wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The main conclusions are that, among all different mechanisms studied, the random partitioning of macro-molecules at division is the only one which may have a significant impact on the variance of protein concentration. In particular, the variability induced by the sharing of RNA-polymerases and ribosomes seems to be of a smaller order of magnitude than what is generally suggested in the literature  .
Revised:  The main important and surprising conclusion of our study is to show that despite the signifcant fuctuations of free RNA polymerases and free ribosomes, they bring little variability to protein production contrary to what has been previously proposed in the literature .
Edit intent: clarity
Original:  In particular, the variability induced by the sharing of RNA-polymerases and ribosomes seems to be of a smaller order of magnitude than what is generally suggested in the literature  .
Revised:  In particular, the variability induced by the sharing of RNA-polymerases and ribosomes seems to be of a smaller order of magnitude than what is generally suggested in the literature . After verifying the robustness of this quite counter-intuitive result, we discuss its possible origin from a theoretical view, and interpret it as the result of a mean-feld efect .
Edit intent: meaning-changed
Original: In this paper, we study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the robustness parameter for worst-case maximization problems with concave reward functions . Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution,
Revised: We study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the robustness parameter for worst-case maximization problems with concave reward functions . Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution,
Edit intent: clarity
Original:  we study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the robustness parameter for worst-case maximization problems with concave reward functions . Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution,
Revised:  we study the out-of-sample properties of robust empirical optimization problems with smooth \phi-divergence penalties and smooth concave objective functions, and develop a theory for data-driven calibration of the robustness parameter for worst-case maximization problems with concave reward functions . Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution,
Edit intent: meaning-changed
Original:  we study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the robustness parameter for worst-case maximization problems with concave reward functions . Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution,
Revised:  we study the out-of-sample properties of robust empirical optimization and develop a theory for data-driven calibration of the non-negative "robustness parameter" \delta that controls the size of the deviations from the nominal model . Building on the intuition that robust optimization reduces the sensitivity of the expected reward to errors in the model by controlling the spread of the reward distribution,
Edit intent: meaning-changed
Original:  we show that the first-order benefit of  little bit of robustness  is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller.
Revised:  we show that the first-order benefit of `` little bit of robustness is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller.
Edit intent: fluency
Original:  we show that the first-order benefit of  little bit of robustness  is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller.
Revised:  we show that the first-order benefit of little bit of robustness " (i.e., \delta small, positive) is a significant reduction in the variance of the out-of-sample reward while the corresponding impact on the mean is almost an order of magnitude smaller.
Edit intent: meaning-changed
Original:  Our examples also show that open loop calibration methods (e.g.  selecting a 90 \% confidence level regardless of the data and objective function) can lead to solutions that are very conservative out-of-sample.
Revised:  Our examples show that robust solutions resulting from "open loop" calibration methods (e.g. selecting a 90 \% confidence level regardless of the data and objective function) can lead to solutions that are very conservative out-of-sample.
Edit intent: clarity
Original:  Our examples also show that open loop calibration methods (e.g.  selecting a 90 \% confidence level regardless of the data and objective function) can lead to solutions that are very conservative out-of-sample.
Revised:  Our examples also show that open loop calibration methods (e.g. , selecting a 90 \% confidence level regardless of the data and objective function) can lead to solutions that are very conservative out-of-sample.
Edit intent: fluency
Original: Contextual bandit algorithms seek to learn a personalized treatment assignment policy, balancing exploration against exploitation. Although a number of algorithms have been proposed, there is little guidance available for applied researchers to select among various approaches. Motivated by the econometrics and statistics literatures on causal effects estimation, we study a new consideration to the exploration vs. exploitation framework , which is that the way exploration is conducted in the present may contribute to the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Revised: Although many contextual bandit algorithms have similar theoretical guarantees, the characteristics of real-world applications oftentimes result in large performance dissimilarities across algorithms. We study a consideration for the exploration vs. exploitation framework , which is that the way exploration is conducted in the present may contribute to the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Edit intent: clarity
Original:  Motivated by the econometrics and statistics literatures on causal effects estimation, we study a new consideration to the exploration vs. exploitation framework , which is that the way exploration is conducted in the present may contribute to the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Revised:  Motivated by the econometrics and statistics literatures on causal effects estimation, we study a new consideration to the exploration vs. exploitation framework that does not arise in non-contextual bandits: the way exploration is conducted in the present may contribute to the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Edit intent: meaning-changed
Original:  Motivated by the econometrics and statistics literatures on causal effects estimation, we study a new consideration to the exploration vs. exploitation framework , which is that the way exploration is conducted in the present may contribute to the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Revised:  Motivated by the econometrics and statistics literatures on causal effects estimation, we study a new consideration to the exploration vs. exploitation framework , which is that the way exploration is conducted in the present may affect the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Edit intent: clarity
Original:  We leverage parametric and non-parametric statistical estimation methods and causal effect estimation methods in order to propose new contextual bandit designs.
