wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Recent progress in deep Inverse Reinforcement Learning (IRL) suggests that the robot could fall back on demonstrations: ask the human for demonstrations of the task , and recover a reward defined over not just the known features, but also the raw state space.
Revised:  Recent progress in deep Inverse Reinforcement Learning (IRL) suggests that the robot could ask for task demonstrations and recover a reward defined over not just the known features, but also the raw state space.
Edit intent: clarity
Original:  Recent progress in deep Inverse Reinforcement Learning (IRL) suggests that the robot could fall back on demonstrations: ask the human for demonstrations of the task , and recover a reward defined over not just the known features, but also the raw state space.
Revised:  Recent progress in deep Inverse Reinforcement Learning (IRL) suggests that the robot could fall back on demonstrations: ask the human for demonstrations of the task , and recover a reward defined over the raw state space.
Edit intent: clarity
Original:  We show this in experiments with a  7DOF robot manipulator . Finally, we discuss our method's potential implications for deep reward learning more broadly: taking a divide-and-conquer approach that focuses on important features separately before learning from demonstrations can improve generalization in tasks where such features are easy for the human to teach .
Revised:  We show this in experiments with a 7DOF robot manipulator , as well as in a user study conducted in a simulated environment .
Edit intent: meaning-changed
Original: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf  \bf U}(t) + {\bf U}(t) B { \bf  + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Revised: We are interested in numerically approximating the solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf \bf U}(t) + {\bf U}(t) B { \bf + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Edit intent: fluency
Original: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf  \bf U}(t) + {\bf U}(t) B { \bf  + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Revised: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional semilinear matrix differential equation \bf U(t) = A { \bf \bf U}(t) + {\bf U}(t) B { \bf + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Edit intent: meaning-changed
Original: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf  \bf U}(t) + {\bf U}(t) B { \bf  + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Revised: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = { \bf \bf U}(t) + {\bf U}(t) B { \bf + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Edit intent: clarity
Original: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf  \bf U}(t) + {\bf U}(t) B { \bf  + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Revised: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf A \bf U}(t) + {\bf U}(t) B { \bf + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Edit intent: meaning-changed
Original: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf  \bf U}(t) + {\bf U}(t) B { \bf  + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Revised: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf \bf U}(t) + {\bf U}(t) { \bf + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Edit intent: coherence
Original: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf  \bf U}(t) + {\bf U}(t) B { \bf  + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Revised: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf \bf U}(t) + {\bf U}(t) B { \bf B + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Edit intent: meaning-changed
Original: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf  \bf U}(t) + {\bf U}(t) B { \bf  + {\cal F}({\bf U},t) + G , with appropriate starting and boundary conditions, and t \in [0, T_f].
Revised: We are interested in approximating the numerical solution {\bf U}(t) of the large dimensional nonlinear matrix differential equation \bf U(t) = A { \bf \bf U}(t) + {\bf U}(t) B { \bf + {\cal F}({\bf U},t) , with appropriate starting and boundary conditions, and t \in [0, T_f].
Edit intent: coherence
Original:  Several numerical experiments based on typical benchmark problems illustrate the effectiveness of the new matrix-oriented setting, also for coupled systems of nonlinear matrix differential equations .
Revised:  By maintaining a matrix-oriented reduction, we are able to employ first order exponential integrators at negligible costs. Numerical experiments on benchmark problems illustrate the effectiveness of the new matrix-oriented setting, also for coupled systems of nonlinear matrix differential equations .
Edit intent: meaning-changed
Original:  We adapt the combinatorial multi-armed bandit (CMAB) framework for the OCIM problem, but unlike the non-competitive setting, the important monotonicity property (influence spread increases when influence probabilities on edges increase) no longer holds due to the competitive nature of propagation, which brings a significant new challenge to the problem.
Revised:  We adapt a combinatorial multi-armed bandit (CMAB) framework for the OCIM problem, but unlike the non-competitive setting, the important monotonicity property (influence spread increases when influence probabilities on edges increase) no longer holds due to the competitive nature of propagation, which brings a significant new challenge to the problem.
