wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this work, we propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering (NSQA)  system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question under-standing ;
Revised:  In this work, we propose Neuro-Symbolic Question Answering (NSQA) system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question under-standing ;
Edit intent: coherence
Original:  In this work, we propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering (NSQA)  system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question under-standing ;
Revised:  In this work, we propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering (NSQA) , a modular KBQA system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question under-standing ;
Edit intent: meaning-changed
Original:  In this work, we propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering (NSQA)  system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question under-standing ;
Revised:  In this work, we propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering (NSQA) system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question understanding ;
Edit intent: fluency
Original:  (3) a neuro-symbolic reasoner called Logical Neural Net-work (LNN) that executes logical queries and reasons over KB facts to provide an answer; (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on  QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
Revised:  (3) a pipeline-based approach which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
Edit intent: clarity
Original:  (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on  QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
Revised:  (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( semantic parser, entity andrelationship linkers, and neuro-symbolic reasoner ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
Edit intent: clarity
Original:  (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on  QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
Revised:  (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on two prominent KBQA datasets based on DBpedia ( QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
Edit intent: meaning-changed
Original:  (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on  QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions .
Revised:  (4) system of systems approach , which integrates multiple, reusable modules that are trained specifically for their individual tasks ( e.g. semantic parsing, entity linking, and relationship linking ) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on QALD-9 and LC-QuAD1.0). Furthermore, our analysis emphasizes that AMR is a powerful tool for KBQA systems .
Edit intent: meaning-changed
Original:  It is known that for every real \alpha>1 and integer \Delta, a fractional coloring of total weight at most \alpha(\Delta+1) can be obtained deterministically in a single round in graphs of maximum degree \Delta, in the LOCAL model of computation.
Revised:  It was proved by Hasemann, Hirvonen, Rybicki and Suomela (2016) that for every real \alpha>1 and integer \Delta, a fractional coloring of total weight at most \alpha(\Delta+1) can be obtained deterministically in a single round in graphs of maximum degree \Delta, in the LOCAL model of computation.
Edit intent: meaning-changed
Original:  Here we prove that even if we impose the more realistic assumption that the output of each vertex has constant size, we can find fractional colourings with a weight arbitrarily close to known tight bounds for the fractional chromatic number in several cases of interest.
Revised:  Here we prove that even if we impose the more realistic assumption that the output of each vertex has constant size, we can find fractional colorings of total weight arbitrarily close to known tight bounds for the fractional chromatic number in several cases of interest.
Edit intent: clarity
Original:  Moreover, we improve on classical bounds on the chromatic number by considering the fractional chromatic number instead, without significantly increasing the output size and the round complexity of the existing algorithms, while finding a fractional coloring of total weight at most \Delta in this case requires \Omega(\log \log n) rounds for randomized algorithms and \Omega( \log n) rounds for deterministic algorithms.
Revised:  More precisely, we show that for any fixed \epsilon > 0 and \Delta, a fractional coloring of total weight at most \Delta+\epsilon can be found in O(\log^*n) rounds in graphs of maximum degree \Delta with no K_{\Delta+1, while finding a fractional coloring of total weight at most \Delta in this case requires \Omega(\log \log n) rounds for randomized algorithms and \Omega( \log n) rounds for deterministic algorithms.
Edit intent: meaning-changed
Original:  This implies the existence of a reachability labelling scheme for digraphs with labels of n/4+o(n) bits per vertex . We also use this result to construct a poset on 2^{n/4+o(n) . All these results are best possible, up to the lower order term , and solve several open problems in the area .
Revised:  This implies the existence of a reachability labelling scheme for digraphs with labels of n/4+o(n) bits per vertex and comparability labelling scheme for posets with labels of n/4+o(n) bits per element . All these results are best possible, up to the lower order term , and solve several open problems in the area .
Edit intent: clarity
Original:  We modeled competing hypotheses regarding the underlying testing mechanisms , thereby providing different prevalence estimates based on case numbers, and used them to predict SARS-CoV-2-attributed death rate trajectories.
Revised:  We develop mathematical models based on competing hypotheses regarding the underlying testing mechanisms , thereby providing different prevalence estimates based on case numbers, and used them to predict SARS-CoV-2-attributed death rate trajectories.
