wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We evaluate MAGIC on several long-term planning tasks, showing that it significantly outperforms planning using primitive actions , hand-crafted macro-actions , as well as naive reinforcement learning in both simulation and on a real robot.
Revised:  We evaluate MAGIC on several long-term planning tasks, showing that it significantly outperforms planning using primitive actions and hand-crafted macro-actions , as well as naive reinforcement learning in both simulation and on a real robot.
Edit intent: coherence
Original:  We evaluate MAGIC on several long-term planning tasks, showing that it significantly outperforms planning using primitive actions , hand-crafted macro-actions , as well as naive reinforcement learning in both simulation and on a real robot.
Revised:  We evaluate MAGIC on several long-term planning tasks, showing that it significantly outperforms planning using primitive actions , hand-crafted macro-actions in both simulation and on a real robot.
Edit intent: coherence
Original:  The partially observable Markov decision process (POMDP) offers a principled approach for planning under uncertainty. However, its computational complexitygrows exponentially with the planning horizon. We propose to use temporally-extended macro-actions  to cut down the effective planning horizon and thus the exponential factor of the complexity.
Revised:  The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required. While temporally-extended macro-actions to cut down the effective planning horizon and thus the exponential factor of the complexity.
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Original:  We propose to use temporally-extended macro-actions  to cut down the effective planning horizon and thus the exponential factor of the complexity. We propose Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator using feedback from a planner, and in turn uses the learned macro-actions to condition long-horizon planning.
Revised:  We propose to use temporally-extended macro-actions to cut down the effective planning horizon and significantly improve computational efficiency, how do we acquire good macro-actions? This paper proposes Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator using feedback from a planner, and in turn uses the learned macro-actions to condition long-horizon planning.
Edit intent: meaning-changed
Original:  We propose Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator using feedback from a planner, and in turn uses the learned macro-actions to condition long-horizon planning.
Revised:  We propose Macro-Action Generator-Critic (MAGIC), which performs offline learning of macro-actions optimized for online POMDP planning. Specifically, MAGIC learns a macro-action generator using feedback from a planner, and in turn uses the learned macro-actions to condition long-horizon planning.
Edit intent: meaning-changed
Original:  We propose Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator using feedback from a planner, and in turn uses the learned macro-actions to condition long-horizon planning. Importantly , the generator is learned to directly maximize the down-stream planning performance.
Revised:  We propose Macro-Action Generator-Critic (MAGIC), an algorithm that learns a macro-action generator end-to-end, using an online planner's performance as the feedback. During online planning , the generator is learned to directly maximize the down-stream planning performance.
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Original:  Importantly , the generator is learned to directly maximize the down-stream planning performance. We evaluate MAGIC on several long-term planning tasks , showing that it significantly outperforms planning using primitive actions and hand-crafted macro-actions in both simulation and on a real robot  .
Revised:  Importantly , the generator generates on the fly situation-aware macro-actions conditioned on the robot's belief and the environment context. We evaluated MAGIC on several long-term planning tasks , showing that it significantly outperforms planning using primitive actions and hand-crafted macro-actions in both simulation and on a real robot .
Edit intent: meaning-changed
Original:  We evaluate MAGIC on several long-term planning tasks , showing that it significantly outperforms planning using primitive actions and hand-crafted macro-actions in both simulation and on a real robot  .
Revised:  We evaluate MAGIC on several long-term planning tasks , showing that it significantly outperforms planning using primitive actions and hand-crafted macro-actions in both simulation and on a real robot . The experimental results show that the learned macro-actions offer significant benefits in online planning performance, compared with primitive actions and handcrafted macro-actions .
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Original:  Our evaluation shows that our approach learns to consistently predict visual keypoints on objects , and can easily adapt a kinematic chain to  the object grasped in various configurations, from a few seconds of  data.
Revised:  Our evaluation shows that our approach learns to consistently predict visual keypoints on objects in the manipulator's hand, and thus can easily facilitate learning an extended kinematic chain to the object grasped in various configurations, from a few seconds of data.
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Original: We propose  several adaptive control functions and a smoothing approximation of the signum function , and apply them to design mapping functions used to construct new mapped weighted essentially non-oscillatory (WENO) schemes.
Revised: We propose a new family of mapped WENO schemes by using several adaptive control functions and a smoothing approximation of the signum function , and apply them to design mapping functions used to construct new mapped weighted essentially non-oscillatory (WENO) schemes.
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Original: We propose  several adaptive control functions and a smoothing approximation of the signum function , and apply them to design mapping functions used to construct new mapped weighted essentially non-oscillatory (WENO) schemes. Our method extends the formulation of the mapping functions used in the WENO-IM(k,A) methods developed in the last years. The new method admits a more extensive permitted range of the parameters in the mapping functions , that it enjoys some mathematical advantages in achieving optimal convergence rates .
