wanyu/IteraTeR_full_sent huggingface dataset:
Original:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset  .
Revised:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure type classification setting new benchmarks on the dataset .
Edit intent: fluency
Original:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset  .
Revised:  To overcome these challenges, in this paper, we present adeep learning based framework which learns multi-spectralfeature embeddings using multiple CNN models in an ensem-ble architecture for accurate cross-patient seizure type clas-sification . Experiments on the recently released TUH EEGSeizure Corpus show that our multi-spectral dense featurelearning produces a weighted f1 score of 0.98 for seizure typeclassification setting new benchmarks on the dataset . We also show that the high-level feature embeddings learnt by SeizureNet considerably improve the classification accuracy of smaller networks through knowledge distillation for applications with low-memory and fast inference speed requirements .
Edit intent: meaning-changed
Original:  Experiments on the recently released TUH EEG Seizure Corpus  show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset .
Revised:  We used the recently released TUH EEG Seizure Corpus show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset .
Edit intent: coherence
Original:  Experiments on the recently released TUH EEG Seizure Corpus  show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset .
Revised:  Experiments on the recently released TUH EEG Seizure Corpus (V1.4.0 and V1.5.2) to evaluate the performance of SeizureNet. Experiments show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset .
Edit intent: meaning-changed
Original:  Experiments on the recently released TUH EEG Seizure Corpus  show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset .
Revised:  Experiments on the recently released TUH EEG Seizure Corpus show that SeizureNet can reach a weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset .
Edit intent: clarity
Original:  Experiments on the recently released TUH EEG Seizure Corpus  show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset .
Revised:  Experiments on the recently released TUH EEG Seizure Corpus show that SeizureNet produces state-of-the-art weighted F1 score of up to 0.95 for seizure-wise cross validation and 0.62 for patient-wise cross validation for scalp EEG based multi-class seizure type classification setting new benchmarks on the dataset .
Edit intent: meaning-changed
Original:  Experiments on the recently released TUH EEG Seizure Corpus  show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification setting new benchmarks on the dataset .
Revised:  Experiments on the recently released TUH EEG Seizure Corpus show that SeizureNet produces state-of-the-art weighted F1 scores of 0.98 for seizure type classification .
Edit intent: clarity
Original:  Experiments show that SeizureNet can reach a weighted F1 score of up to 0.95 for seizure-wise cross validation and 0.62 for patient-wise cross validation for scalp EEG based multi-class seizure type classification.
Revised:  Experiments show that SeizureNet can reach a weighted F1 score of up to 0.94 for seizure-wise cross validation and 0.62 for patient-wise cross validation for scalp EEG based multi-class seizure type classification.
Edit intent: meaning-changed
Original:  Experiments show that SeizureNet can reach a weighted F1 score of up to 0.95 for seizure-wise cross validation and 0.62 for patient-wise cross validation for scalp EEG based multi-class seizure type classification.
Revised:  Experiments show that SeizureNet can reach a weighted F1 score of up to 0.95 for seizure-wise cross validation and 0.59 for patient-wise cross validation for scalp EEG based multi-class seizure type classification.
Edit intent: meaning-changed
Original:   We apply few data preprocessing steps  and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Revised:  Using such a massive dataset is a novelty in the field. We apply few data preprocessing steps and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Edit intent: meaning-changed
Original:   We apply few data preprocessing steps  and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Revised:  We apply few data preprocessing steps , derive specific features and train different classifiers such as logistic regression (LR), feed forward artificial neural network (ANN), artificial neural network combined with principal component analysis (PCA-ANN), support vector machine (SVM) and random forest (RF) on this data to choose the one of the best performance.
Edit intent: meaning-changed
Original:  The task at hand is to decide for each point of P if it is in C  . We show that one can solve this problem in two and three dimensions using O( \mathrm{index (P) \log n) queries, where \mathrm{index (P) is the largest subset of points of P in convex position.
Revised:  The task at hand is to decide for each point of P if it is in C using the fewest number of oracle queries . We show that one can solve this problem in two and three dimensions using O( \mathrm{index (P) \log n) queries, where \mathrm{index (P) is the largest subset of points of P in convex position.
