wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as  nonlinear dynamical systems such as a network of neural mass models  . Finally, we apply our method to real-world observational microelectrode array  data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Revised:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as nonlinear dynamical systems (coupled Lorenz systems and a network of neural mass models . Finally, we apply our method to real-world observational microelectrode array data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Edit intent: meaning-changed
Original:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as  nonlinear dynamical systems such as a network of neural mass models  . Finally, we apply our method to real-world observational microelectrode array  data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Revised:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as nonlinear dynamical systems such as a network of neural mass models ), we demonstrate that our approach can reliably identify the direction of interactions and the associated coupling delays . Finally, we apply our method to real-world observational microelectrode array data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Edit intent: meaning-changed
Original:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as  nonlinear dynamical systems such as a network of neural mass models  . Finally, we apply our method to real-world observational microelectrode array  data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Revised:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as nonlinear dynamical systems such as a network of neural mass models . Finally, we study real-world observational microelectrode array data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Edit intent: clarity
Original:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as  nonlinear dynamical systems such as a network of neural mass models  . Finally, we apply our method to real-world observational microelectrode array  data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Revised:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as nonlinear dynamical systems such as a network of neural mass models . Finally, we apply our method to real-world observational microelectrode array electrophysiology data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Edit intent: meaning-changed
Original:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as  nonlinear dynamical systems such as a network of neural mass models  . Finally, we apply our method to real-world observational microelectrode array  data from rodent brain slices to study the causal effect networks underlying epileptic activity.
Revised:  We demonstrate that our approach can reliably identify the direction of interaction and the corresponding delays with numerical simulations using linear stochastic systems as well as nonlinear dynamical systems such as a network of neural mass models . Finally, we apply our method to real-world observational microelectrode array data from rodent brain slices to identify the causal coupling structures underlying epileptiform activity.
Edit intent: clarity
Original: The growth of model-based control strategies for robotics platforms has led to the need for additional rigid-body-dynamics algorithms to support their operation. Toward addressing this need, this article summarizes efficient numerical methods to compute the Coriolis matrix and underlying Christoffel Symbols (of the first kind) for tree-structure rigid-body systems.
Revised: This article presents methods to efficiently compute the Coriolis matrix and underlying Christoffel Symbols (of the first kind) for tree-structure rigid-body systems.
Edit intent: clarity
Original:  Toward addressing this need, this article summarizes efficient numerical methods to compute the Coriolis matrix and underlying Christoffel Symbols (of the first kind) for tree-structure rigid-body systems.
Revised:  Toward addressing this need, this article summarizes efficient numerical methods to compute the Coriolis matrix and underlying Christoffel symbols (of the first kind) for tree-structure rigid-body systems.
Edit intent: fluency
Original:  The resulting algorithms can be executed purely numerically, without requiring any partial derivatives that would be required in symbolic techniques that do not scale. Properties of the presented algorithms share recursive structure in common with classical methods such as the Composite-Rigid-Body Algorithm .
Revised:  The resulting algorithms can be executed purely numerically, without requiring partial derivatives as in unscalable symbolic techniques. The computations share a recursive structure in common with classical methods such as the Composite-Rigid-Body Algorithm .
Edit intent: clarity
Original:  Properties of the presented algorithms share recursive structure in common with classical methods such as the Composite-Rigid-Body Algorithm . The algorithms presented are of the lowest possible order: O(Nd) for the Coriolis Matrix and O(Nd^2) for the Christoffel symbols, where N is the number of bodies and d is the depth of the kinematic tree.
Revised:  Properties of the presented algorithms share recursive structure in common with classical methods such as the Composite-Rigid-Body Algorithm and are of the lowest possible order: O(Nd) for the Coriolis Matrix and O(Nd^2) for the Christoffel symbols, where N is the number of bodies and d is the depth of the kinematic tree.
Edit intent: coherence
Original:  The algorithms presented are of the lowest possible order: O(Nd) for the Coriolis Matrix and O(Nd^2) for the Christoffel symbols, where N is the number of bodies and d is the depth of the kinematic tree.
Revised:  The algorithms presented are of the lowest possible order: O(Nd) for the Coriolis matrix and O(Nd^2) for the Christoffel symbols, where N is the number of bodies and d is the depth of the kinematic tree.
