wanyu/IteraTeR_v2 huggingface dataset:
Original:  We introduce an information-theoretical formulation of such adversaries and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: deep models that can be accurate in predicting the target attribute, but also can utilize their outputs to secretly encode a sensitive attribute.
Revised:  We introduce an information-theoretical formulation of such adversaries and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation: deep models that can be accurate in predicting the target attribute, but can also exploit their outputs to secretly encode a sensitive attribute.
Edit intent: fluency
Original:  We conclude by showing the difficulties in distinguishing between standard and HBC classifiers and discussing potential proactive defenses against this vulnerability of deep classifiers  .
Revised:  We conclude by showing the difficulties in distinguishing between standard and HBC classifiers , discussing challenges in defending against this vulnerability of deep classifiers .
Edit intent: clarity
Original: It is known that deep neural networks, trained for the classification of a non-sensitive target attribute , can reveal some sensitive attributes of their input data ;
Revised: It is known that deep neural networks, trained for the classification of non-sensitive target attribute , can reveal some sensitive attributes of their input data ;
Edit intent: fluency
Original: It is known that deep neural networks, trained for the classification of a non-sensitive target attribute , can reveal some sensitive attributes of their input data ;
Revised: It is known that deep neural networks, trained for the classification of a non-sensitive target attributes , can reveal some sensitive attributes of their input data ;
Edit intent: fluency
Original: It is known that deep neural networks, trained for the classification of a non-sensitive target attribute , can reveal some sensitive attributes of their input data ;
Revised: It is known that deep neural networks, trained for the classification of a non-sensitive target attribute , can reveal sensitive attributes of their input data ;
Edit intent: clarity
Original:  We take a step forward and show that deep classifiers can be trained to secretly encode a sensitive attribute of users' input data into the classifier's outputs for the target attribute, at inference time.
Revised:  We take a step forward and show that deep classifiers can be trained to secretly encode a sensitive attribute of their input data into the classifier's outputs for the target attribute, at inference time.
Edit intent: clarity
Original:  This results in an attack that works even if users have a full white-box view of the classifier, and can keep all internal representations hidden except for the classifier's outputs for the target attribute.
Revised:  Our proposed attack works even if users have a full white-box view of the classifier, and can keep all internal representations hidden except for the classifier's outputs for the target attribute.
Edit intent: clarity
Original:  This results in an attack that works even if users have a full white-box view of the classifier, and can keep all internal representations hidden except for the classifier's outputs for the target attribute.
Revised:  This results in an attack that works even if users have a full white-box view of the classifier, can keep all internal representations hidden except for the classifier's outputs for the target attribute.
Edit intent: coherence
Original:  This results in an attack that works even if users have a full white-box view of the classifier, and can keep all internal representations hidden except for the classifier's outputs for the target attribute.
Revised:  This results in an attack that works even if users have a full white-box view of the classifier, and can keep all internal representations hidden except for the classifier's estimations for the target attribute.
Edit intent: clarity
Original:  We introduce an information-theoretical formulation of such attacks and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation : classifiers that can be accurate in predicting their target attribute, but can also exploit their outputs to secretly encode a sensitive attribute.
Revised:  We introduce an information-theoretical formulation for such attacks and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation : classifiers that can be accurate in predicting their target attribute, but can also exploit their outputs to secretly encode a sensitive attribute.
Edit intent: fluency
Original:  We introduce an information-theoretical formulation of such attacks and present efficient empirical implementations for training honest-but-curious (HBC) classifiers based on this formulation : classifiers that can be accurate in predicting their target attribute, but can also exploit their outputs to secretly encode a sensitive attribute.
Revised:  We introduce an information-theoretical formulation of such attacks and present efficient empirical implementations for training honest-but-curious (HBC) classifiers : classifiers that can be accurate in predicting their target attribute, but can also exploit their outputs to secretly encode a sensitive attribute.
Edit intent: clarity
Original:  However, because of limited hardware scale and incomplete numerical domain , the majority of existing ONNs are merely studied and benchmarked with basic classification tasks.
Revised:  However, due to the problems of incomplete numerical domain, limited hardware scale and incomplete numerical domain , the majority of existing ONNs are merely studied and benchmarked with basic classification tasks.
Edit intent: clarity
Original:  However, because of limited hardware scale and incomplete numerical domain , the majority of existing ONNs are merely studied and benchmarked with basic classification tasks.
Revised:  However, because of limited hardware scale , or inadequate numerical accuracy , the majority of existing ONNs are merely studied and benchmarked with basic classification tasks.
