wanyu/IteraTeR_full_sent huggingface dataset:
Original:   We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees  ]  .
Revised:  We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model where explainability is achieved via decision trees ] .
Edit intent: fluency
Original:   We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees  ]  .
Revised:  We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees . For the k-means and k-medians problems our upper bounds improve those obtained by ] .
Edit intent: meaning-changed
Original:   We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees  ]  .
Revised:  We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees Moshkovitz et. al, ICML 20] .
Edit intent: meaning-changed
Original:   We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees  ]  .
Revised:  We provide upper and lower bounds on this price as well as efficient algorithms to build explainable clustering for the k-means, k-medians, k-center and the maximum-spacing problems in a natural model in which explainability is achieved via decision trees ] for low dimensions. Another contribution is a simple and efficient algorithm for building explainable clusterings for the k-means problem. We provide empirical evidence that its performance is better than the current state of the art for decision-tree based explainable clustering .
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Original:  This paper introduces a theoretical and algorithmic framework for the fast, parallel solution of the nonlinear equations that arise from IRK methods  applied to nonlinear numerical PDEs, including PDEs with algebraic constraints.
Revised:  This paper introduces a theoretical and algorithmic framework for the fast, parallel solution of the nonlinear equations that arise from IRK methods (and discontinuous Galerkin discretizations in time) applied to nonlinear numerical PDEs, including PDEs with algebraic constraints.
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Original:  Under quite general assumptions on the spatial discretization , it is proven that the preconditioned  operator has a condition number of ~O(1),  with only weak dependence on the number of stages or integration accuracy.
Revised:  Under quite general assumptions on the spatial discretization , it is proven that the preconditioned 2x2 operator has a condition number of ~O(1), with only weak dependence on the number of stages or integration accuracy.
Edit intent: meaning-changed
Original:  Under quite general assumptions on the spatial discretization , it is proven that the preconditioned  operator has a condition number of ~O(1),  with only weak dependence on the number of stages or integration accuracy.
Revised:  Under quite general assumptions on the spatial discretization , it is proven that the preconditioned operator has a condition number of ~O(1), independent of the spatial discretization, and with only weak dependence on the number of stages or integration accuracy.
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Original:   The new methods are applied to several challenging fluid flow problems, including the compressible Euler and Navier Stokes equations, and the vorticity-streamfunction formulation of the incompressible Euler and Navier Stokes equations.
Revised:  Moreover, the new method is built using the same preconditioners needed for backward Euler-type time stepping schemes, so can be readily added to existing codes. The new methods are applied to several challenging fluid flow problems, including the compressible Euler and Navier Stokes equations, and the vorticity-streamfunction formulation of the incompressible Euler and Navier Stokes equations.
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Original:  Up to 10th-order accuracy is demonstrated using Gauss integration , while in all cases 4th-order Gauss integration requires roughly half the number of preconditioner applications as required by standard SDIRK methods.
Revised:  Up to 10th-order accuracy is demonstrated using Gauss IRK , while in all cases 4th-order Gauss integration requires roughly half the number of preconditioner applications as required by standard SDIRK methods.
Edit intent: clarity
Original:  Up to 10th-order accuracy is demonstrated using Gauss integration , while in all cases 4th-order Gauss integration requires roughly half the number of preconditioner applications as required by standard SDIRK methods.
Revised:  Up to 10th-order accuracy is demonstrated using Gauss integration , while in all cases 4th-order Gauss IRK requires roughly half the number of preconditioner applications as required by standard SDIRK methods.
Edit intent: clarity
Original:  Although multilingual MLMs have been introduced to serve many languages, these have limitations as to their capacity and the size and diversity of non-English data they are pre-trained on. In this work, we remedy these issues for Arabic by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT , that have superior performance to all existing models .
Revised:  Although multilingual MLMs have been introduced to serve many languages, these have limitations as to their capacity and the size and diversity of non-English data involved in their pre-training. We remedy these issues for Arabic by introducing two powerful deep bidirectional transformer-based models, ARBERT and MARBERT , that have superior performance to all existing models .
Edit intent: clarity
Original:  To evaluate our models, we propose ArBench , a new benchmark for multi-dialectal Arabic language understanding . ArBench is built using 41 datasets targeting 5 different tasks/ task clusters, allowing us to offer a series of standardized experiments under rich conditions.
