wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Analysing the agents' performances on the test data, we argue that the agents are able to create a dynamic representation of the underlying environment highlighting the occasional regularities present in the data and exploiting them to create long-term profitable trading strategies  .
Revised:  Analysing the agents' performances on the test data, we argue that the agents are able to create a dynamic representation of the underlying environment highlighting the occasional regularities present in the data and exploiting them to create long-term profitable trading strategies . Indeed, agents learn trading strategies able to produce stable positive returns in spite of the highly stochastic and non-stationary environment .
Edit intent: meaning-changed
Original:  Soft moderation interventions refer to warning labels that are attached to potentially questionable or harmful content with the goal of informing other users about the content and its nature , while the content remains accessible, hence alleviating concerns related to censorship and free speech.
Revised:  Soft moderation interventions refer to warning labels attached to potentially questionable or harmful content with the goal of informing other users about the content and its nature , while the content remains accessible, hence alleviating concerns related to censorship and free speech.
Edit intent: clarity
Original:  Soft moderation interventions refer to warning labels that are attached to potentially questionable or harmful content with the goal of informing other users about the content and its nature , while the content remains accessible, hence alleviating concerns related to censorship and free speech.
Revised:  Soft moderation interventions refer to warning labels that are attached to potentially questionable or harmful content to inform other users about the content and its nature , while the content remains accessible, hence alleviating concerns related to censorship and free speech.
Edit intent: clarity
Original:  Soft moderation interventions refer to warning labels that are attached to potentially questionable or harmful content with the goal of informing other users about the content and its nature , while the content remains accessible, hence alleviating concerns related to censorship and free speech.
Revised:  Soft moderation interventions refer to warning labels that are attached to potentially questionable or harmful content with the goal of informing other users about the content and its nature while the content remains accessible, hence alleviating concerns related to censorship and free speech.
Edit intent: fluency
Original: The classic paper of Shapley and Shubik Shapley1971assignment characterized the core of the assignment game using powerful ideas from matching theory and LP-duality theory and their highly non-trivial interplay.
Revised: The classic paper of Shapley and Shubik Shapley1971assignment characterized the core of the assignment game using ideas from matching theory and LP-duality theory and their highly non-trivial interplay.
Edit intent: clarity
Original:  Our profit allocation method goes further: the multiplier on the profit of an agent  \over   lies in the interval [{2 \over 3}, 1], depending on how severely constrained the agent is.
Revised:  Our profit allocation method goes further: the multiplier on the profit of an agent is often better than \over lies in the interval [{2 \over 3}, 1], depending on how severely constrained the agent is.
Edit intent: meaning-changed
Original:  Our profit allocation method goes further: the multiplier on the profit of an agent  \over   lies in the interval [{2 \over 3}, 1], depending on how severely constrained the agent is.
Revised:  Our profit allocation method goes further: the multiplier on the profit of an agent 2\over lies in the interval [{2 \over 3}, 1], depending on how severely constrained the agent is.
Edit intent: meaning-changed
Original:  Our profit allocation method goes further: the multiplier on the profit of an agent  \over   lies in the interval [{2 \over 3}, 1], depending on how severely constrained the agent is.
Revised:  Our profit allocation method goes further: the multiplier on the profit of an agent \over 3 lies in the interval [{2 \over 3}, 1], depending on how severely constrained the agent is.
Edit intent: fluency
Original:  Our profit allocation method goes further: the multiplier on the profit of an agent  \over   lies in the interval [{2 \over 3}, 1], depending on how severely constrained the agent is.
Revised:  Our profit allocation method goes further: the multiplier on the profit of an agent \over and lies in the interval [{2 \over 3}, 1], depending on how severely constrained the agent is.
Edit intent: meaning-changed
Original:  {\em   The core is a key solution concept in cooperative game theory.
Revised:  Next, we provide new insights showing how discerning core imputations of an assignment games are by studying them via the lens of complementary slackness. We present a relationship between the competitiveness of individuals and teams of agents and the amount of profit they accrue in imputations that lie in the core, where by{\em The core is a key solution concept in cooperative game theory.
Edit intent: meaning-changed
Original:  Our imputation, in the 2/3-approximate core, implies that a sub-coalition will gain at most a 3/2 factor by seceding, and less in typical cases . We also study the phenomenon of degeneracy in assignment games, i.e., when the maximum weight matching is not unique. We provide insights of an elementary nature gained by studying this phenomenon through the lens of complementary slackness .
