wanyu/IteraTeR_full_sent huggingface dataset:
Original: Machine learning models often excel in the accuracy of their predictions but are opaque due to their non-linear and non-parametric structure. This makes statistical inferencechallenging and disqualifies them from many applications where model interpretability is crucial. This paper proposes the Shapley regression framework as an approach for statistical inference on non-linear or non-parametric models. Inference is performed based on the Shapley value decomposition of a model , a pay-off concept from cooperative game theory.
Revised: Universal function approximators, such as artificial neural networks, can learn a large variety of target functions arbitrarily well given sufficient training data. This flexibility comes at the cost of the ability to perform parametric inference. We address this gap by proposing a generic framework based on the Shapley-Taylor decomposition of a model. A surrogate parametric regression analysis is performed in the space spanned by the Shapley value expansion of a model , a pay-off concept from cooperative game theory.
Edit intent: meaning-changed
Original:  Inference is performed based on the Shapley value decomposition of a model , a pay-off concept from cooperative game theory. I show that universal approximators from machine learning are estimation consistent and introduce hypothesis tests for individual variable contributions, model bias and parametric functional forms. The inference properties of state-of-the-art machine learning models - like artificial neural networks, support vector machines and random forests - are investigated using numerical simulations and real-world data.
Revised:  Inference is performed based on the Shapley value decomposition of a model . This allows for the testing of standard hypotheses of interest. At the same time, the proposed approach provides novel insights into statistical learning processes themselves derived from the consistency and bias properties of the nonparametric estimators. We apply the framework to the estimation of heterogeneous treatment effects in simulated and real-world data.
Edit intent: clarity
Original:  The inference properties of state-of-the-art machine learning models - like artificial neural networks, support vector machines and random forests - are investigated using numerical simulations and real-world data. The proposed framework is unique in the sense that it is identical to the conventional case of statistical inference on a linear model if the model is linear in parameters. This makes it a well-motivated extension to more general models and strengthens the case for the use of machine learning to inform decisions .
Revised:  The inference properties of state-of-the-art machine learning models - like artificial neural networks, support vector machines and random forests - are investigated using numerical simulations and real-world randomised experiments. We introduce an explicit treatment function based on higher-order Shapley-Taylor indices. This can be used to identify potentially complex treatment channels and help the generalisation of findings from experimental settings. More generally, the presented approach allows for a standardised use and communication of results form machine learning models .
Edit intent: meaning-changed
Original: In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using available Polysomnography (PSG) measurement channels provided in the 2018 PhysioNet challenge database.
Revised: In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using Polysomnography (PSG) measurement channels provided in the 2018 PhysioNet challenge database.
Edit intent: clarity
Original: In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using available Polysomnography (PSG) measurement channels provided in the 2018 PhysioNet challenge database.
Revised: In this work, a dense recurrent convolutional neural network (DRCNN) was constructed to detect sleep disorders including arousal, apnea and hypopnea using available Polysomnography (PSG) measurement channels provided in the 2018 Physionet challenge database.
Edit intent: fluency
Original:  Three binary cross-entropy loss functions corresponding to sleep/wake, arousal presence/absence and apnea-hypopnea/normal detection tasks are summed up to generate our overall network loss function that is optimized using the Adam method.
Revised:  Three binary cross-entropy loss functions corresponding to sleep/wake, target arousal and apnea-hypopnea/normal detection tasks are summed up to generate our overall network loss function that is optimized using the Adam method.
Edit intent: clarity
Original:  Our proposed algorithm achieved the first place in the official stage of the 2018 PhysioNet challenge for detecting sleep arousals with AUPRC of 0.54 on the blind testing dataset.
Revised:  Our proposed algorithm achieved the first place in the official stage of the 2018 Physionet challenge for detecting sleep arousals with AUPRC of 0.54 on the blind testing dataset.
