wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We construct a rich family of practical choice rules, slot-specific priorities with capacity transfers (SSPwCT) , that utilize both independent slot priorities and transferability of vacant slots.
Revised:  Motivated by the need in such real-life matching problems, this paper formulates a family of practical choice rules, slot-specific priorities with capacity transfers (SSPwCT) , that utilize both independent slot priorities and transferability of vacant slots.
Edit intent: clarity
Original:  This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: 1 ) the gender of the speaker, and 2 ) the duration of the emotional episode.
Revised:  This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i ) the gender of the speaker, and 2 ) the duration of the emotional episode.
Edit intent: fluency
Original:  This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: 1 ) the gender of the speaker, and 2 ) the duration of the emotional episode.
Revised:  This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: 1 ) the gender of the speaker, and ii ) the duration of the emotional episode.
Edit intent: coherence
Original:  Using a large dataset of more than 2500 manually annotated videos from YouTube ,  we found that while multimodal features consistently outperformed bimodal and unimodal features, their performances varied significantly for different emotions, gender and duration contexts.
Revised:  Using a large public dataset of 2 , we found that while multimodal features consistently outperformed bimodal and unimodal features, their performances varied significantly for different emotions, gender and duration contexts.
Edit intent: meaning-changed
Original:  Using a large dataset of more than 2500 manually annotated videos from YouTube ,  we found that while multimodal features consistently outperformed bimodal and unimodal features, their performances varied significantly for different emotions, gender and duration contexts.
Revised:  Using a large dataset of more than 2500 manually annotated videos from YouTube , 176 manually annotated YouTube videos, we found that while multimodal features consistently outperformed bimodal and unimodal features, their performances varied significantly for different emotions, gender and duration contexts.
Edit intent: meaning-changed
Original:  Using a large dataset of more than 2500 manually annotated videos from YouTube ,  we found that while multimodal features consistently outperformed bimodal and unimodal features, their performances varied significantly for different emotions, gender and duration contexts.
Revised:  Using a large dataset of more than 2500 manually annotated videos from YouTube , we found that while multimodal features consistently outperformed bimodal and unimodal features, their performance varied significantly across different emotions, gender and duration contexts.
Edit intent: clarity
Original:  This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i) the gender of the speaker, and ii) the duration of the emotional episode.
Revised:  This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i) gender of the speaker, and ii) the duration of the emotional episode.
Edit intent: fluency
Original:  This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i) the gender of the speaker, and ii) the duration of the emotional episode.
Revised:  This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: i) the gender of the speaker, and ii) duration of the emotional episode.
Edit intent: fluency
Original:  {\lambda \left( t\right) }+\alpha \left( b\right) }}\% \] where p_{t}\left( a,A\right) is the probability that alternative a is selected from the set A of feasible alternatives if t is the time available to decide, \lambda is a time dependent noise parameter measuring the unit cost of information, u is a time independent utility function, and \alpha is an alternative-specific bias that determines the initial choice probabilities and possibly reflects prior information .
Revised:  {\lambda \left( t\right) }+\alpha \left( b\right) }}\% \] where p_{t}\left( a,A\right) is the probability that alternative a is selected from the set A of feasible alternatives if t is the time available to decide, \lambda is a time dependent noise parameter measuring the unit cost of information, u is a time independent utility function, and \alpha is an alternative-specific bias that determines the initial choice probabilities reflecting prior information and memory anchoring .
Edit intent: meaning-changed
Original:  The SARS-CoV-2 virus is known to gain entry into various cell types through the binding of one of its surface proteins (spike) to the host Angiotensin Converting Enzyme 2 (ACE2).
Revised:  The SARS-CoV-2 virus is known to gain entry into various cell types through the binding of one of its surface proteins (spike) to the host Angiotensin-Converting Enzyme 2 (ACE2).
Edit intent: fluency
Original:  Importantly, among the different virtual hits, we identified mefloquine, a quinoline-derivative belonging to a class of antimalaria agents (e.g. chloroquine and hydroxychloroquine) recently described for their effects on ACE2 maturation.
Revised:  We identified four compounds (the atypical antipsychotic Ziprasidone, the antihistamine Buclizine, the antiviral Beclabuvir and the antimalaric Artefenomel) as capable of lowering ACE2 maturation.
