wanyu/IteraTeR_v2 huggingface dataset:
Original: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI) , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using  games to build strong AI agentsthat, e. g., achieve human-level performance.
Revised: Multiplayer games have long been used as testbeds in artificial intelligence (AI) , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using games to build strong AI agentsthat, e. g., achieve human-level performance.
Edit intent: clarity
Original: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI) , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using  games to build strong AI agentsthat, e. g., achieve human-level performance.
Revised: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence research , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using games to build strong AI agentsthat, e. g., achieve human-level performance.
Edit intent: clarity
Original: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI) , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using  games to build strong AI agentsthat, e. g., achieve human-level performance.
Revised: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI) , aptly referred to as the Drosophila of artificial intelligence . Traditionally, researchers have focused on using games to build strong AI agentsthat, e. g., achieve human-level performance.
Edit intent: fluency
Original: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI) , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using  games to build strong AI agentsthat, e. g., achieve human-level performance.
Revised: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI) , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using well-known games to build strong AI agentsthat, e. g., achieve human-level performance.
Edit intent: clarity
Original: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI) , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using  games to build strong AI agentsthat, e. g., achieve human-level performance.
Revised: Multiplayer games have a long history in being used as key testbeds for evaluation and training in artificial intelligence (AI) , aptly referred to as the "Drosophila of AI" . Traditionally, researchers have focused on using games to build strong agents.
Edit intent: clarity
Original:  This progress, however, also requires a classification of how 'interesting' a game is for an artificial agent, which requires characterization of games and their topological landscape.
Revised:  This progress, however, can be better informed by characterizing games and their topological landscape.
Edit intent: clarity
Original:  Tackling this latter question not only facilitates an understanding of the characteristics of learnt AI agents in games, but can also help determine what game an AI should address next as part of its training.
Revised:  Tackling this latter question can facilitate understanding of agents and help determine what game an AI should address next as part of its training.
Edit intent: clarity
Original:  Tackling this latter question not only facilitates an understanding of the characteristics of learnt AI agents in games, but can also help determine what game an AI should address next as part of its training.
Revised:  Tackling this latter question not only facilitates an understanding of the characteristics of learnt AI agents in games, but can also help determine what game an agent should target next as part of its training.
Edit intent: clarity
Original:  Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes , characteristics, and complexities .
Revised:  Here, we show how network measures applied to response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes , characteristics, and complexities .
Edit intent: clarity
Original:  Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes , characteristics, and complexities .
Revised:  Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a landscape of games, quantifying the relationships between games of widely varying sizes , characteristics, and complexities .
Edit intent: clarity
Original:  Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes , characteristics, and complexities .
Revised:  Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying relationships between games of widely varying sizes , characteristics, and complexities .
Edit intent: fluency
Original:  Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of widely varying sizes , characteristics, and complexities .
Revised:  Here, we show how network measures applied to so-called response graphs of large-scale games enable the creation of a useful landscape of games, quantifying the relationships between games of varying sizes and characteristics .
Edit intent: clarity
Original:  We illustrate our findings in various domains , ranging from well-studied canonical games to significantly more complex empirical games capturing the performance of trained AI agents pitted against one another.
Revised:  We illustrate our findings in domains ranging from canonical games to significantly more complex empirical games capturing the performance of trained AI agents pitted against one another.
Edit intent: clarity
Original:  We illustrate our findings in various domains , ranging from well-studied canonical games to significantly more complex empirical games capturing the performance of trained AI agents pitted against one another.
Revised:  We illustrate our findings in various domains , ranging from well-studied canonical games to complex empirical games capturing the performance of trained AI agents pitted against one another.
Edit intent: clarity
Original:  We illustrate our findings in various domains , ranging from well-studied canonical games to significantly more complex empirical games capturing the performance of trained AI agents pitted against one another.
Revised:  We illustrate our findings in various domains , ranging from well-studied canonical games to significantly more complex empirical games capturing the performance of trained agents pitted against one another.
Edit intent: clarity
Original:  Our results culminate in a demonstration of how one can leverage this information to automatically generate new and interesting games, including mixtures of empirical games synthesized from real world games.
