wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions reducing the problem to balanced graph partitioning followed bysupervised classification.
Revised:  Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions reducing the problem to followed bysupervised classification.
Edit intent: clarity
Original:  Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions reducing the problem to balanced graph partitioning followed bysupervised classification.
Revised:  Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions reducing the problem to balanced graph partitioning supervised classification.
Edit intent: clarity
Original:  Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions reducing the problem to balanced graph partitioning followed bysupervised classification.
Revised:  Inspired by recent theoretical work on NNS for general metric spaces [Andoni, Naor, Nikolov, Razenshteyn, Waingarten STOC 2018, FOCS 2018], we develop a new framework for building space partitions reducing the problem to balanced graph partitioning followed bybalanced graph partitioning followed by supervised classification.
Edit intent: fluency
Original:  On several standard benchmarks for NNS, our experiments show that the partitions obtained by Neural LSH consistently outperform partitions found by quantization-based and tree-based methods  .
Revised:  On several standard benchmarks for NNS, our experiments show that the partitions obtained by Neural LSH consistently outperform partitions found by quantization-based and tree-based methods as well as classic, data-oblivious LSH .
Edit intent: meaning-changed
Original:  We show that if the population risk satisfies a condition called Neyman orthogonality, the impact of the first stage error on the excess risk bound achieved by the meta-algorithm is of second order.
Revised:  We show that if the population risk satisfies a condition called Neyman orthogonality, the impact of the nuisance estimation error on the excess risk bound achieved by the meta-algorithm is of second order.
Edit intent: meaning-changed
Original:  We highlight the applicability of our results via four applications of primary importance : 1) heterogeneous treatment effect estimation, 2) offline policy optimization, 3) domain adaptation, and 4) learning with missing data.
Revised:  We highlight the applicability of our results in four settings of central importance in the literature : 1) heterogeneous treatment effect estimation, 2) offline policy optimization, 3) domain adaptation, and 4) learning with missing data.
Edit intent: meaning-changed
Original: The present paper aims to provide the first systematic study of high-dimensional statistical arbitrage using both factor models and tools from stochastic control theory, obtaining closed-form optimal strategies in most cases and extending in multiple new directions the existing literature .
Revised: The present paper aims to provide a systematic study of high-dimensional statistical arbitrage using both factor models and tools from stochastic control theory, obtaining closed-form optimal strategies in most cases and extending in multiple new directions the existing literature .
Edit intent: clarity
Original: The present paper aims to provide the first systematic study of high-dimensional statistical arbitrage using both factor models and tools from stochastic control theory, obtaining closed-form optimal strategies in most cases and extending in multiple new directions the existing literature .
Revised: The present paper aims to provide the first systematic study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control theory, obtaining closed-form optimal strategies in most cases and extending in multiple new directions the existing literature .
Edit intent: clarity
Original: The present paper aims to provide the first systematic study of high-dimensional statistical arbitrage using both factor models and tools from stochastic control theory, obtaining closed-form optimal strategies in most cases and extending in multiple new directions the existing literature .
Revised: The present paper aims to provide the first systematic study of high-dimensional statistical arbitrage using both factor models and tools from stochastic control , obtaining explicit closed-form optimal strategies in most cases and extending in multiple new directions the existing literature .
Edit intent: clarity
Original: The present paper aims to provide the first systematic study of high-dimensional statistical arbitrage using both factor models and tools from stochastic control theory, obtaining closed-form optimal strategies in most cases and extending in multiple new directions the existing literature .
Revised: The present paper aims to provide the first systematic study of high-dimensional statistical arbitrage using both factor models and tools from stochastic control theory, obtaining closed-form optimal strategies which are both easily interpretable and computationally implementable in a high-dimensional setting .
Edit intent: clarity
Original:  Our setup is based on a general statistically-constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyze the problem of investing both in continuous time and along a discrete sequence of optimally chosen deterministic times, with different utilities and finite horizon .
Revised:  Our setup is based on a general statistically-constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyze the problem of investing optimally in continuous time and along a discrete sequence of optimally chosen deterministic times, with different utilities and finite horizon .
