wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In order to make statistical inferences and account for parameter uncertainty , we propose a method whereby a likelihood function can be constructed for QF-valued data, and develop an adaptive MCMC sampling algorithm for simulating from the posterior distribution. Compared to modelling realised measures,modelling the entire quantile functions of intra-daily returns allows one to gain more insight into the dynamic structure of price movements.
Revised:  In order to make statistical inferences and account for parameter uncertainty ; an efficient MCMC algorithm is developed for sampling-based posterior inference. Using ten international market indices and approximately 2,000 days of out-of-sample data from each market, the performance of the DQF model compares favourably, in terms of forecasting VaR of intra-daily returns allows one to gain more insight into the dynamic structure of price movements.
Edit intent: meaning-changed
Original:  Compared to modelling realised measures,modelling the entire quantile functions of intra-daily returns allows one to gain more insight into the dynamic structure of price movements. Via simulations, we show that the proposed MCMC algorithm is effective in recovering the posterior distribution, and that the posterior means are reasonable point estimates of the model parameters. For empirical studies, the new model is applied to analysing one-minute returns of major international stock indices. Through quantile scaling, we further demonstrate the usefulness of our method by forecasting one-step-ahead the Value-at-Risk of daily returns.
Revised:  Compared to modelling realised measures,modelling the entire quantile functions of intra-daily returns , against the interval-valued and histogram-valued time-series models. Additionally, we demonstrate that the QF-valued forecasts can be used to forecast VaR measures at the daily timescale via a simple quantile regression model on daily returns (QR-DQF). In certain markets, the resulting QR-DQF model is able to provide competitive VaR forecasts for daily returns.
Edit intent: meaning-changed
Original: To effectively connect animal behaviorsto activities and patterns in the nervous system, it is ideal have a precise, accurate, and complete description of stereotyped modules and their dynamics in behaviors. In case of rodent behaviors, observers have identified and described several stereotyped behaviors, such as grooming and lateral threat. Discovering behavioralrepertoires in this way is imprecise, slow and contaminated with biases and individual differences. As a replacement, we propose a framework for unbiased, efficient and precise investigation of rat locomotor activities. We propose that locomotion possesses multiscale dynamics that can be well approximated by multiple Markov processes running in parallelat different spatial-temporal scales. To capture motifs and transition dynamics on multiple scales, we developed a segmentation-decomposition procedure, which imposes explicit constraints on timescales on parallel Hidden Markov Models (HMM). Each HMM describes the motifs and transition dynamics at its respective timescale. We showed that the motifs discovered across timescales have experimental significance and space-dependent heterogeneity. Through statistical tests, we show that locomotor dynamics largely conforms with Markov property across scales. Finally, using layered HMMs, we showed that motif assembly is strongly constrained to a few fixed sequences. The motifs potentially reflect outputs of canonical underlying behavioral output motifs. Our approach and results for the first time capture behavioral dynamics at different spatial-temporal scales, painting a more complete picture of how behaviorsURLanized  .
Revised: Behaviors of several laboratory animals can be modeled as sequences of stereotyped behaviors, or behavioral motifs. However, identifying such motifs is a challenging problem. Behaviors have a multi-scale structure: the animal can be simultaneously performing a small-scale motif and a large-scale one (e.g. .
Edit intent: meaning-changed
Original:  Our approach and results for the first time capture behavioral dynamics at different spatial-temporal scales, painting a more complete picture of how behaviorsURLanized  .
Revised:  Our approach and results for the first time capture behavioral dynamics at different spatial-temporal scales, painting a more complete picture of how behaviorsURLanized ). Motifs are compositional: a large-scale motif is a chain of smaller-scale ones, folded in (some behavioral) space in a specific manner. We demonstrate an approach which captures these structures, using rat locomotor data as an example. From the same dataset, we used a preprocessing procedure to create different versions, each describing motifs of a different scale. We then trained several Hidden Markov Models (HMMs) in parallel, one for each dataset version. This approach essentially forced each HMM to learn motifs on a different scale, allowing us to capture behavioral structures lost in previous approaches. By comparing HMMs with models representing different null hypotheses, we found that rat locomotion was composed of distinct motifs from second scale to minute scale. We found that transitions between motifs were modulated by rats' location in the environment, leading to non-Markovian transitions. To test the ethological relevance of motifs we discovered, we compared their usage between rats with differences in a high-level trait, prosociality. We found that these rats had distinct motif repertoires, suggesting that motif usage statistics can be used to infer internal states of rats. Our method is therefore an efficient way to discover multi-scale, compositional structures in animal behaviors. It may also be applied as a sensitive assay for internal states .
