wanyu/IteraTeR_full_sent huggingface dataset:
Original:  An integrated view of brain function reflecting this non-linear nature implies that an ongoing focus on personal meaning and understanding  provides positive conditions for  all aspects of literacy learning. Assessment of literacy teaching at all its stages should include indicators that take into account these foundational features relating reading and writing to neuroscience .
Revised:  An integrated view of brain function reflecting this non-linear nature implies that an ongoing focus on personal meaning and understanding provides positive conditions for all aspects of reading and writing to neuroscience .
Edit intent: clarity
Original:  Clearance involves three structures that extend down the length of the nerve: glia, extracellular space, and axon and so need to be described in the tradition of the ' cable equations' of nerve used to explain nerve conduction since the work of Hodgkin in 1937.
Revised:  Clearance involves three structures that extend down the length of the nerve: glia, extracellular space, and axon and so need to be described as systems distributed in space in the tradition used for electrical potential in the ` cable equations' of nerve used to explain nerve conduction since the work of Hodgkin in 1937.
Edit intent: meaning-changed
Original:  Clearance involves three structures that extend down the length of the nerve: glia, extracellular space, and axon and so need to be described in the tradition of the ' cable equations' of nerve used to explain nerve conduction since the work of Hodgkin in 1937.
Revised:  Clearance involves three structures that extend down the length of the nerve: glia, extracellular space, and axon and so need to be described in the tradition of the ' cable equations' of nerve since the work of Hodgkin in 1937.
Edit intent: clarity
Original:  Nash equilibrium, its reality and accuracy , is firstly illustrated by the O'Neill (1987) game experiment . In this game experiments (O'Neill (1987), Binmore (2001) and Okano (2013)), we discover the fine structure in the human social cycling spectrum. With the eigencycle set from the eigenvectors in  game  dynamics equations, we can have the accuracy increased by an order of magnitude on the dynamics testing .
Revised:  Nash equilibrium, its reality and accuracy , is firstly illustrated by the O'Neill game experiment in game dynamics equations, we can have the accuracy increased by an order of magnitude on the dynamics testing .
Edit intent: coherence
Original:  With the eigencycle set from the eigenvectors in  game  dynamics equations, we can have the accuracy increased by an order of magnitude on the dynamics testing .
Revised:  With the eigencycle set from the eigenvectors in game dynamics theory, the central approach is dynamics equations, however, its reality and accuracy is rare known, especially in high dimensional games. By develop a new approach, namely the eigencycle approach, with the eigenvectors from the game dynamics equations, we can have the accuracy increased by an order of magnitude on the dynamics testing .
Edit intent: meaning-changed
Original:  With the eigencycle set from the eigenvectors in  game  dynamics equations, we can have the accuracy increased by an order of magnitude on the dynamics testing .
Revised:  With the eigencycle set from the eigenvectors in game dynamics equations, we discover the fine structure of the cycles in the same experiments. We show that, the eigencycle approach can increase the accuracy by an order of magnitude on the dynamics testing .
Edit intent: meaning-changed
Original:  With the eigencycle set from the eigenvectors in  game  dynamics equations, we can have the accuracy increased by an order of magnitude on the dynamics testing .
Revised:  With the eigencycle set from the eigenvectors in game dynamics equations, we can have the accuracy increased by an order of magnitude in the human dynamic hehavior data. As the eigenvector is fundamental in dynamical systems theory which has applications in natural, social, and virtual worlds, the power of the eigencycles is expectedly. Inspired by the high dimensional eigencycles, we suggest that, the mathematical concept, namely 'invariant manifolds', could be a candidate as the central concept for the game dynamics theory, like the fixed point concept for classical game theory .
Edit intent: meaning-changed
Original: Sequence-to-sequence methods have achieved promising results for textual abstractive meeting summarization . Different from documents like news and scientific papers, a meeting is naturally full of dialogue-specific structural information. However, previous works model a meeting in a sequential manner, while ignoring the rich structural information.
Revised: Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data. Existing methods view the meeting as a linear sequence of utterances while ignoring the rich structural information.
Edit intent: clarity
Original:  In this paper, we develop a Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization by utilizing dialogue discourse , which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance.
