wanyu/IteraTeR_v2 huggingface dataset:
Original:  We propose an algorithm for identification of ultimate controllingentities in such networks that unifies direct and indirect control and allows for continuousinterpolation between the two concepts via a factor damping long paths.
Revised:  We propose an algorithm for identification of ultimate controllingentities in such networks that unifies direct and indirect control and allows for continuous interpolation between the two concepts via a factor damping long paths.
Edit intent: fluency
Original:  By exploiting onion-likeproperties of ownership networks the algorithm scales linearly with the network size and handlescircular ownership by design.
Revised:  By exploiting onion-like properties of ownership networks the algorithm scales linearly with the network size and handlescircular ownership by design.
Edit intent: fluency
Original:  By exploiting onion-likeproperties of ownership networks the algorithm scales linearly with the network size and handlescircular ownership by design.
Revised:  By exploiting onion-likeproperties of ownership networks the algorithm scales linearly with the network size and handles circular ownership by design.
Edit intent: fluency
Original:  We apply it to the universe of 4.2 mln UK companies and 4 mln oftheir holders to understand the distribution of control in the country.
Revised:  We apply it to the universe of 4.2 mln UK companies and 4 mln of their holders to understand the distribution of control in the country.
Edit intent: fluency
Original:  Furthermore, we providethe first independent evaluation of the control identification results.
Revised:  Furthermore, we provide the first independent evaluation of the control identification results.
Edit intent: fluency
Original:  We reveal that the proposed \alpha -ICON algorithm identifies more than 96\% of beneficiary entities from the evaluation set andsupersedes the existing approaches reported in the literature.
Revised:  We reveal that the proposed \alpha -ICON algorithm identifies more than 96\% of beneficiary entities from the evaluation set andsupersedes the existing approaches reported in the literature.
Edit intent: fluency
Original:  We reveal that the proposed \alpha -ICON algorithm identifies more than 96\% of beneficiary entities from the evaluation set andsupersedes the existing approaches reported in the literature.
Revised:  We reveal that the proposed \alpha -ICON algorithm identifies more than 96\% of beneficiary entities from the evaluation set and supersedes the existing approaches reported in the literature.
Edit intent: fluency
Original:  We refer the superiority of \alpha -ICONalgorithm to its ability to correctly identify the parents long away from their subsidiaries in thenetwork .
Revised:  We refer the superiority of \alpha \alpha-ICON algorithm to its ability to correctly identify the parents long away from their subsidiaries in thenetwork .
Edit intent: fluency
Original:  We refer the superiority of \alpha -ICONalgorithm to its ability to correctly identify the parents long away from their subsidiaries in thenetwork .
Revised:  We refer the superiority of \alpha -ICONalgorithm to its ability to correctly identify the parents long away from their subsidiaries in the network .
Edit intent: fluency
Original:  Two well known classifications of modular lattices are geometric and distributive lattices  .
Revised:  Two well known classifications of semimodular lattices are geometric and distributive lattices .
Edit intent: fluency
Original:  The challenge is to learn from data , the representations that have traditionally been crafted by hand.
Revised:  The challenge is to learn from data the representations that have traditionally been crafted by hand.
Edit intent: fluency
Original:  The goals of the paper and talk are to make these ideas explicit, to place them in a broader context where the design of the target language is crucial, and to illustrate them in the context of learning to act and plan.
Revised:  The goals of this paper are to make these ideas explicit, to place them in a broader context where the design of the target language is crucial, and to illustrate them in the context of learning to act and plan.
Edit intent: clarity
Original:  In these cases, learning is formulated as a combinatorial optimization problem but nothing prevents the use  deep learning techniques instead.
Revised:  In these cases, learning is formulated as a combinatorial problem but nothing prevents the use deep learning techniques instead.
Edit intent: clarity
Original:  In these cases, learning is formulated as a combinatorial optimization problem but nothing prevents the use  deep learning techniques instead.