Edit intent: meaning-changed
Original:  The task at hand is to decide for each point of P if it is in C  . We show that one can solve this problem in two and three dimensions using O( \mathrm{index (P) \log n) queries, where \mathrm{index (P) is the largest subset of points of P in convex position.
Revised:  The task at hand is to decide for each point of P if it is in C . We show that one can solve this problem in two and three dimensions using O( h (P) \log n) queries, where \mathrm{index (P) is the largest subset of points of P in convex position.
Edit intent: meaning-changed
Original:  The task at hand is to decide for each point of P if it is in C  . We show that one can solve this problem in two and three dimensions using O( \mathrm{index (P) \log n) queries, where \mathrm{index (P) is the largest subset of points of P in convex position.
Revised:  The task at hand is to decide for each point of P if it is in C . We show that one can solve this problem in two and three dimensions using O( \mathrm{index (P) \log n) queries, where h (P) is the largest subset of points of P in convex position.
Edit intent: meaning-changed
Original:  Furthermore, we show that in two dimensions one can solve this problem using O( \mathrm{price (P,C) \log^2 n ) oracle queries, where \mathrm{price (P, C) is the minimal number of queries that any algorithm for this specific instance requires.
Revised:  Furthermore, we show that in two dimensions one can solve this problem using O( v (P,C) \log^2 n ) oracle queries, where \mathrm{price (P, C) is the minimal number of queries that any algorithm for this specific instance requires.
Edit intent: meaning-changed
Original:  Furthermore, we show that in two dimensions one can solve this problem using O( \mathrm{price (P,C) \log^2 n ) oracle queries, where \mathrm{price (P, C) is the minimal number of queries that any algorithm for this specific instance requires.
Revised:  Furthermore, we show that in two dimensions one can solve this problem using O( \mathrm{price (P,C) \log^2 n ) oracle queries, where v (P, C) is the minimal number of queries that any algorithm for this specific instance requires.
Edit intent: meaning-changed
Original:  Furthermore, we show that in two dimensions one can solve this problem using O( \mathrm{price (P,C) \log^2 n ) oracle queries, where \mathrm{price (P, C) is the minimal number of queries that any algorithm for this specific instance requires.
Revised:  Furthermore, we show that in two dimensions one can solve this problem using O( \mathrm{price (P,C) \log^2 n ) oracle queries, where \mathrm{price (P, C) is a lower bound on the minimum number of queries that any algorithm for this specific instance requires.
Edit intent: clarity
Original: In this paper, we complement the existing literature on the occupation time in the red (below level 0) of a spectrally negative L\'evy process, and later extend the analysis to the refracted spectrally negative L\'evy process.
Revised: In this paper, we obtain analytical expression for the distribution of the occupation time in the red (below level 0) of a spectrally negative L\'evy process, and later extend the analysis to the refracted spectrally negative L\'evy process.
Edit intent: meaning-changed
Original: In this paper, we complement the existing literature on the occupation time in the red (below level 0) of a spectrally negative L\'evy process, and later extend the analysis to the refracted spectrally negative L\'evy process.
Revised: In this paper, we complement the existing literature on the occupation time in the red (below level 0) up to an (independent) exponential horizon for spectrally negative L\'{e refracted spectrally negative L\'evy process.
Edit intent: meaning-changed
Original: In this paper, we complement the existing literature on the occupation time in the red (below level 0) of a spectrally negative L\'evy process, and later extend the analysis to the refracted spectrally negative L\'evy process. For both classes of processes, we derive an explicit expression for the distribution of such occupation timeup to an independent exponential time. As an application, we consider theinverse occupation time (also known as the time of cumulative Parisian ruin in \mbox{%DIFAUXCMD guerinrenaud20150pt%DIFAUXCMD ), where ruin is deemed to occur at the earliest time the risk process cumulatively stays below a critical level over a pre-determined time-threshold.
Revised: In this paper, we complement the existing literature on the occupation time in the red (below level 0) of a spectrally negative L\'evy process, and later extend the analysis to the refracted spectrally negative inverse occupation time (also known as the time of cumulative Parisian ruin in \mbox{%DIFAUXCMD guerinrenaud20150pt%DIFAUXCMD ), where ruin is deemed to occur at the earliest time the risk process cumulatively stays below a critical level over a pre-determined time-threshold.
