wanyu/IteraTeR_full_sent huggingface dataset:
Original: Biomedical researchers working on a specific disease need up-to-date and unified access to knowledge relevant to the disease of their interest. Knowledge is continuously accumulated in scientific literature and other resourcessuch as biomedical ontologies.
Revised: Biomedical researchers working on a specific disease need up-to-date and unified access to up-to-date domain-specific knowledge is crucial, as such knowledge is continuously accumulated in scientific literature and other resourcessuch as biomedical ontologies.
Edit intent: meaning-changed
Original:  The proposed approach is  applied to three different case studies: Two prevalent diseases , Lung Cancerand Dementia, for which a lot of knowledge is available, and one rare disease, Duchenne Muscular Dystrophy , for which knowledge is less abundant and difficult to locate.
Revised:  The proposed approach is implemented as an open-source framework and applied to three different case studies: Two prevalent diseases , Lung Cancerand Dementia, for which a lot of knowledge is available, and one rare disease, Duchenne Muscular Dystrophy , for which knowledge is less abundant and difficult to locate.
Edit intent: meaning-changed
Original:  The proposed approach is  applied to three different case studies: Two prevalent diseases , Lung Cancerand Dementia, for which a lot of knowledge is available, and one rare disease, Duchenne Muscular Dystrophy , for which knowledge is less abundant and difficult to locate.
Revised:  The proposed approach is applied to three diseases (Lung Cancer, Dementia, and Duchenne Muscular Dystrophy , for which knowledge is less abundant and difficult to locate.
Edit intent: clarity
Original:  The proposed approach is  applied to three different case studies: Two prevalent diseases , Lung Cancerand Dementia, for which a lot of knowledge is available, and one rare disease, Duchenne Muscular Dystrophy , for which knowledge is less abundant and difficult to locate. Results from exemplary queries are presented, investigating the potential of this approach in integrating and accessing knowledge as an automatically generated semantic graph  .
Revised:  The proposed approach is applied to three different case studies: Two prevalent diseases , Lung Cancerand Dementia, for which a lot of knowledge is available, and one rare disease, Duchenne Muscular Dystrophy ). Exemplary queries are presented, investigating the potential of this approach in integrating and accessing knowledge as an automatically generated semantic graph .
Edit intent: coherence
Original:  A central theme is that one should evaluate as-if optimization or any other model-based decision rule by its performance across the state space  listing all states of nature that one believes feasible, not  the model space.
Revised:  A central theme is that one should evaluate as-if optimization or any other model-based decision rule by its performance across the state space , listing all states of nature that one believes feasible, not the model space.
Edit intent: fluency
Original:  A central theme is that one should evaluate as-if optimization or any other model-based decision rule by its performance across the state space  listing all states of nature that one believes feasible, not  the model space.
Revised:  A central theme is that one should evaluate as-if optimization or any other model-based decision rule by its performance across the state space listing all states of nature that one believes feasible, not across the model space.
Edit intent: fluency
Original:  In this paper, we present an algorithm for reconstructing embedded graphs in R^d (plane graphs in R^2) with n vertices from n^2 - n + d + 1 directional PDs. Lastly , we empirically validate the correctness and time-complexity of our algorithm in R^2 on randomly generated plane graphs using our implementation, and explain the numerical limitations of implementing our algorithm.
Revised:  In this paper, we present an algorithm for reconstructing embedded graphs in R^d (plane graphs in R^2) with n vertices from n^2 - n + d + 1 directional (augmented) PDs. Additionally , we empirically validate the correctness and time-complexity of our algorithm in R^2 on randomly generated plane graphs using our implementation, and explain the numerical limitations of implementing our algorithm.
Edit intent: meaning-changed
Original:  Most of the work is limited to either independent and identically distributed setting, or time series with independent and/or (sub-)Gaussian innovations . We extend current literatureto a broader set of innovation processes, by assuming that the error process is non-sub-Gaussian and conditionally heteroscedastic, and the generating process is not necessarily sparse. This setting covers fat tailed, conditionally dependent innovations which is of particular interest for financial risk modeling. It covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications  .
