wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Our best single model achieves state-of-the-art on Visual Dialog, outperforming prior published work (including model ensembles) by more than 1\% absolute on NDCG and MRR. Next, we carefully analyse our model and find that additional finetuning using 'dense' annotations i.e. relevance scores for all 100 answer options corresponding to each question on a subset of the training set, leads to even higher NDCG -
Revised:  Our best single model outperforms prior published work (including model ensembles) by more than 1\% absolute on NDCG and MRR. Next, we carefully analyse our model and find that additional finetuning using 'dense' annotations i.e. relevance scores for all 100 answer options corresponding to each question on a subset of the training set, leads to even higher NDCG -
Edit intent: coherence
Original:  Our best single model achieves state-of-the-art on Visual Dialog, outperforming prior published work (including model ensembles) by more than 1\% absolute on NDCG and MRR. Next, we carefully analyse our model and find that additional finetuning using 'dense' annotations i.e. relevance scores for all 100 answer options corresponding to each question on a subset of the training set, leads to even higher NDCG -
Revised:  Our best single model achieves state-of-the-art on Visual Dialog, outperforming prior published work (including model ensembles) by more than 1\% absolute on NDCG and MRR. Next, we find that additional finetuning using 'dense' annotations i.e. relevance scores for all 100 answer options corresponding to each question on a subset of the training set, leads to even higher NDCG -
Edit intent: coherence
Original:  Our best single model achieves state-of-the-art on Visual Dialog, outperforming prior published work (including model ensembles) by more than 1\% absolute on NDCG and MRR. Next, we carefully analyse our model and find that additional finetuning using 'dense' annotations i.e. relevance scores for all 100 answer options corresponding to each question on a subset of the training set, leads to even higher NDCG -
Revised:  Our best single model achieves state-of-the-art on Visual Dialog, outperforming prior published work (including model ensembles) by more than 1\% absolute on NDCG and MRR. Next, we carefully analyse our model and find that additional finetuning using "dense" annotations in VisDial leads to even higher NDCG -
Edit intent: clarity
Original:  This highlights a stark trade-off between the two primary metrics for this task -- NDCG and MRR . We find that this is because dense annotations in the dataset do not correlate well with the original ground-truth answers to questions , often rewarding the model for generic responses (e.g. "can't tell") .
Revised:  This highlights a trade-off between the two primary metrics for this task -- NDCG and MRR . We find that this is because dense annotations in the dataset do not correlate well with the original ground-truth answers to questions , often rewarding the model for generic responses (e.g. "can't tell") .
Edit intent: clarity
Original:  This highlights a stark trade-off between the two primary metrics for this task -- NDCG and MRR . We find that this is because dense annotations in the dataset do not correlate well with the original ground-truth answers to questions , often rewarding the model for generic responses (e.g. "can't tell") .
Revised:  This highlights a stark trade-off between the two primary metrics -- NDCG and MRR . We find that this is because dense annotations in the dataset do not correlate well with the original ground-truth answers to questions , often rewarding the model for generic responses (e.g. "can't tell") .
Edit intent: clarity
Original:  This highlights a stark trade-off between the two primary metrics for this task -- NDCG and MRR . We find that this is because dense annotations in the dataset do not correlate well with the original ground-truth answers to questions , often rewarding the model for generic responses (e.g. "can't tell") .
Revised:  This highlights a stark trade-off between the two primary metrics for this task -- NDCG and MRR -- which we find is due to dense annotations not correlating well with the original ground-truth answers to questions , often rewarding the model for generic responses (e.g. "can't tell") .
Edit intent: clarity
Original:  This highlights a stark trade-off between the two primary metrics for this task -- NDCG and MRR . We find that this is because dense annotations in the dataset do not correlate well with the original ground-truth answers to questions , often rewarding the model for generic responses (e.g. "can't tell") .
Revised:  This highlights a stark trade-off between the two primary metrics for this task -- NDCG and MRR . We find that this is because dense annotations in the dataset do not correlate well with the original ground-truth answers to questions .
Edit intent: clarity
Original:  In this work, we develop a workflow for synthesizing insurance datasets leveraging state-of-the-art neural network techniques. We evaluate the predictive modeling efficacy of datasets synthesized from publicly available data in the domains of general insurance pricing and life insurance shock lapse modeling .
