the imbalance data problem , in the second stage, in order to eliminate irrelevant features; genetic algorithm is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Edit intent: coherence
Original:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Revised:  Finally, in Phase III, a ten-fold cross-validation method is used to select significant features. This later reduces the computational complexity and speed up the data mining process. In the third stage, Random forest classifier is employed to make a decision for two different categories of lesions (Spitz nevus or Atypical Spitz Tumors). The performance of our proposed scheme is evaluated using accuracy, sensitivity, specificity, G-mean, F- measure, ROC and AUC . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Edit intent: meaning-changed
Original:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Revised:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with our SMOTE-GA-RF model with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Edit intent: meaning-changed
Original:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Revised:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 16 features show a great performance with accuracy 0.97, F-measure 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Edit intent: meaning-changed
Original:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 ), a sensitivity of 0.99, and a specificity of 0.98, outperforming other Spitzoid lesionsclassification algorithms .
Revised:  Finally, in Phase III, a ten-fold cross-validation method is used to compare the performance of seven machine-learning algorithms for classification . Results obtained with SMOTE-Multilayer Perceptron with GA-based 14 features show the highest classification accuracy ( 0.98 , AUC 0.98, and G-mean 0.97.Results obtained in this study have potential to open new opportunities in diagnosis of spitzoid lesions .
Edit intent: clarity
Original:  In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads  .
Revised:  In this position paper, we present the current landscape of TinyML and discuss the challenges and direction towards developing a fair and useful hardware benchmark for TinyML workloads . Furthermore, we present our three preliminary benchmarks and discuss our selection methodology .
Edit intent: meaning-changed
Original:  where W is the absolute maximum weight of an edge in the graph . For sparse graphs, this improves over the best known running time for this problem and, by well-known reductions, also implies improved running times for the shortest path problem with negative weights, minimum cost bipartite \mathit{b}-matching when \|\mathit{b}\|_1 = O(m), and recovers the running time of the currently fastest algorithm for maximum flow in graphs with unit capacities (Liu-Sidford  ).
Revised:  where W is the maximum absolute value of any edge weight . For sparse graphs, this improves over the best known running time for this problem and, by well-known reductions, also implies improved running times for the shortest path problem with negative weights, minimum cost bipartite \mathit{b}-matching when \|\mathit{b}\|_1 = O(m), and recovers the running time of the currently fastest algorithm for maximum flow in graphs with unit capacities (Liu-Sidford ).
Edit intent: clarity
Original:  where W is the absolute maximum weight of an edge in the graph . For sparse graphs, this improves over the best known running time for this problem and, by well-known reductions, also implies improved running times for the shortest path problem with negative weights, minimum cost bipartite \mathit{b}-matching when \|\mathit{b}\|_1 = O(m), and recovers the running time of the currently fastest algorithm for maximum flow in graphs with unit capacities (Liu-Sidford  ).
Revised:  where W is the absolute maximum weight of an edge in the graph . For sparse graphs, this improves over the best known running time for this problem and, by well-known reductions, also implies improved running times for the shortest path problem with negative weights, minimum cost bipartite \mathit{b}-matching when \|\mathit{b}\|_1 = O(m), and recovers the running time of the currently fastest algorithm for maximum flow in graphs with unit capacities (Liu-Sidford , 2020 ).
Edit intent: meaning-changed
Original:  These circulations are derived by computing a regularized version of the standard Newton step, which can be done efficiently due to recent developments on computing \ell_p-norm minimizing flows (Kyng-Peng-Sachdeva-Wang  ).
Revised:  These circulations are derived by computing a regularized version of the standard Newton step, which is partially inspired by previous work on the unit-capacity maximum flow problem (Liu-Sidford, 2019), and subsequently refined based on the very recent progress on this problem (Liu-Sidford, 2020). The resulting step problem can then be computed efficiently using the recent work on \ell_p-norm minimizing flows (Kyng-Peng-Sachdeva-Wang ).
Edit intent: meaning-changed
Original:  These circulations are derived by computing a regularized version of the standard Newton step, which can be done efficiently due to recent developments on computing \ell_p-norm minimizing flows (Kyng-Peng-Sachdeva-Wang  ).
Revised:  These circulations are derived by computing a regularized version of the standard Newton step, which can be done efficiently due to recent developments on computing \ell_p-norm minimizing flows (Kyng-Peng-Sachdeva-Wang , 2019 ).
