wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We propose Weakly-supervised VisualBERT with the key idea of conducting " mask-and-predict " pre-training on language-only and image-only corpora . Additionally, we introduce the object tags detected by an object recognition model as anchor points to bridge two modalities.
Revised:  We propose Weakly-supervised VisualBERT with the key idea of conducting " mask-and-predict " pre-training on language-only and image-only corpora and introduce the object tags detected by an object recognition model as anchor points to bridge two modalities.
Edit intent: coherence
Original:  Evaluation on four V L benchmarks shows that Weakly-supervised VisualBERT achieves similar performance with a model pre-trained with paired data. Besides, pre-training on more image-only data further improves a model that already has access to aligned data , suggesting the possibility of utilizing billions of raw images available to enhance & V&L models.
Revised:  Evaluation on four V L benchmarks shows that Weakly-supervised VisualBERT achieves similar performance with a model pre-trained with aligned data , suggesting the possibility of utilizing billions of raw images available to enhance & V&L models.
Edit intent: coherence
Original:  Besides, pre-training on more image-only data further improves a model that already has access to aligned data , suggesting the possibility of utilizing billions of raw images available to enhance & V&L models.
Revised:  Besides, pre-training on more image-only data further improves a model that already has access to aligned data , suggesting the possibility of utilizing billions of raw images available to enhance L benchmarks. Our work challenges the widely held notion that aligned data is necessary for V& V&L models.
Edit intent: meaning-changed
Original:  Besides, pre-training on more image-only data further improves a model that already has access to aligned data , suggesting the possibility of utilizing billions of raw images available to enhance & V&L models.
Revised:  Besides, pre-training on more image-only data further improves a model that already has access to aligned data , suggesting the possibility of utilizing billions of raw images available to enhance &L pre-training, while significantly reducing the amount of supervision needed for V&L models.
Edit intent: meaning-changed
Original: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge .
Revised: Recently, knowledge graph (KG) augmented models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge .
Edit intent: meaning-changed
Original: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge .
Revised: Recently, neural-symbolic models have achieved noteworthy success on various commonsense reasoning tasks , like question answering (QA) . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge .
Edit intent: clarity
Original: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge .
Revised: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge .
Edit intent: clarity
Original: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, fact sparsity, inherent in human-annotated KGs, can hinder such models from retrieving task-relevant knowledge .
Revised: Recently, neural-symbolic models have achieved noteworthy success in leveraging knowledge graphs (KGs) for commonsense reasoning tasks , like question answering (QA) . However, KG edge (fact) sparsity and noisy edge extraction/generation often hinder models from obtaining useful knowledge to reason over .
Edit intent: clarity
Original:  To address these issues, we propose  Hybrid Graph Network (HGN) , a neural-symbolic model that reasons over both extracted (human-labeled) and generated facts within the same learned graph structure.
Revised:  To address these issues, we propose a new KG-augmented model: Hybrid Graph Network (HGN) , a neural-symbolic model that reasons over both extracted (human-labeled) and generated facts within the same learned graph structure.
Edit intent: meaning-changed
Original:  Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts .
Revised:  Given the task input context and an extracted KG subgraph , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts .
Edit intent: clarity
Original:  Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts .
Revised:  Given a KG subgraphof extracted facts , HGN is trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts .
Edit intent: clarity
Original:  Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts .
Revised:  Given a KG subgraphof extracted facts , HGN is jointly trained to generate embeddings for the subgraph's missing edges to form a "hybrid" subgraph, and filter out task-irrelevant facts .
Edit intent: meaning-changed
Original:  Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" subgraph, and filter out task-irrelevant facts .
Revised:  Given a KG subgraphof extracted facts , HGN is jointly trained to generate complementary facts, encode relational information in the resulting "hybrid" graph, then reason over the hybrid graph while filtering out context-irrelevant edges .
Edit intent: clarity
Original: Recently the synchrosqueezing transform (SST) was developed as an empirical mode decomposition (EMD)-like tool to enhance the time-frequency resolution and energy concentration of a multicomponent non-stationary signal and provides more accurate component recovery (mode retrieval) .