Revised:  In this work, we develop a workflow for synthesizing insurance datasets leveraging CTGAN, a recently proposed neural network architecture for generating tabular data. Applying the proposed workflow to publicly available data in the domains of general insurance pricing and life insurance shock lapse modeling .
Edit intent: meaning-changed
Original:  We evaluate the predictive modeling efficacy of datasets synthesized from publicly available data in the domains of general insurance pricing and life insurance shock lapse modeling . The trained synthesizers are able to capture representative characteristics of the real datasets .
Revised:  We evaluate the predictive modeling efficacy of datasets synthesized from publicly available data in the domains of general insurance pricing and life insurance shock lapse modeling , we evaluate the synthesized datasets from a few perspectives: machine learning efficacy, distributions of variables, and stability of model parameters .
Edit intent: meaning-changed
Original:  Since assembly ASM291031v2 was obtained from a specimen originated from the marginal population of Northern Dolly Varden  isolated from the main range of the species and with some traces of introgressive hybridization, this assembly can hardly be considered as a description of a typical genome of S. malma malma.
Revised:  Since assembly ASM291031v2 was obtained from a specimen originated from the marginal population of Northern Dolly Varden (Tree R.) isolated from the main range of the species and with some traces of introgressive hybridization, this assembly can hardly be considered as a description of a typical genome of S. malma malma.
Edit intent: meaning-changed
Original: We undertake a formal study of persistence diagrams and their metrics. We show that barcodes and persistence diagrams together with the bottleneck distance and the Wasserstein distances are obtained via universal constructions and thus have corresponding universal properties. In addition, the 1-Wasserstein distance satisfies Kantorovich-Rubinstein duality. Our constructions and results apply to any metric space with a distinguished basepoint.
Revised: We prove that persistence diagrams with the p-Wasserstein distance form the universal p-subadditive commutative monoid on an underlying metric space with a distinguished basepoint.
Edit intent: meaning-changed
Original: Brain ageing is a continuous process that is affected by many factors including neurodegenerative diseases. Understanding this process is of great value for both neuroscience research and clinical applications. However, revealing underlying mechanisms is challenging due to the lack of longitudinal data. In this paper, we propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data.
Revised: How will my face look when I get older? Or, for a more challenging question: How will my brain look when I get older? To answer this question one must devise (and learn from data) a multivariate auto-regressive function which given an image and a desired target age generates an output image. While collecting data for faces may be easier, collecting longitudinal brain data is not trivial. We propose a deep learning-based method that learns to simulate subject-specific brain ageing trajectories without relying on longitudinal data.
Edit intent: meaning-changed
Original:  Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD ) for this work, as an ordinal variable .
Revised:  Our method synthesises images conditioned on two clinical variables: age as a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD ) for this work, as an ordinal variable .
Edit intent: clarity
Original:  Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD ) for this work, as an ordinal variable .
Revised:  Our method synthesises aged images using a network conditioned on two factors: age ( a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD ) for this work, as an ordinal variable .
Edit intent: clarity
Original:  Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD ) for this work, as an ordinal variable .
Revised:  Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable ), and status of Alzheimer's Disease (AD ) for this work, as an ordinal variable .
Edit intent: coherence
Original:  Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD ) for this work, as an ordinal variable .
Revised:  Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD , an ordinal variable .
Edit intent: coherence
Original:  Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD ) for this work, as an ordinal variable . We adopt an adversarial loss to learn the joint distribution of brain appearance  and clinical variables and define reconstruction losses that help preserve subject identity.
Revised:  Our method synthesises aged images using a network conditioned on two clinical variables: age as a continuous variable , and health state, i.e. status of Alzheimer's Disease (AD ) for this work, as an ordinal variable ). With an adversarial formulation we learn the joint distribution of brain appearance and clinical variables and define reconstruction losses that help preserve subject identity.
Edit intent: clarity
Original:  We use ground-truth longitudinal data from ADNI to evaluate the quality  of synthesised images . A pre-trained age predictor , which estimates the apparent age of a brain image, is used to assess age accuracy.
Revised:  We use ground-truth longitudinal data from ADNI to evaluate the quality of synthesised images using ground-truth longitudinal data and a pre-trained age predictor , which estimates the apparent age of a brain image, is used to assess age accuracy.
Edit intent: meaning-changed
Original:  A pre-trained age predictor , which estimates the apparent age of a brain image, is used to assess age accuracy. In addition, we show that we can train the model on Cam-CAN data and evaluate on the longitudinal data from ADNI, indicating the generalisation power of our approach.
