wanyu/IteraTeR_v2 huggingface dataset:
Original:  The high-level circuit defining the simulation needs to be transformed into one that compiles with hardware limitations such as qubit connectivity and hardware gateset.
Revised:  The high-level circuit defining the simulation needs to be compiled into one that compiles with hardware limitations such as qubit connectivity and hardware gateset.
Edit intent: clarity
Original:  The high-level circuit defining the simulation needs to be transformed into one that compiles with hardware limitations such as qubit connectivity and hardware gateset.
Revised:  The high-level circuit defining the simulation needs to be transformed into one that complies with hardware limitations such as qubit connectivity and hardware gateset.
Edit intent: clarity
Original:  In particular, we  propose permutation-aware qubit mapping, qubit routing, gate optimization and scheduling techniques to minimize the compilation overhead.
Revised:  In particular, we propose permutation-aware techniques for qubit routing, gate optimization and scheduling techniques to minimize the compilation overhead.
Edit intent: clarity
Original:  In particular, we  propose permutation-aware qubit mapping, qubit routing, gate optimization and scheduling techniques to minimize the compilation overhead.
Revised:  In particular, we propose permutation-aware qubit mapping, qubit routing, gate optimization and scheduling to minimize compilation overhead.
Edit intent: clarity
Original:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Revised:  2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Edit intent: clarity
Original:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Revised:  We evaluate 2QAN can target different qubit topologies and different hardware gate sets. Compilation results on four applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Edit intent: clarity
Original:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Revised:  We evaluate 2QAN by compiling three applications (up to 50 qubits) and three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Edit intent: clarity
Original:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Revised:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers ( namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Edit intent: clarity
Original:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Revised:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen ) show that 2QAN outperforms state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Edit intent: coherence
Original:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Revised:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Edit intent: fluency
Original:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by up to 21X.
Revised:  We evaluate 2QAN by compiling three applications (up to 50 qubits) onto three quantum computers that have different qubit topologies and hardware two-qubit gates, namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen . Compared to state-of-the-art quantum compilers , 2QAN can reduce the number of inserted SWAP gates by up to 11.5X, reduce overhead in hardware gate count by up to 30.7 X, and reduce overhead in circuit depth by 21X.
Edit intent: fluency
Original:  Experimental results on the Montreal device demonstrate that benchmarks compiled by 2QAN achieve  highest fidelity.
Revised:  Experimental results on the Montreal device demonstrate that benchmarks compiled by 2QAN achieve the highest fidelity.
Edit intent: fluency
Original:  We achieve this by imposing a novel point discrimination loss on the middle level and global level point features produced in the backbone network.
Revised:  We achieve this by imposing a novel point discrimination loss on the middle level and global level features produced by the backbone network.
Edit intent: clarity
Original:  Our method is simple in design, which works by adding an extra adaptation module and a point consistency module for unsupervised training of the encoder in the backbone network .
Revised:  Our method is simple in design, which works by adding an extra adaptation module and a point consistency module for unsupervised training of the backbone encoder .
Edit intent: clarity
Original:  Once trained, these two modules can be discarded during supervised training of the classifier or decoder for down-stream tasks.
Revised:  Once trained, these two modules can be discarded during supervised training of the classifier or decoder for downstream tasks.
Edit intent: fluency
Original:  The new whole graph embedding scheme can obtain a trade-off between the simplicity and the quality under some supervised classification learning tasks, using molecular, social, and brain networks.
Revised:  The new whole graph embedding scheme can obtain a trade-off between simplicity and quality under supervised classification learning tasks, using molecular, social, and brain networks.
Edit intent: clarity
Original:  The new methodology is overall simple, hyperparameter-free, extensible, and explainable for whole graph embedding with promising potential for exploring graph classification, prediction, and lower-dimensional graph visualization.
Revised:  Overall, the new methodology is overall simple, hyperparameter-free, extensible, and explainable for whole graph embedding with promising potential for exploring graph classification, prediction, and lower-dimensional graph visualization.
Edit intent: coherence
Original:  The new methodology is overall simple, hyperparameter-free, extensible, and explainable for whole graph embedding with promising potential for exploring graph classification, prediction, and lower-dimensional graph visualization.
Revised:  The new methodology is simple, hyperparameter-free, extensible, and explainable for whole graph embedding with promising potential for exploring graph classification, prediction, and lower-dimensional graph visualization.
