wanyu/IteraTeR_v2 huggingface dataset:
Original:  Solving such tasks often involves complex modeling of interaction between two manipulators and their coupling at a control level.
Revised:  Solving such tasks often involves complex modeling of interactions between two manipulators and their coupling at a control level.
Edit intent: fluency
Original:  In this paper , we explore the applicability of model-free reinforcement learning to dual-arm manipulation based on a modular approach with two decentralized single-arm controllers and a single centralized  policy.
Revised:  Instead, in this work , we explore the applicability of model-free reinforcement learning to dual-arm manipulation based on a modular approach with two decentralized single-arm controllers and a single centralized policy.
Edit intent: clarity
Original:  In this paper , we explore the applicability of model-free reinforcement learning to dual-arm manipulation based on a modular approach with two decentralized single-arm controllers and a single centralized  policy.
Revised:  In this paper , we explore the applicability of model-free RL to dual-arm manipulation based on a modular approach with two decentralized single-arm controllers and a single centralized policy.
Edit intent: clarity
Original:  Finally we zero-shot transfer policies trained in simulation to the real-world and evaluate their performance.
Revised:  Finally we zero-shot transfer policies trained in simulation to the real world and evaluate their performance.
Edit intent: fluency
Original:  However, under certain challenging peculiar circumstances (e.g., marking degradation, serious vehicle occlusion), it is difficult to get satisfactory performance in accurately detecting the lane markings from one single image which is often the case in current literature.
Revised:  However, under certain challenging peculiar circumstances , it is difficult to get satisfactory performance in accurately detecting the lane markings from one single image which is often the case in current literature.
Edit intent: clarity
Original:  However, under certain challenging peculiar circumstances (e.g., marking degradation, serious vehicle occlusion), it is difficult to get satisfactory performance in accurately detecting the lane markings from one single image which is often the case in current literature.
Revised:  However, under certain challenging peculiar circumstances (e.g., marking degradation, serious vehicle occlusion), it is difficult to get satisfactory performance in accurately detecting the lanes from one single image which is often the case in current literature.
Edit intent: clarity
Original:  Since road markings are continuous lines on the road , the lanes that are difficult to be accurately detected in the current image frame might potentially be better inferred out if information from previous frames is incorporated.
Revised:  Since lane markings are continuous lines on the road , the lanes that are difficult to be accurately detected in the current image frame might potentially be better inferred out if information from previous frames is incorporated.
Edit intent: fluency
Original:  Since road markings are continuous lines on the road , the lanes that are difficult to be accurately detected in the current image frame might potentially be better inferred out if information from previous frames is incorporated.
Revised:  Since road markings are continuous lines , the lanes that are difficult to be accurately detected in the current image frame might potentially be better inferred out if information from previous frames is incorporated.
Edit intent: clarity
Original:  Since road markings are continuous lines on the road , the lanes that are difficult to be accurately detected in the current image frame might potentially be better inferred out if information from previous frames is incorporated.
Revised:  Since road markings are continuous lines on the road , the lanes that are difficult to be accurately detected in the single current image can potentially be better inferred out if information from previous frames is incorporated.
Edit intent: clarity
Original:  Since road markings are continuous lines on the road , the lanes that are difficult to be accurately detected in the current image frame might potentially be better inferred out if information from previous frames is incorporated.
Revised:  Since road markings are continuous lines on the road , the lanes that are difficult to be accurately detected in the current image frame might potentially be better deduced if information from previous frames is incorporated.
Edit intent: clarity
Original:  For this, we propose a novel hybrid spatial-temporal sequence-to-one deep learning architecture making full use of the spatial-temporal information in multiple frames of a continuous sequence of images to detect lane markings in the very last current image frame.
Revised:  This study proposes a novel hybrid spatial-temporal sequence-to-one deep learning architecture making full use of the spatial-temporal information in multiple frames of a continuous sequence of images to detect lane markings in the very last current image frame.
