wanyu/IteraTeR_v2 huggingface dataset:
Original:  Here, a k-graph property \mathbf{P is testable if there is a randomized algorithm which makes a bounded number of edge queries and distinguishes with probability 2/3 between k-graphs that satisfy \mathbf{P and those that are far from satisfying \mathbf{P . For the 2-graph case, such a combinatorial characterization was obtained by Alon, Fischer, Newman and Shapira.
Revised:  Here, a k-graph property \mathbf{P is testable if there is a randomized algorithm which makes a bounded number of edge queries and distinguishes with probability 2/3 between k-graphs that satisfy P and those that are far from satisfying \mathbf{P . For the 2-graph case, such a combinatorial characterization was obtained by Alon, Fischer, Newman and Shapira.
Edit intent: fluency
Original:  Here, a k-graph property \mathbf{P is testable if there is a randomized algorithm which makes a bounded number of edge queries and distinguishes with probability 2/3 between k-graphs that satisfy \mathbf{P and those that are far from satisfying \mathbf{P . For the 2-graph case, such a combinatorial characterization was obtained by Alon, Fischer, Newman and Shapira.
Revised:  Here, a k-graph property \mathbf{P is testable if there is a randomized algorithm which makes a bounded number of edge queries and distinguishes with probability 2/3 between k-graphs that satisfy \mathbf{P and those that are far from satisfying P . For the 2-graph case, such a combinatorial characterization was obtained by Alon, Fischer, Newman and Shapira.
Edit intent: fluency
Original: Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders and is traditionally performed by a sleep expert who assigns to each 30s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG).
Revised: Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders . It is traditionally performed by a sleep expert who assigns to each 30s of signal a sleep stage, based on the visual inspection of signals such as electroencephalograms (EEG), electrooculograms (EOG), electrocardiograms (ECG) and electromyograms (EMG).
Edit intent: coherence
Original:  In this paper, we introduce the first end-to-end deep learning approach that performs automatic temporal sleep stage classification from multivariate and multimodal Polysomnography (PSG) signals .
Revised:  We introduce here the first deep learning approach that performs automatic temporal sleep stage classification from multivariate and multimodal Polysomnography (PSG) signals .
Edit intent: clarity
Original:  In this paper, we introduce the first end-to-end deep learning approach that performs automatic temporal sleep stage classification from multivariate and multimodal Polysomnography (PSG) signals .
Revised:  In this paper, we introduce the first end-to-end deep learning approach for sleep stage classification from multivariate and multimodal Polysomnography (PSG) signals .
Edit intent: clarity
Original:  Our model , which is unique in its ability to learn a feature representation from multiple modalities, is compared to alternative automatic approaches based on convolutional networks or decisions trees.
Revised:  Our model is compared to alternative automatic approaches based on convolutional networks or decisions trees.
Edit intent: clarity
Original:  Also exploiting one minute of data before and after each data segment to be classified offers the strongest improvement when a limited number of channels is available.
Revised:  Also exploiting one minute of data before and after each data segment offers the strongest improvement when a limited number of channels is available.
Edit intent: clarity
Original:  Our approach aims to improve a key step in the study of sleep disorders.
Edit intent: coherence
Original:  As sleep experts, our system exploits the multivariate and multimodal character of PSG signals  to deliver state-of-the-art classification performance at a very low complexity cost.
Revised:  As sleep experts, our system exploits the multivariate and multimodal nature of PSG signals to deliver state-of-the-art classification performance at a very low complexity cost.
Edit intent: clarity
Original:  As sleep experts, our system exploits the multivariate and multimodal character of PSG signals  to deliver state-of-the-art classification performance at a very low complexity cost.
Revised:  As sleep experts, our system exploits the multivariate and multimodal character of PSG signals in order to deliver state-of-the-art classification performance at a very low complexity cost.
Edit intent: coherence
Original:  As sleep experts, our system exploits the multivariate and multimodal character of PSG signals  to deliver state-of-the-art classification performance at a very low complexity cost.
Revised:  As sleep experts, our system exploits the multivariate and multimodal character of PSG signals to deliver state-of-the-art classification performance with a small computational cost.