Revised:  In these cases, learning is formulated as a combinatorial optimization problem but nothing prevents the use of deep learning techniques instead.
Edit intent: fluency
Original:  We also mention how could this be extended to any qunit gate.
Edit intent: coherence
Original: One-class novelty detection is conducted to iden-tify anomalous instances, with different distributions from theexpected normal instances.
Revised: One-class novelty detection is conducted to identify anomalous instances, with different distributions from theexpected normal instances.
Edit intent: fluency
Original: One-class novelty detection is conducted to iden-tify anomalous instances, with different distributions from theexpected normal instances.
Revised: One-class novelty detection is conducted to iden-tify anomalous instances, with different distributions from the expected normal instances.
Edit intent: fluency
Original:  In this paper, the Generative Adver-sarial Network based on the Encoder-Decoder-Encoder scheme (EDE-GAN) achieves state-of-the-art performance.
Revised:  In this paper, the Generative Adversarial Network based on the Encoder-Decoder-Encoder scheme (EDE-GAN) achieves state-of-the-art performance.
Edit intent: fluency
Original:  The two fac-tors bellow serve the above purpose: 1) The EDE-GAN calculatesthe distance between two latent vectors as the anomaly score, which is unlike the previous methods by utilizing the reconstruc-tion error between images.
Revised:  The two factors bellow serve the above purpose: 1) The EDE-GAN calculatesthe distance between two latent vectors as the anomaly score, which is unlike the previous methods by utilizing the reconstruc-tion error between images.
Edit intent: fluency
Original:  The two fac-tors bellow serve the above purpose: 1) The EDE-GAN calculatesthe distance between two latent vectors as the anomaly score, which is unlike the previous methods by utilizing the reconstruc-tion error between images.
Revised:  The two fac-tors bellow serve the above purpose: 1) The EDE-GAN calculates the distance between two latent vectors as the anomaly score, which is unlike the previous methods by utilizing the reconstruc-tion error between images.
Edit intent: fluency
Original:  The two fac-tors bellow serve the above purpose: 1) The EDE-GAN calculatesthe distance between two latent vectors as the anomaly score, which is unlike the previous methods by utilizing the reconstruc-tion error between images.
Revised:  The two fac-tors bellow serve the above purpose: 1) The EDE-GAN calculatesthe distance between two latent vectors as the anomaly score, which is unlike the previous methods by utilizing the reconstruction error between images.
Edit intent: fluency
Original:  2) The model obtains best resultswhen the batch size is set to 1.
Revised:  2) The model obtains best results when the batch size is set to 1.
Edit intent: fluency
Original:  To illustrate their superiority, we design a new GAN architecture, and compareperformances according to different batch sizes.
Revised:  To illustrate their superiority, we design a new GAN architecture, and compare performances according to different batch sizes.
Edit intent: fluency
Original:  Moreover, withexperimentation leads to discovery, our result implies there is alsoevidence of just how beneficial constraint on the latent space arewhen engaging in model training.
Revised:  Moreover, with experimentation leads to discovery, our result implies there is alsoevidence of just how beneficial constraint on the latent space arewhen engaging in model training.
Edit intent: fluency
Original:  Moreover, withexperimentation leads to discovery, our result implies there is alsoevidence of just how beneficial constraint on the latent space arewhen engaging in model training.
Revised:  Moreover, withexperimentation leads to discovery, our result implies there is also evidence of just how beneficial constraint on the latent space arewhen engaging in model training.
Edit intent: fluency
Original:  Moreover, withexperimentation leads to discovery, our result implies there is alsoevidence of just how beneficial constraint on the latent space arewhen engaging in model training.
Revised:  Moreover, withexperimentation leads to discovery, our result implies there is alsoevidence of just how beneficial constraint on the latent space are when engaging in model training.
Edit intent: fluency
Original:  In an attempt to learn compact and fast models, we present anew technology, Progressive Knowledge Distillation with GANs (P-KDGAN), which connects two standard GANs through thedesigned distillation loss.