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Original:  We  establish consistency and asymptotic normality  under general conditions that allow for weakly dependent data and nonlinear structural models , and we explore options for testing general nonlinear hypotheses.
Revised:  We propose a method of moments estimator for exactly identified models, as well as a generalized method of moments estimator for over-identified models. We establish consistency and asymptotic normality under general conditions that allow for weakly dependent data and nonlinear structural models , and we explore options for testing general nonlinear hypotheses.
Edit intent: meaning-changed
Original:  We  establish consistency and asymptotic normality  under general conditions that allow for weakly dependent data and nonlinear structural models , and we explore options for testing general nonlinear hypotheses. Simulations with iid and time series data illustrate the finite-sample properties of the estimators and tests .
Revised:  We establish consistency and asymptotic normality under general conditions that allow for weakly dependent data and nonlinear structural models . Simulations illustrate the finite-sample properties of the estimators and tests .
Edit intent: clarity
Original:  For the four countries we examine, the quantile estimates of discount factor and elasticity of intertemporal substitution are economically reasonable for a range of quantiles just above the median, even when two-stage least squares estimates are not reasonable . Code is provided for all methods, simulations, and applications at the third author's website .
Revised:  For the four countries we examine, the quantile estimates of discount factor and elasticity of intertemporal substitution are economically reasonable for a range of quantiles above the median, even when two-stage least squares estimates are not reasonable . Code is provided for all methods, simulations, and applications at the third author's website .
Edit intent: clarity
Original:  For the four countries we examine, the quantile estimates of discount factor and elasticity of intertemporal substitution are economically reasonable for a range of quantiles just above the median, even when two-stage least squares estimates are not reasonable . Code is provided for all methods, simulations, and applications at the third author's website .
Revised:  For the four countries we examine, the quantile estimates of discount factor and elasticity of intertemporal substitution are economically reasonable for a range of quantiles just above the median, even when two-stage least squares estimates are not reasonable .
Edit intent: clarity
Original: We consider a modelwhen private banks with interbank cash flows as in (Carmona, Fouque, Sun, 2013) borrow from the outside economy at a certain interest rate, controlled by the central bank, and invest in risky assets.
Revised: In our model, private actors with interbank cash flows as in (Carmona, Fouque, Sun, 2013) borrow from the outside economy at a certain interest rate, controlled by the central bank, and invest in risky assets.
Edit intent: clarity
Original: We consider a modelwhen private banks with interbank cash flows as in (Carmona, Fouque, Sun, 2013) borrow from the outside economy at a certain interest rate, controlled by the central bank, and invest in risky assets.
Revised: We consider a modelwhen private banks with interbank cash flows similar to, but nore general than (Carmona, Fouque, Sun, 2013) borrow from the outside economy at a certain interest rate, controlled by the central bank, and invest in risky assets.
Edit intent: meaning-changed
Original:  The central bank, in turn, aims to control the overall size of financial system. A default occurs when the net worth of a bank goes below a certain threshold. We solve stochastic optimal control problems , and consider systemic risk by studying probability of a certain number of defaults over fixed finite time horizon .
Revised:  The central bank, in turn, aims to control the overall economy by means of an exponential utility function. We solve all stochastic optimal control problems , and consider systemic risk by studying probability of a certain number of defaults over fixed finite time horizon .
Edit intent: meaning-changed
Original:  The accuracy of statistical inferences is 20 \\%DIF < . The most probable Bitcoin price in 180 days is 4537 USD.abstract %DIF > . The most probable Bitcoin price in 180 days is 6358 USD.
Revised:  The accuracy of statistical inferences is 10 \\%DIF < . The most probable Bitcoin price in 180 days is 4537 USD.abstract %DIF > . The most probable Bitcoin price in 180 days is 6358 USD.
Edit intent: meaning-changed
Original: Given a convex polygon P with n vertices, we show that all the maximal area triangles in P can be computed ] in linear time .
Revised: Given a convex polygon P with n vertices, we show that all the maximal area triangles in P can be computed ], the widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al., and whether it can still be solved in linear time .
