wanyu/IteraTeR_full_sent huggingface dataset:
Original: Neural networks have proven to be effective approximators of signed distance fields (SDFs) for solid 3D objects.
Revised: A neural implicit outputs a number indicating whether the given query point in space is inside, outside, or on a surface. Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input. While affording latent-space interpolation, this comes at the cost of reconstruction accuracy for any _single_ shape. Training a specific network for each 3D objects.
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Original: Neural networks have proven to be effective approximators of signed distance fields (SDFs) for solid 3D objects. While prior work has focused on the generalization power of such approximations, we instead explore their suitability as a compact - if purposefully overfit - SDF representation of individual shapes. Specifically, we ask whether neural networks can serve as first-class implicit shape representations in computer graphics. We call such overfit networks Neural Implicits. Similar to SDFs stored on a regular grid, Neural Implicits have fixed storage profiles and memory layout, but afford far greater accuracy. At equal storage cost, Neural Implicits consistently match or exceed the accuracy of irregularly-sampled triangle meshes. We achieve this with a combination of a novel loss function, sampling strategy and supervision protocol designed to facilitate robust shape overfitting. We demonstrate the flexibility of our representationon a variety of standard rendering and modelling tasks .
Revised: Neural networks have proven to be effective approximators of signed distance fields (SDFs) for solid 3D shape, a _weight-encoded_ neural implicit may URLo the latent vector and focus reconstruction accuracy on the details of a single shape. While previously considered as an intermediary representation for 3D scanning tasks or as a toy-problem leading up to latent-encoding tasks, weight-encoded neural implicits have not yet been taken seriously as a 3D shape representation. In this paper, we establish that weight-encoded neural implicits meet the criteria of a first-class 3D shape representation. We introduce a suite of technical contributions to improve reconstruction accuracy, convergence, and robustness when learning the signed distance field induced by a polygonal mesh -- the _de facto_ standard representation. Viewed as a lossy compression, our conversion outperforms standard techniques from geometry processing. Compared to previous latent- and weight-encoded neural implicits we demonstrate superior robustness, scalability, and performance .
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Original:   Our study is filling this gap by presenting a broad evaluation of the approach showing the resulting average Sharpe ratio of 6.32. However, we do not take into account order execution queues, which of course affect the result in the live-trading setting. The obtained performance results give us confidence that this approach is worthwhile .
Revised:  Our study is filling this gap by presenting a broad evaluation of the approach supported by statistical tools which make it generalisable to unseen data and comparable to other approaches .
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Original: By exploiting McFadden (1974)'s results on conditional logit estimation, we show that there exists a one-to-one mapping between existence and uniqueness of conditional maximum likelihood estimates of the binary logit model with fixed effects and the spatial configuration of data points.
Revised: By exploiting McFadden (1974)'s results on conditional logit estimation, we show that there exists a one-to-one mapping between existence and uniqueness of conditional maximum likelihood estimates of the binary logit model with fixed effects and the configuration of data points.
Edit intent: clarity
Original:  However, the traditional game theoretic assumption of complete rationality is too strong for the purpose of human driving, and there is a need for understanding human driving as a bounded rational activity through a behavioral game theoretic lens.
Revised:  However, the traditional game theoretic assumption of complete rationality is too strong for the purpose of human driving, and there is a need for understanding human driving as a bounded rational activity through a behavioral game theoretic lens.
Edit intent: fluency
Original:  Our results suggest that among the behavior models evaluated, modeling driving behavior as pure strategy NE with quantal errors at the level of maneuvers with bounds sampling of actions at the level of trajectories provides the best fit to naturalistic driving behavior  .
Revised:  Our results suggest that among the behavior models evaluated, modeling driving behavior as pure strategy NE with quantal errors at the level of maneuvers with bounds sampling of actions at the level of trajectories provides the best fit to naturalistic driving behavior , and there is a significant impact of situational factors on the performance of behavior models .
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Original:  However, we argue that this evaluation can potentially underestimate the actual performance of runs based on text-generation approaches (e. g. our run). This is due to the fact that although some predictions of such runs answer the slot questions well, they may not be an exact string match for the gold standard answers.
Revised:  However, we argue that this evaluation can potentially underestimate the actual performance of runs based on text-generation . Although some such runs may answer the slot questions well, they may not be an exact string match for the gold standard answers.
Edit intent: clarity
Original:  We explore how contextual banditsproblem is modelled as an extension of the reinforcement learning (RL) problem and build a novel algorithm to solve the problem using an array of action-based learners.
Revised:  We explore how contextual banditsproblem is modelled as an extension of the reinforcement learning (RL) problem and build a novel algorithm to solve this problem using an array of action-based learners.