Revised:  In this paper, we try to mitigate the above challenges by introducing dialogue-discourse relations. First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations. The core module is a relational graph encoder, where the utterances and discourse relations are modeled in a graph interaction manner. Moreover, we devise a Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization by utilizing dialogue discourse , which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance.
Edit intent: meaning-changed
Original:  In this paper, we develop a Dialogue Discourse-Aware Graph Convolutional Networks (DDA-GCN) for meeting summarization by utilizing dialogue discourse , which is a dialogue-specific structure that can provide pre-defined semantic relationships between each utterance. We first transform the entire meeting text with dialogue discourse relations into a discourse graph and then use DDA-GCN to encode the semantic representation of the graph. Finally, we employ a Recurrent Neural Network to generate the summary. In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus , which can be used to pre-train our model . Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
Revised:  In this paper, we develop a Dialogue Discourse-Aware Data Augmentation (DDADA) strategy to construct a pseudo-summarization corpus , which can be used to pre-train our model . Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
Edit intent: clarity
Original:  In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus , which can be used to pre-train our model . Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
Revised:  In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus from existing input meetings, which is 20 times larger than the original dataset and can be used to pre-train our model . Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
Edit intent: meaning-changed
Original:  In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus , which can be used to pre-train our model . Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
Revised:  In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus , which can be used to pretrain DDAMS . Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
Edit intent: meaning-changed
Original:  In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus , which can be used to pre-train our model . Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
Revised:  In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus , which can be used to pre-train our model . Experimental results on AMI and ICSI meeting datasets show that our model outperforms various baselines and can achieve state-of-the-art performance.
Edit intent: meaning-changed
Original:  In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus , which can be used to pre-train our model . Experimental results on the AMI dataset show that our model outperforms various baselines and can achieve state-of-the-art performance.
Revised:  In addition, we utilize the question-answer discourse relation to construct a pseudo-summarization corpus , which can be used to pre-train our model . Experimental results on the AMI dataset show that our full system can achieve SOTA performance. Our codes will be available at: URL
Edit intent: meaning-changed
Original:  The grand-averaged accuracies were 51.1\% (8.02) , 53.2\% (3.11) , and 41.9\% (6.09) in MI, VI, and SI, respectively.
Revised:  The grand-averaged accuracies were 51.1\% , 53.2\% (3.11) , and 41.9\% (6.09) in MI, VI, and SI, respectively.
Edit intent: meaning-changed
Original:  The grand-averaged accuracies were 51.1\% (8.02) , 53.2\% (3.11) , and 41.9\% (6.09) in MI, VI, and SI, respectively.
Revised:  The grand-averaged accuracies were 51.1\% (8.02) , 53.2\% , and 41.9\% (6.09) in MI, VI, and SI, respectively.
Edit intent: meaning-changed
Original:  The grand-averaged accuracies were 51.1\% (8.02) , 53.2\% (3.11) , and 41.9\% (6.09) in MI, VI, and SI, respectively.
Revised:  The grand-averaged accuracies were 51.1\% (8.02) , 53.2\% (3.11) , and 41.9\% in MI, VI, and SI, respectively.
Edit intent: meaning-changed
Original: In a longer document, the topic often slightly shifts from one passage to the next, where topic boundaries are usually indicated by semantically coherent segments. Discovering this latent structure in a document improves the readability and is essential for passage retrieval and summarization tasks. We formulate the task of text segmentation as an independent supervised prediction task, making it suitable to train on Transformer-based language models. By fine-tuning on paragraphs of similar sections, we are able to show that learned features encode topic information, which can be used to find the section boundaries and divide the text into coherent segments. Unlike previous approaches, which mostly operate on sentence-level, we consistently use a broader context of an entire paragraph and assume topical independence of preceeding and succeeding text . We lastly introduce a novel large-scale dataset constructed from online Terms-of-Service documents, on which we compare against various traditional and deep learning baselines, showing significantly better performance of Transformer-based methods.
Revised: The growing complexity of legal cases has lead to an increasing interest in legal information retrieval systems that can effectively satisfy user-specific information needs. However, such downstream systems typically require documents to be properly formatted and segmented, which is often done with relatively simple pre-processing steps, disregarding topical coherence of segments. Systems generally rely on representations of individual sentences or paragraphs, which may lack crucial context, or document-level representations, which are too long for meaningful search results. To address this issue, we propose a segmentation system that can predict topical coherence of sequential text segments spanning several paragraphs, effectively segmenting a document and providing a more balanced representation for downstream applications. We build our model on top of popular transformer networks and formulate structural text segmentation as topical change detection, by performing a series of independent classifications that allow for efficient fine-tuning on task-specific data. We crawl a novel dataset consisting of roughly 74,000 online Terms-of-Service documents, on which we compare against various traditional and deep learning baselines, showing significantly better performance of Transformer-based methods.