Edit intent: meaning-changed
Original:  Importantly, among the different virtual hits, we identified mefloquine, a quinoline-derivative belonging to a class of antimalaria agents (e.g. chloroquine and hydroxychloroquine) recently described for their effects on ACE2 maturation. Thus, our results suggest that these drugs could act against SARS-CoV-2 by altering the folding pathway of its receptor ACE2  .
Revised:  Importantly, among the different virtual hits, we identified mefloquine, a quinoline-derivative belonging to a class of antimalaria agents (e.g. chloroquine and hydroxychloroquine) recently described for their effects on ACE2 expression in Vero cells in a dose-dependent fashion. Importantly, these molecules potently inhibited the entry into cells of a pseudotyped retrovirus exposing the SARS-CoV-2 spike protein. Our results indicate that these drugs could act against SARS-CoV-2 by altering the folding pathway of its receptor ACE2 .
Edit intent: meaning-changed
Original:  Thus, our results suggest that these drugs could act against SARS-CoV-2 by altering the folding pathway of its receptor ACE2  .
Revised:  Thus, our results suggest that these drugs could act against SARS-CoV-2 by altering the folding pathway of its receptor ACE2 . Ongoing studies are directly evaluating the possibility of repurposing these drugs for the treatment of COVID-19 .
Edit intent: meaning-changed
Original:  We identified four compounds (the atypical antipsychotic Ziprasidone, the antihistamine Buclizine, the antiviral Beclabuvir and the antimalaric Artefenomel) as capable of  lowering ACE2 expression in Vero cells in a dose-dependent fashion.
Revised:  We identified four compounds (the atypical antipsychotic Ziprasidone, the antihistamine Buclizine, the antiviral Beclabuvir and the antimalaric Artefenomel) as capable of dose-dependently lowering ACE2 expression in Vero cells in a dose-dependent fashion.
Edit intent: clarity
Original:  We identified four compounds (the atypical antipsychotic Ziprasidone, the antihistamine Buclizine, the antiviral Beclabuvir and the antimalaric Artefenomel) as capable of  lowering ACE2 expression in Vero cells in a dose-dependent fashion. Importantly, these molecules potently inhibited the entry into cells of a pseudotyped retrovirus exposing the SARS-CoV-2 spike protein.
Revised:  We identified four compounds (the atypical antipsychotic Ziprasidone, the antihistamine Buclizine, the antiviral Beclabuvir and the antimalaric Artefenomel) as capable of lowering ACE2 expression in Vero cells . These molecules inhibit the entry into cells of a pseudotyped retrovirus exposing the SARS-CoV-2 spike protein.
Edit intent: clarity
Original:  Our results indicate that these drugs could act against SARS-CoV-2 by altering the folding pathway of its receptor ACE2. Ongoing studies are directly evaluating the possibility of repurposing these drugs for the treatment of COVID-19 .
Revised:  Our results indicate that these drugs could act against SARS-CoV-2 (MEX-BC2/2020 strain). One of the selected drugs (Artefenomel) could completely prevent cytopathic effects induced by the presence of the virus, thus showing a promising antiviral activity against SARS-CoV-2 .
Edit intent: meaning-changed
Original:  We identified four compounds (the atypical antipsychotic Ziprasidone, the antihistamine Buclizine, the antiviral Beclabuvir and the antimalaric Artefenomel) as capable of dose-dependently lowering ACE2 expression in Vero cells .
Revised:  We identified four compounds capable of lowering ACE2 expression in Vero cells .
Edit intent: clarity
Original:  We identified four compounds (the atypical antipsychotic Ziprasidone, the antihistamine Buclizine, the antiviral Beclabuvir and the antimalaric Artefenomel) as capable of dose-dependently lowering ACE2 expression in Vero cells . These molecules inhibit the entry into cells of a pseudotyped retrovirus exposing the SARS-CoV-2 spike protein.
Revised:  We identified four compounds (the atypical antipsychotic Ziprasidone, the antihistamine Buclizine, the antiviral Beclabuvir and the antimalaric Artefenomel) as capable of dose-dependently lowering ACE2 expression in Vero cells in a dose-dependent fashion. All these molecules were found to inhibit the entry into cells of a pseudotyped retrovirus exposing the SARS-CoV-2 spike protein.