Edit intent: fluency
Original:  These networks deliver resources and eliminate wastes from early development onward, and even play a vital role in the growth, prognosis, and treatment of tumors and stroke recovery. Because of these basic and applied reasons there is immense interest in identifying key features of vascular branching and their connection to biological function. Here we classify diverse branching networks-mouse lung, human head and torso, angiosperm plants, and gymnosperm plants-by harnessing recent advances in medical imaging, algorithms and software for extracting vascular data, theory for resource-distribution networks, and machine-learning.
Revised:  By combining machine-learning techniques with new theory that relates vascular form to metabolic function, we enable novel classification of diverse branching networks--mouse lung, human head and torso, angiosperm plants, and gymnosperm plants-by harnessing recent advances in medical imaging, algorithms and software for extracting vascular data, theory for resource-distribution networks, and machine-learning.
Edit intent: clarity
Original:  Here we classify diverse branching networks-mouse lung, human head and torso, angiosperm plants, and gymnosperm plants-by harnessing recent advances in medical imaging, algorithms and software for extracting vascular data, theory for resource-distribution networks, and machine-learning. Specifically, we apply standard machine-learning techniques to a variety of feature spaces. Our results show that our theoretically-informed feature spaces-especially those that determine blood flow rate-combined with Kernel Density Estimation are best at distinguishing networks.
Revised:  Here we classify diverse branching networks-mouse lung, human head and torso, angiosperm and gymnosperm plants. We find that ratios of limb radii--which dictate essential biologic functions related to resource transport and supply--are best at distinguishing networks.
Edit intent: clarity
Original:  Our results show that our theoretically-informed feature spaces-especially those that determine blood flow rate-combined with Kernel Density Estimation are best at distinguishing networks. Our categorization of networks enhances the mapping between biologic function-such as the dependence of metabolic rate on body mass-to vascular branching traits URLanisms URLans. We accomplish this by analyzing how variation in metabolic scaling exponents-around the canonical value of 3/4-arises despite differences in vascular traits.
Revised:  Our results show that our theoretically-informed feature spaces-especially those that determine blood flow rate-combined with Kernel Density Estimation are best at distinguishing branching networks. We also show how variation in metabolic scaling exponents-around the canonical value of 3/4-arises despite differences in vascular traits.
Edit intent: clarity
Original:  We accomplish this by analyzing how variation in metabolic scaling exponents-around the canonical value of 3/4-arises despite differences in vascular traits. Our results reveal how network categorization and variation in metabolic scaling are both heavily determined by scaling ratios of vessel radii-changes and asymmetries across branching generations-that strongly constrain rates of fluid flow. These linkages will improve understanding of evolutionary convergence across plants and animals while also potentially aiding prognosis and treatment of vascular pathologies and other diseased states .
Revised:  We accomplish this by analyzing how variation in vascular and branching geometry persists despite observing a convergent relationship URLanisms for how metabolic rate depends on body mass .
Edit intent: clarity
Original:  The complementary "kissing" duplex loops , like RNA codon-anticodon k-mer duplexes , would seed a more extensive pairing interaction that would then extend until limited by lack of homology or other factors.
Revised:  The complementary "kissing" duplex loops - like tRNA anticodon-codon k-mer duplexes , would seed a more extensive pairing interaction that would then extend until limited by lack of homology or other factors.
Edit intent: clarity
Original:  The complementary "kissing" duplex loops , like RNA codon-anticodon k-mer duplexes , would seed a more extensive pairing interaction that would then extend until limited by lack of homology or other factors.
Revised:  The complementary "kissing" duplex loops , like RNA codon-anticodon k-mer duplexes - would seed a more extensive pairing interaction that would then extend until limited by lack of homology or other factors.
Edit intent: fluency
Original:  The complementary "kissing" duplex loops , like RNA codon-anticodon k-mer duplexes , would seed a more extensive pairing interaction that would then extend until limited by lack of homology or other factors.
Revised:  The complementary "kissing" duplex loops , like RNA codon-anticodon k-mer duplexes , would seed a more extensive pairing that would then extend until limited by lack of homology or other factors.
