wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features,  by applying persistent homology to  filtrations of the cubical complexes of segmentation mapsgenerated using height functions in multiple directions .
Revised:  We extract two kinds of persistent featuresfrom binary segmentation maps, namelysparse PI features and amplitude features, by applying persistent homology to filtrations of the cubical complexes representing the object segmentation maps .
Edit intent: clarity
Original:  For performance evaluation, in addition to a widely used shape dataset, we collect new datasets comprising scene images from two different environments, i.e. , a living room and a warehouse. Scene images in both environments consist of up to five different objects with varying poses, chosen from a  set of fourteen objects , taken in varying illumination conditions from different distances and camera viewing angles.
Revised:  For performance evaluation, in addition to a widely used shape dataset, we collect new datasets comprising scene images from two different environments, i.e. , a living room and a mock warehouse. The scenes in both the environments include up to five different objects with varying poses, chosen from a set of fourteen objects , taken in varying illumination conditions from different distances and camera viewing angles.
Edit intent: clarity
Original:  Scene images in both environments consist of up to five different objects with varying poses, chosen from a  set of fourteen objects , taken in varying illumination conditions from different distances and camera viewing angles. The overall performance of our methods, trained using living room images, remains relatively unaltered on the unseen warehouse images , without retraining .
Revised:  Scene images in both environments consist of up to five different objects with varying poses, chosen from a set of fourteen objects . The objects have varying poses and arrangements, and are imaged under different illumination conditions and camera poses. The recognition performance of our methods, trained using living room images, remains relatively unaltered on the unseen warehouse images , without retraining .
Edit intent: clarity
Original:  In contrast, a state-of-the-art object detector's accuracy drops considerably. Our methods also achieve higher overall recall and accuracy than the state-of-the-art in the unseen warehouse environment .
Revised:  In contrast, a state-of-the-art Faster R-CNN method decreases significantly. In fact, the use of sparse PI features yields higher overall recall and accuracy than the state-of-the-art in the unseen warehouse environment .
Edit intent: meaning-changed
Original:  and (ii) incorporation of such models into the energy shaping methodology, extending our prior work by formally accounting for muscle constraints. Extension of the control scheme to the under-actuated muscle control case involves two steps: (i) design of a desired potential energy function whose static minimizer solves a given control task; and (ii) implementing the resulting energy shaping control input into the dynamic model.
Revised:  and (ii) incorporation of such models into the energy shaping control methodology. The control-oriented modeling takes inspiration in equal parts from theories of nonlinear elasticity and energy shaping control input into the dynamic model.
Edit intent: meaning-changed
Original:  and (ii) implementing the resulting energy shaping control input into the dynamic model. Due to the muscle actuator constraints, the desired potential energy function may not be arbitrarily chosen. Indeed, the desired energy must now satisfy a partial differential equation, known as the matching condition, which is derived for the infinite dimensional Hamiltonian control system. A particular solution to those matching conditions is described, paving the way to the application of energy shaping methodology  .
Revised:  and (ii) implementing the resulting energy shaping control . By introducing a stored energy function for muscles, the difficulties associated with explicitly solving the matching conditions of the energy shaping methodology .
Edit intent: clarity
Original:  The overall control design methodology including muscle models is implemented and demonstrated in a  dynamic simulation environment . Results of several bio-inspired numerical experiments involving the control of octopus arms are reported.
Revised:  The overall control design problem is posed as a bilevel optimization problem. Its solution is obtained through iterative algorithms. The methodology is numerically implemented and demonstrated in a dynamic simulation environment . Results of several bio-inspired numerical experiments involving the control of octopus arms are reported.
Edit intent: meaning-changed
Original:  Using model parameters tailored to describe SARS-CoV-2 diffusion, we show thateven when the probability for a contact to be traced is the same, manual contact tracing robustly performs better than the digital protocol in increasing the epidemic threshold, limiting the height of the epidemic peaks and reducing the number of isolated individuals.
Revised:  We show that, even for equal probability of tracing a contact, manual tracing robustly performs better than the digital protocol in increasing the epidemic threshold, limiting the height of the epidemic peaks and reducing the number of isolated individuals.
Edit intent: clarity
Original:  Using model parameters tailored to describe SARS-CoV-2 diffusion, we show thateven when the probability for a contact to be traced is the same, manual contact tracing robustly performs better than the digital protocol in increasing the epidemic threshold, limiting the height of the epidemic peaks and reducing the number of isolated individuals. This remains true even taking into account the intrinsic delay and limited scalability of the manual procedure.
Revised:  Using model parameters tailored to describe SARS-CoV-2 diffusion, we show thateven when the probability for a contact to be traced is the same, manual contact tracing robustly performs better than the digital protocol , also taking into account the intrinsic delay and limited scalability of the manual procedure.
Edit intent: coherence
Original:  This result is explained in terms of the stochastic sampling occurring during the case-by-case manual reconstruction of contacts of infected individuals , contrasted with the intrinsically prearranged nature of digital tracing, determined by the decision to adopt the app or not of each individual.
