wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In particular, when specialized to the \varepsilon-LDP model, our protocol achieves an error of k/(e^{\Theta(\varepsilon)}-1) for all \varepsilon, while the previous protocol (Chen et al., 2020) has error O(\sqrt{k/\min}  \{ \varepsilon, \sqrt{\varepsilon\}) } {\varepsilon} \}) bits of communication and O(k \log u) bits of public randomness, where u is the size of the domain} .
Revised:  In particular, when specialized to the \varepsilon-LDP model, our protocol achieves an error of k/(e^{\Theta(\varepsilon)}-1) for all \varepsilon, while the previous protocol (Chen et al., 2020) has error O(\sqrt{k/\min} \{ \varepsilon, \}) } {\varepsilon} \}) bits of communication and O(k \log u) bits of public randomness, where u is the size of the domain} .
Edit intent: fluency
Original:  In particular, when specialized to the \varepsilon-LDP model, our protocol achieves an error of k/(e^{\Theta(\varepsilon)}-1) for all \varepsilon, while the previous protocol (Chen et al., 2020) has error O(\sqrt{k/\min}  \{ \varepsilon, \sqrt{\varepsilon\}) } {\varepsilon} \}) bits of communication and O(k \log u) bits of public randomness, where u is the size of the domain} .
Revised:  In particular, when specialized to the \varepsilon-LDP model, our protocol achieves an error of k/(e^{\Theta(\varepsilon)}-1) for all \varepsilon, while the previous protocol (Chen et al., 2020) has error O(\sqrt{k/\min} \{ \varepsilon, \sqrt{\varepsilon\}) } \frac{1{\varepsilon} \}) bits of communication and O(k \log u) bits of public randomness, where u is the size of the domain} .
Edit intent: meaning-changed
Original:  The paper describes the main hardware and software components for UAV and UGV platforms. It also presents the main algorithmic components of the system: a 3D LIDAR-based mapping and localization module able to work in GPS-denied scenarios;
Revised:  The paper describes the main hardware and software components for UAV and UGVplatforms and also present the main algorithmic components of the system: a 3D LIDAR-based mapping and localization module able to work in GPS-denied scenarios;
Edit intent: clarity
Original: Tracking endogenous fluctuations in stock prices emerged as a key challenge for empirical work in behavioural and evolutionary finance. This paper uses new data from an online discussion forum, Reddit, to quantify social contagion, or 'hype,' in specific stock market movements, using state of the art opinion dynamics modelling and sentiment analysis. The influence between users on the WallStreetBets (WSB) subreddit is measured by tracing the probability of a user starting a fresh discussion on an asset given their previous involvement in a discussion on the same asset, measured by their comment history .
Revised: This paper develops an empirical and theoretical case for how 'hype' among retail investors can drive large asset fluctuations. We use the dataset of discussions on WallStreetBets (WSB) subreddit is measured by tracing the probability of a user starting a fresh discussion on an asset given their previous involvement in a discussion on the same asset, measured by their comment history .
Edit intent: clarity
Original:  The influence between users on the WallStreetBets (WSB) subreddit is measured by tracing the probability of a user starting a fresh discussion on an asset given their previous involvement in a discussion on the same asset, measured by their comment history .
Revised:  The influence between users on the WallStreetBets (WSB) , an online investor forum with over nine million followers as of April 2021, to show how excitement about trading opportunities can ripple through an investor community with large market impacts .
Edit intent: meaning-changed
Original:  This paper finds that users who comment on one discussion involving a particular asset are approximately four times more likely to start a new discussion about this asset in the future , with the probability increasing with each additional discussion the user engages in. This is a strong indication that investment strategies are reproduced through social interaction. This is further validated by findings that sentiments expressed in the linked submissions are strongly correlated in a set of spatial regression models. In particular, bearish sentiments seem to spread more than their bullish counterparts .
