wanyu/IteraTeR_full_sent huggingface dataset:
Original:   We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020) .
Revised:  We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020) .
Edit intent: fluency
Original:   We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020) . We observe that optimal policies are typically very complex, and thus unlikely to be discovered by a human agent .
Revised:  We illustrate our approach by minimizing the total time required to eradicate COVID-19 within the Susceptible-Exposed-Infected-Recovered (SEIR) model proposed by Kissler et al. (March, 2020) .
Edit intent: coherence
Original:  Recently, a dizzying number of "X-former" models have been proposed - Reformer, Linformer, Performer, Longformer, to name a few - which improve upon the original Transformer architecture, many of which make improvements around computational and memory efficiency .
Revised:  Recently, a dizzying number of "X-former" models have been proposed - Reformer, Linformer, Performer, Longformer, to name a few - which improve upon the original Transformer architecture, many of which make improvements around computational and memory efficiency .
Edit intent: fluency
Original:  We then use the SST framework to create link prediction models for both static and temporal, directed and undirected graphs which produce highly interpretable results that simultaneously match state of the art graph neural network performance .
Revised:  We then use the SST framework to create link prediction models for both static and temporal, directed and undirected graphs which produce highly interpretable results . Quantitatively, our models match out-of-the-box performance of state of the art graph neural network performance .
Edit intent: meaning-changed
Original:  We then use the SST framework to create link prediction models for both static and temporal, directed and undirected graphs which produce highly interpretable results that simultaneously match state of the art graph neural network performance .
Revised:  We then use the SST framework to create link prediction models for both static and temporal, directed and undirected graphs which produce highly interpretable results that simultaneously match state of the art graph neural network models, thereby validating the correctness and meaningfulness of our interpretable results .
Edit intent: clarity
Original: Biological neural networks operate with extraordinary energy efficiency, owing to properties such as spike-based communication and synaptic plasticity driven by local activity. When emulated in silico, such properties also enable highly energy-efficient machine learning and inference systems. However, it is unclear whether these mechanisms only trade off performance for efficiency or rather they are partly responsible for the superiority of biological intelligence.
Revised: Biological neural networks operate with extraordinary energy efficiency, owing to properties such as spike-based communication and local synaptic plasticity. It remains unclear whether these mechanisms only trade off performance for efficiency or rather they are partly responsible for the superiority of biological intelligence.
Edit intent: clarity
Original:  Here, we first address this theoretically, proving rigorously thatindeed the optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on adaptivity through short-term spike-timing dependent plasticity, a hallmark of biological neural networks.
Revised:  Here, we prove rigorously that, indeed, the Bayes-optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on adaptivity through short-term spike-timing dependent plasticity, a hallmark of biological neural networks.
Edit intent: meaning-changed
Original:  Here, we first address this theoretically, proving rigorously thatindeed the optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on adaptivity through short-term spike-timing dependent plasticity, a hallmark of biological neural networks.
Revised:  Here, we first address this theoretically, proving rigorously thatindeed the optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on short-term spike-timing dependent plasticity, a hallmark of biological neural networks.
Edit intent: clarity
Original:  Here, we first address this theoretically, proving rigorously thatindeed the optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on adaptivity through short-term spike-timing dependent plasticity, a hallmark of biological neural networks.
Revised:  Here, we first address this theoretically, proving rigorously thatindeed the optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on adaptivity through short-term spike-timing-dependent plasticity, a hallmark of biological neural networks.
Edit intent: fluency
Original:  Here, we first address this theoretically, proving rigorously thatindeed the optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on adaptivity through short-term spike-timing dependent plasticity, a hallmark of biological neural networks. Secondly, we assess this theoretical optimality via simulations and also demonstrate improved artificial intelligence (AI). For the first time, a largely biologically modelled spiking neural network (SNN) surpasses state-of-the-art artificial neural networks (ANNs) in all relevant aspects, in an example taskof recognizing video frames transformed by moving occlusions.