Edit intent: coherence
Original:  And finally, we have tried to include enough information about the authors that you, the reader, can interpret our descriptions within context and extract value relevant to your situation.
Edit intent: coherence
Original:  We propose a novel approach, LEO, for learning observation models end-to-end with non-differentiable graph optimizers . LEO alternates between sampling trajectories from the graph posterior and updating the model to match these samples to ground truth trajectories.
Revised:  We propose a novel approach, LEO, for learning observation models end-to-end with graph optimizers that may be non-differentiable . LEO alternates between sampling trajectories from the graph posterior and updating the model to match these samples to ground truth trajectories.
Edit intent: clarity
Original: Visualization languages help to standardize the process of designing effective visualizations, one of the most prominent being D3. js.
Revised: Visualization languages help to standardize the process of designing effective visualizations, one of the most prominent being D3. 
Edit intent: coherence
Original:  However, few researchers have analyzed at scale how users incorporate these languages into existing visualization implementation processes, i.e., implementation workflows.
Revised:  However, few researchers have analyzed at scale how users incorporate these languages into existing visualization programming processes, i.e., implementation workflows.
Edit intent: clarity
Original:  We examine this question through a qualitative lens by interviewing journalists and news consumers---the two stakeholders in a news system.
Revised:  We examine this question through a qualitative lens by interviewing journalists and news consumers -- the two stakeholders in a news system.
Edit intent: fluency
Original:  The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained  reorientation, endo-atmospheric flight including ascent and re-entry , and orbit transfer and injection.
Revised:  The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained attitude reorientation, endo-atmospheric flight including ascent and re-entry , and orbit transfer and injection.
Edit intent: clarity
Original:  The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained  reorientation, endo-atmospheric flight including ascent and re-entry , and orbit transfer and injection.
Revised:  The considered applications include planetary landing, rendezvous and proximity operations, small body landing, constrained reorientation, endo-atmospheric flight including ascent and reentry , and orbit transfer and injection.
Edit intent: fluency
Original:  Low and high resolution (LR\simHR) image pairs synthesized by existing degradation models ( %DIFDELCMD < \eg%%% , bicubic downsampling) deviate from those in reality;
Revised:  Low and high resolution (LR\simHR) image pairs synthesized by existing degradation models ( %DIFDELCMD < \eg%%% e.g. bicubic downsampling) deviate from those in reality;
Edit intent: clarity
Original:  There are three distinctive advantages with our method that allow us to collect high-quality training datasets for image super-resolution.
Revised:  There are three distinctive advantages of our method for image super-resolution.
Edit intent: clarity
Original:  Second, we can display special markers on the image margin to further improve the registration precision.
Revised:  Second, we can display specially designed markers on the image margin to further improve the registration precision.
Edit intent: clarity
Original:  Second, we can display special markers on the image margin to further improve the registration precision.
Revised:  Second, we can display special markers on the image to improve the registration precision.
Edit intent: clarity
Original:  Third , the displayed digital image file can be exploited as a reference to optimize the high frequency content of the restored image  . Experimental results show that training a super-resolution CNN by our LR\simHR dataset has superior restoration performance than training it by existing datasets on real world images at the inference stage.
Revised:  Second , the displayed digital image file can be exploited as a reference to optimize the high frequency content of the restored image . Experimental results show that training a super-resolution CNN by our LR\simHR dataset has superior restoration performance than training it by existing datasets on real world images at the inference stage.
Edit intent: fluency
Original:  In this paper, we introduce a pluggable block called diversity penalty (dp ) to alleviate mode collapse of GANs.
Revised:  In this paper, we introduce a pluggable diversity penalty module (DPM ) to alleviate mode collapse of GANs.
Edit intent: clarity
Original:  It is used to reduce the similarity of image pairs in feature space, i.e., if two latent vectors are different, then we enforce the generator to generate two images with different features.
Revised:  It reduces the similarity of image pairs in feature space, i.e., if two latent vectors are different, then we enforce the generator to generate two images with different features.
Edit intent: clarity
Original:  The normalized Gram Matrix is used to measure the similarity.
Revised:  The normalized Gram matrix is used to measure the similarity.
Edit intent: fluency
Original:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture  more modes of the data distribution.