Edit intent: clarity
Original:  Assuming that individuals were tested based solely on a predefined risk of being infectious implied the absolute case numbers reflected prevalence, but turned out to be a poor predictor  .
Revised:  Assuming that individuals were tested based solely on a predefined risk of being infectious implied the absolute case numbers reflected prevalence, but turned out to be a poor predictor , consistently overestimating growth rates at the beginning of two COVID-19 epidemic waves .
Edit intent: meaning-changed
Original:  This puts forward the percentage of positive tests as a robust indicator of epidemic dynamics in absence of country-specific information. We next demonstrated this strongly affects data interpretation. Notably absolute case numbers trajectories consistently overestimated growth rates at the beginning of two COVID-19 epidemic waves. Overall, this supports non-trivial testing mechanisms can be inferred from data and should be scrutinized .
Revised:  This puts forward the percentage of positive tests as a robust indicator of epidemic dynamics , however we find it is subject to a saturation phenomenon that needs to be accounted for as the number of tests becomes larger .
Edit intent: clarity
Original:  Existing literature demonstrates robot success in executing handovers with humans, albeit with substantial reliance on external sensing or with primitive signal processing methods, deficient compared to the rich set of information humans can detect. In contrast, we present models capable of distinguishing between four classes of human tactile gestures at a robot's end effector, using only a non-collocated six-axis force sensor at the wrist. Due to the absence in the literature, this work describes 1 ) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction .
Revised:  Interpreting human touch gestures is a nuanced, challenging task with extreme gaps between human and robot capability. Among prior works that demonstrate human touch recognition capability, differences in sensors, gesture classes, feature sets, and classification algorithms yield a conglomerate of non-transferable results and a glaring lack of a standard. To address this gap, this work presents 1) four proposed touch gesture classes that cover the majority of the gesture characteristics identified in the literature, this work describes 1 ) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction .
Edit intent: clarity
Original:  Due to the absence in the literature, this work describes 1 ) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction .
Revised:  Due to the absence in the literature, 2 ) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction .
Edit intent: clarity
Original:  Due to the absence in the literature, this work describes 1 ) the collection of an extensive force dataset characterized by human-robot contact events, and 2) classification models informed by this datasetto determine the nature of the interaction .
Revised:  Due to the absence in the literature, this work describes 1 ) the collection of an extensive force dataset on a common pHRI robotic arm with only its internal wrist force-torque sensor, and 3) an exhaustive performance comparison of combinations of feature sets and classification algorithms on this dataset .
Edit intent: meaning-changed
Original:  Therefore, MoleculeKit not only serves as a comprehensive tool, but also contributes towards developing novel and advanced graph and sequence learning methodologies.
Revised:  In order to learn effectively from highly imbalanced datasets, we develop advanced loss functions that optimize areas under precision-recall curves. Altogether, our work not only serves as a comprehensive tool, but also contributes towards developing novel and advanced graph and sequence learning methodologies.
Edit intent: meaning-changed
Original:  Results on both online and offline antibiotics discovery and molecular property prediction tasks show that MoleculeKit achieves consistent improvements over prior methods  .
Revised:  Results on both online and offline antibiotics discovery and molecular property prediction tasks show that MoleculeKit achieves consistent improvements over prior methods . In particular, our methods achieve #1 ranking in terms of both ROC-AUC and PRC-AUC on the AI Cures Open Challenge for drug discovery related to COVID-19. Our software is released as part of the MoleculeX library under AdvProp .
Edit intent: meaning-changed
Original: COVID-19 pandemic has changed and challenged nearly every aspect of modern life. In the absence of a cure, non-pharmaceutical intervention and behavioral changes have played a crucial role in controlling its spread . Joint efforts of the scientific community have aimed to evaluate through models the effectiveness of different actions and to forecast the detrimental effects of this disease.
Revised: Non-pharmaceutical interventions (NPIs) have played a crucial role in controlling its spread . Joint efforts of the scientific community have aimed to evaluate through models the effectiveness of different actions and to forecast the detrimental effects of this disease.