Edit intent: clarity
Original:  One of the selected drugs (Artefenomel) could completely prevent cytopathic effects induced by the presence of the virus, thus showing a promising antiviral activity against SARS-CoV-2  .
Revised:  One of the selected drugs (Artefenomel) could completely prevent cytopathic effects induced by the presence of the virus, thus showing antiviral activity against SARS-CoV-2 .
Edit intent: clarity
Original:  One of the selected drugs (Artefenomel) could completely prevent cytopathic effects induced by the presence of the virus, thus showing a promising antiviral activity against SARS-CoV-2  .
Revised:  One of the selected drugs (Artefenomel) could completely prevent cytopathic effects induced by the presence of the virus, thus showing a promising antiviral activity against SARS-CoV-2 . Ongoing studies are further evaluating the possibility of repurposing these drugs for the treatment of COVID-19 .
Edit intent: meaning-changed
Original:  Thus, we systematically analyze the LIF neuron. We start by deriving simple integration equations to which even a gradient can be assigned. Additionally, we prove that a Long-Short-Term-Memory unit can be tuned to show similar spiking properties. Additionally, LIF units are applied to an image classification task, trained with backpropagation .
Revised:  Thus, we derived simple update-rules for the LIF units from the differential equations, which are easy to numerically integrate. We apply a novel approach to train the LIF units supervisedly via backpropagation, by assigning a constant value to the derivative of the neuron activation function exclusively for the backpropagation step. This simple mathematical trick helps to distribute the error between the neurons of the pre-connected layer. We apply our method to the IRIS blossoms image data set and show that the training technique can be used to train LIF neurons on image classification tasks. Furthermore, we show how to integrate our method in the KERAS (tensorflow) framework and efficiently run it on GPUs. To generate a deeper understanding of the mechanisms during training we developed interactive illustrations, which we provide online .
Edit intent: meaning-changed
Original:  In terms of both computational efficiency and ease of learning, it is preferable to dynamically vary the numbers of computations according to the hardness of the input words. However, how to find a suitable estimation for such hardness , then explicitly modeling adaptive computation depths are still not investigated.
Revised:  In terms of both computational efficiency and ease of learning, it is preferable to dynamically vary the numbers of computations according to the hardness of input words , then explicitly modeling adaptive computation depths are still not investigated.
Edit intent: coherence
Original:  However, how to find a suitable estimation for such hardness , then explicitly modeling adaptive computation depths are still not investigated.
Revised:  However, how to find a suitable estimation for such hardness , and thus improve efficiency. The main challenge is how to measure such hardness and decide the required depths (i.e., layers) to conduct. Previous works generally build a halting unit to decide whether the computation should continue or stop at each layer. As there is no specific supervision of depth selection, the halting unit may be under-optimized and inaccurate, which results in suboptimal and unstable performance when modeling sentences.
Edit intent: meaning-changed
Original:  In this paper, we try to solve this issue, and propose two effective approaches , namely 1) mutual information  based estimation and 2) reconstruction loss based estimation , to measure the hardness of learning the representation for a word and determine its computational depth.
Revised:  In this paper, we get rid of the halting unit and estimate the required depths in advance, which yields a faster depth-adaptive model. Specifically, two approaches are proposed to explicitly measure the hardness of input words and estimate corresponding adaptive depth , namely 1) mutual information based estimation and 2) reconstruction loss based estimation , to measure the hardness of learning the representation for a word and determine its computational depth.
Edit intent: meaning-changed
Original:  In this paper, we try to solve this issue, and propose two effective approaches , namely 1) mutual information  based estimation and 2) reconstruction loss based estimation , to measure the hardness of learning the representation for a word and determine its computational depth.
Revised:  In this paper, we try to solve this issue, and propose two effective approaches , namely 1) mutual information (MI) based estimation and 2) reconstruction loss based estimation , to measure the hardness of learning the representation for a word and determine its computational depth.
Edit intent: clarity
Original:  In this paper, we try to solve this issue, and propose two effective approaches , namely 1) mutual information  based estimation and 2) reconstruction loss based estimation , to measure the hardness of learning the representation for a word and determine its computational depth. Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models.