Edit intent: meaning-changed
Original:  We extracted both blacklists compared them with four other blacklists , including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN.
Revised:  We extracted both blocklists compared them with four other blacklists , including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN.
Edit intent: fluency
Original:  We extracted both blacklists compared them with four other blacklists , including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN.
Revised:  We extracted both blacklists compared them with four other blocklists , including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN.
Edit intent: fluency
Original:  We extracted both blacklists compared them with four other blacklists , including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blacklists for 4- and 6-digit PINs that always excluded the first-choice PIN.
Revised:  We extracted both blacklists compared them with four other blacklists , including a small 4-digit (27 PINs), a large 4-digit (2740 PINs), and two placebo blocklists for 4- and 6-digit PINs that always excluded the first-choice PIN.
Edit intent: fluency
Original:  In this paper, we show that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events , as well as exploit additional web resources for training efficient information filtering models during an ongoing crisis.
Revised:  In this paper, we hypothesize that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events , as well as exploit additional web resources for training efficient information filtering models during an ongoing crisis.
Edit intent: clarity
Original:  In this paper, we show that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events , as well as exploit additional web resources for training efficient information filtering models during an ongoing crisis.
Revised:  In this paper, we show that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events for training efficient information filtering models during an ongoing crisis.
Edit intent: coherence
Original:  In this paper, we show that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events , as well as exploit additional web resources for training efficient information filtering models during an ongoing crisis.
Revised:  In this paper, we show that unsupervised domain adaptation through multi-task learning can be a useful framework to leverage data from past crisis events , as well as exploit additional web resources for training efficient information filtering models during the sudden onset of a new crisis.
Edit intent: clarity
Original:  We present a novel method to classify relevant tweets during an ongoing crisis without seeing any new examples, using the publicly available dataset of TREC incident streams that provides labeled tweets with 4 relevant classes across 10 different crisis events. Additionally, our method addresses a crucial but missing component from current research in web science for crisis data filtering models: interpretability.
Revised:  We present a novel method to classify relevant tweets during an ongoing crisis without seeing any new examples, using the publicly available dataset of TREC incident streams .
Edit intent: coherence
Original:  Specifically, we first identify a standard single-task attention-based neural network architecture and then construct a customized multi-task architecture for the crisis domain: Multi-Task Domain Adversarial Attention Network .
Revised:  Specifically, we first identify a standard single-task attention-based neural network architecture and then construct a customized multi-task architecture with a multi-domain discriminator for crisis analytics: multi-task domain adversarial attention network .
Edit intent: meaning-changed
Original:  Our results show that the multi-task model outperformed its single-task counterpartand also, training with additional web-resources showed further performance boost. Furthermore , we show that the attention layer can be used as a guide to explain the model predictions  by showcasing the words in a tweet that are deemed important in the classification process.
Revised:  Our results show that the multi-task model outperformed its single task counterpart. For the qualitative evaluation of interpretability , we show that the attention layer can be used as a guide to explain the model predictions by showcasing the words in a tweet that are deemed important in the classification process.
Edit intent: meaning-changed
Original:  Using the average R from January 21 to 23, 2020 as a proxy of non-intervened transmission intensity, we find, under a linear regression framework for 100 Chinese cities, high temperature and high relative humidity  reduce the transmission of COVID-19 with a significance level of 1\% and 5\% , respectively, even after controlling for population density and GDP per capita of cities.
Revised:  Using the daily R values from January 21 to 23, 2020 as a proxy of non-intervened transmission intensity, we find, under a linear regression framework for 100 Chinese cities, high temperature and high relative humidity reduce the transmission of COVID-19 with a significance level of 1\% and 5\% , respectively, even after controlling for population density and GDP per capita of cities.
Edit intent: clarity
Original:  Using the average R from January 21 to 23, 2020 as a proxy of non-intervened transmission intensity, we find, under a linear regression framework for 100 Chinese cities, high temperature and high relative humidity  reduce the transmission of COVID-19 with a significance level of 1\% and 5\% , respectively, even after controlling for population density and GDP per capita of cities.
Revised:  Using the average R from January 21 to 23, 2020 as proxies of non-intervened transmission intensity, we find, under a linear regression framework for 100 Chinese cities, high temperature and high relative humidity reduce the transmission of COVID-19 with a significance level of 1\% and 5\% , respectively, even after controlling for population density and GDP per capita of cities.
Edit intent: fluency