wanyu/IteraTeR_full_sent huggingface dataset:
Original:  We train our agent in realistic AirSim simulations using both  hand-crafted and human reward functions. We deploy the same agent on a real DJI M210 drone in order to test  generalization capability of our approach to real world conditions.
Revised:  We train our agent in realistic AirSim simulations using both hand-crafted reward function as well as reward from direct human input. We then deploy the same agent on a real DJI M210 drone in order to test generalization capability of our approach to real world conditions.
Edit intent: meaning-changed
Original: With a brief letter to Nature in 1972, Robert May triggered a worldwide research program in theoretical ecology and complex systems that continues to this day. Building on powerful mathematical results about large random matrices, he argued that systems with sufficiently large numbers of interacting components are generically unstable. In the ecological context, May's thesis directly contradicted the longstanding ecological intuition that diversity promotes stability. In economics and finance, May's work helped to consolidate growing concerns about the fragility of an increasingly interconnected global marketplace. In this Letter , we draw on recent theoretical progress in random matrix theory and statistical physics to fundamentally extend and reinterpret May's theorem.
Revised: With a brief letter to Nature in 1972, Robert May triggered a worldwide research program studying ecological communities using random matrix theory. Yet, it remains unclear if and when we can treat real communities as random ecosystems. Here , we draw on recent theoretical progress in random matrix theory and statistical physics to fundamentally extend and reinterpret May's theorem.
Edit intent: clarity
Original:  In this Letter , we draw on recent theoretical progress in random matrix theory and statistical physics to fundamentally extend and reinterpret May's theorem. We confirm that a wide range of ecological models become unstable at the point predicted by May , even when the models do not strictly follow his assumptions.
Revised:  In this Letter , we draw on recent theoretical progress in random matrix theory and statistical physics to fundamentally extend and reinterpret May's approach to generalized consumer-resource models. We show that in diverse ecosystems adding even modest amounts of noise to consumer preferences results in a transition to "typicality" where macroscopic ecological properties of communities are indistinguishable from those of random ecosystems , even when the models do not strictly follow his assumptions.
Edit intent: meaning-changed
Original:  We confirm that a wide range of ecological models become unstable at the point predicted by May , even when the models do not strictly follow his assumptions. Surprisingly, increasing the interaction strength or diversity beyond the May threshold results in a URLanization of the ecosystem -- through extinction of a fixed fraction of species -- into a new stable state whose properties are well described by purely random interactions. This URLanized state remains stable for arbitrarily large ecosystem and suggests a new interpretation of May's original conclusions: when interacting complex systems with many components become sufficiently large, they will generically undergo a transition to a "typical" URLanized, stable state .
Revised:  We confirm that a wide range of ecological models become unstable at the point predicted by May , even when resource preferences have prominent designed structures. We test these ideas using numerical simulations on a wide variety of ecological models. Our work offers an explanation for the success of random consumer-resource models in reproducing experimentally observed ecological patterns in microbial communities and highlights the difficulty of scaling up bottom-up approaches in synthetic ecology to diverse communities .
Edit intent: meaning-changed
Original:  Our objective is to mimic these connections in the visual system with a neurodynamic network of firing-rate neurons . Early subcortical processes (i.e. retinal and thalamic) are functionally simulated.
Revised:  Our objective is to mimic these connections with a neurodynamic network of firing-rate neurons . Early subcortical processes (i.e. retinal and thalamic) are functionally simulated.
Edit intent: clarity
Original:  Our objective is to mimic these connections in the visual system with a neurodynamic network of firing-rate neurons . Early subcortical processes (i.e. retinal and thalamic) are functionally simulated.
Revised:  Our objective is to mimic these connections in the visual system with a neurodynamic network of firing-rate neurons in order to predict visual attention. Early visual subcortical processes (i.e. retinal and thalamic) are functionally simulated.
Edit intent: meaning-changed
Original:  Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions . Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences during free viewing .
Revised:  Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search tasks . Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences during free viewing .
Edit intent: clarity
Original:  Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions . Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences during free viewing .
Revised:  Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions . Results show that our model outpeforms other biologically-inpired models of saliency prediction while predicting visual saccade sequences during free viewing .
Edit intent: clarity
Original:  Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions . Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences during free viewing .
Revised:  Novel computational definitions of top-down inhibition (in terms of inhibition of return and selection mechanisms), are also proposed to predict attention in Free-Viewing and Visual Search conditions . Results show that our model outpeforms other biologically-inpired models of saliency prediction as well as to predict visual saccade sequences with the same model .
Edit intent: clarity
Original:  We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition)  predict attention at distinct image contexts.
Revised:  We also show how temporal and spatial characteristics of inhibition of return can improve prediction of saccades, as well as how distinct search strategies (in terms of feature-selective or category-specific inhibition) can predict attention at distinct image contexts.