Edit intent: clarity
Original:  The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8 %DIFDELCMD < \\%%% %DIF < F1 value on the Causal Explanation Detection task.abstract 
Revised:  The experiments on the commonly used dataset of CEA shows that the PSAN outperforms the state-of-the-art method by 1.8 %DIFDELCMD < \\%%% %DIF < F1 value on the Causal Explanation Detection task.abstract \% F1 value on the Causal Explanation Detection task.
Edit intent: fluency
Original:  The equilibrium satisfies Pareto optimality, approximate envy-freeness and incentive compatibility in the large . * A combinatorial polynomial time algorithm for an \epsilon-approximate ADHZ equilibrium for the case of dichotomous, and more generally bi-valued, utilities.
Revised:  We prove that the equilibrium satisfies Pareto optimality, approximate envy-freeness and incentive compatibility in the large . * A combinatorial polynomial time algorithm for an \epsilon-approximate ADHZ equilibrium for the case of dichotomous, and more generally bi-valued, utilities.
Edit intent: coherence
Original:  The equilibrium satisfies Pareto optimality, approximate envy-freeness and incentive compatibility in the large . * A combinatorial polynomial time algorithm for an \epsilon-approximate ADHZ equilibrium for the case of dichotomous, and more generally bi-valued, utilities.
Revised:  The equilibrium satisfies Pareto optimality, approximate envy-freeness , and approximate weak core stability . * A combinatorial polynomial time algorithm for an \epsilon-approximate ADHZ equilibrium for the case of dichotomous, and more generally bi-valued, utilities.
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Original:  The equilibrium satisfies Pareto optimality, approximate envy-freeness and incentive compatibility in the large . * A combinatorial polynomial time algorithm for an \epsilon-approximate ADHZ equilibrium for the case of dichotomous, and more generally bi-valued, utilities.
Revised:  The equilibrium satisfies Pareto optimality, approximate envy-freeness and incentive compatibility in the large . * A combinatorial polynomial-time algorithm for an \epsilon-approximate ADHZ equilibrium for the case of dichotomous, and more generally bi-valued, utilities.
Edit intent: fluency
Original:  * A rational convex program for HZ under dichotomous utilities; a combinatorial polynomial time algorithm for this case was given by Vazirani and Yannakakis (2020) . The \epsilon-approximate ADHZ model fills a void, described in the paper, in the space of general mechanisms for one-sided matching markets .
Revised:  Since computing an equilibrium for HZ is likely to be highly intractable and because of the difficulty of extending HZ to more general utility functions, Hosseini and Vazirani proposed (a rich collection of) Nash-bargaining-based matching market models. For the dichotomous-utilities case of their model linear Arrow-Debreu Nash bargaining one-sided matching markets .
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Original:  The \epsilon-approximate ADHZ model fills a void, described in the paper, in the space of general mechanisms for one-sided matching markets .
Revised:  The \epsilon-approximate ADHZ model fills a void, described in the paper, in the space of general mechanisms for one-sided matching market (1LAD), we give a combinatorial, strongly polynomial-time algorithm and show that it admits a rational convex program .
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Original:  The Kazakh speech corpus (KSC) contains around 335 hours of transcribed audio comprising over 154 ,000 utterances spoken by participants from different regions , age groups, and gender .
Revised:  The Kazakh speech corpus (KSC) contains around 332 hours of transcribed audio comprising over 154 ,000 utterances spoken by participants from different regions , age groups, and gender .
Edit intent: meaning-changed
Original:  The Kazakh speech corpus (KSC) contains around 335 hours of transcribed audio comprising over 154 ,000 utterances spoken by participants from different regions , age groups, and gender .
Revised:  The Kazakh speech corpus (KSC) contains around 335 hours of transcribed audio comprising over 153 ,000 utterances spoken by participants from different regions , age groups, and gender .
Edit intent: fluency
Original:  The Kazakh speech corpus (KSC) contains around 335 hours of transcribed audio comprising over 154 ,000 utterances spoken by participants from different regions , age groups, and gender .
Revised:  The Kazakh speech corpus (KSC) contains around 335 hours of transcribed audio comprising over 154 ,000 utterances spoken by participants from different regions and age groups, and gender .
Edit intent: fluency
Original:  The Kazakh speech corpus (KSC) contains around 335 hours of transcribed audio comprising over 154 ,000 utterances spoken by participants from different regions , age groups, and gender .
Revised:  The Kazakh speech corpus (KSC) contains around 335 hours of transcribed audio comprising over 154 ,000 utterances spoken by participants from different regions , age groups, as well as both genders .
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Original:  The L(p,1)-labeling problem is to minimize the span of f (i.e.,u\in V(f(u)) - u\in V(f(u))+1).