Revised:  To evaluate our models, we also introduce ARLUE , a new benchmark for multi-dialectal Arabic language understanding . ArBench is built using 41 datasets targeting 5 different tasks/ task clusters, allowing us to offer a series of standardized experiments under rich conditions.
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Original:  To evaluate our models, we propose ArBench , a new benchmark for multi-dialectal Arabic language understanding . ArBench is built using 41 datasets targeting 5 different tasks/ task clusters, allowing us to offer a series of standardized experiments under rich conditions.
Revised:  To evaluate our models, we propose ArBench , a new benchmark for multi-dialectal Arabic language understanding evaluation. ARLUE is built using 41 datasets targeting 5 different tasks/ task clusters, allowing us to offer a series of standardized experiments under rich conditions.
Edit intent: meaning-changed
Original:  To evaluate our models, we propose ArBench , a new benchmark for multi-dialectal Arabic language understanding . ArBench is built using 41 datasets targeting 5 different tasks/ task clusters, allowing us to offer a series of standardized experiments under rich conditions.
Revised:  To evaluate our models, we propose ArBench , a new benchmark for multi-dialectal Arabic language understanding . ArBench is built using 42 datasets targeting six different task clusters, allowing us to offer a series of standardized experiments under rich conditions.
Edit intent: meaning-changed
Original:  When fine-tuned on ArBench, ARBERT and MARBERT collectively achieve new SOTA with sizeable margins compared to all existing models such as mBERT, XLM-R (Base and Large), and AraBERT on 37 out of 45 classification taskson the 41 datasets(\%82.22) . Our models are publicly available for research .
Revised:  When fine-tuned on ARLUE, our models collectively achieve new SOTA with sizeable margins compared to all existing models such as mBERT, XLM-R (Base and Large), and AraBERT on 37 out of 45 classification taskson the 41 datasets(\%82.22) . Our models are publicly available for research .
Edit intent: clarity
Original:  When fine-tuned on ArBench, ARBERT and MARBERT collectively achieve new SOTA with sizeable margins compared to all existing models such as mBERT, XLM-R (Base and Large), and AraBERT on 37 out of 45 classification taskson the 41 datasets(\%82.22) . Our models are publicly available for research .
Revised:  When fine-tuned on ArBench, ARBERT and MARBERT collectively achieve new state-of-the-art results across the majority of tasks ( 37 out of 45 classification taskson the 41 datasets(\%82.22) . Our models are publicly available for research .
Edit intent: clarity
Original:  When fine-tuned on ArBench, ARBERT and MARBERT collectively achieve new SOTA with sizeable margins compared to all existing models such as mBERT, XLM-R (Base and Large), and AraBERT on 37 out of 45 classification taskson the 41 datasets(\%82.22) . Our models are publicly available for research .
Revised:  When fine-tuned on ArBench, ARBERT and MARBERT collectively achieve new SOTA with sizeable margins compared to all existing models such as mBERT, XLM-R (Base and Large), and AraBERT on 37 out of 48 classification tasks, on the 42 datasets). Our best model acquires the highest ARLUE score (77.40) across all six task clusters, outperforming all other models including XLM-R Large (~ 3.4 x larger size) . Our models are publicly available for research .
Edit intent: meaning-changed
Original:  When fine-tuned on ArBench, ARBERT and MARBERT collectively achieve new SOTA with sizeable margins compared to all existing models such as mBERT, XLM-R (Base and Large), and AraBERT on 37 out of 45 classification taskson the 41 datasets(\%82.22) . Our models are publicly available for research .
Revised:  When fine-tuned on ArBench, ARBERT and MARBERT collectively achieve new SOTA with sizeable margins compared to all existing models such as mBERT, XLM-R (Base and Large), and AraBERT on 37 out of 45 classification taskson the 41 datasets(\%82.22) . Our models are publicly available at URL and ARLUE will be released through the same repository .
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Original:  This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design the sentence encoder and the de-noise method.
Revised:  This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design sentence encoder and the de-noise method.
Edit intent: fluency
Original:  This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design the sentence encoder and the de-noise method.
Revised:  This article 1) introduces some general concepts, and further 2) gives a comprehensive overview of DNNs in RE from two points of view: supervised RE, which attempts to improve the standard RE systems, and distant supervision RE, which adopts DNNs to design the sentence encoder and de-noise method.
Edit intent: fluency
Original:  Robot Operating System 2 (ROS2) targets distributed real-time systems  . Especially in tight real-time control loops , latency in data processing and communication can lead to instabilities.