Edit intent: clarity
Original:  Our setup is based on a general statistically-constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyze the problem of investing both in continuous time and along a discrete sequence of optimally chosen deterministic times, with different utilities and finite horizon .
Revised:  Our setup is based on a general statistically-constructed factor model with mean-reverting residuals, in which we show how to construct analytically market-neutral portfolios and we analyze the problem of investing both in continuous time and finite horizon under exponential and mean-variance utilities .
Edit intent: clarity
Original:  We also discuss how to introduce various constraints on the investor's portfolio and  temporary quadratic transaction costs in both discrete and continuous time, provide Monte Carlo simulations of the previous strategies  , and describe further  extensions of our work.
Revised:  We also extend our model to incorporate various constraints on the investor's portfolio and temporary quadratic transaction costs in both discrete and continuous time, provide Monte Carlo simulations of the previous strategies , and describe further extensions of our work.
Edit intent: clarity
Original:  We also discuss how to introduce various constraints on the investor's portfolio and  temporary quadratic transaction costs in both discrete and continuous time, provide Monte Carlo simulations of the previous strategies  , and describe further  extensions of our work.
Revised:  We also discuss how to introduce various constraints on the investor's portfolio and market frictions in the form of temporary quadratic transaction costs in both discrete and continuous time, provide Monte Carlo simulations of the previous strategies , and describe further extensions of our work.
Edit intent: meaning-changed
Original:  We also discuss how to introduce various constraints on the investor's portfolio and  temporary quadratic transaction costs in both discrete and continuous time, provide Monte Carlo simulations of the previous strategies  , and describe further  extensions of our work.
Revised:  We also discuss how to introduce various constraints on the investor's portfolio and temporary quadratic transaction costs in both discrete and continuous time, provide Monte Carlo simulations of the previous strategies with 100 assets , and describe further extensions of our work.
Edit intent: meaning-changed
Original:  We also discuss how to introduce various constraints on the investor's portfolio and  temporary quadratic transaction costs in both discrete and continuous time, provide Monte Carlo simulations of the previous strategies  , and describe further  extensions of our work.
Revised:  We also discuss how to introduce various constraints on the investor's portfolio and temporary quadratic transaction costs in both discrete and continuous time, provide Monte Carlo simulations of the previous strategies , and describe further possible extensions of our work.
Edit intent: clarity
Original: The present paper aims to provide a systematic study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining explicit closed-form optimal strategies which are both easily interpretable and computationally implementable in a high-dimensional setting.
Revised: The present paper provides a study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining explicit closed-form optimal strategies which are both easily interpretable and computationally implementable in a high-dimensional setting.
Edit intent: clarity
Original: The present paper aims to provide a systematic study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining explicit closed-form optimal strategies which are both easily interpretable and computationally implementable in a high-dimensional setting.
Revised: The present paper aims to provide a systematic study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining closed-form optimal strategies which are both easily interpretable and computationally implementable in a high-dimensional setting.
Edit intent: clarity
Original: The present paper aims to provide a systematic study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining explicit closed-form optimal strategies which are both easily interpretable and computationally implementable in a high-dimensional setting.
Revised: The present paper aims to provide a systematic study of high-dimensional statistical arbitrage that combines factor models with the tools from stochastic control, obtaining explicit closed-form optimal strategies which are both interpretable and computationally implementable in a high-dimensional setting.
Edit intent: clarity
Original:  We also extend our model to incorporate various constraints on the investor's portfolio  and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.
Revised:  We also extend our model to incorporate constraints on the investor's portfolio and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.
Edit intent: clarity
Original:  We also extend our model to incorporate various constraints on the investor's portfolio  and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.
Revised:  We also extend our model to incorporate various constraints on the investor's portfolio like dollar-neutrality and market frictions in the form of temporary quadratic transaction costs, provide extensive Monte Carlo simulations of the previous strategies with 100 assets, and describe further possible extensions of our work.
Edit intent: meaning-changed
Original:  Previous modeling of one such two-component system - the pyrenoid, URLanelle found in algae and plants - surprisingly revealed that phase separation is suppressed for certain combinations of numbers of binding sites.