Revised:  This result is explained in terms of the stochastic sampling occurring during the case-by-case manual reconstruction of contacts , contrasted with the intrinsically prearranged nature of digital tracing, determined by the decision to adopt the app or not of each individual.
Edit intent: clarity
Original:  This result is explained in terms of the stochastic sampling occurring during the case-by-case manual reconstruction of contacts of infected individuals , contrasted with the intrinsically prearranged nature of digital tracing, determined by the decision to adopt the app or not of each individual.
Revised:  This result is explained in terms of the stochastic sampling occurring during the case-by-case manual reconstruction of contacts of infected individuals , contrasted with the intrinsically prearranged nature of digital tracing, determined by the decision to adopt the app or not by each individual.
Edit intent: clarity
Original:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: a superspreader not adopting the app is completely invisible to digital contact tracing, while she can be traced manually, due to her multiple contacts.
Revised:  The better performance of manual tracing is enhanced by heterogeneity in agent behavior: a superspreader not adopting the app is completely invisible to digital contact tracing, while she can be traced manually, due to her multiple contacts.
Edit intent: clarity
Original:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: a superspreader not adopting the app is completely invisible to digital contact tracing, while she can be traced manually, due to her multiple contacts.
Revised:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: superspreaders not adopting the app is completely invisible to digital contact tracing, while she can be traced manually, due to her multiple contacts.
Edit intent: fluency
Original:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: a superspreader not adopting the app is completely invisible to digital contact tracing, while she can be traced manually, due to her multiple contacts.
Revised:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: a superspreader not adopting the app are completely invisible to digital contact tracing, while she can be traced manually, due to her multiple contacts.
Edit intent: fluency
Original:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: a superspreader not adopting the app is completely invisible to digital contact tracing, while she can be traced manually, due to her multiple contacts.
Revised:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: a superspreader not adopting the app is completely invisible to digital contact tracing, while they can be easily traced manually, due to her multiple contacts.
Edit intent: clarity
Original:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: a superspreader not adopting the app is completely invisible to digital contact tracing, while she can be traced manually, due to her multiple contacts.
Revised:  The better performance of manual tracing is enhanced by the heterogeneous features of agent behavior: a superspreader not adopting the app is completely invisible to digital contact tracing, while she can be traced manually, due to their multiple contacts.
Edit intent: fluency
Original: We present a method for learning representations of entities, that uses a Transformer-based architecture as an entity encoder, and link prediction training on a knowledge graph with textual entity descriptions . We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement  on average).
Revised: Knowledge Graphs (KG) are of vital importance for multiple applications on the web, including information retrieval, recommender systems, and metadata annotation. Regardless of whether they are built manually by domain experts or with automatic pipelines, KGs are often incomplete. Recent work has begun to explore the use of textual descriptions available in knowledge graphs to learn vector representations of entities in order to preform link prediction. However, the extent to which these representations learned for link prediction generalize to other tasks is unclear. This is important given the cost of learning such representations. Ideally, we would prefer representations that do not need to be trained again when transferring to a different task, while retaining reasonable performance. In this work, we propose a holistic evaluation protocol for entity representations learned via a link prediction objective. We consider the inductive link prediction and entity classification tasks, which involve entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement on average).
Edit intent: meaning-changed
Original:  We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement  on average).
Revised:  We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training . We also consider an information retrieval task for entity-oriented search. We evaluate an architecture based on a pretrained language model, that exhibits strong generalization to entities not observed during training, and outperforms related state-of-the-art methods (22\% MRR improvement on average).
Edit intent: meaning-changed
Original:  We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement  on average).
Revised:  We demonstrate that our approach can be applied effectively for link prediction in different inductive settings involving entities not seen during training , outperforming related state-of-the-art methods (22\% MRR improvement in link prediction on average).
Edit intent: clarity
Original:  We  provide evidence that the learned representations transfer  to other tasks that do not require fine-tuning the entity encoder. In an entity classification task we obtain an average improvement of 16\%  accuracy compared with baselines that also employ pre-trained models.
Revised:  We provide evidence that the learned representations transfer to other tasks that do not require fine-tuning . In the entity classification task we obtain an average improvement of 16\% accuracy compared with baselines that also employ pre-trained models.
Edit intent: clarity
Original:  For an information retrieval task,  significant improvements of up to 8.8\% in NDCG@10 were obtained for natural language queries  .
Revised:  For an information retrieval task, significant improvements of up to 8.8\% in NDCG@10 were obtained for natural language queries . We thus show that the learned representations are not limited KG-specific tasks, and have greater generalization properties than evaluated in previous work .
