wanyu/IteraTeR_full_sent huggingface dataset:
Original:  ~\mbox{%DIFAUXCMD \cite{bcg-ffq-13}\hspace{0pt}%DIFAUXCMD } [ [ \eps\eps .
Revised:  ~\mbox{%DIFAUXCMD \cite{bcg-ffq-13}\hspace{0pt}%DIFAUXCMD } [ [ \eps\eps^2) that can approximately return the regions containing at least \theta points whose times are within W in O(1) query time .
Edit intent: meaning-changed
Original: In  Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death- despite maximal supportive care.
Revised: In severe pneumonias, including Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death- despite maximal supportive care.
Edit intent: meaning-changed
Original: In  Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death- despite maximal supportive care.
Revised: In Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death- despite maximal supportive care.
Edit intent: clarity
Original: In  Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death- despite maximal supportive care.
Revised: In Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death- despite maximal supportive care.
Edit intent: clarity
Original: In  Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome (ARDS), URLan failure, and death- despite maximal supportive care.
Revised: In Coronavirus disease 2019 (COVID-19), the initial viral replication phase is often followed by a hyperinflammatory reaction in the lungs and URLan systems ('cytokine storm syndrome') that leads to acute respiratory distress syndrome and death, despite maximal supportive care.
Edit intent: clarity
Original:  Preventing hyperinflammation is key to avoiding progression to severe stages of COVID-19. We have previously demonstrated that alpha-1 adrenergic receptor (\alpha_1-AR) antagonists can prevent cytokine storm syndrome and resulting death in mice.
Revised:  Preventing hyperinflammation is key to avoiding these outcomes. We previously demonstrated that alpha-1 adrenergic receptor (\alpha_1-AR) antagonists can prevent cytokine storm syndrome and resulting death in mice.
Edit intent: clarity
Original:  Here, we present a retrospective study of outcomes in patients with acute respiratory distress (n = 13,125) or pneumonia (n = 108,956) . Patients with acute respiratory distress who were taking \alpha_1-AR antagonists for other conditions had a 35\% reduced risk of requiring ventilation , and a 56\% reduced risk of ventilation and death, compared to non-users (adjusted OR = 0.41 , 95\% CI 0.17-0.81, p = 0.01).
Revised:  Here, we present a retrospective study of outcomes in patients with acute respiratory distress (n = 13,125) or pneumonia (n = 108,956) from all causes; patients who were incidentally taking \alpha-blockers had a reduced risk of requiring ventilation , and a 56\% reduced risk of ventilation and death, compared to non-users (adjusted OR = 0.41 , 95\% CI 0.17-0.81, p = 0.01).
Edit intent: meaning-changed
Original:  Patients with acute respiratory distress who were taking \alpha_1-AR antagonists for other conditions had a 35\% reduced risk of requiring ventilation , and a 56\% reduced risk of ventilation and death, compared to non-users (adjusted OR = 0.41 , 95\% CI 0.17-0.81, p = 0.01).
Revised:  Patients with acute respiratory distress who were taking \alpha_1-AR antagonists for other conditions had a 35\% reduced risk of requiring ventilation (by 35\% and 16\%, respectively), and a reduced risk of ventilation and death, compared to non-users (adjusted OR = 0.41 , 95\% CI 0.17-0.81, p = 0.01).
Edit intent: meaning-changed
Original:  Patients with acute respiratory distress who were taking \alpha_1-AR antagonists for other conditions had a 35\% reduced risk of requiring ventilation , and a 56\% reduced risk of ventilation and death, compared to non-users (adjusted OR = 0.41 , 95\% CI 0.17-0.81, p = 0.01).
Revised:  Patients with acute respiratory distress who were taking \alpha_1-AR antagonists for other conditions had a 35\% reduced risk of requiring ventilation , and a 56\% reduced risk of being ventilated and dying (by 56\% and 20\%, respectively) , 95\% CI 0.17-0.81, p = 0.01).
Edit intent: meaning-changed
Original:  Patients with acute respiratory distress who were taking \alpha_1-AR antagonists for other conditions had a 35\% reduced risk of requiring ventilation , and a 56\% reduced risk of ventilation and death, compared to non-users (adjusted OR = 0.41 , 95\% CI 0.17-0.81, p = 0.01). By contrast, no significant effect was observed for beta-adrenergic receptor (\beta-AR) antagonists .
Revised:  Patients with acute respiratory distress who were taking \alpha_1-AR antagonists for other conditions had a 35\% reduced risk of requiring ventilation , and a 56\% reduced risk of ventilation and death, compared to non-users (adjusted OR = 0.41 , compared to non-users .
Edit intent: coherence
Original:   These results support studying \alpha_1-AR antagonists for preventing ARDS and reducing mortality in pneumonia and acute respiratory distress, as well as highlight the need for prospective trials of \alpha_1-AR antagonists to assess their efficacy in preventing cytokine storm syndrome and death in COVID-19.
