wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Third, by illustrating the regulatory procedure in linear dynamical systems, we prove that it can align social and financial interests , which are typically at odds . Specifically, we identify conditions under which the regulation imposes a low cost on the SMP's reward (e.g., profits) and incentivizes the SMP to increase content diversity .
Revised:  Third, by illustrating the regulatory procedure in linear dynamical systems, we prove that it can align social and financial interests . Specifically, we identify conditions under which the regulation imposes a low cost on the SMP's reward (e.g., profits) and incentivizes the SMP to increase content diversity .
Edit intent: clarity
Original:  Third, by illustrating the regulatory procedure in linear dynamical systems, we prove that it can align social and financial interests , which are typically at odds . Specifically, we identify conditions under which the regulation imposes a low cost on the SMP's reward (e.g., profits) and incentivizes the SMP to increase content diversity . We provide illustrative simulations .
Revised:  Third, by illustrating the regulatory procedure in linear dynamical systems, we prove that it can align social and financial interests , which are typically at odds . Specifically, we identify conditions under which the regulation imposes a low cost on the SMP's reward (e.g., profits) and incentivizes the SMP to increase content diversity .
Edit intent: coherence
Original: We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs . In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs) , and find that NNC can learn control signals that drive networked dynamical systems into desired target states.
Revised: We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs , which we represent with neural ordinary differential equations (neural ODEs) , and find that NNC can learn control signals that drive networked dynamical systems into desired target states.
Edit intent: coherence
Original:  In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs) , and find that NNC can learn control signals that drive networked dynamical systems into desired target states.
Revised:  In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs) . To do so, we introduce a neural-ODE control (NODEC) framework and find that NNC can learn control signals that drive networked dynamical systems into desired target states.
Edit intent: meaning-changed
Original:  In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs) , and find that NNC can learn control signals that drive networked dynamical systems into desired target states.
Revised:  In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs) , and find that it can learn control signals that drive networked dynamical systems into desired target states.
Edit intent: clarity
Original:  In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs) , and find that NNC can learn control signals that drive networked dynamical systems into desired target states.
Revised:  In particular, we introduce a neural-network control (NNC) framework, which represents dynamical systems by neural ordinary different equations (neural ODEs) , and find that NNC can learn control signals that drive graph dynamical systems into desired target states.
Edit intent: clarity
Original:  To identify the influence of different target states on the NNC performance, we study two types of control: (i) microscopic control and (ii) macroscopic control. Microscopic control minimizes the L2 norm between the current and target state and macroscopic control minimizes the corresponding Wasserstein distance. We find that the proposed NNC framework produces low-energy control  signals that are highly correlated with those of optimal control.
Revised:  While we use loss functions that do not constrain the control signals that are highly correlated with those of optimal control.
Edit intent: meaning-changed
Original:  We find that the proposed NNC framework produces low-energy control  signals that are highly correlated with those of optimal control. Our results are robust for a wide range of graph structures and (non-)linear dynamical systems .
Revised:  We find that the proposed NNC framework produces low-energy control signals that are highly correlated with optimal (or minimum energy) control signals. Finally, we empirically showcase the high performance and versatility of NODEC for various (non-)linear dynamical systems .
Edit intent: meaning-changed
Original:  The solution is created by constructing a single second-order nonlinear differential equation in ln(S ) and analytically continuing its divergent power series solution such that it matches the correct long-time exponential damping of the epidemic model.
Revised:  The solution is created by constructing a single second-order nonlinear differential equation in \ln S and analytically continuing its divergent power series solution such that it matches the correct long-time exponential damping of the epidemic model.
Edit intent: fluency
Original: This note introduces the exact solver Bute for the exact treedepth problem, along with two variants of the solver . Each of these solvers computes an elimination tree  in a bottom-up fashion by finding sets of vertices that induce subgraphs of small treedepth, then combining sets of vertices together with a root vertex to produce larger sets.
Revised: This note introduces Bute-Plus, an exact solver for the treedepth problem. The core of the solver . Each of these solvers computes an elimination tree in a bottom-up fashion by finding sets of vertices that induce subgraphs of small treedepth, then combining sets of vertices together with a root vertex to produce larger sets.
