wanyu/IteraTeR_full_sent huggingface dataset:
Original:  This work aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-bene?t tradeoff ? that can otherwise be an inhibitor for the fast development of aff?ordable systems.
Revised:  This work aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-benefit tradeoff that can otherwise be an inhibitor for the fast development of aff?ordable systems.
Edit intent: fluency
Original:  This work aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-bene?t tradeoff ? that can otherwise be an inhibitor for the fast development of aff?ordable systems.
Revised:  This work aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-bene?t tradeoff ? that can otherwise be an inhibitor for the fast development of affordable systems.
Edit intent: fluency
Original:  Specifically, the generated path shows the consistent collision-free path in real-time by adopting the euclidean signed distance field-based grid search method. Simulations consistently show the safety, and reliability of the proposed path-planning method.
Revised:  Specifically, the generated path shows the consistent collision-free path in real-time by adopting the euclidean signed distance field-based grid-search method. The simulation results consistently showed the safety, and reliability of the proposed path-planning method.
Edit intent: clarity
Original:  Moreover, in order to improve its computational efficiency, the new method uses a switching updating technique of the Jacobian matrix. That is to say, it does not compute the next Jacobian matrix and replaces it with the current jacobian matrix when the linear approximation model of the merit function approximates it well.
Revised:  Moreover, in order to improve its computational efficiency, the new method will not update the Jacobian matrix when the current Jacobian matrix performs well.
Edit intent: clarity
Original:  The numerical results show that the new method is more robust and faster than the traditional optimization method such as the Levenberg-Marquardt method (a variant of trust-region methods, the built-in subroutine fsolve.m of the MATLAB  environment).
Revised:  The numerical results show that the new method is more robust and faster than the traditional optimization method such as the Levenberg-Marquardt method (a variant of trust-region methods, the built-in subroutine fsolve.m of the MATLAB R2020a environment).
Edit intent: meaning-changed
Original: We combine deep learning and Conditional Probabilistic Context Free Grammars ( CPCFG) to create an end-to-end system for extracting structured information from complex documents.
Revised: We address the challenge of extracting structured information from business documents without detailed annotations. We propose Deep Conditional Probabilistic Context Free Grammars ( CPCFG) to create an end-to-end system for extracting structured information from complex documents.
Edit intent: meaning-changed
Original: We combine deep learning and Conditional Probabilistic Context Free Grammars ( CPCFG) to create an end-to-end system for extracting structured information from complex documents.
Revised: We combine deep learning and Conditional Probabilistic Context Free Grammars ( DeepCPCFG) to parse two-dimensional complex documents and use Recursive Neural Networks to create an end-to-end system for extracting structured information from complex documents.
Edit intent: meaning-changed
Original: We combine deep learning and Conditional Probabilistic Context Free Grammars ( CPCFG) to create an end-to-end system for extracting structured information from complex documents. For each class of documents, we create a CPCFG that describes the structure of the information to be extracted.
Revised: We combine deep learning and Conditional Probabilistic Context Free Grammars ( CPCFG) to create an end-to-end system for finding the most probable parse that represents the structured information to be extracted.
Edit intent: clarity
Original:  This gate fires only when the control qubit is in state <0 |  . It is necessary to develop an algorithm, which uses the distribution for the transient probabilities of the process in a simple and intuitive way and then transform those into X-axis offsets.
Revised:  This gate fires only when the control qubit is in state | . It is necessary to develop an algorithm, which uses the distribution for the transient probabilities of the process in a simple and intuitive way and then transform those into X-axis offsets.
Edit intent: clarity
Original:  This gate fires only when the control qubit is in state <0 |  . It is necessary to develop an algorithm, which uses the distribution for the transient probabilities of the process in a simple and intuitive way and then transform those into X-axis offsets.
Revised:  This gate fires only when the control qubit is in state <0 | 1> . It is necessary to develop an algorithm, which uses the distribution for the transient probabilities of the process in a simple and intuitive way and then transform those into X-axis offsets.
Edit intent: meaning-changed
Original:  We improve state of the art on the CIFAR-100 and Tiny-ImageNet classification datasets. In addition to being easy to implement and adding no cost at inference, our models outperform much costlier data augmented deep ensembles .
Revised:  We improve state of the art on the CIFAR-100 and Tiny ImageNet datasets. Our easy to implement and adding no cost at inference, our models outperform much costlier data augmented deep ensembles .