Revised:  A pre-trained age predictor . We show that, despite the use of cross-sectional data, our model learns patterns of gray matter atrophy in the middle temporal gyrus in patients with AD. To demonstrate generalisation ability, we train on the longitudinal data from ADNI, indicating the generalisation power of our approach.
Edit intent: meaning-changed
Original:  In addition, we show that we can train the model on Cam-CAN data and evaluate on the longitudinal data from ADNI, indicating the generalisation power of our approach. Both qualitative and quantitative results show that our method can progressively simulate the ageing process by synthesising realistic brain images. The code will be made publicly available at : URL
Revised:  In addition, we show that we can train the model on Cam-CAN data and evaluate on one dataset and evaluate predictions on the other. In conclusion, our model shows an ability to separate age, disease influence and anatomy using only 2D cross-sectional data that should should be useful in large studies into neurodegenerative disease, that aim to combine several data sources. To facilitate such future studies by the community at large our code is made available at URL
Edit intent: meaning-changed
Original:  Early prediction and identification of high-risk populations can help to prevent its development. With early identification, proper interventions can be used for its prevention .
Revised:  Early prediction and identification of the children at a high risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage this and other related health conditions. Existing predictive tools designed for childhood obesity primarily rely on traditional regression-type methods without exploiting longitudinal patterns of children's data ignoring data temporality .
Edit intent: clarity
Original:  In this paper, we build prediction models to predict future BMI from baseline medical historydata. We used unaugmented Nemours EHR (Electronic Health Record) data as represented in the PEDSnet (A pediatric Learning Health System) common data model.
Revised:  In this paper, we present a machine learning model specifically designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we have used a large unaugmented EHR (Electronic Health Record) data as represented in the PEDSnet (A pediatric Learning Health System) common data model.
Edit intent: clarity
Original:  We used unaugmented Nemours EHR (Electronic Health Record) data as represented in the PEDSnet (A pediatric Learning Health System) common data model. We trained variety of machine learning models to perform binary classification of obese, and non-obese for children in early childhood ages and during adolescence. We explored if deep learning techniques that can model the temporal nature of EHR datawould improve the performance of predicting obesity as compared to other machine learning techniques that ignore temporality. We also added attention layer at top of rnn layer in our model to compute the  attention scores of each hidden layer corresponding to each input timestep.
Revised:  We used unaugmented Nemours EHR (Electronic Health Record) dataset from a major pediatric health system in the US. We adopt a general long short-term memory network architecture for our model for training over dynamic (sequential) and static (demographic) EHR data. We have additionally included a set embedding and attention layers to compute the attention scores of each hidden layer corresponding to each input timestep.
Edit intent: clarity
Original:  We also added attention layer at top of rnn layer in our model to compute the  attention scores of each hidden layer corresponding to each input timestep. The attention score for each timestep were computed as an average score given to all the features associated with the timestep. These attention scores added interpretability at both timestep level and the features associated with the timesteps .
Revised:  We also added attention layer at top of rnn layer in our model to compute the attention scores of each hidden layer corresponding to each input timestamp. These feature ranking and attention scores added interpretability at both timestep level and the features associated with the timesteps .
Edit intent: meaning-changed
Original:  We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their dominant emotions, emotion experiencers and textual cues,  emotion causes and targets, as well as the reader's perception and emotion of the headline.
Revised:  We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their associated emotions, the corresponding emotion experiencers and textual cues, emotion causes and targets, as well as the reader's perception and emotion of the headline.
Edit intent: meaning-changed
Original:  We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their dominant emotions, emotion experiencers and textual cues,  emotion causes and targets, as well as the reader's perception and emotion of the headline.
Revised:  We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their dominant emotions, emotion experiencers and textual cues, related emotion causes and targets, as well as the reader's perception and emotion of the headline.
Edit intent: meaning-changed
Original:  We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their dominant emotions, emotion experiencers and textual cues,  emotion causes and targets, as well as the reader's perception and emotion of the headline.
Revised:  We fill this gap by releasing a dataset of 5000 English news headlines annotated via crowdsourcing with their dominant emotions, emotion experiencers and textual cues, emotion causes and targets, as well as the reader's perception of the emotion of the headline.