Edit intent: meaning-changed
Original: Given a convex polygon P with n vertices, we show that all the maximal area triangles in P can be computed ] in linear time . This settles the problem proposed by Keikha et. al. .
Revised: Given a convex polygon P with n vertices, we show that all the maximal area triangles in P can be computed ] in linear time was proposed as an open problem. We resolved it affirmatively by presenting a new linear time algorithm. Our algorithm is simple and easy to implement, and it computes all the locally maximal triangles .
Edit intent: meaning-changed
Original: In arXiv 1705.11035%DIFDELCMD < ]%%% , the widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al. , and whether it can still be solved in linear time was proposed as an open problem.
Revised:  arXiv 1705.11035%DIFDELCMD < ]%%% , the widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al. , and whether it can still be solved in linear time was proposed as an open problem.
Edit intent: coherence
Original: In arXiv 1705.11035%DIFDELCMD < ]%%% , the widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al. , and whether it can still be solved in linear time was proposed as an open problem.
Revised: In %DIFDELCMD < ]%%% , the widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al. , and whether it can still be solved in linear time was proposed as an open problem.
Edit intent: clarity
Original: In arXiv 1705.11035%DIFDELCMD < ]%%% , the widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al. , and whether it can still be solved in linear time was proposed as an open problem.
Revised: In arXiv 1705.11035%DIFDELCMD < ]%%% The widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al. , and whether it can still be solved in linear time was proposed as an open problem.
Edit intent: fluency
Original: In arXiv 1705.11035%DIFDELCMD < ]%%% , the widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al. , and whether it can still be solved in linear time was proposed as an open problem. We resolved it affirmatively by presenting a new linear time algorithm . Our algorithm is simple and easy to implement, and it computes all the locally maximal triangles  .
Revised: In arXiv 1705.11035%DIFDELCMD < ]%%% , the widely known linear time algorithm for computing the maximum area triangle in a convex polygon was found incorrect by Keikha et. al. arXiv 1705.11035 .
Edit intent: coherence
Original:  Our algorithm is simple and easy to implement, and it computes all the locally maximal triangles  .
Revised:  Our algorithm is simple and easy to implement, and it computes all the locally maximal triangles . We present an alternative algorithm in this paper. Comparing to the only previously known correct solution, ours is much simpler and more efficient. Moreover, it can be easily extended to compute the minimum area triangle enclosing a convex polygon. Also, our new approach seems powerful in solving other related problems .
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Original:  In a side-by-side comparison , TIE Microscopy of MTs shows an improvement in signal-to-background-noise ratio of more than three times that of video-enhanced bright images .
Revised:  In a co-registered comparison of MT signal-to-background-noise ratio , TIE Microscopy of MTs shows an improvement in signal-to-background-noise ratio of more than three times that of video-enhanced bright images .
Edit intent: clarity
Original:  Unlike other label-free techniques for imaging microtubules, in TIE microscopy the background is naturally removed when taking the difference of two defocused images, so the need to frequently update a background subtraction image is eliminated.
Revised:  Unlike other label-free techniques for imaging microtubules, in TIE microscopy background removal is a natural consequence of taking the difference of two defocused images, so the need to frequently update a background subtraction image is eliminated.
Edit intent: clarity
Original:  Unlike other label-free techniques for imaging microtubules, in TIE microscopy the background is naturally removed when taking the difference of two defocused images, so the need to frequently update a background subtraction image is eliminated.
Revised:  Unlike other label-free techniques for imaging microtubules, in TIE microscopy the background is naturally removed when taking the difference of two defocused images, so the need to frequently update a background image is eliminated.
Edit intent: clarity
Original:  In this paper, we present  a new algorithmic approach to effectively decode and characterize the role of environment on phenotypic traits , from complex phenomic data. To the best of our knowledge, this effort represents the first application of topological data analysis on phenomics data.
Revised:  In this paper, we present a new algorithmic approach to effectively decode and characterize the role of environment on phenotypic traits . We model the problem as one of unsupervised structure discovery, and use emerging principles from algebraic topology and graph theory for discovering higher-order structures of complex phenomics data.
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Original:  Our results demonstrate the ability of our approach to delineate emergent behavioramong subpopulations, as dictated by one or more environmental factors; notably, our approach shows how the environment plays a key role in determining the phenotypic behaviorof one of the two genotypes.
Revised:  Our results demonstrate the ability of our approach to delineate divergent subpopulation-level behavior. Notably, our approach shows how the environment plays a key role in determining the phenotypic behaviorof one of the two genotypes.