Revised:  Our imputation, in the 2/3-approximate core, implies that a sub-coalition will gain at most a 3/2 factor by seceding, and less in typical cases .
Edit intent: coherence
Original: We introduce ES-ENAS, a simple neural architecture search (NAS) algorithm for the purpose of reinforcement learning (RL) policy design, by combining Evolutionary Strategies (ES) and  Efficient NAS (ENAS) in a highly scalable and intuitive way.
Revised: We introduce ES-ENAS, a simple yet general evolutionary joint optimization procedure by combining continuous optimization via Evolutionary Strategies (ES) and Efficient NAS (ENAS) in a highly scalable and intuitive way.
Edit intent: meaning-changed
Original: We introduce ES-ENAS, a simple neural architecture search (NAS) algorithm for the purpose of reinforcement learning (RL) policy design, by combining Evolutionary Strategies (ES) and  Efficient NAS (ENAS) in a highly scalable and intuitive way.
Revised: We introduce ES-ENAS, a simple neural architecture search (NAS) algorithm for the purpose of reinforcement learning (RL) policy design, by combining Evolutionary Strategies (ES) and combinatorial optimization via Efficient NAS (ENAS) in a highly scalable and intuitive way.
Edit intent: meaning-changed
Original:  Our main insight is noticing that ES is already a distributed blackbox algorithm , and thus we may simply insert a model controllerfrom ENAS into the central aggregator in ES and obtain weight sharing properties for free .
Revised:  Our main insight is noticing that ES is already a highly distributed algorithm involving hundreds of forward passes which can not only be used for training neural network weights, but also for jointly training a NAS controller, both in a blackbox fashion .
Edit intent: meaning-changed
Original:  By doing so, we  bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different  lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility  of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Revised:  By doing so, we also bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Edit intent: coherence
Original:  By doing so, we  bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different  lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility  of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Revised:  By doing so, we bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different yet common lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Edit intent: clarity
Original:  By doing so, we  bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different  lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility  of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Revised:  By doing so, we bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different lines of research . We demonstrate the utility of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Edit intent: coherence
Original:  By doing so, we  bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different  lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility  of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Revised:  By doing so, we bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility and effectiveness of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Edit intent: clarity
Original:  By doing so, we  bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different  lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility  of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Revised:  By doing so, we bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility of our method over a large search space by training highly combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Edit intent: meaning-changed
Original:  By doing so, we  bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different  lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility  of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and weight sharing .
Revised:  By doing so, we bridge the gap from NAS research in supervised learning settings to the reinforcement learning scenario through this relatively simple marriage between two different lines of research , and are one of the first to apply controller-based NAS techniques to RL . We demonstrate the utility of our method by training combinatorial neural network architectures for RL problems in continuous control, via edge pruning and quantization .
Edit intent: clarity
Original:  We also incorporate a wide variety of popular techniques from modern NAS literature , including multiobjective optimization and varying controller methods, to showcase their promise in the RL field and discuss possible extensions . We achieve >90\% network compression for multiple tasks, which may be special interest in mobile robotics with limited storage and computational resources .
Revised:  We also incorporate a wide variety of popular techniques from modern NAS literature , including multiobjective optimization and varying controller methods, to showcase their promise in the RL field and discuss possible extensions .
Edit intent: coherence
Original:  We present a Gauss-Newton type method, which relies on two relaxation parameters to ensure convergence, and which incorporates a procedure to dynamically estimate the two parameters during iteration , as well as the rank of the Jacobian matrix  .
Revised:  We present a Gauss-Newton type method, which relies on two relaxation parameters to ensure convergence, and which incorporates a procedure to dynamically estimate the two parameters , as well as the rank of the Jacobian matrix .
Edit intent: clarity
Original:  We present a Gauss-Newton type method, which relies on two relaxation parameters to ensure convergence, and which incorporates a procedure to dynamically estimate the two parameters during iteration , as well as the rank of the Jacobian matrix  .
Revised:  We present a Gauss-Newton type method, which relies on two relaxation parameters to ensure convergence, and which incorporates a procedure to dynamically estimate the two parameters during iteration , as well as the rank of the Jacobian matrix , along the iterations .
Edit intent: meaning-changed
Original: There is a growing interest in designing systems for graph pattern mining in recent years. The existing systems mostly focus on small patterns and have difficulty in mining larger patterns.