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Original: Purpose: To stabilize the NLPContributionGraph scheme for the surface structuring of contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: first stage - to define the scheme ;
Revised: Purpose: The aim of this work is to normalize the NLPCONTRIBUTIONS scheme (henceforward, NLPCONTRIBUTIONGRAPH) to structure, directly from article sentences, the contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: first stage - to define the scheme ;
Edit intent: meaning-changed
Original: Purpose: To stabilize the NLPContributionGraph scheme for the surface structuring of contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: first stage - to define the scheme ;
Revised: Purpose: To stabilize the NLPContributionGraph scheme for the surface structuring of contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: 1) pilot stage - to define the scheme ;
Edit intent: meaning-changed
Original: Purpose: To stabilize the NLPContributionGraph scheme for the surface structuring of contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: first stage - to define the scheme ; and second stage - to stabilize the graphing model . Approach: Re-annotate , a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triples .
Revised: Purpose: To stabilize the NLPContributionGraph scheme for the surface structuring of contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: first stage - to define the scheme (described in prior work); and 2) adjudication stage - to stabilize the graphing model . Approach: Re-annotate , a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triples .
Edit intent: meaning-changed
Original:  and second stage - to stabilize the graphing model . Approach: Re-annotate , a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triples .
Revised:  and second stage - to normalize the graphing model . Approach: Re-annotate , a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triples .
Edit intent: clarity
Original:  and second stage - to stabilize the graphing model . Approach: Re-annotate , a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triples .
Revised:  and second stage - to stabilize the graphing model (the focus of this paper). Design/methodology/approach: We re-annotate , a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triples .
Edit intent: meaning-changed
Original:  and second stage - to stabilize the graphing model . Approach: Re-annotate , a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triples .
Revised:  and second stage - to stabilize the graphing model . Approach: Re-annotate , a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triple statements .
Edit intent: clarity
Original:  Findings: The application of NLPContributionGraph on the 50 articles resulted in finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples.
Revised:  Findings: The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted in finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples.
Edit intent: fluency
Original:  Findings: The application of NLPContributionGraph on the 50 articles resulted in finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples.
Revised:  Findings: The application of NLPContributionGraph on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples.
Edit intent: fluency
Original:  The intra-annotation agreement between the first and second stages, in terms of F1, was 67.92\% for sentences, 41.82\% for phrases, and 22.31\% for triples indicating that with an increased granularity of the information, the annotation decision variance is greater.
Revised:  The intra-annotation agreement between the first and second stages, in terms of F1, was 67.92\% for sentences, 41.82\% for phrases, and 22.31\% for triple statements indicating that with an increased granularity of the information, the annotation decision variance is greater.
Edit intent: clarity
Original:  The intra-annotation agreement between the first and second stages, in terms of F1, was 67.92\% for sentences, 41.82\% for phrases, and 22.31\% for triples indicating that with an increased granularity of the information, the annotation decision variance is greater.
Revised:  The intra-annotation agreement between the first and second stages, in terms of F1, was 67.92\% for sentences, 41.82\% for phrases, and 22.31\% for triples indicating that with increased granularity of the information, the annotation decision variance is greater.
Edit intent: fluency
Original:  Practical Implications: Demonstrate NLPContributionGraph data integrated in the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with compute enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks . Value: NLPContributionGraph is a novel scheme to obtain research contribution-centered graphs from NLP articles which to the best of our knowledge does not exist in the community.
Revised:  Practical Implications: We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with compute enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks . Value: NLPContributionGraph is a novel scheme to obtain research contribution-centered graphs from NLP articles which to the best of our knowledge does not exist in the community.
Edit intent: fluency
Original:  Practical Implications: Demonstrate NLPContributionGraph data integrated in the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with compute enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks . Value: NLPContributionGraph is a novel scheme to obtain research contribution-centered graphs from NLP articles which to the best of our knowledge does not exist in the community.
Revised:  Practical Implications: Demonstrate NLPContributionGraph data integrated in the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks . Value: NLPContributionGraph is a novel scheme to obtain research contribution-centered graphs from NLP articles which to the best of our knowledge does not exist in the community.
Edit intent: clarity
Original:  Practical Implications: Demonstrate NLPContributionGraph data integrated in the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with compute enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks . Value: NLPContributionGraph is a novel scheme to obtain research contribution-centered graphs from NLP articles which to the best of our knowledge does not exist in the community. And our quantitative evaluations over the two-stage annotation tasks offer insights into task difficulty .
Revised:  Practical Implications: Demonstrate NLPContributionGraph data integrated in the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with compute enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks .
Edit intent: coherence
Original:  Following the convention in many data mining and machine learning applications such as data summarization, recommender systems, and social network analysis , the problem is formulated as maximizing a monotone submodular function subject to a cardinality constraint -- i.e., the size of the selected subset is restricted to be smaller than or equal to an input integer k.
Revised:  In many data mining and machine learning applications such as data summarization, recommender systems, and social network analysis , the problem is formulated as maximizing a monotone submodular function subject to a cardinality constraint -- i.e., the size of the selected subset is restricted to be smaller than or equal to an input integer k.
Edit intent: clarity