Revised:  In an attempt to learn compact and fast models, we present a new technology, Progressive Knowledge Distillation with GANs (P-KDGAN), which connects two standard GANs through thedesigned distillation loss.
Edit intent: fluency
Original:  In an attempt to learn compact and fast models, we present anew technology, Progressive Knowledge Distillation with GANs (P-KDGAN), which connects two standard GANs through thedesigned distillation loss.
Revised:  In an attempt to learn compact and fast models, we present anew technology, Progressive Knowledge Distillation with GANs (P-KDGAN), which connects two standard GANs through the designed distillation loss.
Edit intent: fluency
Original:  Two-step progressive learning continu-ously augments the performance of student GANs with improvedresults over single-step approach.
Revised:  Two-step progressive learning continuously augments the performance of student GANs with improvedresults over single-step approach.
Edit intent: fluency
Original:  Two-step progressive learning continu-ously augments the performance of student GANs with improvedresults over single-step approach.
Revised:  Two-step progressive learning continu-ously augments the performance of student GANs with improved results over single-step approach.
Edit intent: fluency
Original:  Our experimental results onCIFAR-10 , MNIST, and FMNIST datasets illustrate that P-KDGAN improves the performance of the student GAN by2.
Revised:  Our experimental results on CIFAR-10 , MNIST, and FMNIST datasets illustrate that P-KDGAN improves the performance of the student GAN by2.
Edit intent: fluency
Original: When humans are given a policy to execute, there can be pol-icy execution errors and deviations in execution if there is un-certainty in identifying a state.
Revised: When humans are given a policy to execute, there can be policy execution errors and deviations in execution if there is un-certainty in identifying a state.
Edit intent: fluency
Original: When humans are given a policy to execute, there can be pol-icy execution errors and deviations in execution if there is un-certainty in identifying a state.
Revised: When humans are given a policy to execute, there can be pol-icy execution errors and deviations in execution if there is uncertainty in identifying a state.
Edit intent: fluency
Original:  So an algorithm that computesa policy for a human to execute ought to consider these effectsin its computations.
Revised:  So an algorithm that computes a policy for a human to execute ought to consider these effectsin its computations.
Edit intent: fluency
Original:  So an algorithm that computesa policy for a human to execute ought to consider these effectsin its computations.
Revised:  So an algorithm that computesa policy for a human to execute ought to consider these effects in its computations.
Edit intent: fluency
Original:  An optimal MDP policy that is poorly ex-ecuted (because of a human agent) maybe much worse thananother policy that is executed with fewer errors.
Revised:  An optimal MDP policy that is poorly executed (because of a human agent) maybe much worse thananother policy that is executed with fewer errors.
Edit intent: fluency
Original:  An optimal MDP policy that is poorly ex-ecuted (because of a human agent) maybe much worse thananother policy that is executed with fewer errors.
Revised:  An optimal MDP policy that is poorly ex-ecuted (because of a human agent) maybe much worse than another policy that is executed with fewer errors.
Edit intent: fluency
Original:  In this pa-per , we consider the problems of erroneous execution and ex-ecution delay when computing policies for a human agent thatwould act in a setting modeled by a Markov Decision Process (MDP) .
Revised:  In this paper , we consider the problems of erroneous execution and ex-ecution delay when computing policies for a human agent thatwould act in a setting modeled by a Markov Decision Process (MDP) .
Edit intent: fluency
Original:  In this pa-per , we consider the problems of erroneous execution and ex-ecution delay when computing policies for a human agent thatwould act in a setting modeled by a Markov Decision Process (MDP) .
Revised:  In this pa-per , we consider the problems of erroneous execution and execution delay when computing policies for a human agent thatwould act in a setting modeled by a Markov Decision Process (MDP) .
Edit intent: fluency
Original:  In this pa-per , we consider the problems of erroneous execution and ex-ecution delay when computing policies for a human agent thatwould act in a setting modeled by a Markov Decision Process (MDP) .