Edit intent: clarity
Original:  The analytical power of k-mer differences supports the theses that evolutionary advance  serves the needs of nucleic acids (genomes) rather than proteins (genes), and that such differences have often played a role in early speciation events.
Revised:  The analytical power of k-mer differences supports the theses that evolutionary advance sometimes serves the needs of nucleic acids (genomes) rather than proteins (genes), and that such differences have often played a role in early speciation events.
Edit intent: clarity
Original:  In previous works without STDPs, fast sparsely synchronized rhythms  were found to appear in a range of noise intensity D . Here, by varying D, we investigate the effect of interpopulation STDPs on population states in the I- and the E-populations.
Revised:  In previous works without STDPs, fast sparsely synchronized rhythms , related to diverse cognitive functions, were found to appear in a range of noise intensity D . Here, by varying D, we investigate the effect of interpopulation STDPs on population states in the I- and the E-populations.
Edit intent: meaning-changed
Original:  In previous works without STDPs, fast sparsely synchronized rhythms  were found to appear in a range of noise intensity D . Here, by varying D, we investigate the effect of interpopulation STDPs on population states in the I- and the E-populations.
Revised:  In previous works without STDPs, fast sparsely synchronized rhythms were found to appear in a range of noise intensity D for static synaptic strengths. By varying D, we investigate the effect of interpopulation STDPs on population states in the I- and the E-populations.
Edit intent: meaning-changed
Original:  In previous works without STDPs, fast sparsely synchronized rhythms  were found to appear in a range of noise intensity D . Here, by varying D, we investigate the effect of interpopulation STDPs on population states in the I- and the E-populations.
Revised:  In previous works without STDPs, fast sparsely synchronized rhythms were found to appear in a range of noise intensity D . Here, by varying D, we investigate the effect of interpopulation STDPs on diverse population and individual properties of fast sparsely synchronized rhythms that emerge in both the I- and the E-populations.
Edit intent: meaning-changed
Original:  Depending on values of D, long-term potentiation (LTP) and long-term depression (LTD) for population-averaged values of saturated interpopulation synaptic strengths are found to occur, and they make effects on the degree of population synchronization.
Revised:  Depending on values of D, long-term potentiation (LTP) and long-term depression (LTD) for population-averaged values of saturated interpopulation synaptic strengths are found to occur, and they make effects on the degree of fast sparse synchronization.
Edit intent: meaning-changed
Original:  We note that the equalization effect in interpopulation synaptic plasticity is  in contrast to the Matthew (bipolarization) effect in intrapopulation (I to I and E to E) synaptic plasticity where good (bad) synchronization gets better (worse).
Revised:  We note that the equalization effect in interpopulation synaptic plasticity is distinctly in contrast to the Matthew (bipolarization) effect in intrapopulation (I to I and E to E) synaptic plasticity where good (bad) synchronization gets better (worse).
Edit intent: style
Original:  Moreover , emergences of LTP and LTD of interpopulation synaptic strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times.
Revised:  Finally , emergences of LTP and LTD of interpopulation synaptic strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times.
Edit intent: fluency
Original:  Understanding the development of trends and identifying trend reversals in decadal time series is becoming more and more important.
Revised: Ethane is the most abundant non-methane hydrocarbon in the Earth's atmosphere and an important precursor of tropospheric ozone through various chemical pathways. Ethane is also an indirect greenhouse gas (global warming potential), influencing the atmospheric lifetime of methane through the consumption of the hydroxyl radical (OH). Understanding the development of trends and identifying trend reversals in decadal time series is becoming more and more important.
Edit intent: meaning-changed
Original:  Understanding the development of trends and identifying trend reversals in decadal time series is becoming more and more important. Many climatological and atmospheric time series are characterized by autocorrelation, heteroskedasticity  and seasonal effects.
Revised:  Understanding the development of trends and identifying trend reversals in atmospheric ethane is therefore crucial. Our dataset consists of four series of daily ethane columns obtained from ground-based FTIR measurements. As many other decadal time series, our data are characterized by autocorrelation, heteroskedasticity and seasonal effects.