Revised:  This paper finds empirical evidence of psychological contagion among retail investors by exploiting differences in stock price fluctuations and discussion intensity. We show that asset discussions on WSB are self-perpetuating: an initial set of investors attracts a larger and larger group of excited followers. Sentiments about future stock performance also spread from one individual to the next, net of any fundamental price movements. Leveraging these findings, we develop a model for how social contagion impacts prices. The proposed model and simulations show that social contagion has a destabilizing effect on markets. Finally, we establish a causal relationship between WSB activity and financial markets using an instrumental variable approach .
Edit intent: meaning-changed
Original: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX ( [ 1 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value  .
Revised: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX ( [ 1 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value .
Edit intent: clarity
Original: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX ( [ 1 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value  .
Revised: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX [ 1 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value .
Edit intent: coherence
Original: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX ( [ 1 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value  .
Revised: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX ( [ 9 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value .
Edit intent: meaning-changed
Original: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX ( [ 1 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value  .
Revised: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX ( [ 1 ] , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value .
Edit intent: fluency
Original: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX ( [ 1 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value  .
Revised: In this paper, I propose actor-critic approaches by introducing an actor policy on QMIX ( [ 1 ] ) , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value . We evaluate our actor-critic methods on StarCraft II micromanagement tasks, and show that it has a stronger performance on maps with heterogeneous agent types .
Edit intent: meaning-changed
Original: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for  joint action-value .
Revised: Multi-agent value-based approaches recently make great progress, especially value decomposition methods. However, there are still a lot of limitations in value function factorization. In VDN, the joint action-value function is the sum of per-agent action-value function while the joint action-value function of QMIX is the monotonic mixing of per-agent action-value function. To some extent, QTRAN reduces the limitation of joint action-value functions that can be represented, but it has unsatisfied performance in complex tasks. In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value .
Edit intent: meaning-changed
Original: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for  joint action-value .
Revised: In this paper, in order to extend the class of joint value functions that can be represented, we propose a novel actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value .
Edit intent: meaning-changed
Original: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for  joint action-value .
Revised: In this paper, we propose actor-critic 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value .
Edit intent: clarity
Original: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for  joint action-value .
Revised: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX method called NQMIX. NQMIX introduces an off-policy policy gradient on QMIX and modify its network architecture , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value .
Edit intent: meaning-changed
Original: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for  joint action-value .
Revised: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for the joint action-value .
Edit intent: fluency
Original: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for  joint action-value . We evaluate our actor-critic methods on StarCraft II micromanagement tasks , and show that it has a stronger performance on maps with heterogeneous agent types  .
Revised: In this paper, we propose actor-critic approaches by introducing an actor policy on QMIX 9 , which can remove the monotonicity constraint of QMIX and implement a non-monotonic value function factorization for joint action-value function. In addition, NQMIX takes the state-value as the learning target, which overcomes the problem in QMIX that the learning target is overestimated. Furthermore, NQMIX can be extended to continuous action space settings by introducing deterministic policy gradient on itself. Finally, we evaluate our actor-critic methods on StarCraft II micromanagement tasks , and show that it has a stronger performance on maps with heterogeneous agent types .
Edit intent: meaning-changed
Original:  Although these models offer streamlined solutions to large problems, they may contain biases and treat groups or individuals unfairly . To our knowledge, this review is one of the first to focus specifically on gender bias in applications of machine learning. We first introduce several examples of machine learning gender bias in practice   .
Revised:  Although these models offer streamlined solutions to large problems, they may contain biases and treat groups or individuals unfairly based on protected attributes such as gender. In this paper, we introduce several examples of machine learning gender bias in practice .
Edit intent: clarity
Original:  We first introduce several examples of machine learning gender bias in practice   .
Revised:  We first introduce several examples of machine learning gender bias in practice framework, which employs a fusion of pre-, in-, and post-processing methods to leverage the strengths of each individual technique. We test this method, along with the standard techniques we review, on a deep neural network to analyze bias mitigation in a deep learning setting. We find that our end-to-end bias mitigation framework outperforms the baselines with respect to several fairness metrics, suggesting its promise as a method for improving fairness .