Edit intent: clarity
Original:  This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, and embodiment .
Revised:  This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, object positions and types, and other factors .
Edit intent: clarity
Original:  We term this kind of imitation learning as imitation-from-observation  and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning.
Revised:  We term this kind of imitation learning " imitation-from-observation and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning.
Edit intent: fluency
Original:  We term this kind of imitation learning as imitation-from-observation  and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning.
Revised:  We term this kind of imitation learning as imitation-from-observation ," and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning.
Edit intent: fluency
Original:  This lifts the assumption in imitation learning that the demonstration should consist of observations and actions in the same environment  , and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use.
Revised:  This lifts the assumption in imitation learning that the demonstration should consist of observations in the same environment , and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use.
Edit intent: clarity
Original:  Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled on common household chores , acquiring skills such as sweeping from videos of a human demonstrator . Videos can be found at URL
Revised:  Our experimental results show the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled on common household chores , acquiring skills such as sweeping from videos of a human demonstrator . Videos can be found at URL
Edit intent: clarity
Original:  Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled on common household chores , acquiring skills such as sweeping from videos of a human demonstrator . Videos can be found at URL
Revised:  Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled after common household chores , acquiring skills such as sweeping from videos of a human demonstrator . Videos can be found at URL
Edit intent: fluency
Original:  Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled on common household chores , acquiring skills such as sweeping from videos of a human demonstrator . Videos can be found at URL
Revised:  Our experimental results show that our approach can perform imitation-from-observation for a variety of real-world robotic tasks modeled on common household chores from videos of a human demonstrator . Videos can be found at URL
Edit intent: clarity
Original: We investigate the global dynamics of a general Kermack-McKendrick-type epidemic model formulated in terms of a system of scalar-renewal equations.
Revised: We investigate the global dynamics of a general Kermack-McKendrick-type epidemic model formulated in terms of a system of renewal equations.
Edit intent: clarity
Original:  Specifically, we consider a  model for which both the force of infection and the infected removal rates are arbitrary functions of the infection age, \tau, and use the direct Lyapunov method to establish the global asymptotic stability of the equilibrium solutions.
Revised:  Specifically, we consider a renewal model for which both the force of infection and the infected removal rates are arbitrary functions of the infection age, \tau, and use the direct Lyapunov method to establish the global asymptotic stability of the equilibrium solutions.
Edit intent: fluency
Original:  In our setup, one of the communities can fall under the influence of a strong and knowledgeable adversarial leader, who knows the full network structure, has unlimited computational resources and can completely foresee our planned actions on the network.
Revised:  In our setup, one of the communities can fall under the influence of a knowledgeable adversarial leader, who knows the full network structure, has unlimited computational resources and can completely foresee our planned actions on the network.
Edit intent: clarity
Original:  We prove strong consistency of our results in a setup with minimal assumptions.
Revised:  We prove strong consistency of our results in this setup with minimal assumptions.
Edit intent: clarity
Original: Sensor-based activity recognition seeks the profound high-level knowledge about human activity from multitudes of low-level sensor readings.
Revised: Sensor-based activity recognition seeks the profound high-level knowledge about human activities from multitudes of low-level sensor readings.
Edit intent: fluency
Original:  However, most of those approaches heavily rely on heuristic hand-crafted feature extraction methods, which dramatically hinder their generalization performance.
Revised:  However, those methods often heavily rely on heuristic hand-crafted feature extraction methods, which dramatically hinder their generalization performance.
Edit intent: clarity
Original:  However, most of those approaches heavily rely on heuristic hand-crafted feature extraction methods, which dramatically hinder their generalization performance.
Revised:  However, most of those approaches heavily rely on heuristic hand-crafted feature extraction , which could hinder their generalization performance.
Edit intent: clarity
Original:  Additionally, those methods often produce unsatisfactory results for unsupervised and incremental learning tasks.
Revised:  Additionally, existing methods are undermined for unsupervised and incremental learning tasks.
Edit intent: clarity
Original:  Meanwhile , the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas.