Revised: Frequent Subgraph Mining (FSM) is the key task in many graph mining and machine learning applications. Numerous systems have been proposed for FSM in the past decade. Although these systems show good performance for small patterns (with no more than four vertices), we found that they have difficulty in mining larger patterns.
Edit intent: meaning-changed
Original:  In this work, we propose Angelica, a single-machine graph pattern mining system aiming at supporting large patterns. We first propose a new computation model called multi-vertex exploration . The model allows us to divide a large pattern mining task into smaller matching tasks. Different from the existing systems which perform vertex-by-vertex exploration , we explore larger subgraphs by joining small subgraphs. Based on the new computation model, we further enhance the performance through an index-based quick pattern technique that addresses the issue of expensive isomorphism check, and approximate join that mitigates the issue of subgraph explosion of large patterns .
Revised:  In this work, we propose a novel two-vertex exploration strategy to accelerate the mining process. Compared with the single-vertex exploration adopted by previous systems, our two-vertex exploration avoids the large memory consumption issue and significantly reduces the memory access overhead. We further enhance the performance through an index-based quick pattern technique that addresses the issue of expensive isomorphism check, and approximate join that mitigates the issue of subgraph explosion of large patterns .
Edit intent: coherence
Original:  Based on the new computation model, we further enhance the performance through an index-based quick pattern technique that addresses the issue of expensive isomorphism check, and approximate join that mitigates the issue of subgraph explosion of large patterns .
Revised:  Based on the new computation model, we further enhance the performance through an index-based quick pattern technique that reduces the overhead of isomorphism checks, and a subgraph sampling technique that mitigates the issue of subgraph explosion of large patterns .
Edit intent: clarity
Original:  Based on the new computation model, we further enhance the performance through an index-based quick pattern technique that addresses the issue of expensive isomorphism check, and approximate join that mitigates the issue of subgraph explosion of large patterns .
Revised:  Based on the new computation model, we further enhance the performance through an index-based quick pattern technique that addresses the issue of expensive isomorphism check, and approximate join that mitigates the issue of subgraph explosion .
Edit intent: clarity
Original:  We have developed a live dashboardto allow people to track hashtag changes over time.The dataset can be used in studies about the impact of online information on COVID-19 vaccine uptake and health outcomes .
Revised:  Additionally, we develop and present the CoVaxxy dashboard, allowing people to visualize the relationship between COVID-19 vaccine adoption and U.S. geo-located posts in our dataset. This dataset can be used in studies about the impact of online information on COVID-19 vaccine uptake and health outcomes .
Edit intent: meaning-changed
Original:  For point sets of size n and m, the Hausdorff distance under translation can be computed in time \tilde O(nm) for the L_1 and L _\inf norm [Chew, Kedem SWAT'92] and \tilde O(nm (n+m)) for the L_2 norm [Huttenlocher, Kedem, Sharir DCG'93].
Revised:  For point sets of size n and m, the Hausdorff distance under translation can be computed in time \tilde O(nm) for the L_1 and L _\infty norm [Chew, Kedem SWAT'92] and \tilde O(nm (n+m)) for the L_2 norm [Huttenlocher, Kedem, Sharir DCG'93].
Edit intent: meaning-changed
Original:  We show (i) a matching lower bound of (nm)^{1-o(1)} for L_1 and L _\inf assuming the Orthogonal Vectors Hypothesis and (ii) a matching lower bound of n^{2-o(1)} for L_2 in the imbalanced case of m = O(1) assuming the 3SUM Hypothesis.
Revised:  We show (i) a matching lower bound of (nm)^{1-o(1)} for L_1 and L _\infty assuming the Orthogonal Vectors Hypothesis and (ii) a matching lower bound of n^{2-o(1)} for L_2 in the imbalanced case of m = O(1) assuming the 3SUM Hypothesis.
Edit intent: meaning-changed
Original:  We show that for any n^{-2}\le\epsilon\le 1, if %DIFDELCMD < \[\#\{\xi \in \{0,1\}^{n}: \langle \xi, w \rangle = r\} \ge 2^{-\epsilon n}\cdot 2^{n}\] %%% for some r \[\#\{\xi \in \{0,1\}^{n}: \langle \xi, w \rangle = \tau\} \ge 2^{-\epsilon n}\cdot 2^{n}\]  \in R, then \[\#\{\langle \xi, w \rangle : \xi \in \{0,1\}^{n}\} \le 2^{O(\epsilonn)}.