Edit intent: fluency
Original:  A method of order O(Nd) is also provided to compute the time derivative of the mass matrix. A numerical implementation of these algorithms in C/C++ is benchmarked showing computation times on the order of 10-20 \mus for the computation of the Coriolis matrix and 40-120 \mus for the computation of the Christoffel symbols for systems with 20 degrees of freedom.
Revised:  Implementation in C/C++ is benchmarked showing computation times on the order of 10-20 \mus for the computation of the Coriolis matrix and 40-120 \mus for the computation of the Christoffel symbols for systems with 20 degrees of freedom.
Edit intent: clarity
Original:  A numerical implementation of these algorithms in C/C++ is benchmarked showing computation times on the order of 10-20 \mus for the computation of the Coriolis matrix and 40-120 \mus for the computation of the Christoffel symbols for systems with 20 degrees of freedom.
Revised:  A numerical implementation of these algorithms in C/C++ shows computation times on the order of 10-20 \mus for the computation of the Coriolis matrix and 40-120 \mus for the computation of the Christoffel symbols for systems with 20 degrees of freedom.
Edit intent: clarity
Original:  A numerical implementation of these algorithms in C/C++ is benchmarked showing computation times on the order of 10-20 \mus for the computation of the Coriolis matrix and 40-120 \mus for the computation of the Christoffel symbols for systems with 20 degrees of freedom.
Revised:  A numerical implementation of these algorithms in C/C++ is benchmarked showing computation times on the order of 10-20 \mus for the Coriolis matrix and 40-120 \mus for the computation of the Christoffel symbols for systems with 20 degrees of freedom.
Edit intent: fluency
Original:  A numerical implementation of these algorithms in C/C++ is benchmarked showing computation times on the order of 10-20 \mus for the computation of the Coriolis matrix and 40-120 \mus for the computation of the Christoffel symbols for systems with 20 degrees of freedom.
Revised:  A numerical implementation of these algorithms in C/C++ is benchmarked showing computation times on the order of 10-20 \mus for the computation of the Coriolis matrix and 40-120 \mus for the Christoffel symbols on systems with 20 degrees of freedom.
Edit intent: fluency
Original:  Our experiments with such metrics show that probe's performance curves often fail to align with widely accepted rankings between language representations (with, e .g.,  non-contextual representations outperforming contextual ones).
Revised:  Our experiments with such metrics show that probe's performance curves often fail to align with widely accepted rankings between language representations (with, e .g., why should the non-contextual representations outperforming contextual ones).
Edit intent: meaning-changed
Original:  Our experiments with such metrics show that probe's performance curves often fail to align with widely accepted rankings between language representations (with, e .g.,  non-contextual representations outperforming contextual ones). These results lead us to argue, second, that commonsimplistic probe taskssuch as POS labeling and dependency arc labeling, are inadequate to evaluate the properties encoded in contextual word representations.
Revised:  Our experiments with such metrics show that probe's performance curves often fail to align with widely accepted rankings between language representations (with, e .g., non-contextual fastText representations encode more morpho-syntactic information than the contextual BERT representations? These results suggest that common, simplistic probing tasks, such as part-of-speech labeling and dependency arc labeling, are inadequate to evaluate the properties encoded in contextual word representations.
Edit intent: meaning-changed
Original:  These results lead us to argue, second, that commonsimplistic probe taskssuch as POS labeling and dependency arc labeling, are inadequate to evaluate the properties encoded in contextual word representations.
Revised:  These results lead us to argue, second, that commonsimplistic probe taskssuch as POS labeling and dependency arc labeling, are inadequate to evaluate the linguistic structure encoded in contextual word representations.
Edit intent: clarity
Original: This paper considers a  linear Fisher market with n buyers and a continuum of items.
Revised: Linear Fisher markets are a fundamental economic model with applications in fair division as well as large-scale Internet markets. In the finite-dimensional case of n buyers and m items, a market equilibrium can be computed using the Eisenberg-Gale convex program. Motivated by large-scale Internet advertising and fair division applications, this paper considers a linear Fisher market with n buyers and a continuum of items.
Edit intent: meaning-changed
Original:  In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual . Regarding the new convex programs, we establish existence of optimal solutions, the existence of KKT-type conditions , as well as strong duality .