Revised:  Our results culminate in a demonstration leveraging this information to automatically generate new and interesting games, including mixtures of empirical games synthesized from real world games.
Edit intent: clarity
Original:  Our results culminate in a demonstration of how one can leverage this information to automatically generate new and interesting games, including mixtures of empirical games synthesized from real world games.
Revised:  Our results culminate in a demonstration of how one can leverage this information to generate new and interesting games, including mixtures of empirical games synthesized from real world games.
Edit intent: clarity
Original:  We present a new technique for proving factorization theorems for compound rewriting systems in a modular way, which is inspired by  Hindley-Rosen result for confluence.
Revised:  We present a new technique for proving factorization theorems for compound rewriting systems in a modular way, which is inspired by the Hindley-Rosen result for confluence.
Edit intent: fluency
Original:  We present a new technique for proving factorization theorems for compound rewriting systems in a modular way, which is inspired by  Hindley-Rosen result for confluence.
Revised:  We present a new technique for proving factorization theorems for compound rewriting systems in a modular way, which is inspired by Hindley-Rosen technique for confluence.
Edit intent: clarity
Original:  In particular, we isolate a simple sufficient condition (called linear swap) for lifting factorization from components to the compound system  .
Revised:  We isolate a sufficient condition (called linear swap) for lifting factorization from components to the compound system .
Edit intent: coherence
Original:  We then closely analyze some common factorization schemas for the lambda-calculus , and show that the technique simplifies even more, reducing to the test of elementary local commutations . Concretely, we apply our technique to diverse extensions of the \lambda-calculus , among which de' Liguoro and Piperno's non-deterministic lambda-calculus and -- for call-by-value -- Carraro and Guerrieri's shuffling calculus.
Revised:  We then closely analyze some common factorization schemas for the lambda-calculus . Concretely, we apply our technique to diverse extensions of the \lambda-calculus , among which de' Liguoro and Piperno's non-deterministic lambda-calculus and -- for call-by-value -- Carraro and Guerrieri's shuffling calculus.
Edit intent: coherence
Original:  We then closely analyze some common factorization schemas for the lambda-calculus , and show that the technique simplifies even more, reducing to the test of elementary local commutations . Concretely, we apply our technique to diverse extensions of the \lambda-calculus , among which de' Liguoro and Piperno's non-deterministic lambda-calculus and -- for call-by-value -- Carraro and Guerrieri's shuffling calculus.
Revised:  We then closely analyze some common factorization schemas for the lambda-calculus , and show that the technique simplifies even more, reducing to the test of elementary local commutations . Concretely, we apply our technique to diverse extensions of the lambda-calculus , among which de' Liguoro and Piperno's non-deterministic lambda-calculus and -- for call-by-value -- Carraro and Guerrieri's shuffling calculus.
Edit intent: fluency
Original:  For each, we obtain a novel proof which is neat and strikingly short .
Revised:  In both cases, we give a new proof which is neat and strikingly short .
Edit intent: clarity
Original:  As the existing online learning techniques do not yield no-regret mechanisms for this problem, we develop a new online learning framework defined over deterministic Markov decision processes with dynamic state transition and reward functions.
Revised:  As the existing online learning techniques do not yield vanishing-regret mechanisms for this problem, we develop a new online learning framework defined over deterministic Markov decision processes with dynamic state transition and reward functions.
Edit intent: fluency
Original:  As the existing online learning techniques do not yield no-regret mechanisms for this problem, we develop a new online learning framework defined over deterministic Markov decision processes with dynamic state transition and reward functions.
Revised:  As the existing online learning techniques do not yield no-regret mechanisms for this problem, we develop a novel online learning framework defined over deterministic Markov decision processes with dynamic state transition and reward functions.
Edit intent: clarity
Original:  We then prove that if the Markov decision process is guaranteed to admit a dominant state in each round and there exists an oracle that can switch the internal state with bounded loss , a condition that is satisfied in the DRACC problem , then the online learning problem can be solved with vanishing regret.
Revised:  We then prove that if the Markov decision process is guaranteed to admit an oracle that can switch the internal state with bounded loss , a condition that is satisfied in the DRACC problem , then the online learning problem can be solved with vanishing regret.