Edit intent: fluency
Original: We show how to set up a forward rate model in the presence of volatility uncertainty by using the theory of G-Brownian motion. In order to formulate the model, we extend the G-framework to integration with respect to two integrators and prove a version of Fubini's theorem for stochastic integrals. The evolution of the forward rate in the model is described by a diffusion process , which is driven by a G-Brownian motion  .
Revised: We show how to set up a forward rate model in the presence of volatility uncertainty . The forward rate is modeled as a diffusion process , which is driven by a G-Brownian motion .
Edit intent: clarity
Original:  In contrast to the traditional model, the drift condition consists of two equations and two market prices of risk , respectively, uncertainty. Furthermore, we examine the connection to short rate models and discuss some examples .
Revised:  In contrast to the traditional model, the drift condition consists of two equations and two market prices of risk and uncertainty, respectively. The drift condition is still consistent with the classical one if there is no volatility uncertainty. Similar to the traditional model, the risk-neutral dynamics of the forward rate are completely determined by the diffusion coefficient. Furthermore, we obtain some classical term structures under volatility uncertainty as examples of our model .
Edit intent: meaning-changed
Original: Given two point sets S and T on a line, we present a linear time algorithm for  the limited capacity many-to-many matching  between S and T which improves the previous quadratic algorithm .
Revised: Given two point sets S and T on a line, we present a linear time algorithm for the limited capacity many-to-many matching (LCMM) between S and T which improves the previous quadratic algorithm .
Edit intent: meaning-changed
Original: Given two point sets S and T on a line, we present a linear time algorithm for  the limited capacity many-to-many matching  between S and T which improves the previous quadratic algorithm .
Revised: Given two point sets S and T on a line, we present a linear time algorithm for the limited capacity many-to-many matching between S and T improving the previous best known quadratic time algorithm. The aim of the LCMM is to match each point of S (T) to at least one point of T (S) such that the matching costs is minimized and the number of the points matched to each point is limited to a given number .
Edit intent: meaning-changed
Original: Schematic metro maps in practice as well as metro map layout algorithms usually adhere to an octilinear layout style with all paths composed of horizontal, vertical, and 45 ^\circ -diagonal edges.
Revised: Traditionally, most schematic metro maps in practice as well as metro map layout algorithms usually adhere to an octilinear layout style with all paths composed of horizontal, vertical, and 45 ^\circ -diagonal edges.
Edit intent: coherence
Original: Schematic metro maps in practice as well as metro map layout algorithms usually adhere to an octilinear layout style with all paths composed of horizontal, vertical, and 45 ^\circ -diagonal edges.
Revised: Schematic metro maps in practice as well as metro map layout algorithms adhere to an octilinear layout style with all paths composed of horizontal, vertical, and 45 ^\circ -diagonal edges.
Edit intent: coherence
Original: Schematic metro maps in practice as well as metro map layout algorithms usually adhere to an octilinear layout style with all paths composed of horizontal, vertical, and 45 ^\circ -diagonal edges.
Revised: Schematic metro maps in practice as well as metro map layout algorithms usually adhere to an octolinear layout style with all paths composed of horizontal, vertical, and 45 ^\circ -diagonal edges.
Edit intent: fluency
Original: Schematic metro maps in practice as well as metro map layout algorithms usually adhere to an octilinear layout style with all paths composed of horizontal, vertical, and 45 ^\circ -diagonal edges.
Revised: Schematic metro maps in practice as well as metro map layout algorithms usually adhere to an octilinear layout style with all paths composed of horizontal, vertical, and 45 \deg -diagonal edges.
Edit intent: meaning-changed
Original:  In this paper we present and implement an adaptation of the octilinear mixed-integer linear programming approach of N\"ollenburg and Wolff (2011) that can draw k-linear metro maps .
Revised:  In this paper we present and implement an adaptation of the octilinear mixed-integer linear programming approach of N\"ollenburg and Wolff (2011) that can draw metro maps schematized to any set C of arbitrary orientations. We further present a data-driven approach to determine a suitable set C by either detecting the best rotation of an equidistant orientation system or by clustering the input edge orientations using a k-means algorithm. We demonstrate the new possibilities of our method using several real-world examples .
Edit intent: meaning-changed
Original: We propose an analytically tractable class of models for the dynamics of a limit order book, described as the solution of a stochastic partial differential equation (SPDE) with multiplicative noise  .
Revised: We propose an analytically tractable class of models for the dynamics of a limit order book, described as the solution of a stochastic partial differential equation (SPDE) with multiplicative noise for the order book centered at the mid-price, along with stochastic dynamics for the mid-price which is consistent with the order flow dynamics .
Edit intent: meaning-changed
Original:  For each model we perform a detailed analysis of the role of different parameters, study the dynamics of the price, order book depth, volume and order imbalance, provide an intuitive financial interpretation of the variables involved and show how the model reproduces statistical properties of price changes, market depth and order flow in limit order markets.