Edit intent: clarity
Original:  For this, we propose a novel hybrid spatial-temporal sequence-to-one deep learning architecture making full use of the spatial-temporal information in multiple frames of a continuous sequence of images to detect lane markings in the very last current image frame.
Revised:  For this, we propose a novel hybrid spatial-temporal sequence-to-one deep learning architecture making full use of the spatial-temporal information in multiple continuous image frames to detect lane markings in the very last current image frame.
Edit intent: clarity
Original:  For this, we propose a novel hybrid spatial-temporal sequence-to-one deep learning architecture making full use of the spatial-temporal information in multiple frames of a continuous sequence of images to detect lane markings in the very last current image frame.
Revised:  For this, we propose a novel hybrid spatial-temporal sequence-to-one deep learning architecture making full use of the spatial-temporal information in multiple frames of a continuous sequence of images to detect lane markings in the very last current frame.
Edit intent: clarity
Original:  Specifically, the hybrid model integrates the  spatial convolutional neural network (SCNN) , which is powerful in extracting spatial features and relationships in one single image, with convolutional long-short term memory (ConvLSTM) neural network , which can capture the spatial-temporal correlations and time dependencies among the image sequences.
Revised:  Specifically, the hybrid model integrates the spatial convolutional neural network (SCNN) embedded for excavating spatial features and relationships in one single image, with convolutional long-short term memory (ConvLSTM) neural network , which can capture the spatial-temporal correlations and time dependencies among the image sequences.
Edit intent: clarity
Original:  Treating lane detection as the image  segmentation problem , we applied encoder-decoder structures to make it work in an end-to-end way .
Revised:  Treating lane detection as the image segmentation problem work in an end-to-end way .
Edit intent: coherence
Original:  Extensive experiments on two large-scale datasets reveal that our proposed model can effectively handle challenging driving scenes and outperforms previous state-of-the-art methods  .
Revised:  Extensive experiments reveal that the proposed model can effectively handle challenging driving scenes and outperforms previous state-of-the-art methods .
Edit intent: clarity
Original:  Extensive experiments on two large-scale datasets reveal that our proposed model can effectively handle challenging driving scenes and outperforms previous state-of-the-art methods  .
Revised:  Extensive experiments on two large-scale datasets reveal that our proposed model can effectively handle challenging driving scenes and outperforms available state-of-the-art methods .
Edit intent: clarity
Original:  Including 3D molecular structure as input to learned models  their performance for many molecular tasks.
Revised:  Including 3D molecular structure as input to learned models improves their performance for many molecular tasks.
Edit intent: clarity
Original: With the proposition of neural networks for point clouds, deep learning has started to shine in the field of 3D object recognition while researchers have shown an increased interest to investigate the reliability of point cloud networks by fooling them with perturbed instances .
Revised: With the proposition of neural networks for point clouds, deep learning has started to shine in the field of 3D object recognition while researchers have shown an increased interest to investigate the reliability of point cloud networks by adversarial attacks .
Edit intent: clarity
Original:  Our results show that the popular point cloud networks can be deceived with almost 100 \% success rate by shifting only one point from the input instance.
Revised:  Our results show that popular point cloud networks can be deceived with almost 100 \% success rate by shifting only one point from the input instance.
Edit intent: fluency
Original:  The pebbling complexity of the graph G characterized the amortized cost of evaluating f_{G,H} multiple times or the total cost to run a brute-force preimage attack over a fixed domain X, i.e., given y \in \{0,1\}^* find x \in X such that f_{G,H}(x)=y.
Revised:  The pebbling complexity of the graph G characterizes the amortized cost of evaluating f_{G,H} multiple times or the total cost to run a brute-force preimage attack over a fixed domain X, i.e., given y \in \{0,1\}^* find x \in X such that f_{G,H}(x)=y.
Edit intent: fluency
Original:  The pebbling complexity of the graph G characterized the amortized cost of evaluating f_{G,H} multiple times or the total cost to run a brute-force preimage attack over a fixed domain X, i.e., given y \in \{0,1\}^* find x \in X such that f_{G,H}(x)=y.