Edit intent: meaning-changed
Original:  Our autoregressive wild bootstrap approach  combined with a seasonal filter, is able to handle all issues mentioned above . We apply our methods to a set of atmospheric ethane series with a focus on the measurements obtained above the Jungfraujoch in the Swiss Alps. Ethane is the most abundant non-methane hydrocarbon in the Earth's atmosphere, an important precursor of tropospheric ozone and a good indicator of oil and gas production as well as transport. Its monitoring is therefore crucial for the characterization of air quality and of the transport of tropospheric pollution .
Revised:  Our autoregressive wild bootstrap approach combined with a seasonal filter, is able to handle all issues mentioned above .
Edit intent: coherence
Original:  Here we present a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Revised:  Here we use an analogy with art to provide an accessible explanation of why this happens. Additionally, we introduce a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Edit intent: meaning-changed
Original:  Here we present a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Revised:  Here we present a numerical method for decomposing differential equations into two terms: the gradient term that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Edit intent: clarity
Original:  Here we present a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Revised:  Here we present a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential , and the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Edit intent: clarity
Original:  Here we present a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Revised:  Here we present a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term that lacks it . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Edit intent: clarity
Original:  Here we present a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we can still make approximate use of the concept of potential .
Revised:  Here we present a numerical method for decomposing differential equations of any size in two terms, one that has an associated potential (the gradient term), and another one that lacks it ( the non-gradient term ) . In regions of the state space where the magnitude of the non-gradient term is small compared to the gradient part, we use the gradient term to approximate the potential as quasi-potential .
Edit intent: meaning-changed
Original:  On  one hand, the long range dependence has been faced replacing the traditional Gauss-Wiener process (Brownian motion), characterized by their stationary independent increments, by a fractional version.
Revised:  On the one hand, the long range dependence has been faced replacing the traditional Gauss-Wiener process (Brownian motion), characterized by their stationary independent increments, by a fractional version.
Edit intent: fluency
Original:  On  one hand, the long range dependence has been faced replacing the traditional Gauss-Wiener process (Brownian motion), characterized by their stationary independent increments, by a fractional version.
Revised:  On one hand, the long-range dependence has been faced replacing the traditional Gauss-Wiener process (Brownian motion), characterized by their stationary independent increments, by a fractional version.
Edit intent: fluency
Original:  On  one hand, the long range dependence has been faced replacing the traditional Gauss-Wiener process (Brownian motion), characterized by their stationary independent increments, by a fractional version.
Revised:  On one hand, the long range dependence has been faced replacing the traditional Gauss-Wiener process (Brownian motion), characterized by stationary independent increments, by a fractional version.
Edit intent: fluency
Original:  Using the fractional versions of both the Ito's calculus and the Fokker-Planck equation, the transition probability density function of the asset price is obtained as a solution of a non-stationary Feller process with time-varying coefficients ;
Revised:  Using the fractional versions of both the Ito's calculus and the Fokker-Planck equation, the transition probability density function of the asset price is obtained as the solution of a non-stationary Feller process with time-varying coefficients ;
Edit intent: fluency
Original:  Using the fractional versions of both the Ito's calculus and the Fokker-Planck equation, the transition probability density function of the asset price is obtained as a solution of a non-stationary Feller process with time-varying coefficients ; and the analytical valuation formula for the European Call Option is provided .
Revised:  Using the fractional versions of both the Ito's calculus and the Fokker-Planck equation, the transition probability density function of the asset price is obtained as a solution of a non-stationary Feller process with time-varying coefficients , getting an analytical valuation formula for the European Call Option is provided .
Edit intent: coherence
Original:  Finally, recurrent deep neural network models clearly outperform  feedforward models in terms of their ability to jointly capture the multi-region cortical dynamics  .
Revised:  Finally, recurrent deep neural network models clearly outperform feedforward models in terms of their ability to jointly capture the multi-region cortical dynamics . Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections .
Edit intent: meaning-changed