Edit intent: meaning-changed
Original:  Meanwhile, 3D point cloud is an important form of data for obtaining environmental information , which is widely used in robotics, autonomous driving and other fields. In order to work better , construction robots need to be able to understand their surroundings .
Revised:  Meanwhile, 3D point cloud is an important form of data for obtaining environmental information , which is widely used in robotics, autonomous driving , and so on. With a good understanding of environmental information , construction robots need to be able to understand their surroundings .
Edit intent: clarity
Original:  However, as the robot renovates a house, the point cloud information changes dynamically. For the purpose of making the robot dynamically adapt to the changes of the environment, this paper proposes a semantic segmentation method for point cloud based on meta-learning.
Revised:  However, the dynamic changes of 3D point cloud data may bring difficulties for construction robots to understand environmental information, such as when construction robots renovate houses. The paper proposes a semantic segmentation method for point cloud based on meta-learning.
Edit intent: clarity
Original:  In the former, we show that a wide range of robust combinatorial  problems can be decomposed into polynomially many subproblems, which in turn can often be solved in polynomial time  .
Revised:  In the former, we show that a wide range of robust combinatorial problems can be decomposed into polynomially many subproblems, which in turn can often be solved in polynomial time for example in the case of (representative) selection .
Edit intent: meaning-changed
Original:   AI technologies provide accurate prediction and classifcation; however, the models do not provide any evidence on their output making them hard to trust for a human operator . In addition, there are currently no methods that can be used to describe requirements of how the data should be annotated in order to train an accurate model  .
Revised:  AI technologies provide accurate predictions and classifications there is an ambiguity regarding the correctness of their outputs. This can make it difficult for the human operator to trust the system. Today there are no methods that can be used to describe requirements of how the data should be annotated in order to train an accurate model .
Edit intent: clarity
Original:  In addition, there are currently no methods that can be used to describe requirements of how the data should be annotated in order to train an accurate model  .
Revised:  In addition, there are currently no methods that can be used to describe requirements of how the data should be annotated in order to train an accurate model . Another challenge related to trust is explainability, while the models have difficulties explaining how they come to a certain conclusion it is difficult for humans to trust it .
Edit intent: meaning-changed
Original: An essential step in the discovery of new drugs and materials is the synthesis of a molecule that exists so far only as an idea to test its biological and physical properties. While computer-aided design of virtual molecules has made large progress , computer-assisted synthesis planning (CASP) to realize physical molecules is still in its infancy and lacks a performance level that would enable large-scale molecule discovery.
Revised: An essential step in the discovery of new drugs and materials . To find such routes , computer-assisted synthesis planning (CASP) to realize physical molecules is still in its infancy and lacks a performance level that would enable large-scale molecule discovery.
Edit intent: clarity
Original:  While computer-aided design of virtual molecules has made large progress , computer-assisted synthesis planning (CASP) to realize physical molecules is still in its infancy and lacks a performance level that would enable large-scale molecule discovery. CASP supports the search for multi-step synthesis routes, which is very challenging due to high branching factors in each synthesis step and the hidden rules that govern the reactions. The central and repeatedly applied step in CASP is reaction prediction, for which machine learning methods yield the best performance. We propose a novel reaction prediction approach that uses a deep learning architecture with modern Hopfield networks (MHNs) that is optimized by contrastive learning.
Revised:  While computer-aided design of virtual molecules has made large progress , computer-assisted synthesis planning (CASP) methods are employed which rely on a model of chemical reactivity. In this study, we model single-step retrosynthesis in a template-based approach using modern Hopfield networks (MHNs) that is optimized by contrastive learning.