Revised:  We show that contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We propose new contextual bandit designs, combining parametric and non-parametric statistical estimation methods and causal effect estimation methods in order to propose new contextual bandit designs.
Edit intent: meaning-changed
Original:  We leverage parametric and non-parametric statistical estimation methods and causal effect estimation methods in order to propose new contextual bandit designs. Through a variety of simulations, we show how alternative design choices impact the learning performance and provide insights on why we observe these effects .
Revised:  We leverage parametric and non-parametric statistical estimation methods and causal effect estimation methods in order to reduce the estimation bias that results from adaptive treatment assignment. We provide empirical evidence that guides the choice among the alternatives in different scenarios, such as prejudice (non-representative user contexts) in the initial training data .
Edit intent: meaning-changed
Original:  We study a consideration for the exploration vs. exploitation framework that does not arise in non-contextual bandits : the way exploration  is conducted in the present may affect the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Revised:  We study a consideration for the exploration vs. exploitation framework that does not arise in multi-armed bandits but is crucial in contextual bandits; the way exploration is conducted in the present may affect the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Edit intent: meaning-changed
Original:  We study a consideration for the exploration vs. exploitation framework that does not arise in non-contextual bandits : the way exploration  is conducted in the present may affect the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Revised:  We study a consideration for the exploration vs. exploitation framework that does not arise in non-contextual bandits : the way exploration and exploitation is conducted in the present may affect the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Edit intent: clarity
Original:  We study a consideration for the exploration vs. exploitation framework that does not arise in non-contextual bandits : the way exploration  is conducted in the present may affect the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Revised:  We study a consideration for the exploration vs. exploitation framework that does not arise in non-contextual bandits : the way exploration is conducted in the present affects the bias and variance in the potential outcome model estimation in subsequent stages of learning.
Edit intent: clarity
Original:  We show that contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We propose new contextual bandit designs, combining parametric and non-parametric statistical estimation methods with causal inference methods in order to reduce the estimation biasthat results from adaptive treatment assignment .
Revised:  We develop parametric and non-parametric statistical estimation methods with causal inference methods in order to reduce the estimation biasthat results from adaptive treatment assignment .
Edit intent: clarity
Original:  We propose new contextual bandit designs, combining parametric and non-parametric statistical estimation methods with causal inference methods in order to reduce the estimation biasthat results from adaptive treatment assignment .
Revised:  We propose new contextual bandit designs, combining parametric and non-parametric contextual bandits that integrate balancing methods from the causal inference literature in their estimation to make it less prone to problems of estimation bias .
Edit intent: meaning-changed
Original:  We provide empirical evidence that guides the choice among the alternatives in different scenarios, such as prejudice (non-representative user contexts) in the initial training data  .
Revised:  We provide the first regret bound analyses for contextual bandits with balancing in the domain of linear contextual bandits that match the state of the art regret bounds. We demonstrate the strong practical advantage of balanced contextual bandits on a large number of supervised learning datasets and on a synthetic example that simulates model mis-specification and prejudice in the initial training data .
Edit intent: meaning-changed
Original:  We provide empirical evidence that guides the choice among the alternatives in different scenarios, such as prejudice (non-representative user contexts) in the initial training data  .
Revised:  We provide empirical evidence that guides the choice among the alternatives in different scenarios, such as prejudice (non-representative user contexts) in the initial training data . Additionally, we develop contextual bandits with simpler assignment policies by leveraging sparse model estimation methods from the econometrics literature and demonstrate empirically that in the early stages they can improve the rate of learning and decrease regret .
Edit intent: meaning-changed
Original:  Due to the difficulty in modeling and quantifying capabilities , research efforts have so far concentrated on the exported products as a main proxy to infer the complexity of the productive system. To bridge this gap, we propose a novel probabilistic framework which leverages Bayesian non-parametric techniques to extract the dominant features behind the comparative advantage in exported products.
Revised:  In general, measuring the capabilities behind economic complexity directly is difficult, and indirect measures have been suggested which exploit the fact that the presence of the capabilities is expressed in a country's mix of products. We complement these studies by introducing a probabilistic framework which leverages Bayesian non-parametric techniques to extract the dominant features behind the comparative advantage in exported products.