Revised:  Robot Operating System 2 (ROS2) targets distributed real-time systems and is widely used in the robotics community . Especially in tight real-time control loops , latency in data processing and communication can lead to instabilities.
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Original:  However, this assumption rarely holds in practice, as diseases typically spread through connections between individuals . We introduce an infection model for networks, inspired by characteristics of COVID-19 and similar diseases,  which generalizes the traditional i.i.d. model from probabilistic group testing.
Revised:  However, this assumption rarely holds in practice, as diseases typically spread through interactions between individuals and therefore cause infections to be correlated. Inspired by characteristics of COVID-19 and similar diseases, which generalizes the traditional i.i.d. model from probabilistic group testing.
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Original:  Through both theory and simulations, we show that when the network and infection parameters are conducive to "strong community structure," our proposed adaptive, graph-aware algorithm outperforms the baseline binary splitting algorithm, and is even order-optimal in certain parameter regimes .
Revised:  We prove that when the network and infection parameters are conducive to "strong community structure," our proposed adaptive, graph-aware algorithm outperforms the baseline binary splitting algorithm, and is even order-optimal in certain parameter regimes .
Edit intent: clarity
Original:  Through both theory and simulations, we show that when the network and infection parameters are conducive to "strong community structure," our proposed adaptive, graph-aware algorithm outperforms the baseline binary splitting algorithm, and is even order-optimal in certain parameter regimes . Finally, we derive novel information-theoretic lower bounds which highlight the fundamental limits of adaptive group testing in our networked setting .
Revised:  Through both theory and simulations, we show that when the network and infection parameters are conducive to "strong community structure," our proposed adaptive, graph-aware algorithm outperforms the baseline binary splitting algorithm, and is even order-optimal in certain parameter regimes .
Edit intent: coherence
Original:  We prove that when the network and infection parameters are conducive to "strong community structure," our proposed adaptive, graph-aware algorithm outperforms the baseline binary splitting algorithm, and is even order-optimal in certain parameter regimes  .
Revised:  We prove that when the network and infection parameters are conducive to "strong community structure," our proposed adaptive, graph-aware algorithm outperforms the baseline binary splitting algorithm, and is even order-optimal in certain parameter regimes . We support our results with numerical simulations .
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Original:  We propose a class of dominant strategy incentive compatible (DSIC)  and ex post individual rational (EPIR) mechanisms that balance the budget and approximates the first-best welfare level when the number of participants is large .
Revised:  We propose a class of informationally robust, dominant-strategy incentive compatible (DSIC) and ex post individual rational (EPIR) mechanisms that balance the budget and approximates the first-best welfare level when the number of participants is large .
Edit intent: meaning-changed
Original:  We propose a class of dominant strategy incentive compatible (DSIC)  and ex post individual rational (EPIR) mechanisms that balance the budget and approximates the first-best welfare level when the number of participants is large .
Revised:  We propose a class of dominant strategy incentive compatible (DSIC) , and ex post individual rational (EPIR) mechanisms that balance the budget and approximates the first-best welfare level when the number of participants is large .
Edit intent: fluency
Original:  We propose a class of dominant strategy incentive compatible (DSIC)  and ex post individual rational (EPIR) mechanisms that balance the budget and approximates the first-best welfare level when the number of participants is large .
Revised:  We propose a class of dominant strategy incentive compatible (DSIC) and ex post individual rational (EPIR) mechanisms that are asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) as the population grows .
Edit intent: meaning-changed
Original:  The decision rule is based on thresholding the sum of transformed valuations of the agents. We show that the rate of change of the threshold is the key to characterizing the trade-off between balancing the budget and maximizing the welfare .
Revised:  The decision rule is based on thresholding the sum of transformed valuations exceeds a threshold that only depends on the population size n. The increasing function can be chosen arbitrarily. For example, it can simply be the identity function. Our results show that the rate of change of the threshold is the key to characterizing the trade-off between balancing the budget and maximizing the welfare .
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Original:  In particular, we find that when this rate is controlled in a certain range , the two goals can be achieved to \sqrt{n \log n}, the mechanism can be AEABB and AE } at the same time asymptotically.
Revised:  In particular, using the multivariate Berry-Esseen theorem, our results demonstrate that when this rate is controlled in a certain range , the two goals can be achieved to \sqrt{n \log n}, the mechanism can be AEABB and AE } at the same time asymptotically.