Revised: Recently the synchrosqueezed transform (SST) was developed as an empirical mode decomposition (EMD)-like tool to enhance the time-frequency resolution and energy concentration of a multicomponent non-stationary signal and provides more accurate component recovery (mode retrieval) .
Edit intent: fluency
Original: Recently the synchrosqueezing transform (SST) was developed as an empirical mode decomposition (EMD)-like tool to enhance the time-frequency resolution and energy concentration of a multicomponent non-stationary signal and provides more accurate component recovery (mode retrieval) .
Revised: Recently the synchrosqueezing transform (SST) was developed as an empirical mode decomposition (EMD)-like tool to enhance the time-frequency resolution and energy concentration of a multi-component non-stationary signal and provides more accurate component recovery (mode retrieval) .
Edit intent: fluency
Original: Recently the synchrosqueezing transform (SST) was developed as an empirical mode decomposition (EMD)-like tool to enhance the time-frequency resolution and energy concentration of a multicomponent non-stationary signal and provides more accurate component recovery (mode retrieval) .
Revised: Recently the synchrosqueezing transform (SST) was developed as an empirical mode decomposition (EMD)-like tool to enhance the time-frequency resolution and energy concentration of a multicomponent non-stationary signal and provides more accurate component recovery .
Edit intent: clarity
Original:  In this paper we propose a direct method of signal separation based on the adaptive continuous wavelet transform (CWT) . We introduce two models of the adaptive CWT-based approach for signal separation: the sinusoidal signal-based model and the linear chirp-based model, which are derived respectively from sinusoidal signal approximation and the linear chirp approximation at any local time .
Revised:  In this paper we propose a direct method of signal separation based on the adaptive continuous wavelet-like transform (CWLT) by introducing two models of the adaptive CWT-based approach for signal separation: the sinusoidal signal-based model and the linear chirp-based model, which are derived respectively from sinusoidal signal approximation and the linear chirp approximation at any local time .
Edit intent: coherence
Original:  We introduce two models of the adaptive CWT-based approach for signal separation: the sinusoidal signal-based model and the linear chirp-based model, which are derived respectively from sinusoidal signal approximation and the linear chirp approximation at any local time .
Revised:  We introduce two models of the adaptive CWLT-based approach for signal separation: the sinusoidal signal-based model and the linear chirp-based model, which are derived respectively from sinusoidal signal approximation and the linear chirp approximation at any local time .
Edit intent: fluency
Original:  We introduce two models of the adaptive CWT-based approach for signal separation: the sinusoidal signal-based model and the linear chirp-based model, which are derived respectively from sinusoidal signal approximation and the linear chirp approximation at any local time .
Revised:  We introduce two models of the adaptive CWT-based approach for signal separation: the sinusoidal signal-based model and the linear chirp-based model, which are derived respectively from sinusoidal signal approximation and the linear chirp approximation at any time instant .
Edit intent: clarity
Original: The problem of maintaining a maximal independent set (MIS) in a dynamic graph has received growing attention in recent years. In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic;
Revised: The field of dynamic graph algorithms aims at achieving a thorough understanding of real-world networks whose topology evolves with time. Traditionally, the focus has been on the classic sequential, centralized setting where the main quality measure of an algorithm is its update time, i.e. the time needed to restore the solution after each update. While real-life networks are very often distributed across multiple machines, the fundamental question of finding efficient dynamic, distributed graph algorithms received little attention to date. The goal in this setting is to optimize both the round and message complexities incurred per update step, ideally achieving a message complexity that matches the centralized update time in O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right) ] Assadi et al. is deterministic;
Edit intent: meaning-changed
Original:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic;
Revised:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( perhaps amortized) rounds. Toward initiating a systematic study of dynamic, distributed algorithms, we study some of the most central symmetry-breaking problems: maximal independent set (MIS), maximal matching/(approx-) maximum cardinality matching (MM/MCM), and (\Delta + 1)-vertex coloring. This paper focuses on dynamic, distributed algorithms that are deterministic, and in particular -- robust against an adaptive adversary. Most of our focus is on the MIS algorithm, which achieves O\left(\log^2 n}\right) \left(\right) ] Assadi et al. is deterministic;
Edit intent: meaning-changed
Original:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic;
Revised:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(m^{2/3\log^2 n}\right) \left(\right) ] Assadi et al. is deterministic;
Edit intent: meaning-changed
Original:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic;
Revised:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) amortized messages in O\left(\right) ] Assadi et al. is deterministic;
Edit intent: meaning-changed
Original:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic;