Edit intent: coherence
Original:  A computational investigation to evaluate the relative binding ability of a group of anti- Angiopoietin-2 aptamersis proposed and tested against data already available in the literature. Results: The procedure consists of three steps: a. the production of a large set of conformations for each candidate aptamer ;
Revised:  A novel strategy is proposed to validate the reliability of the 3D structures of aptamers, produced in silico by using free software. The procedure consists of three steps: a. the production of a large set of conformations for each candidate aptamer ;
Edit intent: meaning-changed
Original:  Steps a and b allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes.
Revised:  Steps a . and b. allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes.
Edit intent: fluency
Original:  Steps a and b allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes.
Revised:  Steps a and b allow a global binding score of the ligand-receptor complexes based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes.
Edit intent: fluency
Original:  Steps a and b allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes.
Revised:  Steps a and b allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energy . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes.
Edit intent: fluency
Original:  Steps a and b allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes.
Revised:  Steps a and b allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employs a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes.
Edit intent: fluency
Original:  Steps a and b allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the complexes. Finally, a comparison of the theoretical results with the measurements obtained for the same aptamers in experiments on the SPR biosensor is performed, revealing a good agreement .
Revised:  Steps a and b allow a global binding score of the ligand-receptor complexes , based on the distribution of the "effective affinity", i.e. the sum of the conformational and the docking energies . Step c. employes a complex network approach (Proteotronics) to characterize the electrical properties of the aptamers and the ligand-receptor complexes. The test-bed is represented by a group of anti- Angiopoietin-2 aptamers. In a previous literature these aptamers were processed both in vitro and in silico, by using an approach different from that here presented, and finally tested with a SPS experiment. Computational expectations and experimental outcomes did not agree, while our results show a good agreement with the known measurements. The devised procedure is not aptamer-specific and, integrating structure production with structure selection, candidates itself as a quite complete theoretical approach for aptamer selection .
Edit intent: meaning-changed
Original: This paper provides empirical evidences that corporate firms risk assessment could benefit from taking quantitatively into account the network of interactions among firms. Indeed, the structure of interactions between firms is critical to identify risk concentration and the possible pathways of propagation of financial distress. In this work, we consider the interactions by investigating a large proprietary dataset of payments among Italian firms.
Revised: This paper provides empirical evidences that corporate firms risk assessment could benefit from taking quantitatively into account the network of interactions among them. While a significant attention has been given to aggregate risk assessment and risk propagation once the above two properties are given, less is known about how the risk is distributed in the network and its relations with the topology. We study this problem by investigating a large proprietary dataset of payments among Italian firms.
Edit intent: meaning-changed
Original:  In this work, we consider the interactions by investigating a large proprietary dataset of payments among Italian firms. We first characterise the topological properties of the payment networks, and then we focus our attention on the relation between the network and the riskof firms.
Revised:  In this work, we consider the interactions by investigating a large proprietary dataset of payments among 2.4M Italian firms, whose credit risk rating is known. We document significant correlations between local topological properties of the payment networks, and then we focus our attention on the relation between the network and the riskof firms.
Edit intent: meaning-changed
Original:  We first characterise the topological properties of the payment networks, and then we focus our attention on the relation between the network and the riskof firms. Our main finding is to document the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves.
Revised:  We first characterise the topological properties of a node (firm) and its risk. Moreover we show the existence of an homophily of risk, i.e. the tendency of firms with similar risk profile to be statistically more connected among themselves.
Edit intent: clarity
Original:  The aim of this paper is to present a  heuristic-free , exact and computationally tractable model, which solves the market clearing problem in the presence of both curtailable block orders and the UPP.
Revised:  The aim of this paper is to present a non-iterative and heuristic-free , exact and computationally tractable model, which solves the market clearing problem in the presence of both curtailable block orders and the UPP.
Edit intent: meaning-changed
Original:   By resorting to an equivalent UPP formulation, the proposed approach results in a mixed-integer linear program, which is built starting from a non-linear integer bilevel programming problem.
Revised:  The solution is exact, with no approximation up to the level of resolution of current market data. By resorting to an equivalent UPP formulation, the proposed approach results in a mixed-integer linear program, which is built starting from a non-linear integer bilevel programming problem.
Edit intent: meaning-changed
Original:  To single out the collective influence of geometry and gain access to the geometric regulation of individual cell behaviours, we develop a mathematical model of the infilling of cortical bone pores and use it with available experimental data on cortical infilling rates.
Revised:  To factor out the collective influence of geometry and gain access to the geometric regulation of individual cell behaviours, we develop a mathematical model of the infilling of cortical bone pores and use it with available experimental data on cortical infilling rates.
Edit intent: clarity