Edit intent: clarity
Original:  notably, our approach shows how the environment plays a key role in determining the phenotypic behaviorof one of the two genotypes. Source code for our implementation and test dataare freely available with detailed instructions at URL
Revised:  notably, our approach shows how the environment plays a key role in determining the phenotypic behaviorof one of the first approaches to systematically formalize the problem of hypothesis extraction for phenomics data. Considering the infancy of the phenomics field, tools that help users explore complex data and extract plausible hypotheses in a data-guided manner will be critical to future advancements in the use of such data.
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Original:  Two branches of the solution exist, having high and low switching rates, such that the low switching rate branch approaches deterministic behavior and the high switching rate branch exhibits sub-Fano behavior  .
Revised:  Two branches of the solution exist, having high and low switching rates, such that the low switching rate branch approaches deterministic behavior and the high switching rate branch exhibits sub-Fano behavior . Average protein number differs from the deterministically expected value. Bimodal probability distributions appear as the protein number becomes a readout of the ON/OFF state of the gene .
Edit intent: meaning-changed
Original: In this paper we  extend the classic reduced-form setting for credit and insurance markets to the case under model uncertainty, when we consider a family of priors possibly mutually singular to each other .
Revised: In this paper we introduce a sublinear conditional expectation with respect to a family of possibly nondominated probability measures on a progressively enlarged filtration. In this way, we extend the classic reduced-form setting for credit and insurance markets to the case under model uncertainty, when we consider a family of priors possibly mutually singular to each other .
Edit intent: meaning-changed
Original: In this paper we  extend the classic reduced-form setting for credit and insurance markets to the case under model uncertainty, when we consider a family of priors possibly mutually singular to each other . To this end, we introduce on a progressively enlarged filtration a sublinear conditional expectation with respect to a family of possibly nondominated probability measures .
Revised: In this paper we extend the classic reduced-form setting for credit and insurance markets to the case under model uncertainty, when we consider a family of priors possibly mutually singular to each other .
Edit intent: clarity
Original:  This approach avoids the computationally expensive step of inferring latent variables, e.g. the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or  single-cell properties.
Revised:  This fitting procedure avoids the computationally expensive step of inferring latent variables, e.g. the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or single-cell properties.
Edit intent: meaning-changed
Original:  This approach avoids the computationally expensive step of inferring latent variables, e.g. the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or  single-cell properties.
Revised:  This approach avoids the computationally expensive step of inferring latent variables, e.g. the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or of single-cell properties.
Edit intent: fluency
Original: Mobile applications are benefiting significantly from the advancement in deep learning, e.g. providing new features . Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values.
Revised: Modern mobile applications are benefiting significantly from the advancement in deep learning, e.g. providing new features . Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values.
Edit intent: clarity
Original: Mobile applications are benefiting significantly from the advancement in deep learning, e.g. providing new features . Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values.
Revised: Mobile applications are benefiting significantly from the advancement in deep learning, e.g. , implementing real-time image recognition and conversational system . Given a trained deep learning model, applications usually need to perform a series of matrix operations based on the input data, in order to infer possible output values.
Edit intent: meaning-changed
Original:  However, because mobile hardware, e.g. GPU and memory size, can be very different and limited when compared to  desktop counterpart, it is important to understand the feasibility of this new device-based deep learning inference architecture.
Revised:  However, because mobile hardware, such as GPU and memory size, can be very different and limited when compared to desktop counterpart, it is important to understand the feasibility of this new device-based deep learning inference architecture.
Edit intent: clarity
Original:  However, because mobile hardware, e.g. GPU and memory size, can be very different and limited when compared to  desktop counterpart, it is important to understand the feasibility of this new device-based deep learning inference architecture.
Revised:  However, because mobile hardware, e.g. GPU and memory size, can be very limited when compared to desktop counterpart, it is important to understand the feasibility of this new device-based deep learning inference architecture.
Edit intent: coherence
Original:  However, because mobile hardware, e.g. GPU and memory size, can be very different and limited when compared to  desktop counterpart, it is important to understand the feasibility of this new device-based deep learning inference architecture.
Revised:  However, because mobile hardware, e.g. GPU and memory size, can be very different and limited when compared to its desktop counterpart, it is important to understand the feasibility of this new device-based deep learning inference architecture.
Edit intent: clarity