Revised:  The L(p,1)-labeling problem is to minimize the span of f (i.e.,u\in V(f(u)) - u\in V(f(u))+1).
Edit intent: fluency
Original:  To fill up the gap, in this paper, we propose new fixed-parameter algorithms for L(p,1)-Labeling by the twin cover number plus the maximum clique size and by the tree-width plus the maximum degree.
Revised:  To fill up the gap, in this paper, we propose new fixed-parameter algorithms for L(p,1)-Labeling by the twin cover number plus the maximum clique size and by the tree-width plus the maximum degree.
Edit intent: fluency
Original:  Our analysis may have interesting policy implications for the ecology of fishing areas  .
Revised:  Our analysis may have interesting policy implications for the ecology of fishing areas . It also suggests that herding behaviour here, just as in financial markets, will lead to significant fluctuations in the amount of fish landed, as the boat concentration on one area at a given point in time will diminish the overall catch, such loss not being compensated by the reproduction of fish in the other area. In other terms, individually rational behaviour will not lead to collectively optimal results .
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Original:  In this work, we improve upon this routine in two aspects: we reduce the required qubit overhead from g to \log_2(g) in the bit precision g (at a cost of slightly increasing the count of non-Clifford operations), and show how various sets of N coefficients can be loaded significantly faster than in O(\sqrt N) rounds of amplitude amplification - up to only O(1) many - by bootstrapping the procedure with an optimised initial state .
Revised:  In this work, we improve upon this routine in two aspects: we reduce the required qubit overhead from g to \log_2(g) in the bit precision g (at a cost of slightly increasing the count of non-Clifford operations), and show how various sets of coefficients can be loaded significantly faster than in O(\sqrt N) rounds of amplitude amplification - up to only O(1) many - by bootstrapping the procedure with an optimised initial state .
Edit intent: fluency
Original:  In this work, we improve upon this routine in two aspects: we reduce the required qubit overhead from g to \log_2(g) in the bit precision g (at a cost of slightly increasing the count of non-Clifford operations), and show how various sets of N coefficients can be loaded significantly faster than in O(\sqrt N) rounds of amplitude amplification - up to only O(1) many - by bootstrapping the procedure with an optimised initial state .
Revised:  In this work, we improve upon this routine in two aspects: we reduce the required qubit overhead from g to \log_2(g) in the bit precision g (at a cost of slightly increasing the count of non-Clifford operations), and show how various sets of N coefficients can be loaded significantly faster than in O(\sqrt N) rounds of amplitude amplification , up to only O(1) many - by bootstrapping the procedure with an optimised initial state .
Edit intent: fluency
Original:  In this work, we improve upon this routine in two aspects: we reduce the required qubit overhead from g to \log_2(g) in the bit precision g (at a cost of slightly increasing the count of non-Clifford operations), and show how various sets of N coefficients can be loaded significantly faster than in O(\sqrt N) rounds of amplitude amplification - up to only O(1) many - by bootstrapping the procedure with an optimised initial state .
Revised:  In this work, we improve upon this routine in two aspects: we reduce the required qubit overhead from g to \log_2(g) in the bit precision g (at a cost of slightly increasing the count of non-Clifford operations), and show how various sets of N coefficients can be loaded significantly faster than in O(\sqrt N) rounds of amplitude amplification - up to only O(1) many . This exponential speedup translates beyond the black box case .
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Original: A sampling discretization theorem for the square norm of functions from a finite dimensional subspace satisfying Nikol'skii's inequality is proved. The obtained upper bound on the number of sampling points is of the order of the dimension of the subspace .
Revised: A sampling discretization theorem for the square norm of functions from a finite dimensional subspace satisfying Nikol'skii's inequality with an upper bound on the number of sampling points is of the order of the dimension of the subspace .
Edit intent: clarity
Original:  The paper starts with explaining mathematical basics that Gaussian processes built on including multivariate normal distribution, kernels, non-parametric models, joint and conditional probability . The Gaussian processes regression is then described in an accessible way by balancing showing unnecessary mathematical derivation steps and missing key conclusive results. An illustrative implementation of a standard Gaussian processes regression algorithmis provided . Beyond the standard Gaussian processes regression, existing software packages to implement state-of-the-art Gaussian processes algorithms are reviewed.
Revised:  The paper starts with explaining mathematical basics that Gaussian processes built on including multivariate normal distribution, kernels, non-parametric models, joint and conditional probability were explained first. Next, the GPR was described concisely together with an implementation of a standard Gaussian processes regression algorithmis provided . Beyond the standard Gaussian processes regression, existing software packages to implement state-of-the-art Gaussian processes algorithms are reviewed.