Revised:  For each model we analyze in detail the role of different parameters, study the dynamics of the price, order book depth, volume and order imbalance, provide an intuitive financial interpretation of the variables involved and show how the model reproduces statistical properties of price changes, market depth and order flow in limit order markets.
Edit intent: clarity
Original:  For each model we perform a detailed analysis of the role of different parameters, study the dynamics of the price, order book depth, volume and order imbalance, provide an intuitive financial interpretation of the variables involved and show how the model reproduces statistical properties of price changes, market depth and order flow in limit order markets.
Revised:  For each model we perform a detailed analysis of the role of different parameters, the dynamics of the price, order book depth, volume and order imbalance, provide an intuitive financial interpretation of the variables involved and show how the model reproduces statistical properties of price changes, market depth and order flow in limit order markets.
Edit intent: clarity
Original:  We observe that " effective " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanations ).
Revised:  We observe that " helpful " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanations ).
Edit intent: meaning-changed
Original:  We observe that " effective " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanations ).
Revised:  We observe that " effective " explanations are conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanations ).
Edit intent: coherence
Original:  We observe that " effective " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanations ).
Revised:  We observe that " effective " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanation games), and having at least one "correct" explanation is significantly helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanations ).
Edit intent: clarity
Original:  We observe that " effective " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanations ).
Revised:  We observe that " effective " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are mostly noisy (by almost 30\% compared to no explanations ).
Edit intent: clarity
Original:  We observe that " effective " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanations ).
Revised:  We observe that " effective " explanations are not only conducive to game performance (by almost 22\% for "excellent" rated explanations), but also helpful when VQA system answers are erroneous or noisy (by almost 30\% compared to no explanation games ).
Edit intent: clarity
Original: While there have been many proposals on how to make AI algorithms more transparent , few have attempted to evaluate the impact of AI explanations on human performance on a task using AI. We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG)  as a method of evaluating the efficacy of explanations  in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Revised: While there have been many proposals on how to make AI algorithms more transparent , few have attempted to evaluate the impact of AI explanations on human performance in conducting human-AI collaborative tasks. To bridge the gap, we propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG) as a method of evaluating the efficacy of explanations in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Edit intent: clarity
Original:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG)  as a method of evaluating the efficacy of explanations  in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Revised:  We propose a Twenty-Questions style collaborative image retrieval game, Explanation-assisted Guess Which (ExAG) as a method of evaluating the efficacy of explanations in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Edit intent: clarity
Original:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG)  as a method of evaluating the efficacy of explanations  in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Revised:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG) , as a method of evaluating the efficacy of explanations in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Edit intent: fluency
Original:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG)  as a method of evaluating the efficacy of explanations  in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Revised:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG) as a method of evaluating the efficacy of explanations (visual evidence or textual justification) in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Edit intent: meaning-changed
Original:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG)  as a method of evaluating the efficacy of explanations  in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images.
Revised:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG) as a method of evaluating the efficacy of explanations in the context of Visual Question Answering (VQA) . In our proposed ExAG, a human user needs to guess a secretly picked image by the VQA agent by asking natural language questions on images.
Edit intent: meaning-changed
Original:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG)  as a method of evaluating the efficacy of explanations  in the context of Visual Question Answering (VQA) - the task of answering natural language questions on images. We study the effect of VQA agent explanations on the game performance as a function of explanation type and quality. We observe that "helpful" explanations are conducive to game performance (by almost 22\% for "excellent" rated explanation games), and having at least one "correct " explanation is significantly helpful when VQA system answers are mostly noisy (by almost 30\% compared to no explanation games).
Revised:  We propose a Twenty-Questions style collaborative image guessing game, Explanation-assisted Guess Which (ExAG) as a method of evaluating the efficacy of explanations in the context of Visual Question Answering (VQA) - the task of answering natural language questions . We show that when AI explains its answers, users succeed more often in guessing the secret image correctly. Furthermore, we show that while good explanations improve human performance, incorrect explanations can degrade game performance as compared to no-explanation games. Notably, a few correct explanations can readily improve human performance in game rounds where the AI system's answers are mostly noisy (by almost 30\% compared to no explanation games).
Edit intent: meaning-changed
Original:  We observe that "helpful" explanations are conducive to game performance (by almost 22\% for "excellent" rated explanation games), and having at least one "correct " explanation is significantly helpful when VQA system answers are mostly noisy (by almost 30\% compared to no explanation games). We also see that players develop a preference for explanations even when penalized and that the explanations are mostly rated as "helpful" .
Revised:  We observe that "helpful" explanations are conducive to game performance (by almost 22\% for "excellent" rated explanation games), and having at least one "correct " explanation is significantly helpful when VQA system answers are mostly incorrect as compared to no-explanation games. Our experiments, therefore, show that ExAG is an effective means to evaluate the efficacy of AI-generated explanation on human-AI collaborative tasks .
Edit intent: coherence
Original: Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for  Bayesian estimation of mixed multinomial logit (MMNL) models.
Revised: Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models.
Edit intent: clarity