Edit intent: meaning-changed
Original: This note introduces the exact solver Bute for the exact treedepth problem, along with two variants of the solver . Each of these solvers computes an elimination tree  in a bottom-up fashion by finding sets of vertices that induce subgraphs of small treedepth, then combining sets of vertices together with a root vertex to produce larger sets.
Revised: This note introduces the exact solver Bute for the exact treedepth problem, along with two variants of the solver is a positive-instance driven dynamic program that constructs an elimination tree in a bottom-up fashion by finding sets of vertices that induce subgraphs of small treedepth, then combining sets of vertices together with a root vertex to produce larger sets.
Edit intent: meaning-changed
Original: This note introduces the exact solver Bute for the exact treedepth problem, along with two variants of the solver . Each of these solvers computes an elimination tree  in a bottom-up fashion by finding sets of vertices that induce subgraphs of small treedepth, then combining sets of vertices together with a root vertex to produce larger sets.
Revised: This note introduces the exact solver Bute for the exact treedepth problem, along with two variants of the solver . Each of these solvers computes an elimination tree of minimum depth in a bottom-up fashion by finding sets of vertices that induce subgraphs of small treedepth, then combining sets of vertices together with a root vertex to produce larger sets.
Edit intent: meaning-changed
Original: This note introduces the exact solver Bute for the exact treedepth problem, along with two variants of the solver . Each of these solvers computes an elimination tree  in a bottom-up fashion by finding sets of vertices that induce subgraphs of small treedepth, then combining sets of vertices together with a root vertex to produce larger sets. The algorithms make use of a trie data structure to reduce the effort required to determine acceptable combinations of subtrees.
Revised: This note introduces the exact solver Bute for the exact treedepth problem, along with two variants of the solver . Each of these solvers computes an elimination tree in a bottom-up fashion . Three features greatly improve the algorithm's run time. The first of these is a specialised trie data structure to reduce the effort required to determine acceptable combinations of subtrees.
Edit intent: clarity
Original:  The algorithms make use of a trie data structure to reduce the effort required to determine acceptable combinations of subtrees. Bute-Plus and Bute-Plus-Plus add a heuristic presolve step , which can quickly find a treedepth decomposition of optimal depth for many instances.
Revised:  The algorithms make use of a trie data structure . The second is a domination rule. The third is a heuristic presolve step , which can quickly find a treedepth decomposition of optimal depth for many instances.
Edit intent: meaning-changed
Original:  From these data , we constructed a very large interaction graph ; a directed graph of 9.3 million nodes and 169.9 million edges . Recently, Twitter released datasets on the misinformation campaigns of 8 , 275 state-sponsored accounts linked to Russia, Iran and Venezuela.
Revised:  From these data , we constructed a very large interaction graph of 9.3 million nodes and 169.9 million edges . Recently, Twitter released datasets on the misinformation campaigns of 8 , 275 state-sponsored accounts linked to Russia, Iran and Venezuela.
Edit intent: clarity
Original:  a directed graph of 9.3 million nodes and 169.9 million edges . Recently, Twitter released datasets on the misinformation campaigns of 8 , 275 state-sponsored accounts linked to Russia, Iran and Venezuela. These data serve as a ground-truth identifier of troll users in our dataset. Using graph analysis techniques along with a game-theoretic centrality measure, we quantify the influence of all Twitter accounts (authentic users and trolls) on the overall information exchange as is defined by the retweet cascades.
Revised:  a directed graph of 9.3 million nodes and 169.9 million edges . Recently, Twitter released datasets on the misinformation campaigns of 8 , where using graph analysis techniques along with a game-theoretic centrality measure, we quantify the influence of all Twitter accounts (authentic users and trolls) on the overall information exchange as is defined by the retweet cascades.
Edit intent: coherence
Original:  This along with other findings presents evidence that the authentic users were the driving force of virality and influence in the network  .
Revised:  This along with other findings presents evidence that the authentic users were the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that on average, troll accounts were tens of times more influential than regular users were. Moreover, 23\% and 22\% of regular accounts in the top-100 and top-1000 respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices overall, during the 2016 US presidential election .
Edit intent: meaning-changed
Original:  Inspired by  recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- an unsupervised graph representation learning framework -- to capture the universal network topological properties across multiple networks.