Edit intent: meaning-changed
Original:  We lastly introduce a novel large-scale dataset constructed from online Terms-of-Service documents, on which we compare against various traditional and deep learning baselines, showing significantly better performance of Transformer-based methods.
Revised:  We lastly introduce a novel large-scale dataset constructed from online Terms-of-Service documents, including hierarchical topic annotations, which we use for training. Results show that our proposed system significantly outperforms baselines, and adapts well to structural peculiarities of legal documents. We release both data and trained models to the research community for future URL
Edit intent: meaning-changed
Original:  We find that the benchmark use cases we examine require 7.5k logical qubits and a T-depth of 46 millionand thus estimate that quantum advantage would require a logical clock speed of 10Mhz . While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.
Revised:  We find that the benchmark use cases we examine require 8k logical qubits and a T-depth of 46 millionand thus estimate that quantum advantage would require a logical clock speed of 10Mhz . While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.
Edit intent: meaning-changed
Original:  We find that the benchmark use cases we examine require 7.5k logical qubits and a T-depth of 46 millionand thus estimate that quantum advantage would require a logical clock speed of 10Mhz . While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.
Revised:  We find that the benchmark use cases we examine require 7.5k logical qubits and a T-depth of 54 million. We estimate that quantum advantage would require a logical clock speed of 10Mhz . While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.
Edit intent: meaning-changed
Original:  We find that the benchmark use cases we examine require 7.5k logical qubits and a T-depth of 46 millionand thus estimate that quantum advantage would require a logical clock speed of 10Mhz . While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.
Revised:  We find that the benchmark use cases we examine require 7.5k logical qubits and a T-depth of 46 millionand thus estimate that quantum advantage would require executing this program at the order of a second . While the resource requirements given here are out of reach of current systems, we hope they will provide a roadmap for further improvements in algorithms, implementations, and planned hardware architectures.
Edit intent: meaning-changed
Original:  We combine Bayesian parameter inference with a network-based epidemiological modelthat allows to interpolate between random and small-world transmission networks.
Revised:  We here identify the key parameters of the COVID-19 transmission network for time periods before, during, and after the application of strict NPIs for the first wave of COVID-19 infections in Germany combining Bayesian parameter inference with a network-based epidemiological modelthat allows to interpolate between random and small-world transmission networks.
Edit intent: meaning-changed
Original:  We combine Bayesian parameter inference with a network-based epidemiological modelthat allows to interpolate between random and small-world transmission networks. Using epidemiological data from Germany, we show that NPIs reduced non-local contacts in the transmission network, resulting in a change from an exponential to a constant regime .
Revised:  We combine Bayesian parameter inference with a network-based epidemiological modelthat allows to interpolate between random and small-world network which allows to distinguish contacts within clustered cliques and unclustered, random contacts in the population, which have been shown to be crucial in sustaining the epidemic. In contrast to other works, which use coarse-grained network structures from anonymized data, like cell phone data, we consider the contacts of individual agents explicitly. We show that NPIs reduced non-local contacts in the transmission network, resulting in a change from an exponential to a constant regime .
Edit intent: meaning-changed
Original:  Using epidemiological data from Germany, we show that NPIs reduced non-local contacts in the transmission network, resulting in a change from an exponential to a constant regime . Due to the small-world nature of the inferred network, exponential spread can be efficiently prevented by reducing non-local contacts. Eliminating the disease remains costly as it requires to reduce all contacts. Our code is freely available and can be readily adapted to any country .
Revised:  Using epidemiological data from Germany, we show that NPIs reduced non-local contacts in the transmission network, resulting in a change from an exponential to a constant regime of newcases. In this regime, the disease spreads like a wave with a finite wave speed that depends on the number of contacts in a nonlinear fashion, which we can predict by mean field theory. Our analysis indicates that besides the well-known transitionbetween exponential increase and exponential decrease in the number of new cases, NPIs can induce a transition to another, previously unappreciated regime of constant new cases .