Revised:  We compare the proposed method with Unrolled GAN , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture more modes of the data distribution.
Edit intent: coherence
Original:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture  more modes of the data distribution.
Revised:  We compare the proposed method with Unrolled GAN 17 %DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture more modes of the data distribution.
Edit intent: fluency
Original:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture  more modes of the data distribution.
Revised:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture more modes of the data distribution.
Edit intent: coherence
Original:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture  more modes of the data distribution.
Revised:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 %DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture more modes of the data distribution.
Edit intent: fluency
Original:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture  more modes of the data distribution.
Revised:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture more modes of the data distribution.
Edit intent: coherence
Original:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture  more modes of the data distribution.
Revised:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 %DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture more modes of the data distribution.
Edit intent: coherence
Original:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture  more modes of the data distribution.
Revised:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 %DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture more modes of the data distribution.
Edit intent: coherence
Original:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture  more modes of the data distribution.
Revised:  We compare the proposed method with Unrolled GAN 17 , BourGAN%DIFDELCMD < [%%% 26 , PacGAN%DIFDELCMD < [%%% 14 , VEEGAN%DIFDELCMD < [%%% 23 and ALI%DIFDELCMD < [%%% 4 on 2D synthetic dataset, and results show that our proposed method can help GAN capture much more modes of the data distribution.
Edit intent: clarity
Original:  Further,  we apply this penalty term into image data augmentation on MNIST, Fashion-MNIST and CIFAR-10, and the  testing accuracy is improved by 0.24\%, 1.34\% and 0.52\% compared with WGAN GP 6 , respectively.
Revised:  Further, we apply this method as image data augmentation on MNIST, Fashion-MNIST and CIFAR-10, and the testing accuracy is improved by 0.24\%, 1.34\% and 0.52\% compared with WGAN GP 6 , respectively.
Edit intent: clarity
Original:  Further,  we apply this penalty term into image data augmentation on MNIST, Fashion-MNIST and CIFAR-10, and the  testing accuracy is improved by 0.24\%, 1.34\% and 0.52\% compared with WGAN GP 6 , respectively.
Revised:  Further, we apply this penalty term into image data augmentation on MNIST, Fashion- MNIST and CIFAR-10, and the testing accuracy is improved by 0.24\%, 1.34\% and 0.52\% compared with WGAN GP 6 , respectively.
Edit intent: fluency
Original: Video abnormal event detection (VAD) is a vital semi-supervised task that requires learning with only roughly labeled normal videos, as anomalies are often practically unavailable.
Edit intent: coherence
Original:  Although deep neural networks (DNNs) enable great progress in VAD , existing solutions typically suffer from two issues: (1) The precise and comprehensive localization of video events is ignored . (2) The video semantics and temporal context are under-explored.
Revised:  Although deep neural networks (DNNs) enable great progress in VAD , existing solutions typically suffer from two issues: (1) The localization of video events is ignored . (2) The video semantics and temporal context are under-explored.
Edit intent: clarity
Original:  Although deep neural networks (DNNs) enable great progress in VAD , existing solutions typically suffer from two issues: (1) The precise and comprehensive localization of video events is ignored . (2) The video semantics and temporal context are under-explored.
Revised:  Although deep neural networks (DNNs) enable great progress in VAD , existing solutions typically suffer from two issues: (1) The precise and comprehensive localization of video events is ignored . (2) The semantics and temporal context are under-explored.
Edit intent: fluency
Original:  To address those issues, we are motivated by the prevalent cloze test in education and propose a novel approach named visual cloze completion (VCC), which performs VAD by learning to complete "visual cloze tests" (VCTs).
Revised:  To tackle those issues, we are motivated by the prevalent cloze test in education and propose a novel approach named visual cloze completion (VCC), which performs VAD by learning to complete "visual cloze tests" (VCTs).
Edit intent: fluency
Original:  To address those issues, we are motivated by the prevalent cloze test in education and propose a novel approach named visual cloze completion (VCC), which performs VAD by learning to complete "visual cloze tests" (VCTs).
Revised:  To address those issues, we are motivated by the prevalent cloze test in education and propose a novel approach named Visual Cloze Completion (VCC), which performs VAD by learning to complete "visual cloze tests" (VCTs).
Edit intent: clarity