wanyu/IteraTeR_full_sent huggingface dataset:
Original:  By contrast, as any omnibus test, we cannot distinguish between a rejection due to genuine selection or to generic mis-specification, when the omitted variable is correlated with the propensity score. Under the maintained assumption of no selection , our second test is therefore designed to detect mis-specification.
Revised:  By contrast, as any omnibus test, we cannot distinguish between a rejection due to genuine selection or to misspecification. Since differentiation of the latter has implications for nonparametric (point) identification and estimation of the conditional quantile function , our second test is therefore designed to detect mis-specification.
Edit intent: meaning-changed
Original:  This is achieved by a localized version of the first test, using only individuals with propensity score close to one . Although the second step requires `identification at infinity' , we can allow for cases of irregular identification.
Revised:  This is achieved by a localized version of the first test, using only individuals with propensity score close to one , this test relies on an `identification at infinity' , we can allow for cases of irregular identification.
Edit intent: coherence
Original:  Finally, our testing procedure does not require any parametric assumptions on neither the outcome nor the selection equation(s) , and all our results in the conditional quantile case hold uniformly across quantile ranks in a compact set.
Revised:  Finally, our testing procedure does not require any parametric assumptions on neither the outcome nor the selection equation(s) , and all our results in the quantile case hold uniformly across quantile ranks in a compact set.
Edit intent: clarity
Original:  Existing studies do not exploit the topological structure of EEG signals effectively. Our RGNNmodel is biologically supported and captures both local and global inter-channel relations.
Revised:  Existing studies do not exploit the topological structure of EEG signals . In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition, which is biologically supported and captures both local and global inter-channel relations.
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Original:   In addition, we propose two regularizers, namely NodeDAT and EmotionDL , to improve the robustness of our model against cross-subject EEG variations and noisy labels during recording .
Revised:  Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization. In addition, we propose two regularizers, namely NodeDAT and EmotionDL , to improve the robustness of our model against cross-subject EEG variations and noisy labels during recording .
Edit intent: meaning-changed
Original:   In addition, we propose two regularizers, namely NodeDAT and EmotionDL , to improve the robustness of our model against cross-subject EEG variations and noisy labels during recording .
Revised:  In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL) , to improve the robustness of our model against cross-subject EEG variations and noisy labels during recording .
Edit intent: meaning-changed
Original:   In addition, we propose two regularizers, namely NodeDAT and EmotionDL , to improve the robustness of our model against cross-subject EEG variations and noisy labels during recording .
Revised:  In addition, we propose two regularizers, namely NodeDAT and EmotionDL , to improve the robustness of our model against cross-subject EEG variations and noisy labels , respectively .
Edit intent: clarity
Original:  Our model obtains better performance than a few competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM on most of the tasks .
Revised:  Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM on most of the tasks .
Edit intent: clarity
Original:  Our model obtains better performance than a few competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM on most of the tasks .
Revised:  Our model obtains better performance than a few competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings .
Edit intent: clarity
Original:  In addition,  local inter-channel relations between (FP1, AF3), (F6, F8) and (FP2, AF4) may  provide useful information as well .
Revised:  In addition, experimental results suggest that global inter-channel relations between the left and right hemispheres are important for emotion recognition and local inter-channel relations between (FP1, AF3), (F6, F8) and (FP2, AF4) may provide useful information as well .
Edit intent: meaning-changed
Original:  Accurate detection and segmentation of densely-packed nuclei in images acquired under a variety of conditions is the major challenge. With a sufficient number of training examples, Mask R-CNN based methods have achieved state-of-the-art results on nuclei segmentation.
Revised:  Accurate detection and segmentation of densely-packed nuclei in images acquired under a variety of conditions . With sufficient training examples, Mask R-CNN based methods have achieved state-of-the-art results on nuclei segmentation.
Edit intent: coherence
Original:  However, the current pipeline requires fully-annotated training images, which are time-consuming to create and sometimes infeasible because of the noisy nature of microscopical images. On the other hand , nuclei often have similar appearances within the same image - this could be utilized to segment nuclei with only partially labeled training examples.
Revised:  However, the current pipeline requires fully-annotated training images, which are time-consuming to create and sometimes infeasible because of the noisy nature of microscopic images. Importantly , nuclei often have similar appearances within the same image - this could be utilized to segment nuclei with only partially labeled training examples.