Edit intent: clarity
Original:  In the absence of a cure, non-pharmaceutical intervention and behavioral changes have played a crucial role in controlling its spread . Joint efforts of the scientific community have aimed to evaluate through models the effectiveness of different actions and to forecast the detrimental effects of this disease. In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Revised:  In the absence of a cure, non-pharmaceutical intervention and behavioral changes have played a crucial role in controlling the spread of COVID-19. Nevertheless, NPI efficacy varies enormously between and within countries, mainly because of population and behavioral heterogeneity. In this work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Edit intent: coherence
Original:  In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Revised:  In the present work, we adapted a multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Edit intent: clarity
Original:  In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Revised:  In the present work, we adapted a recently published multi-group SEIRA model to study the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Edit intent: clarity
Original:  In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Revised:  In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, representing geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Edit intent: coherence
Original:  In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Revised:  In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions of the country by different groups. We use national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region.
Edit intent: clarity
Original:  In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity among such groups and the official COVID-19 reported data by the government, we fitted COVID-19 spreading and fatality rates, for every region. Then, we assessed the effectiveness of different governmental interventions on the spread of COVID-19 . A comparison between the reproductive numbers (Rt) separately obtained from raw data and fitted parameters .
Revised:  In the present work, we adapted a recently published multi-group SEIRA model to represent the spreading dynamics of COVID-19 in Chile, where different groups would represent geographically separated regions . Using national mobilization statistics to estimate the connectivity between regions and data from governmental repositories to obtain each region's spreading and infection fatality rate. We then assessed the effectiveness of different governmental interventions on the spread of COVID-19 . A comparison between the reproductive numbers (Rt) separately obtained from raw data and fitted parameters .
Edit intent: clarity
Original:  Then, we assessed the effectiveness of different governmental interventions on the spread of COVID-19 . A comparison between the reproductive numbers (Rt) separately obtained from raw data and fitted parameters .
Revised:  Then, we assessed the effectiveness of different NPIs by studying the temporal evolution of the reproduction number Rt. Analyzing data-driven and model-based estimates of Rt, we found a strong coupling of different regions, highlighting the necessity URLanized, coordinated actions to control the spread of SARS-CoV-2. Finally, we evaluated different scenarios to forecast the evolution of COVID-19 . A comparison between the reproductive numbers (Rt) separately obtained from raw data and fitted parameters .
Edit intent: meaning-changed
Original:  Analyzing data-driven and model-based estimates of Rt, we found a strong coupling of different regions, highlighting the necessity URLanized , coordinated actions to control the spread of SARS-CoV-2.
Revised:  Analyzing data-driven and model-based estimates of Rt, we found a strong coupling of different regions, highlighting the necessity URLanized and coordinated actions to control the spread of SARS-CoV-2.
Edit intent: fluency
Original:  In this paper , we marry these two lines of research by proposing Attention Pruning (AP): a novel pruning framework that collects observations about the attention patterns in a fixed dataset and then induces a global sparseness mask for the model.
Revised:  Here , we marry these two lines of research by proposing Attention Pruning (AP): a novel pruning framework that collects observations about the attention patterns in a fixed dataset and then induces a global sparseness mask for the model.
Edit intent: coherence
Original:  Through attention pruning, we find that about 90\% of the attention computation can be reduced for language modelling and about 50\% for machine translation and \%natural language inference prediction with BERT on GLUE tasks, while maintaining the quality of the results.
Revised:  This can save 90\% of the attention computation can be reduced for language modelling and about 50\% for machine translation and \%natural language inference prediction with BERT on GLUE tasks, while maintaining the quality of the results.
Edit intent: coherence
Original:  Through attention pruning, we find that about 90\% of the attention computation can be reduced for language modelling and about 50\% for machine translation and \%natural language inference prediction with BERT on GLUE tasks, while maintaining the quality of the results.
Revised:  Through attention pruning, we find that about 90\% of the attention computation for language modelling and about 50\% for machine translation and \%natural language inference prediction with BERT on GLUE tasks, while maintaining the quality of the results.
Edit intent: clarity
Original:  Through attention pruning, we find that about 90\% of the attention computation can be reduced for language modelling and about 50\% for machine translation and \%natural language inference prediction with BERT on GLUE tasks, while maintaining the quality of the results.
Revised:  Through attention pruning, we find that about 90\% of the attention computation can be reduced for language modelling and about 50\% for machine translation and for solving GLUE tasks, while maintaining the quality of the results.
Edit intent: clarity