Revised:  Inspired by the recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- an unsupervised graph representation learning framework -- to capture the universal network topological properties across multiple networks.
Edit intent: fluency
Original:  Inspired by  recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- an unsupervised graph representation learning framework -- to capture the universal network topological properties across multiple networks.
Revised:  Inspired by recent advances in pre-training from natural language processing and computer vision, we design Graph Contrastive Coding (GCC) -- a self-supervised graph neural network pre-training framework -- to capture the universal network topological properties across multiple networks.
Edit intent: meaning-changed
Original:  We design GCC's pre-training task as subgraph-level instance discrimination in and across networks and leverage contrastive learning to empower the model to learn the intrinsic and transferable structural representations.
Revised:  We design GCC's pre-training task as subgraph-level instance discrimination in and across networks and leverage contrastive learning to empower graph neural networks to learn the intrinsic and transferable structural representations.
Edit intent: clarity
Original:  By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence and contamination in the binary responses .
Revised:  By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences .
Edit intent: clarity
Original: One aspect of human commonsense reasoning is the ability to make presumptions about daily experiences, activities and social interactions with others. We propose a new commonsensereasoning benchmark where the task is to uncover commonsense presumptions implied by imprecisely stated natural language commands in the form of if-then-because statements .
Revised: In order for conversational AI systems to hold more natural and broad-ranging conversations, they will require much more commonsense, including the ability to identify unstated presumptions of their conversational partners .
Edit intent: clarity
Original:  For example, in the command "If it snows at night then wake me up early because I don't want to be late for work" the speaker relies on commonsense reasoning of the listener to infer the implicit presumption that it must snow enough to cause traffic slowdowns.
Revised:  For example, in the command "If it snows at night then wake me up early because I don't want to be late for work" the speaker relies on commonsense reasoning of the listener to infer the implicit presumption that they wish to be woken only if it snows enough to cause traffic slowdowns.
Edit intent: clarity
Original:  Such if-then-because commands are particularly important when users instruct conversational agents.
Revised:  We consider here the problem of understanding such imprecisely stated natural language commands given in the form of "if-(state), then-(action), because-(goal)" statements. More precisely, we consider the problem of identifying the unstated presumptions of the speaker that allow the requested action to achieve the desired goal from the given state (perhaps elaborated by making the implicit presumptions explicit).
Edit intent: meaning-changed
Original:  In comparison with standard Lloyd's, we find that on benchmark data sets , Fair-Lloyd exhibits unbiased performance by ensuring that all groups have balanced costs in the output k-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever k-means is currently used.
Revised:  In comparison with standard Lloyd's, we find that on benchmark datasets , Fair-Lloyd exhibits unbiased performance by ensuring that all groups have balanced costs in the output k-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever k-means is currently used.
Edit intent: clarity
Original:  In comparison with standard Lloyd's, we find that on benchmark data sets , Fair-Lloyd exhibits unbiased performance by ensuring that all groups have balanced costs in the output k-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever k-means is currently used.
Revised:  In comparison with standard Lloyd's, we find that on benchmark data sets , Fair-Lloyd exhibits unbiased performance by ensuring that all groups have equal costs in the output k-clustering, while incurring a negligible increase in running time, thus making it a viable fair option wherever k-means is currently used.
Edit intent: clarity
Original:  The nematode C. elegans is an ideal URLanism for studying locomotion in an integrated neuromechanical setting because its nervous system is well characterized and its forward swimming gait adapts to the surrounding fluid using sensory feedback.
Revised:  The nematode C. elegans is an ideal URLanism for studying locomotion in an integrated neuromechanical setting because its neural circuit has a well-characterized modular structure and its undulatory forward swimming gait adapts to the surrounding fluid using sensory feedback.
Edit intent: clarity
Original:  The nematode C. elegans is an ideal URLanism for studying locomotion in an integrated neuromechanical setting because its nervous system is well characterized and its forward swimming gait adapts to the surrounding fluid using sensory feedback. However, it is not understood how the gait emerges from mechanical forces, neuronal coupling, and sensory feedback mechanisms.
Revised:  The nematode C. elegans is an ideal URLanism for studying locomotion in an integrated neuromechanical setting because its nervous system is well characterized and its forward swimming gait adapts to the surrounding fluid with a shorter wavelength in higher viscosity environments. This adaptive behavior emerges from the neural modules interacting through a combination of mechanical forces, neuronal coupling, and sensory feedback mechanisms.