Revised:  In this paper, we try to solve this issue, and propose two effective approaches , namely 1) mutual information based estimation and 2) reconstruction loss based estimation . We conduct experiments on the text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models.
Edit intent: coherence
Original:  Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models.
Revised:  Results on the classic text classification task with 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models.
Edit intent: fluency
Original:  Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models.
Revised:  Results on the classic text classification task ( 24 datasets in various sizes and domains . Results confirm that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models.
Edit intent: clarity
Original:  Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models.
Revised:  Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer (up to 7x) while preserving high accuracy. Moreover, efficiency and robustness are significantly improved when compared with other depth-adaptive models.
Edit intent: meaning-changed
Original:  Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive models. More importantly, our approaches lead to more robust depth-adaptive Transformer models with better interpretability of the depth distribution .
Revised:  Results on the classic text classification task ( 24 datasets in various sizes and domains ) show that our approaches achieve superior performance while preserving higher efficiency in computation over the vanilla Transformer and previous depth-adaptive approaches .
Edit intent: clarity
Original:  Results. Our scenarios indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by late May, peaking around mid-June at 7 to 50 times the current supply of beds and ventilators.
Revised:  Results. Our scenarios indicate a surge in COVID-19 patients could overwhelm Chilean hospitals by June, peaking in July or August at 6 to 50 times the current supply of beds and ventilators.
Edit intent: meaning-changed
Original:  To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 117,200 event mention instances, and 207 event types.
Revised:  To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 118,732 event mention instances, and 207 event types.
Edit intent: meaning-changed
Original:  To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 117,200 event mention instances, and 207 event types.
Revised:  To alleviate these problems, we present a MAssive eVENt detection dataset (MAVEN), which contains 4,480 Wikipedia documents, 117,200 event mention instances, and 168 event types.
Edit intent: meaning-changed
Original: We introduce Denise, a deep learning based algorithm for decomposing positive semidefinite matrices into the sum of a low rank plus a sparse matrix.
Revised: The robust PCA of high-dimensional matrices plays an essential role when isolating key explanatory features. The currently available methods for performing such a low-rank plus sparse decomposition are matrix specific, meaning, the algorithm must re-run each time a new matrix should be decomposed. Since these algorithms are computationally expensive, it is preferable to learn and store a function that instantaneously performs this decomposition when evaluated. Therefore, we introduce Denise, a deep learning based algorithm for decomposing positive semidefinite matrices into the sum of a low rank plus a sparse matrix.
Edit intent: meaning-changed
Original: We introduce Denise, a deep learning based algorithm for decomposing positive semidefinite matrices into the sum of a low rank plus a sparse matrix. The deep neural network is trained on a randomly generated dataset using the Cholesky factorization.
Revised: We introduce Denise, a deep learning based algorithm for decomposing positive semidefinite matrices , which learns precisely such a function. Theoretical guarantees that Denise's architecture can approximate the decomposition function, to arbitrary precision and with arbitrarily high probability, are obtained. The training scheme is also shown to convergence to a stationary point of the robust PCA's loss-function. We train Denise on a randomly generated dataset using the Cholesky factorization.
Edit intent: meaning-changed
Original:  The deep neural network is trained on a randomly generated dataset using the Cholesky factorization. This method, benchmarked on synthetic datasets as well as on some S P500 stock returns covariance matrices ,  achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time.
Revised:  The deep neural network is trained on a randomly generated dataset P500 stock returns covariance matrices , achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time.
Edit intent: clarity
Original:  This method, benchmarked on synthetic datasets as well as on some S P500 stock returns covariance matrices ,  achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time.
Revised:  This method, benchmarked on synthetic datasets as well as on some S , achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time.
Edit intent: clarity
Original:  This method, benchmarked on synthetic datasets as well as on some S P500 stock returns covariance matrices ,  achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time.
Revised:  This method, benchmarked on synthetic datasets as well as on some S P500 stock returns covariance matrices , and evaluate the performance of the DNN on synthetic and real-world covariance matrices. Denise achieves comparable results to several state-of-the-art techniques, while outperforming all existing algorithms in terms of computational time.
Edit intent: meaning-changed