Revised:  In time series context, it is mostly restricted to Gaussian autoregressions or mixing sequences. We study oracle properties of LASSO estimation of weakly sparse vector-autoregressive models with heavy tailed, weakly dependent innovations with virtually no assumption on the conditional heteroskedasticity. In contrast to current literature, our innovation process satisfy an L^1 mixingale type condition on the centered conditional covariance matrices. This condition covers L^1-NED sequences and strong (\alpha-) mixing sequences as particular examples. From a modeling perspective, it covers several multivariate-GARCH specifications, such as the BEKK model, and other factor stochastic volatility specifications .
Edit intent: meaning-changed
Original:  The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents'aptitude in the task, and determine when they might need adjustments to improve their performance.
Revised:  The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly understand an agent's strengths and limitations in performing a task, and determine when they might need adjustments to improve their performance.
Edit intent: clarity
Original:  The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents'aptitude in the task, and determine when they might need adjustments to improve their performance.
Revised:  The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents'aptitude in the task, and determine when it might need adjustments to improve their performance.
Edit intent: clarity
Original:  The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents'aptitude in the task, and determine when they might need adjustments to improve their performance.
Revised:  The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly identify the agents'aptitude in the task, and determine when they might need adjustments to improve its performance.
Edit intent: fluency
Original:  Our first result shows that an algebraic abstraction of this problem is NP-hard , which (assuming \mathrm{P}\neq\mathrm{NP} ) suggests that any polynomial time solution will need to exploit some geometric or topological intuition.
Revised:  We formulate an abstract problem that captures the algebraic and combinatorial aspects of this operation, and show that this abstract problem is NP-hard , which (assuming \mathrm{P}\neq\mathrm{NP} ) suggests that any polynomial time solution will need to exploit some geometric or topological intuition.
Edit intent: meaning-changed
Original:  Our first result shows that an algebraic abstraction of this problem is NP-hard , which (assuming \mathrm{P}\neq\mathrm{NP} ) suggests that any polynomial time solution will need to exploit some geometric or topological intuition.
Revised:  Our first result shows that an algebraic abstraction of this problem is NP-hard . Assuming \mathrm{P}\neq\mathrm{NP} ) suggests that any polynomial time solution will need to exploit some geometric or topological intuition.
Edit intent: coherence
Original:  Our first result shows that an algebraic abstraction of this problem is NP-hard , which (assuming \mathrm{P}\neq\mathrm{NP} ) suggests that any polynomial time solution will need to exploit some geometric or topological intuition.
Revised:  Our first result shows that an algebraic abstraction of this problem is NP-hard , which (assuming \mathrm{P}\neq\mathrm{NP} , this result suggests that any polynomial time solution will need to exploit some geometric or topological intuition.
Edit intent: meaning-changed
Original:  Our first result shows that an algebraic abstraction of this problem is NP-hard , which (assuming \mathrm{P}\neq\mathrm{NP} ) suggests that any polynomial time solution will need to exploit some geometric or topological intuition.
Revised:  Our first result shows that an algebraic abstraction of this problem is NP-hard , which (assuming \mathrm{P}\neq\mathrm{NP} ) suggests that any polynomial-time procedure for finding a non-trivial normal sphere or disc will need to exploit some geometric or topological intuition.
Edit intent: clarity
Original:  Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the AI literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources  .
Revised:  Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources .
Edit intent: clarity
Original:  Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the AI literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources  .
Revised:  Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the AI literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016) .
Edit intent: meaning-changed
Original:  And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer a perfect middle ground between these two competing literature streams.
Revised:  And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer an effective middle ground between these two competing literature streams.
Edit intent: clarity
Original:  And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer a perfect middle ground between these two competing literature streams.
Revised:  And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer a perfect middle ground between these two literature streams.
Edit intent: clarity
Original:  We develop a new inference approach that exploits 1) the bouncy particle sampler  based on piecewise deterministic Markov processes  and 2) novel dynamic programming that reduces the cost of likelihood and gradient evaluations  to linear in sample size.
Revised:  We develop a new inference approach that exploits 1) the bouncy particle sampler (BPS) based on piecewise deterministic Markov processes and 2) novel dynamic programming that reduces the cost of likelihood and gradient evaluations to linear in sample size.
Edit intent: meaning-changed
Original:  We develop a new inference approach that exploits 1) the bouncy particle sampler  based on piecewise deterministic Markov processes  and 2) novel dynamic programming that reduces the cost of likelihood and gradient evaluations  to linear in sample size.