Edit intent: clarity
Original:  We propose a novel reaction prediction approach that uses a deep learning architecture with modern Hopfield networks (MHNs) that is optimized by contrastive learning. An MHN is an associative memory that can store and retrieve chemical reactions in each layer of a deep learning architecture. We show that our MHN contrastive learning approach enables few- and zero-shot learning for reaction prediction which, in contrast to previous methods, can deal with rare, single, or even no training example(s) for a reaction. On a well established benchmark, our MHN approach pushes the state-of-the-art performance up by a large margin as it improves the predictive top-100 accuracy from 0.858%DIFDELCMD < \pm0%%% .004 to 0.959%DIFDELCMD < \pm0%%% .004.
Revised:  We propose a novel reaction prediction approach that uses a deep learning architecture with modern Hopfield networks (MHNs) %DIFDELCMD < \pm0%%% .004 to 0.959%DIFDELCMD < \pm0%%% .004.
Edit intent: meaning-changed
Original:  On a well established benchmark, our MHN approach pushes the state-of-the-art performance up by a large margin as it improves the predictive top-100 accuracy from 0.858%DIFDELCMD < \pm0%%% .004 to 0.959%DIFDELCMD < \pm0%%% .004.
Revised:  On a well established benchmark, our MHN approach pushes the state-of-the-art performance up by a large margin as it improves the predictive top-100 accuracy from 0.858%DIFDELCMD < \pm0%%% %DIFDELCMD < \pm0%%% .004.
Edit intent: meaning-changed
Original:  On a well established benchmark, our MHN approach pushes the state-of-the-art performance up by a large margin as it improves the predictive top-100 accuracy from 0.858%DIFDELCMD < \pm0%%% .004 to 0.959%DIFDELCMD < \pm0%%% .004. This advance might pave the way to large-scale molecule discovery}\geq5  .
Revised:  On a well established benchmark, our MHN approach pushes the state-of-the-art performance up by a large margin as it improves the predictive top-100 accuracy from 0.858%DIFDELCMD < \pm0%%% .004 to 0.959%DIFDELCMD < \pm0%%% . We adapt MHNs to associate different modalities, reaction templates and molecules, which allows the model to leverage structural information about reaction templates. This approach significantly improves the performance of template relevance prediction, especially for templates with few or zero training examples. With inference speed several times faster than that of baseline methods, we improve predictive performance for top-k exact match accuracy for \mathrm{k}\geq5 .
Edit intent: meaning-changed
Original:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%%  . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%%  , tight apart from an O(n) term.
Revised:  (i) The precise run time of the %DIFDELCMD < \oea %%% %DIFDELCMD < \leadingones%%% . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%% , tight apart from an O(n) term.
Edit intent: fluency
Original:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%%  . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%%  , tight apart from an O(n) term.
Revised:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%% (1+1) EA on . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%% , tight apart from an O(n) term.
Edit intent: meaning-changed
Original:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%%  . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%%  , tight apart from an O(n) term.
Revised:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%% LeadingOnes . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%% , tight apart from an O(n) term.
Edit intent: meaning-changed
Original:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%%  . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%%  , tight apart from an O(n) term.
Revised:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%% . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% %DIFDELCMD < \onemax%%% , tight apart from an O(n) term.
Edit intent: fluency
Original:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%%  . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%%  , tight apart from an O(n) term.
Revised:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%% . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%% (1+1) EA on , tight apart from an O(n) term.
Edit intent: meaning-changed
Original:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%%  . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%%  , tight apart from an O(n) term.
Revised:  (i) The precise run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \leadingones%%% . (ii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on%DIFDELCMD < \onemax%%% OneMax , tight apart from an O(n) term.
Edit intent: meaning-changed
Original:  (iii) A lower bound for the run time of the %DIFDELCMD < \oea %%%  on long k-paths  .
Revised:  (iii) A lower bound for the run time of the %DIFDELCMD < \oea %%% on long k-paths . We also prove a tighter lower bound for the run time of the (1+1) EA on jump functions by showing that, regardless of the jump size, only with probability O(2^{-n .
Edit intent: meaning-changed