Revised:  Beta-adrenergic receptor antagonists had no significant effects. These results support studying \alpha_1-AR antagonists for preventing ARDS and reducing mortality in pneumonia and acute respiratory distress, as well as highlight the need for prospective trials of \alpha_1-AR antagonists to assess their efficacy in preventing cytokine storm syndrome and death in COVID-19.
Edit intent: meaning-changed
Original:   These results support studying \alpha_1-AR antagonists for preventing ARDS and reducing mortality in pneumonia and acute respiratory distress, as well as highlight the need for prospective trials of \alpha_1-AR antagonists to assess their efficacy in preventing cytokine storm syndrome and death in COVID-19.
Revised:  These results highlight the urgent need for prospective trials of \alpha_1-AR antagonists to assess their efficacy in preventing cytokine storm syndrome and death in COVID-19.
Edit intent: clarity
Original:   These results support studying \alpha_1-AR antagonists for preventing ARDS and reducing mortality in pneumonia and acute respiratory distress, as well as highlight the need for prospective trials of \alpha_1-AR antagonists to assess their efficacy in preventing cytokine storm syndrome and death in COVID-19.
Revised:  These results support studying \alpha_1-AR antagonists for preventing ARDS and reducing mortality in pneumonia and acute respiratory distress, as well as highlight the need for prospective trials testing whether prophylactic \alpha-blockers improve outcomes in diseases with a prominent hyperinflammatory component such as COVID-19.
Edit intent: clarity
Original:  While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images of the specific anatomy and modalities they were trained on .
Revised:  While classical registration methods accurately estimate the spatial correspondence between images , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images of the specific anatomy and modalities they were trained on .
Edit intent: clarity
Original:  While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images of the specific anatomy and modalities they were trained on .
Revised:  While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every image pair . Learning-based techniques are fast at test time, but can only register images of the specific anatomy and modalities they were trained on .
Edit intent: clarity
Original:  While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images of the specific anatomy and modalities they were trained on . In contrast, our approach leverages a generative model to synthesize label maps and gray-scale images that expose a network to a wide range of anatomy and contrast during training.
Revised:  While classical registration methods can accurately align multi-modal image pairs , they solve a costly optimization problem for every new pairof images . Learning-based techniques are fast at test time, but can only register images with image contrast and geometric content that are similar to those available during training. We focus on removing this image-data dependency of learning methods. Our approach leverages a generative model to synthesize label maps and gray-scale images that expose a network to a wide range of anatomy and contrast during training.
Edit intent: clarity
Original:  In contrast, our approach leverages a generative model to synthesize label maps and gray-scale images that expose a network to a wide range of anatomy and contrast during training. We demonstrate that this strategy enables robust registration of arbitrary modalities, without the need to retrain for a new modality.
Revised:  In contrast, our approach leverages a generative model to synthesize label maps and gray-scale images that expose a network to a wide range of variability during training, forcing them to learn features invariant to image type (contrast). This strategy results in powerful networks trained to generalize to a broad array of real input images. We present extensive experiments, with a focus on 3D neuroimaging, showing that this strategy enables robust registration of arbitrary modalities, without the need to retrain for a new modality.
Edit intent: meaning-changed
Original:  Critically, we show that input labels  need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data .
Revised:  Critically, we show that input labels from which we synthesize images need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data .
Edit intent: clarity
Original:  Critically, we show that input labels  need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data .
Revised:  Critically, we show that input labels need not be of actual anatomy: training on randomly generated geometric shapes also results in competitive registration performance and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data .
Edit intent: clarity
Original:  Critically, we show that input labels  need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance and makes the network agnostic to anatomy and contrast, all while eradicating the need for real data . We present extensive experiments demonstrating that this strategy enables registration of modalities not seen during training and surpasses the state of art in cross-contrast registration .
Revised:  Critically, we show that input labels need not be of actual anatomy: training on randomly synthesized shapes , or supervoxels, results in competitive registration performance , albeit slightly less accurate, while alleviating the dependency on real data of any kind .
Edit intent: clarity
Original: While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions.
Revised: Deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions.
Edit intent: coherence
Original: While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions.
Revised: While deep neural networks (DNNs) are being increasingly used to make predictions from functional magnetic resonance imaging (fMRI) data. However , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions.
Edit intent: meaning-changed
Original: While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions.
Revised: While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", as it can be difficult to discover what input information is used to make predictions.
Edit intent: coherence
Original: While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used to make predictions. This ability is particularly important for applications in cognitive neuroscience and neuroinformatics .
Revised: While deep neural networks (DNNs) are being increasingly used to make predictions from high-dimensional, complex data , they are widely seen as uninterpretable "black boxes", since it can be difficult to discover what input information is used by the DNN in the process, something important in both cognitive neuroscience and neuroinformatics .
Edit intent: clarity
Original:  In this paper, we develop two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .
Revised:  In this paper, we review a variety of methods for producing gradient-based saliency maps, and present a new adversarial training method we developed to make DNNs robust to input noise, with the goal of improving interpretability. We introduce two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .
Edit intent: meaning-changed
Original:  In this paper, we develop two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .
Revised:  In this paper, we develop two quantitative evaluation procedures for saliency map methods in fMRI, applicable whenever a DNN or linear model is being trained to decode some information from imaging data. We evaluate the procedures using a synthetic dataset where the complex activation structure is known, and on saliency maps produced for DNN and linear models for task decoding in the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .
Edit intent: meaning-changed
Original:  In this paper, we develop two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .
Revised:  In this paper, we develop two quantitative evaluation procedures for saliency methods, using the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps .
Edit intent: coherence
Original:  In this paper, we develop two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset contains functional magnetic resonance imaging (fMRI ) datafrom multiple tasks per subject to create ground truth saliency maps . We then introduce an adversarial training method that makes DNNs robust to small input noise, and demonstrate that it measurably improves interpretability .
Revised:  In this paper, we develop two quantitative evaluation procedures for saliency methods, using the fact that the Human Connectome Project (HCP) dataset . Our key finding is that saliency maps produced with different methods vary widely in interpretability, in both in synthetic and HCP fMRI data. Strikingly, even when DNN and linear models decode at comparable levels of performance, DNN saliency maps score higher on interpretability than linear model saliency maps (derived via weights or gradient). Finally, saliency maps produced with our adversarial training method that makes DNNs robust to small input noise, and demonstrate that it measurably improves interpretability .
Edit intent: meaning-changed
Original: In many high-stakes domains such as criminal justice, finance, and healthcare, AI systems may recommend actions to a human expert responsible for final decisions, a context known as AI-advised decision making. When AI practitioners deploy the most accurate system in these domains, they implicitly assume that the system will function alone in the world.
Revised: AI practitioners typically strive to develop the most accurate system in these domains, they implicitly assume that the system will function alone in the world.
Edit intent: clarity
Original:  When AI practitioners deploy the most accurate system in these domains, they implicitly assume that the system will function alone in the world.
Revised:  When AI practitioners deploy the most accurate system in these domains, they implicitly assume that the system will function autonomously. However, in practice, AI systems often are used to provide advice to people in domains ranging from criminal justice and finance to healthcare. In such AI-advised decision making, humans and machines form a team, where the human is responsible for making final decisions. But is the world.
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Original:  Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the improvements in utility while being small and varying across datasetsand parameters (such as cost of mistake), are real and consistent with our definition of team utility .
Revised:  Our experiments with linear and non-linear models on real-world, high-stakes datasets show that the most accuracy AI may not lead to highest team performance and show the benefit of modeling teamwork during training through improvements in expected team utility across datasets, considering parameters such as human skill and the cost of mistakes .
Edit intent: meaning-changed
Original:  Alongside common methods in time-series analysis of coupling between financial and economic markets, mapping coupled time-series onto networks is an outstanding measure to provide insight into hidden aspects embedded in couplings intrinsically.
Revised:  Alongside common methods in time-series analysis of coupling between financial and economic markets, mapping the joint structure of two time-series onto networks is an outstanding measure to provide insight into hidden aspects embedded in couplings intrinsically.
Edit intent: meaning-changed
Original:  Alongside common methods in time-series analysis of coupling between financial and economic markets, mapping coupled time-series onto networks is an outstanding measure to provide insight into hidden aspects embedded in couplings intrinsically. In this manner, we discretize the amplitude of coupled time-series and investigate relative simultaneous locations of the corresponding amplitudes(nodes).
Revised:  Alongside common methods in time-series analysis of coupling between financial and economic markets, mapping coupled time-series onto networks is an outstanding measure to provide insight into hidden aspects embedded in the couplings. We discretize the amplitude of coupled time-series and investigate relative simultaneous locations of the corresponding amplitudes(nodes).
Edit intent: clarity
Original:  In this manner, we discretize the amplitude of coupled time-series and investigate relative simultaneous locations of the corresponding amplitudes(nodes). The transmissions between simultaneous amplitudes are clarified by edges in the network.
Revised:  In this manner, we discretize the amplitude of coupled time-series and investigate relative simultaneous locations of those amplitudes. Each segment of a discretized amplitude is considered as a node. The simultaneity of the amplitudes of the two time-series is considered as the edges in the network.
Edit intent: clarity
Original:  The frequency of occurrences of the coupled amplitudes is illustrated by the weighted edges, that is to say, some coupled amplitudes in the time-series can be identified as communities in the network .
Revised:  The frequency of occurrences forms the weighted edges. In order to extract information, we need to measure that to what extent the coupling deviates from the coupling of two uncoupled series. Also, we need to measure that to what extent the couplings inherit their characteristics from a Gaussian distribution or a non-Gaussian distribution. We mapped the network from two surrogate time-series can be identified as communities in the network .
Edit intent: meaning-changed