Revised: We propose several adaptive control functions and a smoothing approximation of the signum function . The proposed schemes introduce the adaptivity and admit an extensive permitted range of the parameters in the mapping functions , that it enjoys some mathematical advantages in achieving optimal convergence rates .
Edit intent: clarity
Original:  The new method admits a more extensive permitted range of the parameters in the mapping functions , that it enjoys some mathematical advantages in achieving optimal convergence rates . Improved numerical results are obtained for one-dimensional linear advection cases, especially over long output times .
Revised:  The new method admits a more extensive permitted range of the parameters in the mapping functions , that it enjoys some mathematical advantages in achieving optimal convergence rates , even near critical points. Particularly, one of these new schemes with fine-tuned parameters illustrates a significant advantage when solving problems with discontinuities. It produces numerical solutions with high resolution without generating spurious oscillations, especially for long output times .
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Original:  Improved numerical results are obtained for one-dimensional linear advection cases, especially over long output times . Numerical solutions of the 1D Euler equations with the Sod's shock tube problem, the Shu-Osher shock-entropy wave interaction problem and the Woodward-Colella blastwave problem are compared with results in references. Quite satisfied performances using the new method are demonstrated in the simulation of two severe problems as the Titarev-Toro shock-entropy wave interaction problem and the 123 problem .
Revised:  Improved numerical results are obtained for one-dimensional linear advection cases, especially over long output times .
Edit intent: coherence
Original:  Since the search for the best structure is time prohibitive, we propose to randomly remove attention heads during training and keep all attention heads at test time, thus the final model can be viewed as an average of models with different architectures.
Revised:  Since the search for the best structure is time prohibitive, we propose to randomly remove attention heads during training and keep all attention heads at test time, thus the final model is an ensemble of models with different architectures.
Edit intent: clarity
Original:  This method gives consistent performance gains  on the Wall Street Journal, AISHELL, Switchboard and AMI ASR tasks.
Revised:  The proposed method also forces each head independently learn the most useful patterns. We apply the proposed method to train Transformer based and Convolution-augmented Transformer (Conformer) based ASR models. Our method gives consistent performance gains on the Wall Street Journal, AISHELL, Switchboard and AMI ASR tasks.
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Original:  This method gives consistent performance gains  on the Wall Street Journal, AISHELL, Switchboard and AMI ASR tasks. On the AISHELL dev/test sets, the proposed method achieves state-of-the-art Transformer results with 5.8\%/6.3\% word error rates .
Revised:  This method gives consistent performance gains on the Wall Street Journal, AISHELL, Switchboard and AMI datasets. To the best of our knowledge, we have achieved state-of-the-art Transformer results with 5.8\%/6.3\% word error rates .
Edit intent: clarity
Original:  It was thought in the numerical linear algebra community that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns, regression, low rank approximation, maximum matching on general graphs and linear matroid union, that one would need to resolve the main open question of Cohen (SODA, 2016 ) regarding the logarithmic factors in existing oblivious subspace embeddings.
Revised:  It was thought in the numerical linear algebra community that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns, regression, low rank approximation, maximum matching on general graphs and linear matroid union, that one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013 ) regarding the logarithmic factors in existing oblivious subspace embeddings.
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Original:  With this recognition, we are able to employ the large body of work in numerical linear algebra to obtain algorithms for these problems that are simpler and faster than existing approaches. We also consider static data structures for the above problems, and obtain close-to-optimal bounds for them. To do this, we introduce a new randomized transform, the Gaussian Randomized Hadamard Transform (GRHT). It was thought in the numerical linear algebra community that to obtain nearly-optimal bounds for various problems such as rank computation, finding a maximal linearly independent subset of columns, regression, low rank approximation, maximum matching on general graphs and linear matroid union, that one would need to resolve the main open question of Nelson and Nguyen (FOCS, 2013) regarding the logarithmic factors in existing oblivious subspace embeddings. We bypass this question, using GRHT, and obtain optimal or nearly-optimal bounds for these problems. For the fundamental problems of rank computation and finding a linearly independent subset of columns, our algorithms improve Cheung, Kwok, and Lau (JACM, 2013) and are optimal to within a constant factor and a \log\log(n)-factor, respectively. Further, for constant factor regression and low rank approximation we give the first optimal algorithms, for the current matrix multiplication exponent .
Revised:  With this recognition, we are able to employ the large body of work in numerical linear algebra to obtain algorithms for these problems that are simpler or faster (or both) than existing approaches .