Edit intent: clarity
Original:  As an application, we consider theinverse occupation time (also known as the time of cumulative Parisian ruin in \mbox{%DIFAUXCMD guerinrenaud20150pt%DIFAUXCMD ), where ruin is deemed to occur at the earliest time the risk process cumulatively stays below a critical level over a pre-determined time-threshold.
Revised:  As an application, we consider the (also known as the time of cumulative Parisian ruin in \mbox{%DIFAUXCMD guerinrenaud20150pt%DIFAUXCMD ), where ruin is deemed to occur at the earliest time the risk process cumulatively stays below a critical level over a pre-determined time-threshold.
Edit intent: clarity
Original:  As an application, we consider theinverse occupation time (also known as the time of cumulative Parisian ruin in \mbox{%DIFAUXCMD guerinrenaud20150pt%DIFAUXCMD ), where ruin is deemed to occur at the earliest time the risk process cumulatively stays below a critical level over a pre-determined time-threshold.
Revised:  As an application, we consider theinverse occupation time 0pt%DIFAUXCMD ), where ruin is deemed to occur at the earliest time the risk process cumulatively stays below a critical level over a pre-determined time-threshold.
Edit intent: clarity
Original: Based on the behavior coordinate system, ideal individual model and quantum probability, the state of an ideal individual is assumed to be described by the behavior state function. Then we present a conjecture that the ideal individuals can be identical. A consequence of the identical individual conjecture is that the existence of the personal space and the behavior differentiation under high population density condition receive a possible theoretical explanation .
Revised: The identical ideal individual hypothesis is proposed. According to this hypothesis, the identical ideal individuals should be classified into two classes: the bosonic individuals and the fermionic individuals. The bosonic individuals can occupy the same behavior state while the fermionic individuals can not be in the same behavior state. We propose that human beings and many species of animals are fermionic, which can not occupy the same behavior state according to the Pauli exclusion principle. An unified theoretical explanation is given for the natures of two important and seemingly irrelated phenomena in psychology: the existence of the personal space and the behavior differentiation under high population density condition receive a possible theoretical explanation .
Edit intent: meaning-changed
Original:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising , calledNormalNet . Different from natural images, for mesh, it is difficult to collect enough examples to build a robust end-to-end training scheme for deep networks.
Revised:  In this work, we propose a learning-based normal filtering scheme for mesh denoising , calledNormalNet . Different from natural images, for mesh, it is difficult to collect enough examples to build a robust end-to-end training scheme for deep networks.
Edit intent: clarity
Original:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising , calledNormalNet . Different from natural images, for mesh, it is difficult to collect enough examples to build a robust end-to-end training scheme for deep networks.
Revised:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising NormalNet . Different from natural images, for mesh, it is difficult to collect enough examples to build a robust end-to-end training scheme for deep networks.
Edit intent: clarity
Original:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising , calledNormalNet . Different from natural images, for mesh, it is difficult to collect enough examples to build a robust end-to-end training scheme for deep networks.
Revised:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising , called . Different from natural images, for mesh, it is difficult to collect enough examples to build a robust end-to-end training scheme for deep networks.
Edit intent: clarity
Original:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising , calledNormalNet . Different from natural images, for mesh, it is difficult to collect enough examples to build a robust end-to-end training scheme for deep networks. To remedy this problem, we propose an iterative framework to generate enough face-normal pairs, based on which a convolutional neural networks (CNNs) based scheme is designed for guidance normal learning. Moreover, to facilitate the 3D convolution operation in CNNs, for each face in mesh, we propose a voxelization strategy to transform irregular local mesh structure into regular 4D-array form.
Revised:  In this work, we propose a deep learning based face normal filtering scheme for mesh denoising , calledNormalNet called NormalNet, which maps the guided normal filtering (GNF) into a deep network. The scheme follows the iterative framework of filtering-based mesh denoising. During each iteration, first, the voxelization strategy is applied on each face in mesh, we propose a voxelization strategy to transform irregular local mesh structure into regular 4D-array form.
Edit intent: coherence
Original:  Moreover, to facilitate the 3D convolution operation in CNNs, for each face in mesh, we propose a voxelization strategy to transform irregular local mesh structure into regular 4D-array form. Finally, guided normal filtering is performed to obtain filtered face normals, according to which denoised positions of vertices are derived . Compared to the state-of-the-art works, the proposed scheme can generate accurate guidance normals and remove noise effectively while preserving original features and avoiding pseudo-features.