Revised:  The pebbling complexity of the graph G characterized the amortized cost of evaluating f_{G,H} multiple times as well as the total cost to run a brute-force preimage attack over a fixed domain X, i.e., given y \in \{0,1\}^* find x \in X such that f_{G,H}(x)=y.
Edit intent: clarity
Original:  As a consequence we are able to present, for example, a general new class of Johnson-Lindenstrauss embedding matrices for O(\log^c N)-dimensional submanifolds of R^N which enjoy O(N \log  \log N))-time matrix vector multiplications.
Revised:  As a consequence we are able to present, for example, a general new class of Johnson-Lindenstrauss embedding matrices for O(\log^c N)-dimensional submanifolds of R^N which enjoy O(N \log ( \log N))-time matrix vector multiplications.
Edit intent: fluency
Original: A key reason for the lack of reliability of deep neural networks in the real world is their heavy reliance on %DIFDELCMD < {\it %%% spurious input features that are causally unrelated to the true label.
Revised: A key reason for the lack of reliability of deep neural networks in the real world is their heavy reliance on %DIFDELCMD < {\it %%% spurious input features that are causally unrelated to the true label.
Edit intent: fluency
Original:  Focusing on image classifications, we define causal attributes as the set of visual features that are always a part of the object while spurious attributes are the ones that are likely to %DIFDELCMD < {\it %DIFDELCMD < %%% co-occur with the object but not a part of it (e.g., attribute `` fingers" for class `` band aid").
Revised:  Focusing on image classifications, we define causal attributes as the set of visual features that are always a part of the object while spurious attributes are the ones that are likely to %DIFDELCMD < {\it %DIFDELCMD < %%% co-occur with the object but not a part of it (e.g., attribute " fingers" for class `` band aid").
Edit intent: fluency
Original:  Focusing on image classifications, we define causal attributes as the set of visual features that are always a part of the object while spurious attributes are the ones that are likely to %DIFDELCMD < {\it %DIFDELCMD < %%% co-occur with the object but not a part of it (e.g., attribute `` fingers" for class `` band aid").
Revised:  Focusing on image classifications, we define causal attributes as the set of visual features that are always a part of the object while spurious attributes are the ones that are likely to %DIFDELCMD < {\it %DIFDELCMD < %%% co-occur with the object but not a part of it (e.g., attribute `` fingers" for class " band aid").
Edit intent: fluency
Original:  In this work, we introduce a %DIFDELCMD < {\it %%% scalable framework to discover a subset of spurious and causal visual attributes used in inferences of a general model and localize them on a large number of images with minimal human supervision.
Revised:  In this work, we introduce a %DIFDELCMD < {\it %%% scalable framework to discover a subset of spurious and causal visual attributes used in inferences of a general model and localize them on a large number of images with minimal human supervision.
Edit intent: fluency
Original:  Our methodology is based on this key idea: to identify spurious or causal visual attributes used in model predictions, we identify spurious or causal neural features (penultimate layer neurons of a robust model) via limited human supervision (e.g., using top 5 activating images per feature).
Revised:  Our methodology is based on this key idea: to identify spurious or causal visual attributes used in model predictions, we identify spurious or causal neural features (penultimate layer neurons of a robust model) via limited human supervision (e.g., using top 5 activating images per feature).
Edit intent: fluency
Original:  We then show that these neural feature annotations %DIFDELCMD < {\it %DIFDELCMD < %%% generalize extremely well to many more images %DIFDELCMD < {\it %%% without any human supervision.
Revised:  We then show that these neural feature annotations %DIFDELCMD < {\it %DIFDELCMD < %%% generalize extremely well to many more images %DIFDELCMD < {\it %%% without any human supervision.