Edit intent: coherence
Original:  To capture the similarities between the unlabeled image regions and labeled nuclei, we apply decomposed self-attention to the learned image features. By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this ,  our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Revised:  To capture the similarities between the unlabeled image regions and labeled nuclei, we apply decomposed self-attention to the learned features. On the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this , our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Edit intent: coherence
Original:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this ,  our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Revised:  By carefully examining the self-attention map , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this , our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Edit intent: clarity
Original:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this ,  our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Revised:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activation at the centers and edges of all nuclei in the image, including those unlabeled . Based on this , our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Edit intent: fluency
Original:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this ,  our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Revised:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei , our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Edit intent: clarity
Original:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this ,  our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Revised:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this , including the unlabeled ones. On this basis, our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Edit intent: clarity
Original:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this ,  our region proposal module propagates the partial annotations to the whole image , and then generates effective bounding box proposals for the bounding box regression and binary mask generation modules.
Revised:  By carefully examining the self-attention map of the labeled nuclei , we observe strong activations on the centers and edges of all nuclei in the image, including those unlabeled . Based on this , our region proposal module propagates the partial annotations to the whole image and then proposes effective bounding boxes for the bounding box regression and binary mask generation modules.
Edit intent: clarity
Original:  When training with only 1/4 of the nuclei annotation , the baseline pipeline suffers from severe false negatives, while our approach can retain a comparable detection accuracy , compared to that of training with the fully annotated data.
Revised:  When trained with only 1/4 of the nuclei annotation , the baseline pipeline suffers from severe false negatives, while our approach can retain a comparable detection accuracy , compared to that of training with the fully annotated data.
Edit intent: fluency
Original:  When training with only 1/4 of the nuclei annotation , the baseline pipeline suffers from severe false negatives, while our approach can retain a comparable detection accuracy , compared to that of training with the fully annotated data.
Revised:  When training with only 1/4 of the nuclei annotated , the baseline pipeline suffers from severe false negatives, while our approach can retain a comparable detection accuracy , compared to that of training with the fully annotated data.
Edit intent: fluency
Original:  When training with only 1/4 of the nuclei annotation , the baseline pipeline suffers from severe false negatives, while our approach can retain a comparable detection accuracy , compared to that of training with the fully annotated data.
Revised:  When training with only 1/4 of the nuclei annotation , the baseline pipeline gives frequent false negatives, while our approach can retain a comparable detection accuracy , compared to that of training with the fully annotated data.
Edit intent: clarity
Original:  When training with only 1/4 of the nuclei annotation , the baseline pipeline suffers from severe false negatives, while our approach can retain a comparable detection accuracy , compared to that of training with the fully annotated data.
Revised:  When training with only 1/4 of the nuclei annotation , the baseline pipeline suffers from severe false negatives, while our approach retains detection accuracy comparable to that of training with the fully annotated data.
Edit intent: clarity
Original:  Here, the robustness means that the GRNs do not lose their functions when exposed to mutation or noises. Both the adaptive response and the robustness have been acquired through the evolution.
Revised:  Here, the robustness means that the GRNs do not lose their functions when exposed to disturbances such as mutations or noise, and is widely observed at many levels in living systems. Both function and robustness have been acquired through the evolution.
Edit intent: meaning-changed
Original:  In this respect, real GRNs are rare among "all the possible GRNs" . In this study, we explore the fitness landscape of GRNs and investigate how the robustness emerge in the "well-fitted" GRNs. For that purpose, we employ the Multi-Canonical Monte Carlo method, which can sample GRNs randomly in wide range of fitness.
Revised:  In this respect, GRNs utilized in living systems are rare among "all the possible GRNs" . In this study, we explore the fitness landscape of GRNs and investigate how the robustness emerge in the "well-fitted" GRNs. For that purpose, we employ the Multi-Canonical Monte Carlo method, which can sample GRNs randomly in wide range of fitness.
Edit intent: meaning-changed
Original:  In this respect, real GRNs are rare among "all the possible GRNs" . In this study, we explore the fitness landscape of GRNs and investigate how the robustness emerge in the "well-fitted" GRNs. For that purpose, we employ the Multi-Canonical Monte Carlo method, which can sample GRNs randomly in wide range of fitness.