Edit intent: clarity
Original: Upsampling sparse, noisy, and non-uniform point clouds is a challenging task. In this paper, we propose 3 novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle.
Revised: The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. We propose three novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle.
Edit intent: meaning-changed
Original:  Our modules use Graph Convolutional Networks (GCNs) to better encode local point information . Our upsampling modules are versatile and can be incorporated into any point cloud upsampling pipeline.
Revised:  Our modules use Graph Convolutional Networks (GCNs) to better encode local point information from the point neighborhood. These upsampling modules are versatile and can be incorporated into any point cloud upsampling pipeline.
Edit intent: meaning-changed
Original:  We show both qualitatively and quantitatively the  advantages of PU-GCN against the state-of-the-art in terms of fine-grained upsampling quality and point cloud uniformity .
Revised:  We show qualitatively and quantitatively the advantages of PU-GCN against the state-of-the-art in terms of fine-grained upsampling quality and point cloud uniformity .
Edit intent: coherence
Original:  We show both qualitatively and quantitatively the  advantages of PU-GCN against the state-of-the-art in terms of fine-grained upsampling quality and point cloud uniformity .
Revised:  We show both qualitatively and quantitatively the significant advantages of PU-GCN against the state-of-the-art in terms of fine-grained upsampling quality and point cloud uniformity .
Edit intent: meaning-changed
Original:  We show both qualitatively and quantitatively the  advantages of PU-GCN against the state-of-the-art in terms of fine-grained upsampling quality and point cloud uniformity .
Revised:  We show both qualitatively and quantitatively the advantages of PU-GCN over the state-of-the-art in terms of fine-grained upsampling quality and point cloud uniformity .
Edit intent: clarity
Original:  We propose three novel point upsampling modules: Multi-branch GCN, Clone GCN, and NodeShuffle. Our modules use Graph Convolutional Networks (GCNs ) to better encode local point information from the point neighborhood.
Revised:  For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN ) to better encode local point information from the point neighborhood.
Edit intent: clarity
Original:  Our modules use Graph Convolutional Networks (GCNs ) to better encode local point information from the point neighborhood. These upsampling modules are versatile and can be incorporated into any point cloud upsampling pipeline.
Revised:  Our modules use Graph Convolutional Networks (GCNs ) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline.
Edit intent: meaning-changed
Original:  The synthetic control method (SCM) is a recent addition to the evaluation toolkit but is designed to study a single treated unitand does not easily accommodate staggered adoption .
Revised:  Estimation remains challenging, however, and common regression methods can give misleading results. A promising alternative is the synthetic control method (SCM) is a recent addition to the evaluation toolkit but is designed to study a single treated unitand does not easily accommodate staggered adoption .
Edit intent: meaning-changed
Original:  We show that the average of separate SCM fits does not necessarily achieve good balance for the average of the treated units , leading to possible bias in the estimated effect. We propose "partially pooled" SCM weights that instead minimize both average and state-specific imbalance, and show that the resulting estimator controls biasunder a linear factor model.
Revised:  We show that the average of separate SCM fits does not necessarily achieve good balance for the average of the treated units . We then propose "partially pooled" SCM weights that instead minimize both average and state-specific imbalance, and show that the resulting estimator controls biasunder a linear factor model.
Edit intent: clarity
Original:  We propose "partially pooled" SCM weights that instead minimize both average and state-specific imbalance, and show that the resulting estimator controls biasunder a linear factor model. We also combine our partially pooled SCM weights with traditional fixed effects methods to obtain an augmented estimator that improves over both SCM weighting and fixed effects estimation alone .
Revised:  We propose "partially pooled" SCM weights to minimize a weighted combination of these measures; approaches that focus only on balancing one of the two components can lead to bias. We extend this approach to incorporate unit-level intercept shifts and auxiliary covariates .
Edit intent: meaning-changed
Original:  The main performance measure is the total number of agent updates required by the algorithms to find those global maxima and the baseline performance, which establishes the worth of the cooperative algorithms, is set by the blind search in which the agents explore the problem space (binary strings) by flipping randomly chosen bits .
Revised:  The main performance measure is the total number of agent updates required by the algorithms to find those global maxima and the baseline performance, which establishes the effectiveness of the cooperative algorithms, is set by the blind search in which the agents explore the problem space (binary strings) by flipping randomly chosen bits .
Edit intent: clarity