wanyu/IteraTeR_v2 huggingface dataset:
Original:  To overcome this limitation, we propose a novel methodology based on model checking to formally analyze security vulnerabilities in sequential circuits  considering side-channel parameters like propagation delay, switching  and leakage power.
Revised:  To overcome this limitation, we propose a novel methodology based on model checking to formally analyze security vulnerabilities in sequential circuits while considering side-channel parameters like propagation delay, switching and leakage power.
Edit intent: coherence
Original:  To overcome this limitation, we propose a novel methodology based on model checking to formally analyze security vulnerabilities in sequential circuits  considering side-channel parameters like propagation delay, switching  and leakage power.
Revised:  To overcome this limitation, we propose a novel methodology based on model checking to formally analyze security vulnerabilities in sequential circuits considering side-channel parameters like propagation delay, switching power, and leakage power.
Edit intent: fluency
Original:  In particular, we present a novel algorithm to efficiently partition the state-space into corresponding smaller state-spaces for faster security analysis of complex sequential circuits and thereby mitigating the associated state-space explosion due to their feedback loops.
Revised:  In particular, we present a novel algorithm to efficiently partition the state-space into corresponding smaller state-spaces to enable distributed security analysis of complex sequential circuits and thereby mitigating the associated state-space explosion due to their feedback loops.
Edit intent: clarity
Original: "Coevolving" or "adaptive" voter models (AVMs) are natural systems for modeling the emergence of mesoscopic structure from local networked processes driven by conflict and homophily.
Revised: Adaptive voter models (AVMs) are natural systems for modeling the emergence of mesoscopic structure from local networked processes driven by conflict and homophily.
Edit intent: clarity
Original: "Coevolving" or "adaptive" voter models (AVMs) are natural systems for modeling the emergence of mesoscopic structure from local networked processes driven by conflict and homophily.
Revised: "Coevolving" or "adaptive" voter models (AVMs) are simple mechanistic systems that model the emergence of mesoscopic structure from local networked processes driven by conflict and homophily.
Edit intent: clarity
Original:  Because of this, many methods for approximating the long-run behavior of AVMs have been proposed over the last decade.
Revised:  Because of this, many methods for approximating the behavior of AVMs have been proposed over the last decade.
Edit intent: clarity
Original:  In this work, we develop a novel, second-order moment closure approximation method for studying the equilibrium mesoscopic structure of AVMs and apply it to binary-state rewire-to-random and rewire-to-same model variants with random state-switching.
Revised:  In this work, we develop a novel, second-order moment closure approximation method for binary-state rewire-to-random and rewire-to-same model variants with random state-switching.
Edit intent: coherence
Original:  The resulting numerical approximations enable the computation of key properties of the model behavior, such as the location of the fragmentation transition and the equilibrium active edge density  , across the entire range of state densities.
Revised:  The resulting numerical approximations enable the computation of key properties of the model behavior, such as the location of the fragmentation transition and the active edge density , across the entire range of state densities.
Edit intent: clarity
Original:  The resulting numerical approximations enable the computation of key properties of the model behavior, such as the location of the fragmentation transition and the equilibrium active edge density  , across the entire range of state densities.
Revised:  The resulting numerical approximations enable the computation of key properties of the model behavior, such as the location of the fragmentation transition and the equilibrium active edge density , across the entire space of parameters and opinion densities.
Edit intent: clarity
Original:  Numerically, they are nearly exact for the rewire-to-random model, and competitive with other current approaches for the rewire-to-same model.
Revised:  Numerically, our results are nearly exact for the rewire-to-random model, and competitive with other current approaches for the rewire-to-same model.
Edit intent: clarity
Original:   We conclude with suggestions for model refinement and extensionsto more complex models .
Revised:  We conclude with suggestions for model refinements and extensions .
Edit intent: clarity
Original:  Most existing work in this area focuses on static undirected networks and do not account for presence of directed edges or temporarily changes.
Revised:  Most existing work in this area focuses on static undirected networks and does not account for presence of directed edges or temporarily changes.