Edit intent: meaning-changed
Original:  Here, an integrated neuromechanical model ofC. elegans forward locomotion is developed and analyzed .
Revised:  Here, an integrated neuromechanical model ofC. elegans However, the relative contributions of these coupling modes to gait adaptation are not understood. The model consists of repeated neuromechanical modules that are coupled through the mechanics of the body, short-range proprioception, and gap-junctions .
Edit intent: meaning-changed
Original:  The model captures the experimentally observed gait adaptation over a wide range of  parameters, provided that the muscle response to input from the nervous system is faster than the body response to changes in internal and external forces.
Revised:  The model captures the experimentally observed gait adaptation over a wide range of mechanical parameters, provided that the muscle response to input from the nervous system is faster than the body response to changes in internal and external forces.
Edit intent: meaning-changed
Original:  The model is analyzed using the theory of weakly coupled oscillators to identify the relative roles of body mechanics, neural coupling, and proprioceptive coupling in coordinating the undulatory gait.
Revised:  The model is analyzed using the theory of weakly coupled oscillators to identify the relative roles of body mechanics, gap-junctional coupling, and proprioceptive coupling in coordinating the undulatory gait.
Edit intent: meaning-changed
Original:  The model suggests that the experimentally observed decrease in wavelength in response to increasing fluid viscosity is the result of an increase in the relative strength of mechanical coupling, which promotes a short wavelength.
Revised:  In a low-viscosity fluid environment, the competition between gap-junctions and proprioception produces a long wavelength undulation, which is only achieved in the model with sufficiently strong gap-junctional coupling.The experimentally observed decrease in wavelength in response to increasing fluid viscosity is the result of an increase in the relative strength of mechanical coupling, which promotes a short wavelength.
Edit intent: meaning-changed
Original:  Starting from the paper [ 12 ], where a two-dimensional model for a single class of vehicles is proposed, we extend the dynamics to a multi-class model leading to a coupled system of conservation laws in two space dimensions.
Revised:  Starting from the paper [ 20 ], where a two-dimensional model for a single class of vehicles is proposed, we extend the dynamics to a multi-class model leading to a coupled system of conservation laws in two space dimensions.
Edit intent: meaning-changed
Original:   We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries in a span of two years.
Revised:  The lockdown has caused substantial disruptions to academic activities, requiring people to work from home. How this disruption affects productivity and the related gender equity is an important operations and societal question. We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries in a span of two years.
Edit intent: meaning-changed
Original:   We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries in a span of two years.
Revised:  We collect data from the largest open-access preprint repository for social science on 41,858 research preprints in 18 disciplines produced by 76,832 authors across 25 countries over a span of two years.
Edit intent: clarity
Original:  We find thatduring the 10 weeks after the lockdown in the United States, although the total research productivity increased by 35\%, female academics' productivity dropped by 13.9\% relative to that of male academics.
Revised:  We use a difference-in-differences approach leveraging the exogenous pandemic shock. Our results indicate that, in the 10 weeks after the lockdown in the United States, although the total research productivity increased by 35\%, female academics' productivity dropped by 13.9\% relative to that of male academics.
Edit intent: meaning-changed
Original:  Finally, we find that this intensified productivity gap is more pronounced for academics in top-ranked universities, and the effect exists in six other countries  .
Revised:  Finally, we find that this intensified productivity gap is more pronounced for academics in top-ranked universities, and the effect exists in six other countries . Our work points out the fairness issue in productivity caused by the lockdown, a finding that universities will find helpful when evaluating faculty productivity. It also URLanizations realize the potential unintended consequences that can arise from telecommuting .
Edit intent: meaning-changed
Original:  Assuming the grid cells form a vector representation of self-position, we elucidate a minimally simple recurrent model for path integral, which models the change of the vector representation given the self-motion, and we discern two matrix Lie algebras and their Lie groups that are naturally coupled together.
Revised:  Assuming the grid cells form a vector representation of self-position, we elucidate a minimally simple recurrent model for grid cells' path integration based on two coupled matrix Lie algebras and their Lie groups that are naturally coupled together.
Edit intent: clarity