Revised:  We introduce generalizations of the Eisenberg-Gale convex program and its dual . Regarding the new convex programs, we establish existence of optimal solutions, the existence of KKT-type conditions , as well as strong duality .
Edit intent: clarity
Original:  In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual . Regarding the new convex programs, we establish existence of optimal solutions, the existence of KKT-type conditions , as well as strong duality .
Revised:  In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual to this infinite-dimensional setting, which leads to Banach-space optimization problems. We establish existence of optimal solutions, the existence of KKT-type conditions , as well as strong duality .
Edit intent: meaning-changed
Original:  In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual . Regarding the new convex programs, we establish existence of optimal solutions, the existence of KKT-type conditions , as well as strong duality .
Revised:  In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual . Regarding the new convex programs, we establish existence of optimal solutions, strong duality , as well as strong duality .
Edit intent: coherence
Original:  In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual . Regarding the new convex programs, we establish existence of optimal solutions, the existence of KKT-type conditions , as well as strong duality .
Revised:  In order to compute market equilibria, we introduce (infinite-dimensional) convex programs over Banach spaces, thereby generalizing the Eisenberg-Gale convex program and its dual . Regarding the new convex programs, we establish existence of optimal solutions, the existence of KKT-type conditions , as well as necessity and sufficiency of KKT-type conditions .
Edit intent: clarity
Original:  When the item space is the unit interval 0,1 and buyers have piecewise linear utilities , we show that \epsilon-approximate equilibrium prices can be computed in time polynomial in the market size and \log \frac{1{\epsilon}.
Revised:  When the item space is 0,1 and buyers have piecewise linear utilities , we show that \epsilon-approximate equilibrium prices can be computed in time polynomial in the market size and \log \frac{1{\epsilon}.
Edit intent: clarity
Original:  When the item space is the unit interval 0,1 and buyers have piecewise linear utilities , we show that \epsilon-approximate equilibrium prices can be computed in time polynomial in the market size and \log \frac{1{\epsilon}.
Revised:  When the item space is the unit interval a closed interval and buyers have piecewise linear utilities , we show that \epsilon-approximate equilibrium prices can be computed in time polynomial in the market size and \log \frac{1{\epsilon}.
Edit intent: meaning-changed
Original:  When the item space is the unit interval 0,1 and buyers have piecewise linear utilities , we show that \epsilon-approximate equilibrium prices can be computed in time polynomial in the market size and \log \frac{1{\epsilon}.
Revised:  When the item space is the unit interval 0,1 and buyers have piecewise linear valuations , we show that \epsilon-approximate equilibrium prices can be computed in time polynomial in the market size and \log \frac{1{\epsilon}.
Edit intent: clarity
Original:  By performing experiments in our environment, we are able to discern the causal factors of variation and infer how they affect the dynamics of our world. Analogously, here we attempt to equip reinforcement learning agents with the ability to perform experiments that facilitate a categorization of the rolled-out trajectories, and to subsequently infer the causal factors of the environment in a hierarchical manner.
Revised:  By performing experiments in our environment, we are able to discern the causal factors of variation and infer how they affect the world's dynamics. Inspired by this, we attempt to equip reinforcement learning agents with the ability to perform experiments that facilitate a categorization of the rolled-out trajectories, and to subsequently infer the causal factors of the environment in a hierarchical manner.
Edit intent: coherence
Original:  We introduce {\em   a novel intrinsic reward, called causal curiosity, and show that it allows our agents to learn optimal sequences of actions , and to discover causal factors in the dynamics  .
Revised:  We introduce {\em causal curiosity a novel intrinsic reward, called causal curiosity, and show that it allows our agents to learn optimal sequences of actions , and to discover causal factors in the dynamics .
Edit intent: meaning-changed
Original:  We introduce {\em   a novel intrinsic reward, called causal curiosity, and show that it allows our agents to learn optimal sequences of actions , and to discover causal factors in the dynamics  .
Revised:  We introduce {\em , a novel intrinsic reward, called causal curiosity, and show that it allows our agents to learn optimal sequences of actions , and to discover causal factors in the dynamics .
Edit intent: fluency
Original:  We introduce {\em   a novel intrinsic reward, called causal curiosity, and show that it allows our agents to learn optimal sequences of actions , and to discover causal factors in the dynamics  .