Edit intent: clarity
Original:  We then prove that if the Markov decision process is guaranteed to admit a dominant state in each round and there exists an oracle that can switch the internal state with bounded loss , a condition that is satisfied in the DRACC problem , then the online learning problem can be solved with vanishing regret.
Revised:  We then prove that if the Markov decision process is guaranteed to admit a dominant state in each round and there exists an oracle that can switch the internal state with bounded loss -- a condition that is satisfied in the DRACC problem , then the online learning problem can be solved with vanishing regret.
Edit intent: fluency
Original:  We then prove that if the Markov decision process is guaranteed to admit a dominant state in each round and there exists an oracle that can switch the internal state with bounded loss , a condition that is satisfied in the DRACC problem , then the online learning problem can be solved with vanishing regret.
Revised:  We then prove that if the Markov decision process is guaranteed to admit a dominant state in each round and there exists an oracle that can switch the internal state with bounded loss , a condition that is satisfied in the DRACC problem -- then the online learning problem can be solved with vanishing regret.
Edit intent: fluency
Original:  Our proof technique is based on a reduction to full information online learning with switching cost (Kalai and Vempala, 2005), in which an online decision maker incurs an extra cost every time she switches from one arm to another.
Revised:  Our proof technique is based on a reduction to online learning with switching cost (Kalai and Vempala, 2005), in which an online decision maker incurs an extra cost every time she switches from one arm to another.
Edit intent: clarity
Original:  Our proof technique is based on a reduction to full information online learning with switching cost (Kalai and Vempala, 2005), in which an online decision maker incurs an extra cost every time she switches from one arm to another.
Revised:  Our proof technique is based on a reduction to full information online learning with switching cost , in which an online decision maker incurs an extra cost every time she switches from one arm to another.
Edit intent: clarity
Original:  We  demonstrate this connection formally, and further show how DRACC can be used in our proposed applications of stateful pricing.
Revised:  We formally demonstrate this connection formally, and further show how DRACC can be used in our proposed applications of stateful pricing.
Edit intent: fluency
Original:  We  demonstrate this connection formally, and further show how DRACC can be used in our proposed applications of stateful pricing.
Revised:  We demonstrate this connection and further show how DRACC can be used in our proposed applications of stateful pricing.
Edit intent: coherence
Original:  In certain domains, the trained model outperforms state-of-the-art selection heuristics both in number of iterations of the algorithm and total number of polynomial additions performed .
Revised:  In certain domains, the trained model outperforms state-of-the-art selection heuristics in total number of polynomial additions performed .
Edit intent: clarity
Original: In this article we propose a probabilistic framework in order to study the fair division of a divisible good, e.g.  a cake, between n players.
Revised: In this article we propose a probabilistic framework in order to study the fair division of a divisible good, e.g. , a cake, between n players.
Edit intent: fluency
Original: In this paper , we evaluate the performance of multiple machine-learning methods in the emulation of agent-based models (ABMs).
Revised: In this proof-of-concept work , we evaluate the performance of multiple machine-learning methods in the emulation of agent-based models (ABMs).
Edit intent: clarity
Original: In this paper , we evaluate the performance of multiple machine-learning methods in the emulation of agent-based models (ABMs).
Revised: In this paper , we evaluate the performance of multiple machine-learning methods as statistical emulators for use in the analysis of agent-based models (ABMs).
Edit intent: clarity
Original:  The analysis of ABM outputs is often not straightforward , as the relationships between input parameters can be non-linear or even chaotic  , and each individual model run can require significant CPU time.
Revised:  The analysis of ABM outputs is often not straightforward , as the relationships between input parameters can be non-linear or even chaotic , and each model run can require significant CPU time.
Edit intent: clarity
Original:  Here we compare multiple machine-learning methods for ABM emulation in order to determine the approaches best-suited to replicating the complex and non-linear behaviour of ABMs.
Revised:  Here we compare multiple machine-learning methods for ABM emulation in order to determine the approaches best suited to emulating the complex behaviour of ABMs.
Edit intent: clarity
Original:  ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times for these emulators were considerably longer than for any other method .
Revised:  ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods .
Edit intent: clarity
Original:  We propose that users of complex ABMs would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for their models as well as reducing CPU time consumption when calibrating and analysing the simulation.
Revised:  We propose that agent-based modelling would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for their models as well as reducing CPU time consumption when calibrating and analysing the simulation.
Edit intent: clarity
Original:  We propose that users of complex ABMs would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for their models as well as reducing CPU time consumption when calibrating and analysing the simulation.
Revised:  We propose that users of complex ABMs would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for the models while also reducing CPU time consumption when calibrating and analysing the simulation.
Edit intent: clarity
Original:  A Bayesian inverse problem approach applied to UK data on COVID-19 deaths and the published disease duration distribution suggests that infections were in decline before UK lockdown, and that infections in Sweden started to decline only a short time later.
Revised:  A Bayesian inverse problem approach applied to UK data on COVID-19 deaths and the disease duration distribution suggests that infections were in decline before UK lockdown, and that infections in Sweden started to decline only a short time later.
Edit intent: clarity
Original:  At each point in time, we partition data points into an appropriate number of clusters and thereby track both the total number of clusters and the individual constituents' cluster memberships changing over time.
Revised:  At each point in time, we partition data points into an appropriate number of clusters and thereby track both the total number of clusters and their changing constituency over time.
Edit intent: clarity
Original:  With this  in mind, we also develop a method to analyse the similarity and anomalies between two related multivariate systems in conjunction, allowing us to identify anomalous countries in the progression of cases to deaths  .
Revised:  With this in mind, we identify anomalous countries in the progression of cases to deaths .
Edit intent: coherence
Original:  We study different properties of the geometric signature, which are  image-based encoding of geometric uncertainty and semantic content.
Revised:  We study different properties of the geometric signature, which are an image-based encoding of geometric uncertainty and semantic content.
Edit intent: fluency
Original:  While the stochastic gradient descent algorithm is still used in the majority of applications, recently also stochastic algorithms for minimizing nonsmooth and nonconvex functions were proposed.
Revised:  While algorithms based on stochastic gradient descent algorithm is still used in the majority of applications, recently also stochastic algorithms for minimizing nonsmooth and nonconvex functions were proposed.
Edit intent: clarity
Original:  While the stochastic gradient descent algorithm is still used in the majority of applications, recently also stochastic algorithms for minimizing nonsmooth and nonconvex functions were proposed.
Revised:  While the stochastic gradient descent are still used in the majority of applications, recently also stochastic algorithms for minimizing nonsmooth and nonconvex functions were proposed.
Edit intent: clarity
Original:  In this paper, we derive an inertial variant of the SPRING algorithm , called iSPRING, and prove linear convergence of the algorithm under certain assumptions.
Revised:  In this paper, we derive an inertial variant of the SPRING algorithm , and prove linear convergence of the algorithm under certain assumptions.
Edit intent: coherence
Original:  Dynamical spectral estimation has been applied for two decades in biomolecular simulation, yet its error properties remain poorly understood.
Revised:  Although the approach has been widely applied in biomolecular simulations, its error properties remain poorly understood.
Edit intent: clarity
Original:  Our analysis establishes VAC's convergence properties and suggests a new approach for tuning the lag time to improve VAC accuracy.
Revised:  Our analysis establishes VAC's convergence properties and suggests new strategies for tuning VAC to improve accuracy.
Edit intent: clarity
Original: Gene expression data for a set of 12 localizations from The Cancer Genome Atlas are processed in order to evaluate an entropy-like magnitude allowing the characterization of tumors and comparison with the corresponding normal tissues .
Revised: Gene expression data for a set of 12 cancer types is studied by means of an entropy-like magnitude allowing the characterization of tumors and comparison with the corresponding normal tissues .
Edit intent: clarity
Original: Gene expression data for a set of 12 localizations from The Cancer Genome Atlas are processed in order to evaluate an entropy-like magnitude allowing the characterization of tumors and comparison with the corresponding normal tissues .
Revised: Gene expression data for a set of 12 localizations from The Cancer Genome Atlas are processed in order to evaluate an entropy-like magnitude , which allows the characterization of tumors and comparison with the corresponding normal tissues .
Edit intent: coherence