Edit intent: fluency
Original:  We prove that the Triggering Probability Modulated (TPM) condition for CMAB still holds , and then utilize the property of competitive diffusion to introduce a new offline oracle, and discuss how to implement this new oracle in various cases.
Revised:  We provide a nontrivial proof showing that the Triggering Probability Modulated (TPM) condition for CMAB still holds , and then utilize the property of competitive diffusion to introduce a new offline oracle, and discuss how to implement this new oracle in various cases.
Edit intent: meaning-changed
Original:  We prove that the Triggering Probability Modulated (TPM) condition for CMAB still holds , and then utilize the property of competitive diffusion to introduce a new offline oracle, and discuss how to implement this new oracle in various cases. We propose an OCIM-OIFU algorithm with such an oracle that achieves logarithmic regret .
Revised:  We prove that the Triggering Probability Modulated (TPM) condition for CMAB still holds in OCIM, which is instrumental for our proposed algorithms OCIM-TS and OCIM-OFU to achieve logarithmic Bayesian regret and frequentist regret, respectively .
Edit intent: meaning-changed
Original:  To formulate such an optimal tracking problem, we envision that the fund manager can dynamically inject capital into the portfolio account such that the total capital dominates the nondecreasing benchmark floor process at each intermediate time.
Revised:  To formulate such an optimal tracking problem, we envision that the fund manager can dynamically inject capital into the portfolio account such that the total capital dominates the non-decreasing benchmark floor process at each intermediate time.
Edit intent: fluency
Original:  By identifying a controlled state process with reflection, we next transform the problem further into an equivalent auxiliary problem, which leads to a nonlinear Hamilton-Jacobi-Bellman (HJB)  with a Neumann boundary condition.
Revised:  By identifying a controlled state process with reflection, we next transform the problem further into an equivalent auxiliary problem, which leads to a nonlinear Hamilton-Jacobi-Bellman (HJB) equation with a Neumann boundary condition.
Edit intent: clarity
Original:  By identifying a controlled state process with reflection, we next transform the problem further into an equivalent auxiliary problem, which leads to a nonlinear Hamilton-Jacobi-Bellman (HJB) equation with a Neumann boundary condition.
Revised:  By identifying a controlled state process with reflection, the problem is further shown to be equivalent to an auxiliary problem, which leads to a nonlinear Hamilton-Jacobi-Bellman (HJB) equation with a Neumann boundary condition.
Edit intent: clarity
Original: We present a non-intrusive reduced basis method (RBM) for unsteady non-linear parametrized partial differential equations (PDEs)based on proper orthogonal decomposition (POD) with regression .
Revised: In recent years, numerical methods in industrial applications have evolved from a pure predictive tool towards a means for optimization and control. Since standard numerical analysis methods have become prohibitively costly in such multi-query settings, a variety of reduced order modeling (ROM) approaches have been advanced towards complex applications. In this context, the driving application for this work is twin-screw extruders (TSEs): manufacturing devices with an important economic role in plastics processing. Modeling the flow through a TSE requires non-linear parametrized partial differential equations (PDEs)based on proper orthogonal decomposition (POD) with regression .
Edit intent: meaning-changed
Original: We present a non-intrusive reduced basis method (RBM) for unsteady non-linear parametrized partial differential equations (PDEs)based on proper orthogonal decomposition (POD) with regression .
Revised: We present a non-intrusive reduced basis method (RBM) for unsteady non-linear material models and coupling with the heat equation alongside intricate mesh deformations, which is a comparatively complex scenario. We investigate how a non-intrusive, data-driven ROM can be constructed for this application. We focus on the well-established proper orthogonal decomposition (POD) with regression .
Edit intent: meaning-changed
Original: We present a non-intrusive reduced basis method (RBM) for unsteady non-linear parametrized partial differential equations (PDEs)based on proper orthogonal decomposition (POD) with regression . Three different regression models are compared, including radial basis function (RBF) regression, Gaussian process regression (GPR)  and artificial neural networks (ANNs).