Edit intent: clarity
Original:  However, because of limited hardware scale and incomplete numerical domain , the majority of existing ONNs are merely studied and benchmarked with basic classification tasks.
Revised:  However, because of limited hardware scale and incomplete numerical domain , the majority of existing ONNs were studied for basic classification tasks.
Edit intent: coherence
Original:  The OCDC adopts optical fields rather than intensities to represent values in the complete real-value domain .
Revised:  The OCDC adopts optical fields to carry out operations in complete real-value domain .
Edit intent: clarity
Original:  Hardware deviations are compensated via in-situ backpropagation control owing to the simplicity of chip architecture , thus enhancing the numerical accuracy of analog computing .
Revised:  Hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture , thus enhancing the numerical accuracy of analog computing .
Edit intent: coherence
Original:  Hardware deviations are compensated via in-situ backpropagation control owing to the simplicity of chip architecture , thus enhancing the numerical accuracy of analog computing .
Revised:  Hardware deviations are compensated via in-situ backpropagation control owing to the simplicity of chip architecture .
Edit intent: clarity
Original:  Therefore, the OCDC meets the fundamental requirement for regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction).
Revised:  Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction).
Edit intent: clarity
Original:  To  best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chip.
Revised:  To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chip.
Edit intent: fluency
Original:  It is anticipated that the OCDC can promote novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, medical diagnosis, and scientific study .
Revised:  It is anticipated that the OCDC can promote novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study .
Edit intent: clarity
Original:  Instead of attempting to transform diagrams into vectorized representations, we hash diagrams into binary codes, which have natural advantages in large-scale tasks.
Revised:  Instead of using standard representations, we hash diagrams into binary codes, which have natural advantages in large-scale tasks.
Edit intent: clarity
Original:  As a consequence, our proposed method is directly applicable to various datasets without the need of retraining the model.
Revised:  As a consequence, our proposed method is directly applicable to various datasets without the need for retraining the model.
Edit intent: fluency
Original:  Moreover, our experimental results demonstrate that our method is significantly faster with  less memory usage, while retaining comparable or better quality comparisons.
Revised:  Moreover, our experimental results demonstrate that our method is significantly faster with the potential of less memory usage, while retaining comparable or better quality comparisons.
Edit intent: clarity
Original:  A detector threshold that guarantees an acceptable false alarm rate is equivalent to a specific quantile of the detector output distribution.
Revised:  A detector threshold that provides an acceptable false alarm rate is equivalent to a specific quantile of the detector output distribution.
Edit intent: clarity
Original:  The first guarantee is based on the Dworetzky-Kiefer-Wolfowitz inequality, the second guarantee utilizes Chebyshev's inequality, and the third guarantee is based on exact confidence intervals for a binomial distribution.
Revised:  The first is based on the Dworetzky-Kiefer-Wolfowitz inequality, the second guarantee utilizes Chebyshev's inequality, and the third guarantee is based on exact confidence intervals for a binomial distribution.
Edit intent: fluency
Original:  The first guarantee is based on the Dworetzky-Kiefer-Wolfowitz inequality, the second guarantee utilizes Chebyshev's inequality, and the third guarantee is based on exact confidence intervals for a binomial distribution.
Revised:  The first guarantee is based on the Dworetzky-Kiefer-Wolfowitz inequality, the second utilizes Chebyshev's inequality, and the third guarantee is based on exact confidence intervals for a binomial distribution.
Edit intent: fluency
Original:  The first guarantee is based on the Dworetzky-Kiefer-Wolfowitz inequality, the second guarantee utilizes Chebyshev's inequality, and the third guarantee is based on exact confidence intervals for a binomial distribution.
Revised:  The first guarantee is based on the Dworetzky-Kiefer-Wolfowitz inequality, the second guarantee utilizes Chebyshev's inequality, and the third is based on exact confidence intervals for a binomial distribution.
Edit intent: fluency
Original:  We use both simulated data as well as data obtained from a Hardware-in-the-Loop simulation with the Temperature Control Lab to verify the guarantees provided.
Revised:  We use both simulated data as well as data obtained from an experimental setup with the Temperature Control Lab to verify the guarantees provided.
Edit intent: clarity
Original:  We use both simulated data as well as data obtained from a Hardware-in-the-Loop simulation with the Temperature Control Lab to verify the guarantees provided.
Revised:  We use both simulated data as well as data obtained from a Hardware-in-the-Loop simulation with the Temperature Control Lab to validate the guarantees provided.