Revised:  Moreover, to facilitate the 3D convolution operation in CNNs, for each face in a mesh to transform the irregular local structure into the regular volumetric representation, therefore, both the structure and face normal information are preserved and the convolution operations in CNN(Convolutional Neural Network) can be easily performed. Second, instead of the guidance normal generation and the guided filtering in GNF, a deep CNN is designed, which takes the volumetric representation as input, and outputs the learned filtered normals. At last, the vertex positions are updated according to the filtered normals. Specifically, the iterative training framework is proposed, in which the generation of training data and the network training are alternately performed, whereas the ground truth normals are taken as the guidance normals in GNF to get the target normals . Compared to the state-of-the-art works, the proposed scheme can generate accurate guidance normals and remove noise effectively while preserving original features and avoiding pseudo-features.
Edit intent: meaning-changed
Original:  Finally, guided normal filtering is performed to obtain filtered face normals, according to which denoised positions of vertices are derived . Compared to the state-of-the-art works, the proposed scheme can generate accurate guidance normals and remove noise effectively while preserving original features and avoiding pseudo-features.
Revised:  Finally, guided normal filtering is performed to obtain filtered face normals, according to which denoised positions of vertices are derived . Compared to state-of-the-art works, the proposed scheme can generate accurate guidance normals and remove noise effectively while preserving original features and avoiding pseudo-features.
Edit intent: fluency
Original:  Finally, guided normal filtering is performed to obtain filtered face normals, according to which denoised positions of vertices are derived . Compared to the state-of-the-art works, the proposed scheme can generate accurate guidance normals and remove noise effectively while preserving original features and avoiding pseudo-features.
Revised:  Finally, guided normal filtering is performed to obtain filtered face normals, according to which denoised positions of vertices are derived . Compared to the state-of-the-art works, NormalNet can effectively remove noise while preserving the original features and avoiding pseudo-features.
Edit intent: clarity
Original:  This is surprising given that an increased mechanistic knowledge of learning by doing-based improvements has the potential to speed up the development of evidence-based teaching practices. Here we use neuroscience methods and a gesture-enriched foreign language (L2) vocabulary learning paradigm to adjudicate between opposing predictions of two neuroscientific learning theories .
Revised:  Here we show that visual association cortex causally contributes to performance benefits of a learning by doing method. This finding derives from transcranial magnetic stimulation (TMS) and a gesture-enriched foreign language (L2) vocabulary learning paradigm to adjudicate between opposing predictions of two neuroscientific learning theories .
Edit intent: meaning-changed
Original:  Here we use neuroscience methods and a gesture-enriched foreign language (L2) vocabulary learning paradigm to adjudicate between opposing predictions of two neuroscientific learning theories .
Revised:  Here we use neuroscience methods and a gesture-enriched foreign language (L2) vocabulary learning paradigm performed by 22 young adults. Inhibitory TMS of visual motion cortex reduced learning outcomes for abstract and concrete gesture-enriched words in comparison to sham stimulation. There were no TMS effects on words learned with pictures. These results adjudicate between opposing predictions of two neuroscientific learning theories .
Edit intent: meaning-changed
Original:  Here we use neuroscience methods and a gesture-enriched foreign language (L2) vocabulary learning paradigm to adjudicate between opposing predictions of two neuroscientific learning theories . We demonstrate that sensory brain regions associated with the processing of gesture-related stimulus information causally contribute to performance benefits following gesture-enriched L2 vocabulary learning . Sensory brain responses benefitted both short- and long-term learning outcomes, as well as the learning of both concrete and abstract words. These findings suggest that responses within specialized visual sensory cortices precipitate sensorimotor-based learning benefits .
Revised:  Here we use neuroscience methods and a gesture-enriched foreign language (L2) vocabulary learning paradigm to adjudicate between opposing predictions of two neuroscientific learning theories : While reactivation-based theories predict no functional role of visual motion cortex in vocabulary learning outcomes, the current study supports the predictive coding theory view that specialized sensory cortices precipitate sensorimotor-based learning benefits .
Edit intent: meaning-changed