Edit intent: fluency
Original:  Using this methodology, we introduce the %DIFDELCMD < {\it %%% Causal Imagenet dataset containing causal and spurious masks for a large set of samples from Imagenet.
Revised:  Using this methodology, we introduce the %DIFDELCMD < {\it %%% Causal Imagenet dataset containing causal and spurious masks for a large set of samples from Imagenet.
Edit intent: fluency
Original: A key reason for the lack of reliability of deep neural networks in the real world is their heavy reliance on spurious input features that are causally unrelated to the true label.
Revised: A key reason for the lack of reliability of deep neural networks in the real world is their heavy reliance on spurious input features that are not essential to the true label.
Edit intent: clarity
Original:  Focusing on image classifications, we define causal attributes as the set of visual features that are always a part of the object  while spurious attributes are the ones that are likely to co-occur with the object but not a part of it (e.g., attribute "fingers" for class "band aid").
Revised:  Focusing on image classifications, we define core attributes as the set of visual features that are always a part of the object while spurious attributes are the ones that are likely to co-occur with the object but not a part of it (e.g., attribute "fingers" for class "band aid").
Edit intent: clarity
Original:  In this work, we introduce a scalable framework to discover a subset of spurious and causal visual attributes used in inferences of a general model and localize them on a large number of images with minimal human supervision.
Revised:  In this work, we introduce a general framework to discover a subset of spurious and causal visual attributes used in inferences of a general model and localize them on a large number of images with minimal human supervision.
Edit intent: clarity
Original:  In this work, we introduce a scalable framework to discover a subset of spurious and causal visual attributes used in inferences of a general model and localize them on a large number of images with minimal human supervision.
Revised:  In this work, we introduce a scalable framework to discover a subset of spurious and core visual attributes used in inferences of a general model and localize them on a large number of images with minimal human supervision.
Edit intent: clarity
Original:  Our methodology is based on this key idea: to identify spurious or causal visual attributes used in model predictions, we identify spurious or causal neural features (penultimate layer neurons of a robust model) via limited human supervision (e.g., using top 5 activating images per feature).
Revised:  Our methodology is based on this key idea: to identify spurious or core visual attributes used in model predictions, we identify spurious or causal neural features (penultimate layer neurons of a robust model) via limited human supervision (e.g., using top 5 activating images per feature).
Edit intent: clarity
Original:  We use the activation maps for these neural features as the soft masks to highlight spurious or causal visual attributes.
Revised:  We use the activation maps for these neural features as the soft masks to highlight spurious or core visual attributes.
Edit intent: clarity
Original:  Using this methodology, we introduce the Causal Imagenet dataset containing causal and spurious masks for a large set of samples from Imagenet.
Revised:  Using this methodology, we introduce the Causal Imagenet dataset containing core and spurious masks for a large set of samples from Imagenet.
Edit intent: clarity
Original:  We assess the performance of several popular Imagenet models and show that they rely heavily on various spurious features in their predictions  .
Revised:  Using this dataset, we show that several popular Imagenet models and show that they rely heavily on various spurious features in their predictions .
Edit intent: clarity
Original:  We assess the performance of several popular Imagenet models and show that they rely heavily on various spurious features in their predictions  .
Revised:  We assess the performance of several popular Imagenet models rely heavily on various spurious features in their predictions .
Edit intent: coherence
Original:  Experimental results using real-ear measured transfer functions confirm that the proposed method achieves a good sound quality compared to the open-ear while outperforming the generic filter design that is based on an average equalization .
Revised:  Experimental results using real-ear measured transfer functions confirm that the proposed method achieves a good sound quality compared to the open-ear while outperforming filter designs that do not leverage the proposed estimator .
Edit intent: clarity
Original:  By time stepping along specific paths in the complex plane, integrators can gain higher orders of accuracy or achieve expanded stability regions.
Revised:  By time stepping along specific paths in the complex plane, integrators can gain higher orders of accuracy and achieve expanded stability regions.