Edit intent: fluency
Original: The major components of any successful autonomous flight system are task completion and collision avoidance.
Revised: The significant components of any successful autonomous flight system are task completion and collision avoidance.
Edit intent: clarity
Original:  Most deep learning algorithms are successful while executing these aspects under the environment and conditions in which they have been trained.
Revised:  Most deep learning algorithms successfully execute these aspects under the environment and conditions in which they have been trained.
Edit intent: clarity
Original:  Most deep learning algorithms are successful while executing these aspects under the environment and conditions in which they have been trained.
Revised:  Most deep learning algorithms are successful while executing these aspects under the environment and conditions they are trained.
Edit intent: clarity
Original:  In this paper we present autonomous UAV flight using Deep Reinforcement Learning augmented with Self-Attention Models  that can effectively reason when subjected to varying inputs.
Revised:  In this paper we present autonomous UAV flight using Deep Reinforcement Learning augmented with Self-Attention Models , that can effectively reason when subjected to varying inputs.
Edit intent: fluency
Original:  In addition to their reasoning ability, they also are interpretable which enables it to be used under real-world conditions.
Revised:  In addition to their reasoning ability, they are also interpretable, enabling it to be used under real-world conditions.
Edit intent: coherence
Original:  We have tested our algorithm under different weather  and environments and found it to be robust compared to conventional Deep Reinforcement Learning algorithms.
Revised:  We have tested our algorithm under different weather conditions and environments and found it to be robust compared to conventional Deep Reinforcement Learning algorithms.
Edit intent: clarity
Original:  We have tested our algorithm under different weather  and environments and found it to be robust compared to conventional Deep Reinforcement Learning algorithms.
Revised:  We have tested our algorithm under different weather and environments and found it robust compared to conventional Deep Reinforcement Learning algorithms.
Edit intent: clarity
Original:  Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to both academics and practitioners.
Revised:  Hence, a comprehensive overview of community detection's latest progress through deep learning is timely to academics and practitioners.
Edit intent: clarity
Original:  This survey devises and proposes a new taxonomy covering different categories of the state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering.
Revised:  This survey devises and proposes a new taxonomy covering different state-of-the-art methods, including deep learning-based models upon deep neural networks, deep nonnegative matrix factorization and deep sparse filtering.
Edit intent: clarity
Original:  The survey also summarizes the popular benchmark data sets, model evaluation metrics, and open-source implementations to address experimentation settings.
Revised:  The survey also summarizes the popular benchmark data sets, evaluation metrics, and open-source implementations to address experimentation settings.
Edit intent: clarity
Original:  We consider the problem of privacy-preserving spectral clustering of SBMs under \varepsilon-edge differential privacy(DP)
Revised:  We consider the problem of spectral clustering of SBMs under \varepsilon-edge differential privacy(DP)
Edit intent: clarity
Original:  for networks, and offer practical interpretations from both the central-DP and local-DP perspectives . Using a randomized response privacy mechanism called the edge-flip mechanism, we take a first step toward theoretical analysis of differentially private community detection by demonstrating conditions under which this strong privacy guarantee can be upheld while achieving spectral clustering convergence rates that match the known rates without privacy.
Revised:  for networks, and offer practical interpretations from both the central-DP and local-DP perspectives . Using a randomized response privacy mechanism called the edge-flip mechanism, we develop theoretical guarantees for differentially private community detection by demonstrating conditions under which this strong privacy guarantee can be upheld while achieving spectral clustering convergence rates that match the known rates without privacy.
Edit intent: clarity
Original:  for networks, and offer practical interpretations from both the central-DP and local-DP perspectives . Using a randomized response privacy mechanism called the edge-flip mechanism, we take a first step toward theoretical analysis of differentially private community detection by demonstrating conditions under which this strong privacy guarantee can be upheld while achieving spectral clustering convergence rates that match the known rates without privacy.
Revised:  for networks, and offer practical interpretations from both the central-DP and local-DP perspectives . Using a randomized response privacy mechanism called the edge-flip mechanism, we take a first step toward theoretical analysis of differentially private community detection , demonstrating conditions under which this strong privacy guarantee can be upheld while achieving spectral clustering convergence rates that match the known rates without privacy.
Edit intent: fluency
Original:  These lead to a version of the nonlinear filtering equation , which can be used to design efficient Monte Carlo inference algorithms  . Existing full-information approaches for phylodynamic inference are special cases of the theory.
Revised:  These lead to a nonlinear filtering equation , which can be used to design efficient Monte Carlo inference algorithms . Existing full-information approaches for phylodynamic inference are special cases of the theory.