Revised:  Here, we first address this theoretically, proving rigorously thatindeed the optimal prediction and inference of randomly but continuously transforming environments, a common natural setting, relies on adaptivity through short-term spike-timing dependent plasticity, a hallmark of biological synapses. Further, this dynamic Bayesian inference through plasticity enables circuits of the cerebral cortex in simulations to recognize previously unseen, highly distorted dynamic stimuli. Strikingly, this also introduces a biologically-modelled AI, the first to overcome multiple limitations of deep learning and outperform artificial neural networks (ANNs) in all relevant aspects, in an example taskof recognizing video frames transformed by moving occlusions.
Edit intent: meaning-changed
Original:  For the first time, a largely biologically modelled spiking neural network (SNN) surpasses state-of-the-art artificial neural networks (ANNs) in all relevant aspects, in an example taskof recognizing video frames transformed by moving occlusions. The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs .  These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments .
Revised:  For the first time, a largely biologically modelled spiking neural network (SNN) surpasses state-of-the-art artificial neural networks in a visual task . These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments .
Edit intent: coherence
Original:  The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs .  These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments .
Revised:  The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs . The cortical-like network is spiking and event-based, trained only with unsupervised and local plasticity, on a small, narrow, and static training dataset, but achieves recognition of unseen, transformed, and dynamic data better than deep neural networks with continuous activations, trained with supervised backpropagation on the transforming data. These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments .
Edit intent: meaning-changed
Original:  The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs .  These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments .
Revised:  The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs . These results link short-term plasticity to high-level cortical function, suggest optimality of natural intelligence for natural environments .
Edit intent: clarity
Original:  The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs .  These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments . Moreover, this expands the goal of exploiting biological neuro-synaptic properties for AI ,  from mere efficiency , to computational supremacy altogether.
Revised:  The SNN recognizes the frames more accurately, even if trained on few, still, and untransformed images, with unsupervised and synaptically-local learning, binary spikes, and a single layer of neurons - all in contrast to the deep-learning-trained ANNs . These results indicate that on-line adaptivity and spike-based computation may optimize natural intelligence for natural environments , from mere efficiency , to computational supremacy altogether.
Edit intent: coherence
Original:  We find that the radius of gyration R_g of the proteins exhibits 1/f fluctuations, that are synchronous with the fluctuations of D_I. Our analysis demonstrates the validity of the local Stokes-Einstein type relation D_I\propto1/ R_g between both quantities\sim0 . From the analysis of different protein types with both strong and weak conformational fluctuations the validity of the Stokes-Einstein type relation appears to be a general property.
Revised:  We find that the radius of gyration R_g of the proteins exhibits 1/f fluctuations, that are synchronous with the fluctuations of D_I. Our analysis demonstrates the validity of the local Stokes-Einstein type relation D_I\propto1/ (R_g + R_0), where R_0\sim0 . From the analysis of different protein types with both strong and weak conformational fluctuations the validity of the Stokes-Einstein type relation appears to be a general property.
Edit intent: meaning-changed
Original:  We find that the radius of gyration R_g of the proteins exhibits 1/f fluctuations, that are synchronous with the fluctuations of D_I. Our analysis demonstrates the validity of the local Stokes-Einstein type relation D_I\propto1/ R_g between both quantities\sim0 . From the analysis of different protein types with both strong and weak conformational fluctuations the validity of the Stokes-Einstein type relation appears to be a general property.
Revised:  We find that the radius of gyration R_g of the proteins exhibits 1/f fluctuations, that are synchronous with the fluctuations of D_I. Our analysis demonstrates the validity of the local Stokes-Einstein type relation D_I\propto1/ R_g between both quantities\sim0.3 nm is assumed to be a hydration layer around the protein . From the analysis of different protein types with both strong and weak conformational fluctuations the validity of the Stokes-Einstein type relation appears to be a general property.