Revised:  Recently , the recent advancement of deep learning makes it possible to perform automatic high-level feature extraction thus achieves promising performance in many areas.
Edit intent: coherence
Original:  In this paper , we survey and highlight the recent advancement of deep learning approaches for sensor-based activity recognition.
Revised:  This paper surveys the recent advance of deep learning approaches for sensor-based activity recognition.
Edit intent: coherence
Original:  In this paper , we survey and highlight the recent advancement of deep learning approaches for sensor-based activity recognition.
Revised:  In this paper , we survey and highlight the recent advancement of deep learning based sensor-based activity recognition.
Edit intent: clarity
Original:  Specifically, we summarize existing literatures from three aspects: sensor modality, deep model  and application.
Revised:  We summarize existing literature from three aspects: sensor modality, deep model and application.
Edit intent: coherence
Original:  Specifically, we summarize existing literatures from three aspects: sensor modality, deep model  and application.
Revised:  Specifically, we summarize existing literatures from three aspects: sensor modality, deep model , and application.
Edit intent: fluency
Original:  We also present a detailed discussion and propose grand challenges for future direction .
Revised:  We also present a detailed discussion and propose grand challenges for future research .
Edit intent: clarity
Original: In this paper, I introduce a Sequence-based Multiscal Model (SeqMM) for the biomolecular data analysis.
Revised: In this paper, I introduce a Sequence-based Multiscale Model (SeqMM) for the biomolecular data analysis.
Edit intent: clarity
Original:  Further, my SeqMM is used to explore the hierarchial structures of chromosomes.
Revised:  Further, my SeqMM is used to explore the hierarchical structures of chromosomes.
Edit intent: fluency
Original: Given a clique-width expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree .
Revised: Given a clique-width k-expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree .
Edit intent: clarity
Original: Given a clique-width expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree .
Revised: Given a clique-width expression of a graph G , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree .
Edit intent: clarity
Original: Given a clique-width expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree .
Revised: Given a clique-width expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree, .
Edit intent: fluency
Original: Given a clique-width expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree .
Revised: Given a clique-width expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner TreeConnected Dominating Set .
Edit intent: fluency
Original: Given a clique-width expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree .
Revised: Given a clique-width expression of a graph G of clique-width k , we provide 2 O(k) \times n O(1)\cdot n } time algorithms for connectivity constraints on locally checkable properties such as Connected Dominating Set, Connected Perfect Dominating Set or Node-Weighted Steiner Tree, or .
Edit intent: coherence
Original:  We also propose an 2 O(k) \times n O(1)\cdot n } time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2 O(k%DIFDELCMD < \timeslog%%% (k)) \times nO(1)\cdot n^{O(1)} or worse} .
Revised:  We also propose a 2 O(k) \times n O(1)\cdot n } time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2 O(k%DIFDELCMD < \timeslog%%% (k)) \times nO(1)\cdot n^{O(1)} or worse} .
Edit intent: fluency
Original:  We also propose an 2 O(k) \times n O(1)\cdot n } time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2 O(k%DIFDELCMD < \timeslog%%% (k)) \times nO(1)\cdot n^{O(1)} or worse} .
Revised:  We also propose an 2 ^{O(k)\cdot n } time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2 O(k%DIFDELCMD < \timeslog%%% (k)) \times nO(1)\cdot n^{O(1)} or worse} .
Edit intent: clarity
Original:  We also propose an 2 O(k) \times n O(1)\cdot n } time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2 O(k%DIFDELCMD < \timeslog%%% (k)) \times nO(1)\cdot n^{O(1)} or worse} .
Revised:  We also propose an 2 O(k) \times n O(1)\cdot n } time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2 O(k%DIFDELCMD < \timeslog%%% (k)) \times nO(1)\cdot n^{O(1)} or worse} .
Edit intent: fluency
Original:  We also propose an 2 O(k) \times n O(1)\cdot n } time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2 O(k%DIFDELCMD < \timeslog%%% (k)) \times nO(1)\cdot n^{O(1)} or worse} .