Revised:  In this respect, real GRNs are rare among all possible GRNs . In this study, we explore the fitness landscape of GRNs and investigate how the robustness emerge in the "well-fitted" GRNs. For that purpose, we employ the Multi-Canonical Monte Carlo method, which can sample GRNs randomly in wide range of fitness.
Edit intent: fluency
Original:  In this respect, real GRNs are rare among "all the possible GRNs" . In this study, we explore the fitness landscape of GRNs and investigate how the robustness emerge in the "well-fitted" GRNs. For that purpose, we employ the Multi-Canonical Monte Carlo method, which can sample GRNs randomly in wide range of fitness.
Revised:  In this respect, real GRNs are rare among "all the possible GRNs" . In this study, we explored the fitness landscape of GRNs and investigate how the robustness emerge in the "well-fitted" GRNs. For that purpose, we employ the Multi-Canonical Monte Carlo method, which can sample GRNs randomly in wide range of fitness.
Edit intent: fluency
Original:  In this respect, real GRNs are rare among "all the possible GRNs" . In this study, we explore the fitness landscape of GRNs and investigate how the robustness emerge in the "well-fitted" GRNs. For that purpose, we employ the Multi-Canonical Monte Carlo method, which can sample GRNs randomly in wide range of fitness. We consider a toy model of GRNs having one input gene and one output gene.
Revised:  In this respect, real GRNs are rare among "all the possible GRNs" . In this study, we explore the fitness landscape of GRNs and investigated how robustness emerged in highly-fit GRNs. We considered a toy model of GRNs having one input gene and one output gene.
Edit intent: coherence
Original:  Thus the more sensitively a GRN responds to the input , the fitter it is.We show the following properties for the GRNs in the "fittest ensemble" : (1) They distinguish two different states of the input by switching the fixed points .
Revised:  Thus the more sensitively a GRN responds to the input .We employed the multicanonical Monte Carlo method, which can sample GRNs randomly in a wide range of fitness levels, and classified the GRNs according to their fitness. The following properties were found : (1) They distinguish two different states of the input by switching the fixed points .
Edit intent: meaning-changed
Original: We show the following properties for the GRNs in the "fittest ensemble" : (1) They distinguish two different states of the input by switching the fixed points .
Revised: We show the following properties for the GRNs in the "fittest ensemble" : (1) Highly-fit GRNs exhibited bistability for intermediate input between "on" and "off". This means that such GRNs responded to two input states by using different fixed points of dynamics. This bistability emerges necessarily as fitness increases .
Edit intent: meaning-changed
Original:  and existing CAV controllers do not assess intents of the follower non-CAVs  . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Revised:  and existing CAV controllers do not assess the following non-CAVs . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Edit intent: fluency
Original:  and existing CAV controllers do not assess intents of the follower non-CAVs  . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Revised:  and existing CAV controllers do not assess intents of the follower non-CAVs intents . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Edit intent: clarity
Original:  and existing CAV controllers do not assess intents of the follower non-CAVs  . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Revised:  and existing CAV controllers do not assess intents of the follower non-CAVs . Based on our simulation study, the situation-aware CAV controller module reduces up to 27 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Edit intent: meaning-changed
Original:  and existing CAV controllers do not assess intents of the follower non-CAVs  . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Revised:  and existing CAV controllers do not assess intents of the follower non-CAVs . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the following non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Edit intent: fluency
Original:  and existing CAV controllers do not assess intents of the follower non-CAVs  . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Revised:  and existing CAV controllers do not assess intents of the follower non-CAVs . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for scenarios with different opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Edit intent: meaning-changed
Original:  and existing CAV controllers do not assess intents of the follower non-CAVs  . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Revised:  and existing CAV controllers do not assess intents of the follower non-CAVs . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles .
Edit intent: fluency
Original:  and existing CAV controllers do not assess intents of the follower non-CAVs  . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle without considering intents of the follower vehicles . For opposite through traffic volumes with 800 and 1000 vphpln, the reduction decreases to 10\%.
Revised:  and existing CAV controllers do not assess intents of the follower non-CAVs . Based on our simulation study, the situation-aware CAV controller module reduces 40 \% of the abrupt braking of the follower non-CAVs for the scenario of 600 vphpl on the opposing through movement , compared to the base scenario with the autonomous vehicle , without considering the following vehicles intent.
Edit intent: meaning-changed