Edit intent: fluency
Original:  Most existing work in this area focuses on static undirected networks and do not account for presence of directed edges or temporarily changes.
Revised:  Most existing work in this area focuses on static undirected networks and do not account for presence of directed edges or temporal changes.
Edit intent: fluency
Original:  In this paper, we propose a novel , flexible and scalable network embedding methodology, gl2vec , for network classification in both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs.
Revised:  In this paper, we propose a novel network embedding methodology, gl2vec , for network classification in both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs.
Edit intent: coherence
Original:  We argue thatgl2vec can be used to classify and compare networks of varying sizes and time period with high accuracy.
Revised:  We gl2vec can be used to classify and compare networks of varying sizes and time period with high accuracy.
Edit intent: clarity
Original:  Experimental results further show that gl2vec , concatenated with a wide range of state-of-the-art methods, improves classification accuracy by up to 10\\%DIF < in real-world applications %DIF >  such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks.
Revised:  We argue that gl2vec , concatenated with a wide range of state-of-the-art methods, improves classification accuracy by up to 10\\%DIF < in real-world applications %DIF > such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks.
Edit intent: clarity
Original:  Experimental results further show that gl2vec , concatenated with a wide range of state-of-the-art methods, improves classification accuracy by up to 10\\%DIF < in real-world applications %DIF >  such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks.
Revised:  Experimental results further show that gl2vec , concatenated with a wide range of state-of-the-art methods, which can significantly improve their classification accuracy by up to 10\\%DIF < in real-world applications %DIF > such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks.
Edit intent: clarity
Original:  In this paper, we propose a novel network embedding methodology, gl2vec %DIFDELCMD < }%%% , for network classification in  both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs  .
Revised:  In this paper, we gl2vec %DIFDELCMD < }%%% , for network classification in both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs .
Edit intent: clarity
Original:  In this paper, we propose a novel network embedding methodology, gl2vec %DIFDELCMD < }%%% , for network classification in  both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs  .
Revised:  In this paper, we propose a novel network embedding methodology, %DIFDELCMD < }%%% , for network classification in both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs .
Edit intent: fluency
Original:  In this paper, we propose a novel network embedding methodology, gl2vec %DIFDELCMD < }%%% , for network classification in  both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs  .
Revised:  In this paper, we propose a novel network embedding methodology, gl2vec %DIFDELCMD < }%%% , for network classification ingraphlets both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs .
Edit intent: fluency
Original:  In this paper, we propose a novel network embedding methodology, gl2vec %DIFDELCMD < }%%% , for network classification in  both static and temporal directed networks. gl2vec constructs vectors for feature representation using static or temporal network graphlet distributions and a null model for comparing them against random graphs  .
Revised:  In this paper, we propose a novel network embedding methodology, gl2vec %DIFDELCMD < }%%% , for network classification in both static and temporal directed networks. gl2vec for network embedding, and .
Edit intent: coherence
Original:  We demonstrate the efficacy and usability of gl2vec over existing state-of-the-art methods on network classification tasks such as network type classification and subgraph identification in  several real-world static and temporal directed networks.
Revised:  We demonstrate the efficacy and usability of gl2vec and several real-world static and temporal directed networks.
Edit intent: clarity
Original:  We demonstrate the efficacy and usability of gl2vec over existing state-of-the-art methods on network classification tasks such as network type classification and subgraph identification in  several real-world static and temporal directed networks.
Revised:  We demonstrate the efficacy and usability of gl2vec over existing state-of-the-art methods on network classification tasks such as network type classification and subgraph identification in several real-world static and temporal networks.
Edit intent: clarity
Original:  We argue thatgl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10%DIFDELCMD < \\%%% %DIF < in real-world applications such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks .
Revised:  We gl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10%DIFDELCMD < \\%%% %DIF < in real-world applications such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks .
Edit intent: clarity
Original:  We argue thatgl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10%DIFDELCMD < \\%%% %DIF < in real-world applications such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks .
Revised:  We argue that provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10%DIFDELCMD < \\%%% %DIF < in real-world applications such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks .
Edit intent: clarity
Original:  We argue thatgl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10%DIFDELCMD < \\%%% %DIF < in real-world applications such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks .
Revised:  We argue thatgl2vec provides additional network features that are not captured by state-of-the-art %DIFDELCMD < \\%%% %DIF < in real-world applications such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks .
Edit intent: clarity
Original:  We argue thatgl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10%DIFDELCMD < \\%%% %DIF < in real-world applications such as detecting departments for subgraphs in an email network or identifying mobile users given their app switching behaviors represented as static or temporal directed networks .
Revised:  We argue thatgl2vec provides additional network features that are not captured by state-of-the-art methods, which can significantly improve their classification accuracy by up to 10%DIFDELCMD < \\%%% %DIF < methods in network analysis .
Edit intent: clarity
Original:  Therefore , we propose FREE as a fine-grained scheduling scheme for reliable and energy-efficient data collection in LoRaWAN.
Revised:  Therefore , we propose FREE , a fine-grained scheduling scheme for reliable and energy-efficient data collection in LoRaWAN.
Edit intent: coherence
Original:  FREE schedules spreading factors, transmission powers, frequency channels, timeslots, and slot numbers in frames for LoRa end-devices.
Revised:  FREE schedules spreading factors, transmission powers, frequency channels, time slots, and schedules slots in frames for LoRa end-devices.
Edit intent: clarity
Original:  As a result, FREE overcomes the scalability issue of LoRaWAN by eliminating collisions and grouping acknowledgments.
Revised:  As a result, FREE overcomes the scalability problem of LoRaWAN by eliminating collisions and grouping acknowledgments.
Edit intent: fluency
Original:  Furthermore, FREE achieves almost 100\% data delivery and the estimated device lifetime is likely over 10 years regardless of the running application and the network size
Revised:  Furthermore, FREE achieves almost 100\% data delivery and the device lifetime is likely over 10 years regardless of the running application and the network size
Edit intent: clarity
Original:  Furthermore, FREE achieves almost 100\% data delivery and the estimated device lifetime is likely over 10 years regardless of the running application and the network size
Revised:  Furthermore, FREE achieves almost 100\% data delivery and the estimated device lifetime is estimated to over 10 years regardless of the running application and the network size
Edit intent: clarity
Original:  Determining the expected behaviour and variability of a stochastic biochemical reaction network (BCRN) requires many probabilistic simulations of its evolution.
Revised:  Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution.
Edit intent: clarity
Original:  Using a BCRN model to assist in the interpretation of time course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities.
Revised:  Using a biochemical reaction network model to assist in the interpretation of time course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities.
Edit intent: fluency
Original:  In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic BCRN models.
Revised:  In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models.
Edit intent: clarity
Original:  As a result, this review provides a practical and accessible introduction to computational methods for stochastic models applicable to the life sciences community 
Revised:  As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community 
Edit intent: clarity
Original:  As a result, this review provides a practical and accessible introduction to computational methods for stochastic models applicable to the life sciences community 
Revised:  As a result, this review provides a practical and accessible introduction to computational methods for stochastic models applicable to the life sciences community .
Edit intent: fluency
Original:  We obtain decay estimates that are uniform in the diffusive limit.
Revised:  We obtain decay rates that are bounded uniformly in the diffusive limit.
Edit intent: clarity
Original:  In this paper, we propose an improved version of the heat method which overcomes its limitation in scalability .
Revised:  In this paper, we propose a parallel and scalable approach for geodesic distance computation on triangle meshes .
Edit intent: clarity
Original:  Our key observation is that the recovery of geodesic distance from approximate gradient directions  can be reformulated as gradient optimization problem that enforces integrability, which can be solved using a parallel fixed-order method that requires no linear system solving and converges quickly.