Revised:  Previous biologically-motivated modeling of such a two-component system - the pyrenoid, URLanelle found in algae and plants - surprisingly revealed that phase separation is suppressed for certain combinations of numbers of binding sites.
Edit intent: clarity
Original:  This phenomenon, dubbed the "magic-number " effect , occurs if the two polymers can form fully-bonded small oligomers by virtue of the number of binding sites in one polymer being an integer multiple of the number of binding sites of the other.
Revised:  This phenomenon, dubbed the "magic-number effect" , occurs if the two polymers can form fully-bonded small oligomers by virtue of the number of binding sites in one polymer being an integer multiple of the number of binding sites of the other.
Edit intent: fluency
Original:  The  aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means.
Revised:  The framework is consistently applicable throughout a range of volatility models -including the rough volatility family- and a range of derivative contracts. The aim of neural networks in this work is an off-line approximation of complex pricing functions, which are difficult to represent or time-consuming to evaluate by other means.
Edit intent: meaning-changed
Original:  As customary for machine learning, the form in which information from available data is extracted and stored is crucial for network performance. With this in mind, we discuss how our approach addresses the usual challenges of machine learning solutions in a financial context (availability of training data, interpretability of results for regulators, control over generalisation errors). We present specific architectures for price approximation and calibration and optimize these with respect to different objectives regarding accuracy, speed and robustness .
Revised:  As customary for machine learning, the form in which information from available data is extracted and stored influences network performance: This approach is inspired by representing the implied volatility and option prices as a collection of pixels. In a number of applications we demonstrate the prowess of this modelling approach regarding accuracy, speed and robustness .
Edit intent: meaning-changed
Original:  We present specific architectures for price approximation and calibration and optimize these with respect to different objectives regarding accuracy, speed and robustness . We also find that including the intermediate step of learning pricing functions of (classical or rough) models before calibration significantly improves network performance compared to direct calibration to data .
Revised:  We present specific architectures for price approximation and calibration and optimize these with respect to different objectives regarding accuracy, speed , robustness and generality and also its potentials towards model recognition .
Edit intent: meaning-changed
Original:  Here we show that a large segment of niche theory is equivalent to a Minimum Environmental Perturbation Principle (MEPP): ecosystems URLanize into a state that minimizes the  impact URLanisms on their environment.
Revised:  Here we show that , for a wide range of modeling assumptions, niche theory is equivalent to a Minimum Environmental Perturbation Principle (MEPP): ecosystems URLanize into a state that minimizes the impact URLanisms on their environment.
Edit intent: meaning-changed
Original:  Here we show that a large segment of niche theory is equivalent to a Minimum Environmental Perturbation Principle (MEPP): ecosystems URLanize into a state that minimizes the  impact URLanisms on their environment.
Revised:  Here we show that a large segment of niche theory is equivalent to a Minimum Environmental Perturbation Principle (MEPP): ecosystems URLanize into a state that minimizes the collective impact URLanisms on their environment.
Edit intent: clarity
Original:  However, even in the presence of such feedbacks, a weaker, local version of the MEPP still applies in a limited region of resource space  .
Revised:  However, even in the presence of such feedbacks, a weaker, local version of the MEPP still applies in a limited region of resource space . We show that the MEPP framework is consistent with classic experiments on competition for substitutable resources in herbivorous zooplankton and propose new experiments for testing MEPP directly .
Edit intent: meaning-changed
Original: Contemporary niche theory is a powerful conceptual framework for understanding URLanisms interact with each other and with their shared environment. Herewe show that, for a wide range of modeling assumptions, niche theoryis equivalent to a Minimum Environmental Perturbation Principle (MEPP): ecosystems URLanize into a state that minimizes the collective impact URLanisms on their environment. Different choices of environmental dynamics naturally give rise to distinct dissimilarity measures for quantifying environmental impact. The MEPP allows for the analysis of ecosystems with large numbers of species and environmental factors and provides a new avenue for analyzing ecological invasions.
Revised: Fifty years ago, Robert MacArthur showed that stable equilibria optimize quadratic functions of the population sizes in several important ecological models. Here, we generalize this finding to a broader class of systems within the framework of contemporary niche theory, and precisely state the conditions under which an optimization principle (not necessarily quadratic) can be obtained.