Revised:  We develop a new inference approach that exploits 1) the bouncy particle sampler based on piecewise deterministic Markov processes to simultaneously sample all truncated normal dimensions, and 2) novel dynamic programming that reduces the cost of likelihood and gradient evaluations to linear in sample size.
Edit intent: meaning-changed
Original:  We develop a new inference approach that exploits 1) the bouncy particle sampler  based on piecewise deterministic Markov processes  and 2) novel dynamic programming that reduces the cost of likelihood and gradient evaluations  to linear in sample size.
Revised:  We develop a new inference approach that exploits 1) the bouncy particle sampler based on piecewise deterministic Markov processes and 2) novel dynamic programming that reduces the cost of likelihood and gradient evaluations for BPS to linear in sample size.
Edit intent: meaning-changed
Original: In the pooling and sharing mechanism of multiplicatively grown individual resources, the evolution of cooperation is uniquely determined by the physical traits of the population.
Revised: In contrast to game-theoretic interaction models with additive payoffs, the temporal evolution of natural and artificial systems in often governed by a multiplicative process. In evolutionary biology, these systems are studied within the general framework of fluctuating environments, where the individual entities are subject to spatio-temporal fluctuations in the environmental conditions. In these environments, evolutionary behavior may emerge that essentially differs from the one observed in standard models. The study of the physical traits of the population.
Edit intent: coherence
Original: In the pooling and sharing mechanism of multiplicatively grown individual resources, the evolution of cooperation is uniquely determined by the physical traits of the population. If the entities in the population exhibit homogeneous traits, cooperation is always favored. However, if there is heterogeneity within the population, the evolution of cooperation is dependent on both the interaction structure and the decision making rules implemented by the entities.
Revised: In the pooling and sharing mechanism of multiplicatively grown individual resources, the evolution of cooperation is uniquely determined by the evolution of cooperation is dependent on both the interaction structure and the decision making rules implemented by the entities.
Edit intent: clarity
Original:  However, if there is heterogeneity within the population, the evolution of cooperation is dependent on both the interaction structure and the decision making rules implemented by the entities. Here, we perform a detailed analysis on the role of the interaction structures in the evolutionary stability of cooperation in heterogeneous multiplicative environments. We utilize this analysis to examine the applicability of a simple state-based decision making rule in a variety of settings.
Revised:  However, if there is heterogeneity within the population, the evolution of cooperation in fluctuating environments typically assumes a homogeneous, well mixed population, whose constituents choose between two behavioral strategies. In this paper, we generalize these results by developing a systematic study of the cooperation dynamics in fluctuating environments under the consideration of structured, heterogeneous populations with individual entities subjected to general behavioral rules. We find that, in the presence of environmental fluctuations, the cooperation dynamics yields the creation of network components with distinct evolutionary properties. We utilize this result to examine the applicability of a simple state-based decision making rule in a variety of settings.
Edit intent: meaning-changed
Original:  We thereby show that the introduced rule leads to steady state cooperative behavior that is always greater than or equal to the one predicted by  evolutionary stability analysis and discuss relevant implications to natural and artificial systems .
Revised:  We thereby show that the introduced rule leads to steady state cooperative behavior that is always greater than or equal to the one predicted by evolutionary stability analysis of unconditional cooperation. As a consequence, the implementation of our results may go beyond explaining the evolution of cooperation. In particular, they can be directly applied in domains that deal with the development of artificial systems able to adequately mimic reality, such as reinforcement learning .
Edit intent: meaning-changed
Original: In contrast to game-theoretic interaction models with additive payoffs, the temporal evolution of natural and artificial systems in often governed by a multiplicative process. In evolutionary biology, these systems are studied within the general framework of fluctuating environments , where the individual entities are subject to spatio-temporal fluctuations in the environmental conditions.
Revised: Fluctuating environments are situations where the spatio-temporal fluctuations in the environmental conditions.
Edit intent: clarity
Original:  In evolutionary biology, these systems are studied within the general framework of fluctuating environments , where the individual entities are subject to spatio-temporal fluctuations in the environmental conditions. In these environments, evolutionary behavior may emerge that essentially differs from the one observed in standard models .