Edit intent: clarity
Original:  In addition to being easy to implement and adding no cost at inference, our models outperform much costlier data augmented deep ensembles . We open a new line of research complementary to previous works, as we operate in features and  better leverage the expressiveness of large networks  .
Revised:  In addition to being easy to implement and adding no cost at inference, our models outperform much costlier data augmented deep ensembles , without the inference and memory overheads. As we operate in features and better leverage the expressiveness of large networks .
Edit intent: meaning-changed
Original:  Using our age-stratified compartmental model and carefully curated epidemiological data from Germany and Europe, we study different scenarios for the long-term containment of COVID-19 in Europe, ranging from i) immediate release of measures and re-implementing NPIs only if risking a hospital overflow, to ii) driving case-numbers further down to profit from the additional stability gained by test-trace-isolate (TTI) measures. We found that in all scenarios restrictions can be lifted steadily -- at the pace of vaccination . Differences in restriction levels are only of short duration: If restrictions are lifted faster than the pace of vaccination , more freedom is gained only transiently until full intensive care units require installing restrictions again.
Revised:  Using our age-stratified model and curated epidemiological and vaccination data, we quantified at which pace European countries can lift restrictions without overwhelming their health-care systems. The analysed scenarios range from immediately lifting restriction (accepting high mortality and morbidity) to reducing case numbers until test-trace-and-isolate programs facilitate control. In general, the age-dependent vaccination roll-out implies a transient decrease in the average age of ICU patients and deaths by about seven years. The pace of vaccination , more freedom is gained only transiently until full intensive care units require installing restrictions again.
Edit intent: meaning-changed
Original:  We found that in all scenarios restrictions can be lifted steadily -- at the pace of vaccination . Differences in restriction levels are only of short duration: If restrictions are lifted faster than the pace of vaccination , more freedom is gained only transiently until full intensive care units require installing restrictions again. Keeping case numbers at a low level instead a) reduces the number of fatalities considerably, b) allows to make full use of TTI programs and , in addition, c) offers a better preparedness if escape variants of the virus challenge the vaccination strategy.
Revised:  We found that in all scenarios restrictions can be lifted steadily -- at the pace of vaccination . Differences in restriction levels are only of short duration: If restrictions are lifted faster than the pace of vaccination determines the speed of lifting restrictions; all scenarios allow to steadily increase contacts starting in May 2021 and relax most restrictions by autumn 2021. Throughout summer, only moderate contact restrictions remain necessary. However, with the assumed vaccination protection, further waves among the unvaccinated population can only be avoided with high vaccine uptake (>90
Edit intent: meaning-changed
Original:  Using our age-stratified SEIRD-ICU compartmental model and curated epidemiological and vaccination data, we quantified the rate (relative to vaccination progress) at which countries from the European Union (EU) can lift non-pharmaceutical interventions without overwhelming their healthcare systems.
Revised:  Using our age-stratified SEIRD-ICU compartmental model and curated epidemiological and vaccination data, we quantified the rate (relative to vaccination progress) at which countries can lift non-pharmaceutical interventions without overwhelming their healthcare systems.
Edit intent: clarity
Original:  In addition, we highlight the need for preventive measures to reduce contagion in school settings throughout the year 2021, where children might be drivers of contagion because of them remaining susceptible . Strategies that maintain low case numbers, instead of high ones, reduce infections and deaths by factors of eleven and five, respectively ...
Revised:  In addition, we highlight the need for preventive measures to reduce contagion in school settings throughout the year 2021, where children might be drivers of contagion because of them remaining susceptible ...
Edit intent: coherence
Original:  Specifically, the proposed method is formulated as to construct a linear mapping from the prior ensemble to the posterior one, and the linear mapping is computed  by minimizing the Kullback-Leibler divergence between the transformed distribution by the linear mapping and the actual posterior  .
Revised:  Specifically, the proposed method is formulated as to construct a linear mapping from the prior ensemble to the posterior one, and the linear mapping is computed via a variational Bayesian formulation, i.e., by minimizing the Kullback-Leibler divergence between the transformed distribution by the linear mapping and the actual posterior .