Edit intent: meaning-changed
Original:  We show that  the presence of environmental feedbacks, URLanisms can produce new resources in addition to depleting them, violates the global MEPP.
Revised:  We show that conducting the optimization in the space of environmental states instead of population sizes leads to a universal and transparent physical interpretation of the objective function. Specifically, the presence of environmental feedbacks, URLanisms can produce new resources in addition to depleting them, violates the global MEPP.
Edit intent: meaning-changed
Original:  We show that  the presence of environmental feedbacks, URLanisms can produce new resources in addition to depleting them, violates the global MEPP. However, even in the presence of such feedbacks, a weaker, local version of the MEPP still applies in a limited region of resource space. We show that the MEPP framework is consistent with classic experiments on competition for substitutable resources in herbivorous zooplankton and propose new experiments for testing MEPP directly .
Revised:  We show that the equilibrium state minimizes the perturbation of the environment induced by the presence of the competing species, subject to the constraint that no species has a positive net growth rate. We use this "minimum environmental perturbation principle" to make new predictions for eco-evolution and community assembly, and describe a simple experimental setting where its conditions of validity have been empirically tested .
Edit intent: clarity
Original:  The method's performance is briefly tested using German Intraday Continuous data. Despite the simplicity of the method, the results are encouraging. The supplementary materials containing the R-codes and the data are attached to this paper .
Revised:  A rolling window forecasting study of future transaction arrival times is conducted in order to gain significant insights to the performance of the considered models. We analyse both the in-sample characteristics and the forecasting efficiency. In the forecasting part, artificial trajectories are simulated and then evaluated by calculating the \mathcal{L .
Edit intent: meaning-changed
Original:  We analyse both the in-sample characteristics and the forecasting efficiency. In the forecasting part, artificial trajectories are simulated and then evaluated by calculating the \mathcal{L .
Revised:  We analyse both the in-sample characteristics and the probabilistic forecasting performance. In a rolling window forecasting study, we simulate many trajectories to evaluate the forecasts and gain significant insights into the model fit. The prediction accuracy is evaluated by a functional version of the MAE (mean absolute error), RMSE (root mean squared error) and CRPS (continuous ranked probability score) for the simulated count processes. This paper fills the gap in the literature regarding the intensity estimation of transaction arrivals and is a major contribution to the topic, yet leaves much of the field for further development .
Edit intent: meaning-changed
Original:  The study presented in this paper is conducted based on the German Intraday Continuous electricity market data, but this method can be easily applied to any other continuous intraday electricity market  .
Revised:  The study presented in this paper is conducted based on the German Intraday Continuous electricity market data, but this method can be easily applied to any other continuous intraday electricity market . For the German market, a specific generalized gamma distribution setup explains the overall behaviour significantly best, especially as the tail behaviour of the process is well covered .
Edit intent: meaning-changed
Original:   Based on an upper bound on the stationary number of active "Lambda servers" considering the execution-time distribution of Lambda functions, we describe an approach that the cloud could use to overbook Lambda functions for improved utilization of IT resources.
Revised:  Such quotas are due to, e.g., limited resources (as in a fog/edge-cloud context) or to prevent excessive unauthorized invocation of numerous instances by malware. Based on an upper bound on the stationary number of active "Lambda servers" considering the execution-time distribution of Lambda functions, we describe an approach that the cloud could use to overbook Lambda functions for improved utilization of IT resources.
Edit intent: meaning-changed
Original:  An earlier bound for a single service tier is extended to the case of multiple service tiers  .
Revised:  An earlier bound for a single service tier is extended to the case of multiple service tiers . For the context of scheduling microservices in a private setting, the framework could be used to determine the required VM resources for a token-bucket constrained workload stream. Finally, we note that the looser Markov inequality may be useful in settings where the job service times are dependent .
Edit intent: meaning-changed
Original: A growing number of neuromorphic spiking neural network processors that emulate biological neural networks create an imminent need for methods and tools to enable them to solve real-world signal processing problems.
Revised: Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neuromorphic spiking neural network processors that emulate biological neural networks create an imminent need for methods and tools to enable them to solve real-world signal processing problems.
Edit intent: meaning-changed