Revised: We present a non-intrusive reduced basis method (RBM) for unsteady non-linear parametrized partial differential equations (PDEs)based on proper orthogonal decomposition (POD) with regression albeit we introduce two adaptations: standardizing both the data and the error measures as well as -- inspired by our space-time simulations -- treating time as a discrete coordinate rather than a continuous parameter. We show that these steps make the POD-regression framework more interpretable, computationally efficient, and problem-independent. We proceed to compare the performance of three different regression models are compared, including radial basis function (RBF) regression, Gaussian process regression (GPR) and artificial neural networks (ANNs).
Edit intent: meaning-changed
Original:  The established method is extended with additional preprocessing steps, including centering before POD, standardization by singular values before regression and a standardized error measure. These steps benefit the interpretability and allow to reuse the presented framework on different problems. Furthermore, we propose to treat time not as a parameter but rather as a discretized coordinate similar to space. The approach is first validated on steady as well as time-dependent driven cavity viscous flows. Additionally, we study the flow of plastic melt inside across-section of a co-rotating twin-screw extruder. This is characterized by a time- and temperature-dependent flowof a generalized Newtonian fluid on a moving domain. The achieved standardized errors are less than 3\% for the predicted velocity, pressure and temperature distributions.
Edit intent: meaning-changed
Original:  We find that GPR can offer several advantages over an ANN, constituting a viable and computationally inexpensive non-intrusive RBM .
Revised:  We find that GPR can offer several advantages over an ANN, constituting a viable and computationally inexpensive non-intrusive ROM. Additionally, the framework is open-sourced to serve as a starting point for other practitioners and facilitate the use of ROM in general engineering workflows .
Edit intent: meaning-changed
Original:  We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that the BioBERT and novel Longformer models surpass all others with almost equivalent micro-F1 and accuracy scores of around 81\% and 69\% on the test set.
Revised:  We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that BioBERT surpasses the others by a small margin with micro-F1 and accuracy scores of around 81\% and 69\% on the test set.
Edit intent: clarity
Original:  We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that the BioBERT and novel Longformer models surpass all others with almost equivalent micro-F1 and accuracy scores of around 81\% and 69\% on the test set.
Revised:  We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that the BioBERT and novel Longformer models surpass all others with almost equivalent micro-F1 and accuracy scores of around 86\% and 75\% respectively on the test set.
Edit intent: meaning-changed
Original: The main success stories of deep learning, starting with ImageNet, depend on  convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines . Is there something special about deep convolutional networks that other learning machines do not possess ? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure.
Revised: The main success stories of deep learning, starting with ImageNet, depend on convolutional networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines , and also better than deep fully connected networks; but what is so special about deep convolutional networks that other learning machines do not possess ? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure.
Edit intent: meaning-changed
Original:  Is there something special about deep convolutional networks that other learning machines do not possess ? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure.
Revised:  Is there something special about deep convolutional networks ? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure.
Edit intent: clarity
Original:  Is there something special about deep convolutional networks that other learning machines do not possess ? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure.
Revised:  Is there something special about deep convolutional networks that other learning machines do not possess ? Recent results in approximation theory proved an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure.
Edit intent: clarity
Original:  Is there something special about deep convolutional networks that other learning machines do not possess ? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional-like networks in approximating functions with hierarchical locality in their compositional structure.
Revised:  Is there something special about deep convolutional networks that other learning machines do not possess ? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional networks with or without shared weights in approximating functions with hierarchical locality in their compositional structure.
Edit intent: meaning-changed
Original:  Among all the possible hierarchically local tasks in vision, text and speech we explore a few of them experimentally by studying how they are affected by disrupting locality in the input images . We also discuss a taxonomy of tasks ranging from local, to hierarchically local, to global and make predictions about the type of networks required to perform efficiently on these different types of tasks .
Revised:  To evaluate this, we consider a set of visual tasks where we disrupt the URLanization of images via "deterministic scrambling" to later perform a visual task on these images structurally-altered in the same way for training and testing. For object recognition we find, as expected, that scrambling does not affect the performance of shallow or deep fully connected networks contrary to the out-performance of convolutional networks. Not all tasks involving images are however affected. Texture perception and global color estimation are much less sensitive to deterministic scrambling showing that the underlying functions corresponding to these tasks are not hierarchically local; and also counter-intuitively showing that these tasks are better approximated by networks that are not deep (texture) nor convolutional (color). Altogether, these results shed light into the importance of matching a network architecture with its embedded prior of the task to be learned .