Edit intent: meaning-changed
Original:  In addition, the simulation results elucidate movement patterns across submarkets, in both spatial and homeownership terms, including renters, first-time home buyers , as well as local and overseas investors.
Revised:  In addition, the simulation results elucidate movement patterns across submarkets, in both spatial and homeownership terms, including renters, first-time home buyers and investors, and between both local and overseas investors.
Edit intent: meaning-changed
Original:  In this paper, we propose a deep neural network for end-to-end learning of radar localization on lidar map to bridge the gap  .
Revised:  In this paper, we propose a deep neural network for end-to-end learning of radar localization on lidar map to bridge the gap , which not only achieves the robust radar localization but also exploits the mature lidar mapping technique, thus reducing the cost of radar mapping .
Edit intent: meaning-changed
Original:   To validate the feasibility and effectiveness, we employ multi-session multi-scene datasets collected from the real world, and the results demonstrate that our proposed system achieves superior performance over 90km driving, even in generalization scenarios where the model training is in UK, while testing in South Korea.
Revised:  The whole learning system is differentiable with the network based measurement model at the front-end and KF at the back-end. To validate the feasibility and effectiveness, we employ multi-session multi-scene datasets collected from the real world, and the results demonstrate that our proposed system achieves superior performance over 90km driving, even in generalization scenarios where the model training is in UK, while testing in South Korea.
Edit intent: meaning-changed
Original: When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance on challenging natural language understanding benchmarks. In this work, we show that performance similar to GPT-3 can be obtained with language models whose parameter count is several orders of magnitude smaller.
Revised: When scaled to hundreds of billions of parameters, pretrained language models such as GPT-3 (Brown et al., 2020) achieve remarkable few-shot performance . However, enormous amounts of compute are required for training and applying such big models, resulting in a large carbon footprint and making it difficult for researchers and practitioners to use them. We show that performance similar to GPT-3 can be obtained with language models whose parameter count is several orders of magnitude smaller.
Edit intent: meaning-changed
Original: We follow the agentization approach and transform the standard-hidden action model introduced by Holmstr\"om  into an agent-based model.
Revised: We follow the agentization approach and transform the standard-hidden action model introduced by Holmstr\"om and, by doing so, provide a model which can be used to further explore theoretical topics in managerial economics, such as the efficiency of incentive mechanisms. We employ the concept of agentization, i.e., we systematically transform the standard hidden-action model into an agent-based model.
Edit intent: meaning-changed
Original:  Doing so allows us to relax some of the incorporated rather "heroic" assumptions  related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their  memory).
Revised:  Our modeling approach allows for a relaxation of some of the incorporated rather "heroic" assumptions related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their memory).
Edit intent: clarity
Original:  Doing so allows us to relax some of the incorporated rather "heroic" assumptions  related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their  memory).
Revised:  Doing so allows us to relax some of the rather "heroic" assumptions related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their memory).
Edit intent: fluency
Original:  Doing so allows us to relax some of the incorporated rather "heroic" assumptions  related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their  memory).
Revised:  Doing so allows us to relax some of the incorporated rather "heroic" assumptions included in the standard hidden-action model, whereby we particularly focus on assumptions related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their memory).
Edit intent: meaning-changed
Original:  Doing so allows us to relax some of the incorporated rather "heroic" assumptions  related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their  memory).
Revised:  Doing so allows us to relax some of the incorporated rather "heroic" assumptions related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their learning capabilities and their memory).
Edit intent: clarity
Original:  The findings suggest that there might exist a representative sample of paradigmatic instances of good reasoning that will suffice to acquire general reasoning skillsand that might form the core of a critical thinking curriculum for language models. 
Revised:  The findings suggest that intermediary pre-training on texts that exemplify basic reasoning abilities (such as typically covered in critical thinking textbooks) might help language models to acquire a broad range of reasoning skills. The synthetic argumentative texts presented in this paper are a promising starting point for building such a " critical thinking curriculum for language models. 