Edit intent: fluency
Original: In this paper we propose the design and implementation of a generic medical knowledge based system (MKBS) for identifying diseases from several symptoms and signs.
Revised: In this paper , we design and implement a generic medical knowledge based system (MKBS) for identifying diseases from several symptoms and signs.
Edit intent: coherence
Original:  In this system  some important aspects like Knowledge bases system, Knowledge representation, Inference Engine has been addressed.
Revised:  In this system , some important aspects like Knowledge bases system, Knowledge representation, Inference Engine has been addressed.
Edit intent: fluency
Original:  In this system  some important aspects like Knowledge bases system, Knowledge representation, Inference Engine has been addressed.
Revised:  In this system some important aspects like knowledge bases system, Knowledge representation, Inference Engine has been addressed.
Edit intent: fluency
Original:  In this system  some important aspects like Knowledge bases system, Knowledge representation, Inference Engine has been addressed.
Revised:  In this system some important aspects like Knowledge bases system, knowledge representation, inference engine have been addressed.
Edit intent: fluency
Original:  New certainty fact has been introduced to get conclusion about same firing rules .
Revised:  The system asks users different questions and inference engines will use the certainty factor to prune out low possible solutions .
Edit intent: coherence
Original:  The proposed system is generic and knowledge based, and it can be integrated with any rule bases system in disease diagnosis.
Revised:  The proposed system is generic and flexible, which can be integrated with any rule bases system in disease diagnosis.
Edit intent: clarity
Original:  Compared with existing attack models, the attacker's ability in the proposed action poisoning attack model is more restricted, and hence the attack model is more practical .
Revised:  Compared with existing attack models, the attacker's ability in the proposed action poisoning attack model is more restricted, which brings some design challenges .
Edit intent: clarity
Original:  In seeking to address these challenges, we employ a football video game, e.g., Google Research Football (GRF), as our testbed and develop an end-to-end learning-based AI system (denoted as TiKick  to complete this challenging task.
Revised:  In seeking to address these challenges, we employ a football video game, e.g., Google Research Football (GRF), as our testbed and develop an end-to-end learning-based AI system (denoted as TiKick ) to complete this challenging task.
Edit intent: fluency
Original:  this class is in the second level of the arithmetical hierarchy and corresponds to formulas of the form `` \forall x \, \exists y \, \phi(x,y) '' . This shows that exactly computing the quantum value is strictly harder than approximating it, and also strictly harder than computing the commuting operator value (either exactly or approximately).
Revised:  this class is in the second level of the arithmetical hierarchy and corresponds to formulas of the form " \forall x \, \exists y \, \phi(x,y) '' . This shows that exactly computing the quantum value is strictly harder than approximating it, and also strictly harder than computing the commuting operator value (either exactly or approximately).
Edit intent: fluency
Original:  this class is in the second level of the arithmetical hierarchy and corresponds to formulas of the form `` \forall x \, \exists y \, \phi(x,y) '' . This shows that exactly computing the quantum value is strictly harder than approximating it, and also strictly harder than computing the commuting operator value (either exactly or approximately).
Revised:  this class is in the second level of the arithmetical hierarchy and corresponds to formulas of the form `` \forall x \, \exists y \, \phi(x,y) " . This shows that exactly computing the quantum value is strictly harder than approximating it, and also strictly harder than computing the commuting operator value (either exactly or approximately).
Edit intent: fluency
Original:  At the core of our \Pi_2-completeness result is a new ``gapless'' compression theorem that holds for both quantum and commuting operator strategies.
Revised:  At the core of our \Pi_2-completeness result is a new "gapless" compression theorem that holds for both quantum and commuting operator strategies.
Edit intent: fluency
Original:  We also show how a `` gap-preserving '' compression theorem for commuting operator strategies would imply that approximating the commuting operator value is complete for \Pi_1.
Revised:  We also show how a " gap-preserving '' compression theorem for commuting operator strategies would imply that approximating the commuting operator value is complete for \Pi_1.
Edit intent: fluency