Revised:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\log^2 n\right) ] Assadi et al. is deterministic;
Edit intent: fluency
Original:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic;
Revised:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right) amortized rounds in the Congest model. Notably, the amortized message complexity of our algorithm matches the amortized update time of the best-known deterministic centralized MIS algorithm by Gupta and Khan ] Assadi et al. is deterministic;
Edit intent: meaning-changed
Original:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic;
Revised:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right) SOSA'21] Assadi et al. is deterministic;
Edit intent: meaning-changed
Original:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic;
Revised:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right) ] up to a polylog n factor. The previous best deterministic distributed MIS algorithm, by Assadi et al. is deterministic;
Edit intent: meaning-changed
Original:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right)  ]  Assadi et al. is deterministic; to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o  (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Revised:  In STOC'18, Assadi et al. presented a distributed algorithm for maintaining an MIS with O(1) ( amortized) round complexity and O(m^{3/4\left(\log^2 n}\right) \left(\right) ] Assadi et al. STOC'18 (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Edit intent: meaning-changed
Original:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o  (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Revised:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o , uses O (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Edit intent: clarity
Original:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o  (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Revised:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o (m^{3/4}) (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Edit intent: clarity
Original:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o  (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Revised:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 ) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Edit intent: coherence
Original:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o  (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Revised:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% %DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Edit intent: meaning-changed
Original:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o  (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Revised:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% amortized messages in O(1) amortized rounds, i.e., we achieve a polynomial improvement in the message complexity by a polylog n increase to the round complexity; moreover, the algorithm of Assadi et al. makes an implicit assumption that the 
Edit intent: meaning-changed
Original:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o  (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. 
Revised:  to the best of our knowledge, the state-of-the-art distributed algorithm for this problem with a message complexity of o (m^{3/4}) incurs a round complexity of \Omega(m^{1/3 (m^{2/3) and an amortized round complexityof }%DIFDELCMD < \tilde{O}(1) %%% under fully dynamic edge updates in the CONGEST model, where%DIFDELCMD < \tilde{O}%%% (\cdot) suppresses polylogarithmic factors. ...
Edit intent: fluency
Original:  Our method uses this octree to sample viewpoints that increase the information around the fruit regions and estimates their position and size .
Revised:  Our method uses this octree to sample viewpoints that increase the information around the fruit regions and evaluates them using a heuristic utility function that takes into account the expected information gain. Our system automatically switches between ROI targeted sampling and exploration sampling, which considers general frontier voxels, depending on the estimated utility. When the plants have been sufficiently covered with the RGB-D sensor, our system clusters the ROI voxels and estimates the position and size .
Edit intent: meaning-changed
Original:  Depending on the online selected strategy, a set of time-varying hyperplanes in the AV's motion space is generated and included in the lower level control. The latter is a Strategy-Guided Optimization-Based Collision Avoidance (SG-OBCA) algorithm where the strategy-dependent hyperplane constraints are used to drive a model-based receding horizon controller towards a predicted feasiblearea.
Revised:  Depending on the online selected strategy, a set of time-varying hyperplanes in the AV's motion space is generated online and the corresponding halfspace constraints are included in a lower-level model-based receding horizon controller towards a predicted feasiblearea.
Edit intent: clarity
Original:  The latter is a Strategy-Guided Optimization-Based Collision Avoidance (SG-OBCA) algorithm where the strategy-dependent hyperplane constraints are used to drive a model-based receding horizon controller towards a predicted feasiblearea. The strategy also informs switching from the SG-OBCA control policy to a safety or emergency control policy .
Revised:  The latter is a Strategy-Guided Optimization-Based Collision Avoidance (SG-OBCA) algorithm where the strategy-dependent hyperplane constraints are used to drive a model-based receding horizon controller . These strategy-dependent constraints drive the vehicle towards areas where it is likely to remain feasible. Moreover, the predicted strategy also informs switching from the SG-OBCA control policy to a safety or emergency control policy .
Edit intent: clarity