Revised:  In this pa-per , we consider the problems of erroneous execution and ex-ecution delay when computing policies for a human agent that would act in a setting modeled by a Markov Decision Process (MDP) .
Edit intent: fluency
Original:  In this pa-per , we consider the problems of erroneous execution and ex-ecution delay when computing policies for a human agent thatwould act in a setting modeled by a Markov Decision Process (MDP) .
Revised:  In this pa-per , we consider the problems of erroneous execution and ex-ecution delay when computing policies for a human agent thatwould act in a setting modeled by a Markov Decision Process .
Edit intent: clarity
Original:  We present a framework to model the likelihood ofpolicy execution errors and likelihood of non-policy actionslike inaction (delays) due to state uncertainty.
Revised:  We present a framework to model the likelihood of policy execution errors and likelihood of non-policy actionslike inaction (delays) due to state uncertainty.
Edit intent: fluency
Original:  We present a framework to model the likelihood ofpolicy execution errors and likelihood of non-policy actionslike inaction (delays) due to state uncertainty.
Revised:  We present a framework to model the likelihood ofpolicy execution errors and likelihood of non-policy actions like inaction (delays) due to state uncertainty.
Edit intent: fluency
Original:  This is followedby a hill climbing algorithm to search for good policies thataccount for these errors.
Revised:  This is followed by a hill climbing algorithm to search for good policies thataccount for these errors.
Edit intent: fluency
Original:  This is followedby a hill climbing algorithm to search for good policies thataccount for these errors.
Revised:  This is followedby a hill climbing algorithm to search for good policies that account for these errors.
Edit intent: fluency
Original:  We then use the best policy found byhill climbing with a branch and bound algorithm to find theoptimal policy.
Revised:  We then use the best policy found by hill climbing with a branch and bound algorithm to find theoptimal policy.
Edit intent: fluency
Original:  We then use the best policy found byhill climbing with a branch and bound algorithm to find theoptimal policy.
Revised:  We then use the best policy found byhill climbing with a branch and bound algorithm to find the optimal policy.
Edit intent: fluency
Original:  We show experimental results in a Gridworlddomain and analyze the performance of the two algorithms.
Revised:  We show experimental results in a Gridworld domain and analyze the performance of the two algorithms.
Edit intent: fluency
Original:  We also present human studies that verify if our assumptionson policy execution by humans under state-aliasing are rea-sonable .
Revised:  We also present human studies that verify if our assumptions on policy execution by humans under state-aliasing are rea-sonable .
Edit intent: fluency
Original:  We also present human studies that verify if our assumptionson policy execution by humans under state-aliasing are rea-sonable .
Revised:  We also present human studies that verify if our assumptionson policy execution by humans under state-aliasing are reasonable .
Edit intent: fluency
Original:  In this paper, we propose deep Bregman divergences for contrastive learning of visual representation and we aim to enhance contrastive loss used in self-supervised learning by training additional networks based on functional Bregman divergence.
Revised:  In this paper, we propose deep Bregman divergences for contrastive learning of visual representation where we aim to enhance contrastive loss used in self-supervised learning by training additional networks based on functional Bregman divergence.
Edit intent: coherence
Original:  By combining conventional contrastive loss with the proposed divergence loss , our method outperforms baseline and most of  previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets.
Revised:  We show the combination of conventional contrastive loss with the proposed divergence loss , our method outperforms baseline and most of previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets.
Edit intent: coherence
Original:  By combining conventional contrastive loss with the proposed divergence loss , our method outperforms baseline and most of  previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets.
Revised:  By combining conventional contrastive loss and our proposed divergence loss , our method outperforms baseline and most of previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets.
Edit intent: fluency
Original:  By combining conventional contrastive loss with the proposed divergence loss , our method outperforms baseline and most of  previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets.
Revised:  By combining conventional contrastive loss with the proposed divergence loss outperforms baseline and most of previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets.