Edit intent: meaning-changed
Original:  Specifically, the proposed method is formulated as to construct a linear mapping from the prior ensemble to the posterior one, and the linear mapping is computed  by minimizing the Kullback-Leibler divergence between the transformed distribution by the linear mapping and the actual posterior  .
Revised:  Specifically, the proposed method is formulated as to construct a linear mapping from the prior ensemble to the posterior one, and the linear mapping is computed by minimizing the Kullback-Leibler divergence between the transformed distribution by the linear mapping and the actual posterior . A gradient descent scheme is proposed to solve the resulting optimization problem. With numerical examples we demonstrate that the method has competitive performance against existing methods .
Edit intent: meaning-changed
Original:  Being based on an Image to Sequence architecture, it can be trained to extract text present in an image and  sequence it correctly without imposing any constraints on language, shape of characters or orientationand layout of text and non-text.
Revised:  Being based on Image to Sequence architecture, it can be trained to extract text present in an image and sequence it correctly without imposing any constraints on language, shape of characters or orientationand layout of text and non-text.
Edit intent: fluency
Original:  Being based on an Image to Sequence architecture, it can be trained to extract text present in an image and  sequence it correctly without imposing any constraints on language, shape of characters or orientationand layout of text and non-text.
Revised:  Being based on an Image to Sequence architecture, it can extract text present in an image and sequence it correctly without imposing any constraints on language, shape of characters or orientationand layout of text and non-text.
Edit intent: clarity
Original:  Being based on an Image to Sequence architecture, it can be trained to extract text present in an image and  sequence it correctly without imposing any constraints on language, shape of characters or orientationand layout of text and non-text.
Revised:  Being based on an Image to Sequence architecture, it can be trained to extract text present in an image and then sequence it correctly without imposing any constraints on language, shape of characters or orientationand layout of text and non-text.
Edit intent: coherence
Original:  Being based on an Image to Sequence architecture, it can be trained to extract text present in an image and  sequence it correctly without imposing any constraints on language, shape of characters or orientationand layout of text and non-text.
Revised:  Being based on an Image to Sequence architecture, it can be trained to extract text present in an image and sequence it correctly without imposing any constraints regarding orientation, layout and size of text and non-text.
Edit intent: clarity
Original:  The model achieves a new state-of-art in full page recognition on the IAM dataset and when evaluated on scans of real world handwritten free form test answers - a dataset beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR  APIs.
Revised:  The model achieves a new state-of-art in paragraph level recognition on the IAM dataset and when evaluated on scans of real world handwritten free form test answers - a dataset beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR APIs.
Edit intent: meaning-changed
Original:  The model achieves a new state-of-art in full page recognition on the IAM dataset and when evaluated on scans of real world handwritten free form test answers - a dataset beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR  APIs.
Revised:  The model achieves a new state-of-art in full page recognition on the IAM dataset . When evaluated on scans of real world handwritten free form test answers - a dataset beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR APIs.
Edit intent: coherence
Original:  The model achieves a new state-of-art in full page recognition on the IAM dataset and when evaluated on scans of real world handwritten free form test answers - a dataset beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR  APIs.
Revised:  The model achieves a new state-of-art in full page recognition on the IAM dataset and when evaluated on scans of real world handwritten free form test answers - beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR APIs.
Edit intent: clarity
Original:  The model achieves a new state-of-art in full page recognition on the IAM dataset and when evaluated on scans of real world handwritten free form test answers - a dataset beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR  APIs.
Revised:  The model achieves a new state-of-art in full page recognition on the IAM dataset and when evaluated on scans of real world handwritten free form test answers - a dataset beset with curved and slanted lines, drawings, tables, math, chemistry and other symbols - it performs better than all commercially available HTR cloud APIs.
Edit intent: meaning-changed
Original: The potential outcomes approach to causal inference, most commonly known as the Rubin Causal Model (RCM) , is a framework that allows to define the causal effect of a treatment (or "intervention ") as a contrast of potential outcomes , to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions .
Revised: The Rubin Causal Model (RCM) , is a framework that allows to define the causal effect of a treatment (or "intervention ") as a contrast of potential outcomes , to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions .
Edit intent: clarity
Original: The potential outcomes approach to causal inference, most commonly known as the Rubin Causal Model (RCM) , is a framework that allows to define the causal effect of a treatment (or "intervention ") as a contrast of potential outcomes , to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions .
Revised: The potential outcomes approach to causal inference, most commonly known as the Rubin Causal Model (RCM) is a framework that allows to define the causal effect of a treatment (or "intervention ") as a contrast of potential outcomes , to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions .
Edit intent: fluency
Original: The potential outcomes approach to causal inference, most commonly known as the Rubin Causal Model (RCM) , is a framework that allows to define the causal effect of a treatment (or "intervention ") as a contrast of potential outcomes , to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions .
Revised: The potential outcomes approach to causal inference, most commonly known as the Rubin Causal Model (RCM) , is a framework that allows to define the causal effect of an intervention as a contrast of potential outcomes , to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions .
Edit intent: clarity
Original: The potential outcomes approach to causal inference, most commonly known as the Rubin Causal Model (RCM) , is a framework that allows to define the causal effect of a treatment (or "intervention ") as a contrast of potential outcomes , to discuss assumptions enabling to identify such causal effects from available data, as well as to develop methods for estimating causal effects under these assumptions .
Revised: The potential outcomes approach to causal inference, most commonly known as the Rubin Causal Model (RCM) , is a framework that allows to define the causal effect of a treatment (or "intervention ") as a contrast of potential outcomes .
Edit intent: coherence
Original:  In this paper, we present CANINE, a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a pre-training strategy with soft inductive biases in place of hard token boundaries .
Revised:  In this paper, we present CANINE, a neural encoder that operates directly on character sequences, without explicit tokenization or vocabulary, and a pre-training strategy that operates either directly on characters or optionally uses subwords as a soft inductive bias .
Edit intent: meaning-changed
Original:  CANINE outperforms a comparable mBERT model by >= 1 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28\% fewer model parameters.
Revised:  CANINE outperforms a comparable mBERT model by 2.8 F1 on TyDi QA, a challenging multilingual benchmark, despite having 28\% fewer model parameters.
Edit intent: meaning-changed
Original:  There are two findings: 1) Trained NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training.
Revised:  There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training.
Edit intent: clarity
Original:  We apply the time-integration scheme following the ideas developed by the finite difference community [M. Hochbruck, T. Jahnke, R. Schnaubelt, Convergence of an ADI splitting for Maxwell's equations, Numerishe Mathematik, 2015] to derive a weak formulations resulting in a discretization with Kronecker product matrices.
Revised:  We apply the time-integration scheme following the ideas developed by the finite difference community [M. Hochbruck, T. Jahnke, R. Schnaubelt, Convergence of an ADI splitting for Maxwell's equations, Numerishe Mathematik, 2015] to derive a weak formulation resulting in discretization with Kronecker product matrices.
Edit intent: fluency
Original:  Going further, we investigate the application of the residual minimization (RM) method for stabilization of the Maxwell equations within the isogeometric analysis setup. The residual minimization method is introduced in every time step of the implicit time integration scheme. We introduce the RM in such a way that we preserve the Kronecker product structure of the matrix. We take the tensor product structure of the computational patch of elements from  IGA framework as an advantage, allowing for linear computational cost factorization in every time step , with the automatic stabilization guaranteed by the RM method .
Revised:  We take the tensor product structure of the computational patch of elements from IGA framework as an advantage, allowing for linear computational cost factorization in every time step , with the automatic stabilization guaranteed by the RM method .
Edit intent: coherence
Original:  We take the tensor product structure of the computational patch of elements from  IGA framework as an advantage, allowing for linear computational cost factorization in every time step , with the automatic stabilization guaranteed by the RM method .
Revised:  We take the tensor product structure of the computational patch of elements from the IGA framework as an advantage, allowing for linear computational cost factorization in every time step , with the automatic stabilization guaranteed by the RM method .
Edit intent: fluency
Original:  We take the tensor product structure of the computational patch of elements from  IGA framework as an advantage, allowing for linear computational cost factorization in every time step , with the automatic stabilization guaranteed by the RM method .
Revised:  We take the tensor product structure of the computational patch of elements from IGA framework as an advantage, allowing for linear computational cost factorization in every time step . We design our solver to target simulations of electromagnetic waves propagations in non-regular biological tissues. We show that the linear cost of the alternating direction solver is preserved when we arbitrarily vary material data coefficients across the computational domain. We verify the solver using the manufactured solution and the problem of propagation of electromagnetic waves on the human head .
Edit intent: meaning-changed