Edit intent: meaning-changed
Original: Fluctuations are ubiquitous and prominent in microscopic systems. Observations in vitro have confirmed motor proteins are subjected to thermal fluctuations. The walking molecular motor, kinesin-1 (hereafter called kinesin), which carries vesicles on microtubules in cells, has long been proposed to utilize thermal fluctuations to make directed movements. In addition to thermal fluctuations, living cells actively generate non-thermal fluctuations using energy derived from metabolic activities. However, it is not clear whether and how these active fluctuations affect the function of kinesins in living cells. To investigate the effect of active fluctuations on single kinesin molecules , we used an in vitro measurement system to apply actively fluctuating external forces (i.e. noise), artificially mimicking intracellular active fluctuations.
Revised: The movement of single kinesin molecules , we used an in vitro measurement system to apply actively fluctuating external forces (i.e. noise), artificially mimicking intracellular active fluctuations.
Edit intent: clarity
Original:  Here we show that kinesin accelerates in response to the applied noise, especially under the application of a large average hindering force (load ). The acceleration was quantitatively explained with a mathematical model using independently determined parameters .
Revised:  Here we show that kinesin accelerates in response to the applied noise, especially when a large hindering load is added. The behavior quantitatively conformed to a theoretical model that describes the kinesin movement with simple two-state reactions .
Edit intent: clarity
Original:  The universality of the theory behind the model suggests that intracellular enzymes share a similar noise-induced acceleration mechanism, meaning that the active fluctuations in cells are not just noise but utilized to improve the activity of biomolecules .
Revised:  The universality of the kinetic theory suggests that intracellular enzymes share a similar noise-induced acceleration mechanism, meaning that the active fluctuations in cells are not just noise but utilized to improve the activity of biomolecules .
Edit intent: clarity
Original:  The universality of the theory behind the model suggests that intracellular enzymes share a similar noise-induced acceleration mechanism, meaning that the active fluctuations in cells are not just noise but utilized to improve the activity of biomolecules .
Revised:  The universality of the theory behind the model suggests that intracellular enzymes share a similar noise-induced acceleration mechanism, i.e. active fluctuations in cells are not just noise but utilized to improve the activity of biomolecules .
Edit intent: coherence
Original:  The universality of the theory behind the model suggests that intracellular enzymes share a similar noise-induced acceleration mechanism, meaning that the active fluctuations in cells are not just noise but utilized to improve the activity of biomolecules .
Revised:  The universality of the theory behind the model suggests that intracellular enzymes share a similar noise-induced acceleration mechanism, meaning that the active fluctuations in cells are not just noise but are utilized to promote various physiological processes .
Edit intent: clarity
Original:  Strong evaluation methods should explore the diverse scenarios of interaction between humans and robots . We propose quality diversity (QD) algorithms as a method for simultaneously exploring both environments and human actions to discover diverse failure scenarios .
Revised:  Strong evaluation methods should explore the diverse scenarios of interaction between humans and robots in simulation can improve understanding of the system and avoid potentially costly failures in real-world settings. We formulate this as a quality diversity (QD) algorithms as a method for simultaneously exploring both environments and human actions to discover diverse failure scenarios .
Edit intent: meaning-changed
Original:  We focus on the shared autonomy domain, where the robot attempts to infer the goal of a human operator . We evaluate our approach by automatically generating scenarios for two published algorithms in this domain: shared autonomy via hindsight optimization and linear policy blending.
Revised:  We focus on the shared autonomy domain, where the robot attempts to infer the goal of a human operator , and adopt the QD algorithm MAP-Elites to generate scenarios for two published algorithms in this domain: shared autonomy via hindsight optimization and linear policy blending.
Edit intent: meaning-changed
Original: Progress to-date towards the ambitious global 2030 agenda for sustainable development has been limited, and upheaval from the COVID-19 pandemic will further exacerbate the already significant challenges to Sustainable Development Goal (SDG) achievement.
Revised: Progress to-date towards the UN Agenda 2030 agenda for sustainable development has been limited, and upheaval from the COVID-19 pandemic will further exacerbate the already significant challenges to Sustainable Development Goal (SDG) achievement.
Edit intent: clarity