Edit intent: clarity
Original:  These lead to a version of the nonlinear filtering equation , which can be used to design efficient Monte Carlo inference algorithms  . Existing full-information approaches for phylodynamic inference are special cases of the theory.
Revised:  These lead to a version of the nonlinear filtering equation which can be used to design efficient Monte Carlo inference algorithms . Existing full-information approaches for phylodynamic inference are special cases of the theory.
Edit intent: fluency
Original:  This paper presents a deep neural network (DNN) for monocular relative localization between multiple tiny flying robots.
Revised:  This paper presents a deep neural network (DNN) for monocular relative localization of multiple tiny flying robots.
Edit intent: clarity
Original:  This approach does not require any ground-truth data from external systems or manual labeling.
Revised:  This approach does not require any groundtruth data from external systems or manual labeling.
Edit intent: fluency
Original:  Our system is able to label real-world images with 3D relative positions between robots by another onboard relative estimation technology .
Revised:  Our system is able to label real-world images with 3D relative positions between robots based on another onboard relative estimation technology .
Edit intent: clarity
Original:  After the training from scratch in this self-supervised way, the DNN can predict the relative positions of peer robots by purely using the monocular image.
Revised:  After the training from scratch in this self-supervised manner, the proposed deep neural network (DNN) can predict relative positions of peer robots by purely using the monocular image.
Edit intent: clarity
Original:  Training and testing of the proposed network on these real-world datasets further validate the self-supervised localization effectiveness in real environment .
Revised:  Training and testing of the proposed network on these real-world datasets further validate the self-supervised multi-robot localization method .
Edit intent: clarity
Original:  XOmiVAE is able to obtain contribution values of each gene and latent dimension for a specific prediction, and the correlation between genes and the latent dimensions .
Revised:  XOmiVAE is capable of revealing the contribution of each gene and latent dimension for a specific prediction, and the correlation between genes and the latent dimensions .
Edit intent: clarity
Original:  XOmiVAE is able to obtain contribution values of each gene and latent dimension for a specific prediction, and the correlation between genes and the latent dimensions .
Revised:  XOmiVAE is able to obtain contribution values of each gene and latent dimension for each classification prediction, and the correlation between genes and the latent dimensions .
Edit intent: clarity
Original:  XOmiVAE is able to obtain contribution values of each gene and latent dimension for a specific prediction, and the correlation between genes and the latent dimensions .
Revised:  XOmiVAE is able to obtain contribution values of each gene and latent dimension for a specific prediction, and the correlation between each gene and each latent dimension .
Edit intent: clarity
Original:  It is also revealed that XOmiVAE can explain both the supervised classification and the unsupervised clustering results from the deep learning network.
Revised:  It is also demonstrated that XOmiVAE can explain both the supervised classification and the unsupervised clustering results from the deep learning network.
Edit intent: clarity
Original:  It is also revealed that XOmiVAE can explain both the supervised classification and the unsupervised clustering results from the deep learning network.
Revised:  It is also revealed that XOmiVAE can explain not only the supervised classification and the unsupervised clustering results from the deep learning network.
Edit intent: coherence
Original:  It is also revealed that XOmiVAE can explain both the supervised classification and the unsupervised clustering results from the deep learning network.
Revised:  It is also revealed that XOmiVAE can explain both the supervised classification but the unsupervised clustering results from the deep learning network.
Edit intent: coherence
Original:  To the best of our knowledge, XOmiVAE is one of the first activated-based deep learning interpretation method to explain novel clusters generated by variational autoencoders.
Revised:  To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by variational autoencoders.
Edit intent: clarity
Original: What is the influence of chronic maternal prenatal stress (PS) on neonatal iron homeostasis?
Revised: What is the influence of chronic maternal prenatal stress (PS) on fetal iron homeostasis?
Edit intent: clarity
Original:  The total effect of PS exposure on neonatal ferritin revealed a decrease of 15.4\% compared with controls.
Revised:  The total effect of PS exposure on fetal ferritin revealed a decrease of 15.4\% compared with controls.
Edit intent: fluency
Original:  Game levels often consist of several segments, sometimes repeated directly or with variation, organized into an engaging pattern . Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs).
Revised:  Game levels often consist of several segments, sometimes repeated directly or with variation, and organized into patterns (the symmetric eagle in Level 1 of The Legend of Zelda, or repeated pipe motifs in Super Mario Bros) . Such patterns can be produced with Compositional Pattern Producing Networks (CPPNs).
Edit intent: clarity