Revised:  Our key observation is that the recovery of geodesic distance from approximate gradient directions can be reformulated as gradient optimization problem that enforces integrability, which can be solved using an efficient fixed-order method that requires no linear system solving and converges quickly.
Edit intent: clarity
Original:  Afterwards, the geodesic distance are efficiently recovered by parallel integration of the optimized gradients in breadth-first order.
Revised:  Afterwards, the geodesic distance is efficiently recovered by parallel integration of the optimized gradients in breadth-first order.
Edit intent: fluency
Original:  Moreover,  a similar breadth-first strategy is developed for a parallel Gauss-Seidel solver for heat diffusion .
Revised:  Moreover, we employ a similar breadth-first strategy is developed for a parallel Gauss-Seidel solver for heat diffusion .
Edit intent: clarity
Original:  Moreover,  a similar breadth-first strategy is developed for a parallel Gauss-Seidel solver for heat diffusion .
Revised:  Moreover, a similar breadth-first strategy to derive a parallel Gauss-Seidel solver for heat diffusion .
Edit intent: clarity
Original:  Experimental results show that our method can efficiently compute geodesic distance on meshes with up to 99 million vertices on a workstation with a workstation with an octa-core CPU and 128GB RAM, outperforming the original heat method and other state-of-the-art geodesic distance solvers.
Revised:  Experimental results show that it can efficiently compute geodesic distance on meshes with up to 99 million vertices on a workstation with a workstation with an octa-core CPU and 128GB RAM, outperforming the original heat method and other state-of-the-art geodesic distance solvers.
Edit intent: clarity
Original:  Our key observation is that the recovery of geodesic distance in the heat method  [Crane et al. 2017 ] can be reformulated as an optimization of its gradients subject to integrability, which can be solved using an efficient fixed-order method that requires no linear system solving and converges quickly.
Revised:  Our key observation is that the recovery of geodesic distance with the heat method [Crane et al. 2017 ] can be reformulated as an optimization of its gradients subject to integrability, which can be solved using an efficient fixed-order method that requires no linear system solving and converges quickly.
Edit intent: clarity
Original:  Our key observation is that the recovery of geodesic distance in the heat method  [Crane et al. 2017 ] can be reformulated as an optimization of its gradients subject to integrability, which can be solved using an efficient fixed-order method that requires no linear system solving and converges quickly.
Revised:  Our key observation is that the recovery of geodesic distance in the heat method from [Crane et al. 2017 ] can be reformulated as an optimization of its gradients subject to integrability, which can be solved using an efficient fixed-order method that requires no linear system solving and converges quickly.
Edit intent: clarity
Original:  Our key observation is that the recovery of geodesic distance in the heat method  [Crane et al. 2017 ] can be reformulated as an optimization of its gradients subject to integrability, which can be solved using an efficient fixed-order method that requires no linear system solving and converges quickly.
Revised:  Our key observation is that the recovery of geodesic distance in the heat method [Crane et al. 2017 ] can be reformulated as optimization of its gradients subject to integrability, which can be solved using an efficient fixed-order method that requires no linear system solving and converges quickly.
Edit intent: fluency
Original:  Afterwards , the geodesic distance is efficiently recovered by parallel integration of the optimized gradients in breadth-first order.
Revised:  Afterward , the geodesic distance is efficiently recovered by parallel integration of the optimized gradients in breadth-first order.
Edit intent: fluency
Original:  To further lower the memory consumption from gradient optimization on faces, we also propose a formulation that optimizes the projected gradients on edges, further reducing the memory footprint by about 50 %DIFDELCMD < \\%%% %DIF < . Our approach is  trivially parallelizable, with a low memory footprint that grows linearly with respect to the model size.
Revised:  To further lower the memory consumption from gradient optimization on faces, we also propose a formulation that optimizes the projected gradients on edges, which reduces the memory footprint by about 50 %DIFDELCMD < \\%%% %DIF < . Our approach is trivially parallelizable, with a low memory footprint that grows linearly with respect to the model size.
Edit intent: coherence