Revised:  We introduce {\em a novel intrinsic reward, called causal curiosity, and show that it allows our agents to learn optimal sequences of actions , and to discover causal factors in the dynamics of the environment .
Edit intent: meaning-changed
Original:  Additionally, we find that these experimental behaviors are semantically meaningful (e.g., to differentiate between heavy and light blocks, our agents learn to lift them ), and are learnt in a self-supervised manner with approximately 2.5 times less data than conventional supervised planners.
Revised:  Additionally, we find that these experimental behaviors are semantically meaningful (e.g., our agents learn to lift them ), and are learnt in a self-supervised manner with approximately 2.5 times less data than conventional supervised planners.
Edit intent: coherence
Original:  Additionally, we find that these experimental behaviors are semantically meaningful (e.g., to differentiate between heavy and light blocks, our agents learn to lift them ), and are learnt in a self-supervised manner with approximately 2.5 times less data than conventional supervised planners.
Revised:  Additionally, we find that these experimental behaviors are semantically meaningful (e.g., to differentiate between heavy and light blocks, our agents learn to lift blocks to categorize them by weight ), and are learnt in a self-supervised manner with approximately 2.5 times less data than conventional supervised planners.
Edit intent: meaning-changed
Original:  In this paper, an internal reward function-based driving model that emulates the human's internal decision-making mechanism is proposed. Besides, a sampling-based inverse reinforcement learning (IRL) algorithm that learns the reward function from human naturalistic driving data is also developed.
Revised:  In this paper, an internal reward function-based driving model that emulates the human's internal decision-making mechanism is utilized. To infer the reward function from naturalistic human driving data, we propose a structural assumption about human driving behavior that focuses on discrete latent driving intentions. It converts the continuous behavior modeling problem to a discrete setting and thus makes maximum entropy inverse reinforcement learning (IRL) algorithm that learns the reward function from human naturalistic driving data is also developed.
Edit intent: meaning-changed
Original:  Besides, a sampling-based inverse reinforcement learning (IRL) algorithm that learns the reward function from human naturalistic driving data is also developed. A polynomial trajectory sampler is adopted to generate feasible trajectories and approximate the partition function in the maximum entropy IRL framework, and a dynamic and interactive environment is built upon the static driving dataset to estimate the generated trajectories considering the mutual dependency of agents' actions .
Revised:  Besides, a sampling-based inverse reinforcement learning (IRL) tractable to learn reward functions. Specifically, a polynomial trajectory sampler is adopted to generate feasible trajectories and approximate the partition function in the maximum entropy IRL framework, and a dynamic and interactive environment is built upon the static driving dataset to estimate the generated trajectories considering the mutual dependency of agents' actions .
Edit intent: clarity
Original:  A polynomial trajectory sampler is adopted to generate feasible trajectories and approximate the partition function in the maximum entropy IRL framework, and a dynamic and interactive environment is built upon the static driving dataset to estimate the generated trajectories considering the mutual dependency of agents' actions .
Revised:  A polynomial trajectory sampler is adopted to generate candidate trajectories considering high-level intentions and approximate the partition function in the maximum entropy IRL framework, and a dynamic and interactive environment is built upon the static driving dataset to estimate the generated trajectories considering the mutual dependency of agents' actions .
Edit intent: meaning-changed
Original:  A polynomial trajectory sampler is adopted to generate feasible trajectories and approximate the partition function in the maximum entropy IRL framework, and a dynamic and interactive environment is built upon the static driving dataset to estimate the generated trajectories considering the mutual dependency of agents' actions .
Revised:  A polynomial trajectory sampler is adopted to generate feasible trajectories and approximate the partition function in the maximum entropy IRL framework, and an environment model considering interactive behaviors among the ego and surrounding vehicles is built to better estimate the generated trajectories considering the mutual dependency of agents' actions .
Edit intent: clarity
Original:  A polynomial trajectory sampler is adopted to generate feasible trajectories and approximate the partition function in the maximum entropy IRL framework, and a dynamic and interactive environment is built upon the static driving dataset to estimate the generated trajectories considering the mutual dependency of agents' actions .
Revised:  A polynomial trajectory sampler is adopted to generate feasible trajectories and approximate the partition function in the maximum entropy IRL framework, and a dynamic and interactive environment is built upon the static driving dataset to estimate the generated trajectories .
Edit intent: clarity