Edit intent: coherence
Original:  An illustrative implementation of a standard Gaussian processes regression algorithmis provided . Beyond the standard Gaussian processes regression, existing software packages to implement state-of-the-art Gaussian processes algorithms are reviewed. Lastly, more advanced Gaussian processes regression models are specified. The paper is written in an accessible way , thus undergraduate science and engineering background will find no difficulties in following the content .
Revised:  An illustrative implementation of a standard Gaussian processes regression algorithmis provided . Beyond the standard Gaussian processes regression, existing software packages to implement state-of-the-art Gaussian processes algorithms were reviewed. This tutorial was written in an accessible way , thus undergraduate science and engineering background will find no difficulties in following the content .
Edit intent: clarity
Original:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 0.04 -PICs, 0.09 -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Revised:  In addition, PIC and it's soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 0.04 -PICs, 0.09 -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Edit intent: fluency
Original:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 0.04 -PICs, 0.09 -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Revised:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 10\% -FABRIK, 0.04 -PICs, 0.09 -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Edit intent: meaning-changed
Original:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 0.04 -PICs, 0.09 -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Revised:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 4\% -PICs, 0.09 -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Edit intent: meaning-changed
Original:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 0.04 -PICs, 0.09 -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Revised:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 0.04 -PICs, 9\% -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Edit intent: meaning-changed
Original:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 0.04 -PICs, 0.09 -PICs) and a lower percentage of obstruction during assembly (0.09-FABRIK, 0.08-PICs, 0.07- PICs) .
Revised:  In addition, PIC and its soft version achieve a lower percentage of occlusion during incision ( 0.10 -FABRIK, 0.04 -PICs, 0.09 -PICs) .
Edit intent: coherence
Original:  These experiments were performed on the benchmark dataset Vietnamese Social Media Emotion Corpus (UIT-VSMEC) . We explored Vietnamese social media characteristics to proposed different pre-processing techniques, and key-clause extraction with emotional context to improve the machine performance on UIT-VSMEC.
Revised:  These experiments were performed on the benchmark dataset Vietnamese Social Media Emotion Corpus (UIT-VSMEC) as the benchmark dataset. We explore Vietnamese social media characteristics to proposed different pre-processing techniques, and key-clause extraction with emotional context to improve the machine performance on UIT-VSMEC.
Edit intent: clarity
Original:  Our experimental evaluation shows that with appropriate pre-processing techniques  , Multinomial Logistic Regression (MLR) achieves the best F1-score of 64.40 %DIFDELCMD < \\%%% %DIF < , a significant improvement of 4.66\\% over the CNN model built by the authors of UIT-VSMEC (59.74\\%).abstract 
Revised:  Our experimental evaluation shows that with appropriate pre-processing techniques based on Vietnamese social media characteristics , Multinomial Logistic Regression (MLR) achieves the best F1-score of 64.40 %DIFDELCMD < \\%%% %DIF < , a significant improvement of 4.66\\% over the CNN model built by the authors of UIT-VSMEC (59.74\\%).abstract 
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Original:  Our experimental evaluation shows that with appropriate pre-processing techniques  , Multinomial Logistic Regression (MLR) achieves the best F1-score of 64.40 %DIFDELCMD < \\%%% %DIF < , a significant improvement of 4.66\\% over the CNN model built by the authors of UIT-VSMEC (59.74\\%).abstract 
Revised:  Our experimental evaluation shows that with appropriate pre-processing techniques , Multinomial Logistic Regression (MLR) achieves the best F1-score of 64.40 %DIFDELCMD < \\%%% %DIF < , a significant improvement of 4.66\\% over the CNN model built by the authors of UIT-VSMEC (59.74\\%).abstract \%, a significant improvement of 4.66\% over the CNN model built by the authors of UIT-VSMEC (59.74\%).
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Original:  In the present work, we present this key question: given a particular number of ancestors at each generation, how many different hypothetical trees can be constructed? Building upon our previous works, we propose an approach to estimate this number. Given that this number is quite large, we also address the non-trivial issue of tree representation .
Revised:  In previous works \mbox{%DIFAUXCMD PhysRevE.92.052132, PhysRevE.90.022125, JARNE20191 .
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Original:  The  code presented here, available in Git-Hub, may be easily modified to be applied to other areas of interest involving connections between individuals, such as models of opinion spread .
Revised:  The algorithm includes a feature to average several tree realizations, obtain the connectivity distribution, and calculate the average and mean value. We used this feature to compare trees with a different number of generations and nodes. The code presented here, available in Git-Hub, may be easily modified to be applied to other areas of interest involving connections between individuals, such as models of opinion spread .
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