Revised:  In evolutionary biology, these systems are studied within the general framework of fluctuating environments , where the individual entities are subject to spatio-temporal stochasticity plays a significant role in the evolutionary dynamics .
Edit intent: meaning-changed
Original:   We find that, in the presence of environmental fluctuations, the cooperation dynamics yields the creation of network components with distinct evolutionary properties.
Revised:  Considering complex network topologies, and a behavioral rule based on generalized reciprocity, we perform a detailed analysis of the effect of the underlying interaction structure on the evolutionary stability of cooperation. We find that, in the presence of environmental fluctuations, the cooperation dynamics yields the creation of network components with distinct evolutionary properties.
Edit intent: meaning-changed
Original:  This paper investigates the possibility of identification of optimal dynamic regimes when data are generated from sequential (natural) experiments. We propose a framework in which we can partially learn the optimal dynamic regime and ordering of welfares, relaxing sequential randomization assumptions commonly employed in the literature .
Revised:  We introduce a framework in which we can partially learn the optimal dynamic regime and ordering of welfares, relaxing sequential randomization assumptions commonly employed in the literature .
Edit intent: coherence
Original:  We propose a framework in which we can partially learn the optimal dynamic regime and ordering of welfares, relaxing sequential randomization assumptions commonly employed in the literature . We establish the sharp partial ordering of counterfactual welfares with respect to dynamic regimes by using a series of linear programs.
Revised:  We propose a framework in which we can partially learn the optimal dynamic regime and ordering of welfares, relaxing sequential randomization assumptions commonly employed in the literature but instead using (binary) instrumental variables. We propose the notion of sharp partial ordering of counterfactual welfares with respect to dynamic regimes by using a series of linear programs.
Edit intent: meaning-changed
Original:  The identified set of the optimal regime is then characterized as the set of maximal elements of the partial order. We also propose topological sorts of the partial order as a policy menu. We show how policymaking can be further guided by imposing assumptions such as monotonicity/uniformity of different stringency, agent's learning, Markovian structure, and stationarity .
Revised:  The identified set of the optimal regime is then characterized as the set of maximal elements associated with the partial ordering. One main contribution of this paper is that we develop simple analytical conditions to establish the ordering, which bypass solving a large number of large-scale linear programs, and thus facilitate estimation and inference. This paper's analytical framework has broader applicability beyond the current context, e.g., in establishing signs of various treatment effects and rankings of policies across different counterfactual scenarios .
Edit intent: meaning-changed
Original:  One main contribution of this paper is that we develop simple analytical conditions to establish the ordering, which bypass solving a large number of large-scale linear programs, and thus facilitate estimation and inference. This paper's analytical framework has broader applicability beyond the current context, e.g., in establishing signs of various treatment effects and rankings of policies across different counterfactual scenarios.
Revised:  We relate the notion of partial ordering with a more conventional notion of partial identification using topological sorts. Practically, topological sorts can be served as a policy benchmark for a policymaker. We apply our method to understand returns to schooling and post-school training as a sequence of treatments by combining data from multiple sources. The framework of this paper can be used beyond the current context, e.g., in establishing signs of various treatment effects and rankings of policies across different counterfactual scenarios.
Edit intent: meaning-changed
Original: We present a Residual Logit (ResLogit) model for seamlessly integrating a data-driven Deep Neural Network (DNN) architecture in the random utility maximization paradigm.
Revised: This paper presents a novel deep learning-based travel behaviour choice model.Our proposed Residual Logit (ResLogit) model for seamlessly integrating a data-driven Deep Neural Network (DNN) architecture in the random utility maximization paradigm.
Edit intent: meaning-changed
Original:  DNN models such as the Multi-layer Perceptron (MLP)  have shown remarkable success in modelling complex data accurately, but recent studies have consistently demonstrated that their black-box properties are incompatible with discrete choice analysis for the purpose of interpreting decision making behaviour.
Revised:  DNN models such as the Multi-layer Perceptron (MLP) and the Recurrent Neural Network (RNN) have shown remarkable success in modelling complex data accurately, but recent studies have consistently demonstrated that their black-box properties are incompatible with discrete choice analysis for the purpose of interpreting decision making behaviour.
Edit intent: meaning-changed