Edit intent: clarity
Original:  By combining conventional contrastive loss with the proposed divergence loss , our method outperforms baseline and most of  previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets.
Revised:  By combining conventional contrastive loss with the proposed divergence loss , our method outperforms baseline and most of the previous methods for self-supervised and semi-supervised learning on multiple classifications and object detection tasks and datasets.
Edit intent: fluency
Original:  Numerical simulations confirm our intuition that the proposed architectures reliably produce stabilizing feedback controllers without sacrificing performance .
Revised:  Numerical simulations confirm our intuition that the proposed architectures reliably produce stabilizing feedback controllers without sacrificing optimality .
Edit intent: clarity
Original:  Here we show that ED can be solved in polynomial time for (P_9,S_{1, 1 , 6 },S_{ 1 , 2 , 5 })-free chordal bipartite graphs.
Revised:  Here we show that ED can be solved in polynomial time for (P_9,S_{1, 2 , 6 },S_{ 1 , 2 , 5 })-free chordal bipartite graphs.
Edit intent: fluency
Original: We study a canonical problem in adaptive control;
Revised: Ito stochastic differential equations are ubiquitous models for dynamic environments. A canonical problem in adaptive control;
Edit intent: clarity
Original:  Then, an easy-to-implement algorithm for balancing exploration versus exploitation is proposed, followed by theoretical guarantees showing a square-root of time regret bound .
Revised:  Then, an easy-to-implement algorithm for balancing exploration versus exploitation is proposed, followed by theoretical guarantees showing a square-root rate as time grows .
Edit intent: clarity
Original:  It contains over 70 interesting scenes, 111 ,464 frames, and 232,913 annotated 3D vehicle bounding boxes, collected from 8 towns in CARLA and a digital town of Culver City, Los Angeles.
Revised:  It contains over 70 interesting scenes, 11 ,464 frames, and 232,913 annotated 3D vehicle bounding boxes, collected from 8 towns in CARLA and a digital town of Culver City, Los Angeles.
Edit intent: fluency
Original:  In addition, we perform a case study of OptChain, which is the state-of-the-art transaction placement algorithm for sharded blockchains, and find a defect of it.
Revised:  In addition, we perform a case study of OptChain, which is the state-of-the-art transaction placement algorithm for sharded blockchains, and find a shortcoming of it.
Edit intent: clarity
Original:  A  fix is proposed, and our evaluation results demonstrate that the fix helps OptChain improve the system throughput by 4x .
Revised:  A simple fix is proposed, and our evaluation results demonstrate that the fix helps OptChain improve the system throughput by 4x .
Edit intent: clarity
Original:  We demonstrate the successful deployment of the model learned in simulation to highly resource-constrained physical quadrotors performing stationkeeping and goal swapping behaviors.
Revised:  We demonstrate the successful deployment of the model learned in simulation to highly resource-constrained physical quadrotors performing station keeping and goal swapping behaviors.
Edit intent: fluency
Original:  Code and video demonstrations are available at the project website  URL
Revised:  Code and video demonstrations are available on the project website URL
Edit intent: fluency
Original:  Code and video demonstrations are available at the project website  URL
Revised:  Code and video demonstrations are available at the project website at URL
Edit intent: fluency
Original:  We hope these empirical-driven techniques will pave way for more effective ways to prompt LMs in  future.
Revised:  We hope these empirical-driven techniques will pave way for more effective ways to prompt LMs in the future.
Edit intent: fluency
Original: In computational geometry, the construction of essential primitives like convex hulls, Voronoi diagrams and Delaunay triangulations require the evaluation of the signs of determinants, which are sums of products.
Revised: IIn computational geometry, the construction of essential primitives like convex hulls, Voronoi diagrams and Delaunay triangulations require the evaluation of the signs of determinants, which are sums of products.
Edit intent: fluency
Original:  We prove that the algorithm is correct and provide efficient and tested C++ code for it.
Revised:  We prove that the algorithm is correct and provide efficient and tested C++ code for it.
Edit intent: fluency