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Original: The purpose of this paper is to test the multi-factor beta model implied by the generalized arbitrage pricing theory ( APT)and the Adaptive Multi-Factor (AMF ) model with the Groupwise Interpretable Basis Selection (GIBS) algorithm , without imposing the exogenous assumption of constant betas.
Revised: The purpose of this paper is to test the time-invariance of the beta coefficients estimated by the Adaptive Multi-Factor (AMF) model. The AMF model is implied by the generalized arbitrage pricing theory ( APT)and the Adaptive Multi-Factor (AMF ) model with the Groupwise Interpretable Basis Selection (GIBS) algorithm , without imposing the exogenous assumption of constant betas.
Edit intent: meaning-changed
Original: The purpose of this paper is to test the multi-factor beta model implied by the generalized arbitrage pricing theory ( APT)and the Adaptive Multi-Factor (AMF ) model with the Groupwise Interpretable Basis Selection (GIBS) algorithm , without imposing the exogenous assumption of constant betas.
Revised: The purpose of this paper is to test the multi-factor beta model implied by the generalized arbitrage pricing theory ( GAPT), which implies constant beta coefficients. The AMF model utilizes a Groupwise Interpretable Basis Selection (GIBS) algorithm , without imposing the exogenous assumption of constant betas.
Edit intent: meaning-changed
Original: The purpose of this paper is to test the multi-factor beta model implied by the generalized arbitrage pricing theory ( APT)and the Adaptive Multi-Factor (AMF ) model with the Groupwise Interpretable Basis Selection (GIBS) algorithm , without imposing the exogenous assumption of constant betas. The intercept (arbitrage) tests validate both the AMF and the Fama-French 5-factor (FF5) model.
Revised: The purpose of this paper is to test the multi-factor beta model implied by the generalized arbitrage pricing theory ( APT)and the Adaptive Multi-Factor (AMF ) model with the Groupwise Interpretable Basis Selection (GIBS) algorithm to identify the relevant factors from among all traded ETFs. We compare the AMF model with the Fama-French 5-factor (FF5) model.
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Original:  We do the time-invariance tests for the betas for both the AMF model and the FF5 in various time periods. We show that for nearly all time periods with length less than 6 years, the beta coefficients are time-invariant for the AMF model, but not  the FF5 model.
Revised:  We show that for nearly all time periods with length less than 6 years, the beta coefficients are time-invariant for the AMF model, but not the FF5 model.
Edit intent: coherence
Original:  The beta coefficients are time-varying for both AMF and FF5 models for longer time periods. Therefore, using the dynamic AMF model with a decent rolling window (such as 5 years) is more powerful and stable than the FF5 model.
Revised:  This implies that the AMF model with a decent rolling window (such as 5 years) is more powerful and stable than the FF5 model.
Edit intent: coherence
Original:  Towards addressing this, we consider an existing noise model in which there is probabilistic information about the protected attributes (e.g., [ 19, 32, 56, 44 ]), and ask is fair selection is possible under noisy conditions?
Revised:  Towards addressing this, we consider an existing noise model in which there is probabilistic information about the protected attributes (e.g., [ 58, 34, 20, 46 ]), and ask is fair selection is possible under noisy conditions?
Edit intent: meaning-changed
Original:  Towards addressing this, we consider an existing noise model in which there is probabilistic information about the protected attributes (e.g., [ 19, 32, 56, 44 ]), and ask is fair selection is possible under noisy conditions?
Revised:  Towards addressing this, we consider an existing noise model in which there is probabilistic information about the protected attributes (e.g., [ 19, 32, 56, 44 ]), and ask is fair selection possible under noisy conditions?
Edit intent: fluency
Original:  We address these shortcomings and develop a novel strategy which produces unbiased and consistent sparse allocations. We demonstrate that: (1) failing to correct for the bias leads to low out-of-sample portfolio return; (2) only sparse portfolios achieved positive cumulative return during several economic downturns, including the dot-com bubble of 2000, the financial crisis of 2007-09, and COVID-19 outbreak .
Revised:  We address these shortcomings and develop an approach to construct sparse portfolios in high dimensions. Our contribution is twofold: from the theoretical perspective, we establish the oracle bounds of sparse weight estimators and provide guidance regarding their distribution. From the empirical perspective, we examine the merit of sparse portfolios during different market scenarios. We find that in contrast to non-sparse counterparts, our strategy is robust to recessions and can be used as a hedging vehicle during such times .
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Original:  We prove that  our multilevel EnKBF can achieve a mean square error (MSE) of O(\epsilon^2), \ \epsilon>0 with a cost of order O(\epsilon^{-2}\log(\epsilon)^2).
Revised:  We prove that an ideal version of our multilevel EnKBF can achieve a mean square error (MSE) of O(\epsilon^2), \ \epsilon>0 with a cost of order O(\epsilon^{-2}\log(\epsilon)^2).