Edit intent: clarity
Original:  A major concern in using CNNs is the problem of catastrophic URLetting when a model tries to learn new data. In spite of various recent proposed solutions to mitigate this problem, there still exist a few side-effects (such as time/computational complexity) of such solutions.
Revised:  A major concern in using CNNs is the problem of catastrophic URLetting when a model tries to learn a new task. Despite various proposed solutions to mitigate this problem, there still exist a few side-effects (such as time/computational complexity) of such solutions.
Edit intent: clarity
Original:  In spite of various recent proposed solutions to mitigate this problem, there still exist a few side-effects (such as time/computational complexity) of such solutions. We propose a model capable of learning 3D objects in an open-ended fashion by employing deep transfer learning-based approach combined with dynamically expandable layers, which also makes sure that these side-effects are minimized to a great extent.
Revised:  In spite of various recent proposed solutions to mitigate this problem, there still exist some downsides of such solutions, e.g., computational complexity, especially when learning substantial number of tasks. These downsides can pose major problems in robotic scenarios where real-time response plays an essential role. Towards addressing this challenge, we propose a new deep transfer learning approach based on a dynamic architectural method to make robots capable of open-ended learning about new 3D objects in an open-ended fashion by employing deep transfer learning-based approach combined with dynamically expandable layers, which also makes sure that these side-effects are minimized to a great extent.
Edit intent: meaning-changed
Original:  To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to better select relevant information for expansion.
Revised:  To bridge this gap, inspired by recent advances in applying contextualized models like BERT to the document retrieval task, this paper proposes a novel query expansion model that leverages the strength of the BERT model to select relevant document chunks for expansion.
Edit intent: clarity
Original:  In evaluations on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models commonly used for document retrieval .
Revised:  In evaluation on the standard TREC Robust04 and GOV2 test collections, the proposed BERT-QE model significantly outperforms BERT-Large models commonly used for document retrieval .
Edit intent: fluency
Original: We give a classical algorithm for linear regression analogous to the quantum matrix inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters'09] for low-rank matrices [ Chakraborty et al., ICALP'19 ], when the input matrix A is stored in a data structure applicable for QRAM-based state preparation.
Revised: We give a classical algorithm for linear regression analogous to the quantum matrix inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters'09] for low-rank matrices [ Wossnig et al., ICALP'19 ], when the input matrix A is stored in a data structure applicable for QRAM-based state preparation.
Edit intent: meaning-changed
Original: We give a classical algorithm for linear regression analogous to the quantum matrix inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters'09] for low-rank matrices [ Chakraborty et al., ICALP'19 ], when the input matrix A is stored in a data structure applicable for QRAM-based state preparation.
Revised: We give a classical algorithm for linear regression analogous to the quantum matrix inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters'09] for low-rank matrices [ Chakraborty et al., Physical Review Letters'18 ], when the input matrix A is stored in a data structure applicable for QRAM-based state preparation.
Edit intent: meaning-changed
Original:  Namely, if the input model supports efficient \ell_2-norm importance sampling, and given A \in C^{m\times n} with minimum singular value \sigma and  b \in C^m as input , we can output a description of an x  such that \|x -
Revised:  Namely, given an A \in C^{m\times n} with minimum singular value \sigma and b \in C^m as input , we can output a description of an x such that \|x -
Edit intent: clarity
Original:  Namely, if the input model supports efficient \ell_2-norm importance sampling, and given A \in C^{m\times n} with minimum singular value \sigma and  b \in C^m as input , we can output a description of an x  such that \|x -
Revised:  Namely, if the input model supports efficient \ell_2-norm importance sampling, and given A \in C^{m\times n} with minimum singular value \sigma and which supports certain efficient \ell_2-norm importance sampling queries, along with a b \in C^m as input , we can output a description of an x such that \|x -
Edit intent: meaning-changed