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Original:  In particular, we find that when this rate is controlled in a certain range , the two goals can be achieved to \sqrt{n \log n}, the mechanism can be AEABB and AE } at the same time asymptotically.
Revised:  In particular, we find that when this rate is controlled within the range from \sqrt{n to \sqrt{n \log n}, the mechanism can be AEABB and AE } at the same time asymptotically.
Edit intent: clarity
Original:  In particular, we find that when this rate is controlled in a certain range , the two goals can be achieved to \sqrt{n \log n}, the mechanism can be AEABB and AE } at the same time asymptotically. The proposed mechanisms are robust in two ways: first, they depend on the valuation distributions only through certain moments ; second, they allow for asymmetric and irregular valuation distributions.
Revised:  In particular, we find that when this rate is controlled in a certain range , the two goals can be achieved to \sqrt{n \log n}, the mechanism can be AEABB and AE } at the same time as long as the cost does not grow too rapidly. One advantage of the proposed mechanisms is their informational robustness as they depend only on certain moments of the valuation distributions.
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Original:  More specifically, we show that the Myerson regularity condition plays no role in this constrained problem of welfare approximation. In addition to welfare approximation, we also study profit approximation using similar mechanisms but with a different threshold and show that a fraction of the optimal profit can be achieved with a large number of participants.
Revised:  In contrast, previous mechanisms proposed to solve this question, such as the second-best mechanism, typically require the knowledge of the entire valuation distribution. Also, our study extends the results to non-binary decision environments with general utility functions. Lastly, we show that if the threshold is instead set equal to the cost, the proposed mechanism can achieve a non-negligible fraction of the optimal profit can be achieved with a large number of participants.
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Original:  In addition to welfare approximation, we also study profit approximation using similar mechanisms but with a different threshold and show that a fraction of the optimal profit can be achieved with a large number of participants. Lastly, we extend the model to accommodate for non-binary decision and general utility functions .
Revised:  In addition to welfare approximation, we also study profit approximation using similar mechanisms but with a different threshold and show that a fraction of the optimal profit in the limit, where the fraction is the correlation between the virtual value and the aforementioned transformed value .
Edit intent: meaning-changed
Original:  We propose a class of informationally robust, dominant-strategy incentive compatible (DSIC) , and ex post individual rational (EPIR) mechanisms that are asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) as the population grows .
Revised:  We propose a class of dominant-strategy incentive compatible (DSIC) , and ex post individual rational (EPIR) mechanisms that are asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) as the population grows .
Edit intent: clarity
Original:  We propose a class of informationally robust, dominant-strategy incentive compatible (DSIC) , and ex post individual rational (EPIR) mechanisms that are asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) as the population grows .
Revised:  We propose a class of informationally robust, dominant-strategy incentive compatible (DSIC) and ex post individual rational (EPIR) mechanisms that are asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) as the population grows .
Edit intent: fluency
Original:  We propose a class of informationally robust, dominant-strategy incentive compatible (DSIC) , and ex post individual rational (EPIR) mechanisms that are asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) as the population grows .
Revised:  We propose a class of informationally robust, dominant-strategy incentive compatible (DSIC) , and ex post individual rational (EPIR) mechanisms which we call the adjusted mean-thresholding (AMT) mechanisms. We show that when the cost of provision grows slower than the \sqrt{n asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) as the population grows .
Edit intent: meaning-changed
Original:  We propose a class of informationally robust, dominant-strategy incentive compatible (DSIC) , and ex post individual rational (EPIR) mechanisms that are asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) as the population grows . The decision rule is constructed in two steps: First, each valuation is transformed with an increasing function and centered to be mean zero . Then the public good is allocated if the sum of transformed valuations exceeds a threshold that only depends on the population size n. The increasing function can be chosen arbitrarily. For example, it can simply be the identity function. Our results show that the rate of change of the threshold is the key tocharacterizing the trade-off between budget balance and efficiency. In particular, using the multivariate Berry-Esseen theorem, our results demonstrate that when this rate is controlled within the range from \sqrt{n to n \log n, the mechanismcan be AEABB and AE at the same time as long as the cost does not grow too rapidly.
Revised:  We propose a class of informationally robust, dominant-strategy incentive compatible (DSIC) , and ex post individual rational (EPIR) mechanisms that are asymptotically ex ante budget balanced (AEABB) and asymptotically efficient (AE) to n \log n, the mechanismcan be AEABB and AE at the same time as long as the cost does not grow too rapidly.
Edit intent: meaning-changed