Edit intent: meaning-changed
Original:   This implies a reduction in cost compared to the (single level) EnKBF which requires a cost of O(\epsilon^{-3}) to achieve an MSE of O(\epsilon^2).
Revised:  In order to prove this result we provide a Monte Carlo convergence and approximation bounds associated to time-discretized EnKBFs. This implies a reduction in cost compared to the (single level) EnKBF which requires a cost of O(\epsilon^{-3}) to achieve an MSE of O(\epsilon^2).
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Original: Recently, contextualized word embeddings outperform static word embeddings on many NLP tasks. However, we still do not know much about the mechanism inside these representations. Do they have any common patterns? If so, where do these patterns come from? We find that almost all the contextualized word vectors of BERT and RoBERTa have a commonpattern.
Revised: In this work, we demonstrate that the contextualized word vectors of BERT and RoBERTa have a commonpattern.
Edit intent: clarity
Original:  We find that almost all the contextualized word vectors of BERT and RoBERTa have a commonpattern. For BERT, the 557^{th neuron-level method to analyze where these "tails" come from.
Revised:  We find that almost all the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. Namely, we find cases of persistent outlier neurons within BERT and RoBERTa's hidden state vectors that consistently bear the smallest or largest values in said vectors. In an attempt to investigate the source of this information, we introduce a neuron-level method to analyze where these "tails" come from.
Edit intent: coherence
Original:  We also investigate what will happen if we "cutting the tails" (zero-out). Our results show that "tails" are the major cause of anisotropy of vector space.
Revised:  We also pre-train the RoBERTa-base models from scratch and find that the outliers disappear without using positional embeddings. These outliers, we find, are the major cause of anisotropy of vector space.
Edit intent: meaning-changed
Original:  Our results show that "tails" are the major cause of anisotropy of vector space. After "cutting the tails", a word's different vectors are more similar to each other. The internal representations have a better ability to distinguish a word 's different senseswith the word-in-context (WiC) dataset. The performance on the word sense disambiguation task is better for BERT and unchanged for RoBERTa. We can also better induce phrase grammar from the vector space. These suggest that "tails" are less related to the sense and syntax information in vectors. These findings provide insights into the inner workings of contextualized word vectors .
Revised:  Our results show that "tails" are the major cause of anisotropy of encoders' raw vector spaces, and clipping them leads to increased similarity across vectors. We demonstrate this in practice by showing that clipped vectors can more accurately distinguish word senses, as well as lead to better sentence embeddings when mean pooling. In three supervised tasks, we find that clipping does not affect the performance .
Edit intent: coherence
Original: Motivated by recent applications of sequential decision making in matching markets, in this paper we attempt at formulating and abstracting market designs in peer lending. In the rest of this paper, what will follow is a paradigm to set the stage for how peer lending can be conceived from a matching market perspective with sequential design making embedded in it.
Revised: Motivated by recent applications of sequential decision making in matching markets, in this paper we attempt at formulating and abstracting market designs for P2P lending. We describe a paradigm to set the stage for how peer lending can be conceived from a matching market perspective with sequential design making embedded in it.
Edit intent: clarity
Original:  In the rest of this paper, what will follow is a paradigm to set the stage for how peer lending can be conceived from a matching market perspective with sequential design making embedded in it. We attempt at laying the stepping stones toward understanding how sequential decision making can be made more flexible in peer lending platforms and as a way to devise more fair and equitable outcomes for both borrowers and lenders.
Revised:  In the rest of this paper, what will follow is a paradigm to set the stage for how peer lending can be conceived from a matching market perspective , especially when both borrower and lender preferences are respected. We model these specialized markets as an optimization problem and consider different utilities for agents on both sides of the market while also understanding the impact of equitable allocations to borrowers. We devise a technique based on sequential decision making can be made more flexible in peer lending platforms and as a way to devise more fair and equitable outcomes for both borrowers and lenders.
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Original:  We attempt at laying the stepping stones toward understanding how sequential decision making can be made more flexible in peer lending platforms and as a way to devise more fair and equitable outcomes for both borrowers and lenders. The goal of this paper is to provide some ideas on how and why lending platforms conceived from the perspective of matching markets can allow for incorporating fairness and equitable outcomes when we design lending platforms .
Revised:  We attempt at laying the stepping stones toward understanding how sequential decision making that allow the lenders to adjust their choices based on the dynamics of uncertainty from competition over time and that also impacts the rewards in return for their investments. Using simulated experiments we show the dynamics of the regret based on the optimal borrower-lender matching and find that the lender regret depends on the initial preferences set by the lenders which could affect their learning over decision making steps .
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