Revised:  We show that for any n^{-2}\le\epsilon\le 1, if %DIFDELCMD < \[\#\{\xi \in \{0,1\}^{n}: \langle \xi, w \rangle = r\} \ge 2^{-\epsilon n}\cdot 2^{n}\] %%% \[\#\{\xi \in \{0,1\}^{n}: \langle \xi, w \rangle = \tau\} \ge 2^{-\epsilon n}\cdot 2^{n}\] \in R, then \[\#\{\langle \xi, w \rangle : \xi \in \{0,1\}^{n}\} \le 2^{O(\epsilonn)}.
Edit intent: meaning-changed
Original:  We show that for any n^{-2}\le\epsilon\le 1, if %DIFDELCMD < \[\#\{\xi \in \{0,1\}^{n}: \langle \xi, w \rangle = r\} \ge 2^{-\epsilon n}\cdot 2^{n}\] %%% for some r \[\#\{\xi \in \{0,1\}^{n}: \langle \xi, w \rangle = \tau\} \ge 2^{-\epsilon n}\cdot 2^{n}\]  \in R, then \[\#\{\langle \xi, w \rangle : \xi \in \{0,1\}^{n}\} \le 2^{O(\epsilonn)}.
Revised:  We show that for any n^{-2}\le\epsilon\le 1, if %DIFDELCMD < \[\#\{\xi \in \{0,1\}^{n}: \langle \xi, w \rangle = r\} \ge 2^{-\epsilon n}\cdot 2^{n}\] %%% for some r \[\#\{\xi \in \{0,1\}^{n}: \langle \xi, w \rangle = \tau\} \ge 2^{-\epsilon n}\cdot 2^{n}\] for some \tau \in R, then \[\#\{\langle \xi, w \rangle : \xi \in \{0,1\}^{n}\} \le 2^{O(\epsilonn)}.
Edit intent: fluency
Original: \] This exponentially improves  a recent result of Nederlof, Pawlewicz, Swennenhuis, and W\k{e}grzycki and leads to a similar improvement in the parameterized (by the number of bins) runtime of bin packing.
Revised: \] This exponentially improves the \epsilon dependence in a recent result of Nederlof, Pawlewicz, Swennenhuis, and W\k{e}grzycki and leads to a similar improvement in the parameterized (by the number of bins) runtime of bin packing.
Edit intent: meaning-changed
Original:  Since firms are embedded in production networks, these direct shocks to supply and demand will propagate downstream and upstream . We show that existing input-output models which allow for binding demand and supply constraints yield infeasible solutions when applied to pandemic shocksof three major European countries (Germany, Italy, Spain). We then introduce a mathematical optimization procedure which is able to determine best-case feasible market allocations, giving a lower bound on total shock propagation.
Revised:  Since firms are embedded in production networks, these direct shocks to supply and demand will propagate downstream and upstream through production networks. Given the exogenous shocks, we derive a lower bound on total shock propagation.
Edit intent: clarity
Original:  To provide at least 2 Mbps per user, 4G is often the cheapest option , whereas a minimum 10 Mbps per user is cheapest with 5G non-standalone (NSA) . The cost of meeting the UN Broadband Commission target of a minimum 10 Mbps per user is estimated at USD 1.4 trillion using 5G NSA, equating to approximately 0.5 \% of annual GDP for the developing world over the next decade.
Revised:  To provide at least 2 Mbps per user, 4G is often the cheapest option to reach universal coverage . The cost of meeting the UN Broadband Commission target of a minimum 10 Mbps per user is estimated at USD 1.4 trillion using 5G NSA, equating to approximately 0.5 \% of annual GDP for the developing world over the next decade.
Edit intent: clarity
Original:  To provide at least 2 Mbps per user, 4G is often the cheapest option , whereas a minimum 10 Mbps per user is cheapest with 5G non-standalone (NSA) . The cost of meeting the UN Broadband Commission target of a minimum 10 Mbps per user is estimated at USD 1.4 trillion using 5G NSA, equating to approximately 0.5 \% of annual GDP for the developing world over the next decade.
Revised:  To provide at least 2 Mbps per user, 4G is often the cheapest option , whereas a minimum 10 Mbps per user is cheapest with 5G non-standalone (NSA) . The cost of meeting the UN Broadband Commission target of a minimum 10 Mbps per user is estimated at 1.7 trillion using 5G NSA, equating to approximately 0.5 \% of annual GDP for the developing world over the next decade.
Edit intent: meaning-changed