Edit intent: meaning-changed
Original:  Specifically, a standard deviation increase in excitement is associated with an 8.9 basis points lower announcement return, which translates into an approximately 7.2 \% annualized loss . Motivated by this finding, I then construct a zero-cost portfolio leveraging social media emotions and opinions around earnings announcements, and show performance exceeding the market by a factor of 1.75 over the decade .
Revised:  Specifically, a standard deviation increase in excitement is associated with an 8.9 basis points lower announcement return, which translates into an approximately 7.2 \% annualized loss .
Edit intent: coherence
Original:  Energy identities and stability estimates of the discrete problem are proved for a slightly more general problem , that are used to prove optimal order a priori error estimates with minimal regularity requirement . Uniform norm in time error estimates are proved for the constant and linear cases . Numerical experiments are performed to verify the theoretical rate of convergence.
Revised:  The main goal of this paper is to present a simple and straight forward a priori error analysis of optimal order with minimal regularity requirement on the solution. Uniform norm in time error estimates are also proved for the constant and linear cases. To this end, energy identities and stability estimates of the discrete problem are proved for a slightly more general problem , that are used to prove optimal order a priori error estimates with minimal regularity requirement . Uniform norm in time error estimates are proved for the constant and linear cases . Numerical experiments are performed to verify the theoretical rate of convergence.
Edit intent: meaning-changed
Original:  Energy identities and stability estimates of the discrete problem are proved for a slightly more general problem , that are used to prove optimal order a priori error estimates with minimal regularity requirement . Uniform norm in time error estimates are proved for the constant and linear cases . Numerical experiments are performed to verify the theoretical rate of convergence.
Revised:  Energy identities and stability estimates of the discrete problem are proved for a slightly more general problem . These are used to prove optimal order a priori error estimates with minimal regularity requirement . Uniform norm in time error estimates are proved for the constant and linear cases . Numerical experiments are performed to verify the theoretical rate of convergence.
Edit intent: coherence
Original:  Energy identities and stability estimates of the discrete problem are proved for a slightly more general problem , that are used to prove optimal order a priori error estimates with minimal regularity requirement . Uniform norm in time error estimates are proved for the constant and linear cases . Numerical experiments are performed to verify the theoretical rate of convergence.
Revised:  Energy identities and stability estimates of the discrete problem are proved for a slightly more general problem , that are used to prove optimal order a priori error estimates with minimal regularity requirement on the solution. The combination with the classic continuous Galerkin finite element discretization in space variable is used, to formulate a full-discrete scheme. The a priori error analysis is presented . Numerical experiments are performed to verify the theoretical rate of convergence.
Edit intent: meaning-changed
Original:  It is assumed that the proper rational  matrix-valued function is expressed in the transfer function form H(s) = C (sI - A)^{-1} B, where the middle factor is large, whereas the number of rows of C and the number of columns of B are equal and small.
Revised:  It is assumed that the proper rational part of the rational matrix-valued function is expressed in the transfer function form H(s) = C (sI - A)^{-1} B, where the middle factor is large, whereas the number of rows of C and the number of columns of B are equal and small.
Edit intent: meaning-changed
Original:  In the second part, we extend the proposed framework to locate the eigenvalues of a general square large-scale nonlinear meromorphic matrix-valued function T(\cdot), where we exploit a representation \mathcal R (s) = C(s) A(s)^{-1} B(s) - D(s) defined in terms of the block components of T(\cdot).
Revised:  In the second part, we extend the proposed framework to locate the eigenvalues of a general square large-scale nonlinear meromorphic matrix-valued function T(\cdot), where we exploit a representation \mathcal{R (s) = C(s) A(s)^{-1} B(s) - D(s) defined in terms of the block components of T(\cdot).
Edit intent: fluency