Revised:  We also propose an 2 O(k) \times n O(1)\cdot n } time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2 %DIFDELCMD < \timeslog%%% (k)) \times nO(1)\cdot n^{O(1)} or worse} .
Edit intent: fluency
Original: Given a clique-width k-expression of a graph G, we provide 2^{O(k)}\cdot n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree ,Connected Dominating Set , orConnected Vertex Cover .
Revised: Given a clique-width k-expression of a graph G, we provide 2^{O(k)}\cdot n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree Connected Dominating Set , orConnected Vertex Cover .
Edit intent: fluency
Original: Given a clique-width k-expression of a graph G, we provide 2^{O(k)}\cdot n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree ,Connected Dominating Set , orConnected Vertex Cover .
Revised: Given a clique-width k-expression of a graph G, we provide 2^{O(k)}\cdot n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree , , orConnected Vertex Cover .
Edit intent: clarity
Original: Given a clique-width k-expression of a graph G, we provide 2^{O(k)}\cdot n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree ,Connected Dominating Set , orConnected Vertex Cover .
Revised: Given a clique-width k-expression of a graph G, we provide 2^{O(k)}\cdot n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree ,Connected Dominating Set Connected Vertex Cover .
Edit intent: fluency
Original: Given a clique-width k-expression of a graph G, we provide 2^{O(k)}\cdot n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree ,Connected Dominating Set , orConnected Vertex Cover .
Revised: Given a clique-width k-expression of a graph G, we provide 2^{O(k)}\cdot n time algorithms for connectivity constraints on locally checkable properties such as Node-Weighted Steiner Tree ,Connected Dominating Set , orNode-Weighted Steiner Tree, Connected Dominating Set, or Connected Vertex Cover .
Edit intent: fluency
Original:  We also propose a 2^{O(k)}\cdot n time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2^{O(k\cdot \log(k))}\cdot n^{O(1)} or worse.
Revised:  We also propose a 2^{O(k)}\cdot n time algorithm for Feedback Vertex Set . The best running times for all the considered cases were either 2^{O(k\cdot \log(k))}\cdot n^{O(1)} or worse.
Edit intent: fluency
Original: Variability on the external conditions , such as temperature, humidity, pH, available nutrients, etc., has important consequences for the dynamics and organization of communities of living systems.
Revised: Variability on external conditions has important consequences for the dynamics and organization of communities of living systems.
Edit intent: clarity
Original: Variability on the external conditions , such as temperature, humidity, pH, available nutrients, etc., has important consequences for the dynamics and organization of communities of living systems.
Revised: Variability on the external conditions , such as temperature, humidity, pH, available nutrients, etc., has important consequences for the dynamics and the organization of biological systems.
Edit intent: clarity
Original:  In many cases, the characteristic timescale of environmental changes and their correlations play a fundamental role in the way living systems adapt and respond to it.
Revised:  In many cases, the characteristic timescale of environmental changes as well as their correlations play a fundamental role in the way living systems adapt and respond to it.
Edit intent: fluency
Original:  Thus, a proper mathematical modeling requires of approaches more refined than  e.g.  simple white-noise approximations to population dynamics.
Revised:  A proper mathematical approach to understand population dynamics, thus, requires approaches more refined than e.g. simple white-noise approximations to population dynamics.
Edit intent: clarity
Original:  Thus, a proper mathematical modeling requires of approaches more refined than  e.g.  simple white-noise approximations to population dynamics.
Revised:  Thus, a proper mathematical modeling requires of approaches more refined than , e.g. simple white-noise approximations to population dynamics.
Edit intent: fluency
Original:  Thus, a proper mathematical modeling requires of approaches more refined than  e.g.  simple white-noise approximations to population dynamics.
Revised:  Thus, a proper mathematical modeling requires of approaches more refined than e.g. , simple white-noise approximations to population dynamics.
Edit intent: fluency
Original:  For the sake of specificity, we present the method we have devised using --as a guideline-- a variation of the contact process, where the propagation rate varies stochastically in time.
Revised:  For the sake of specificity, we present our approach using as a guideline a variation of the contact process, where the propagation rate varies stochastically in time.
Edit intent: clarity