wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning  . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal expectations about the transition structures underlying sequences of ordered events .
Revised:  Here we propose a competing perspective: that higher-order associations actually arise from natural errors in learning . Combining ideas from information theory and reinforcement learning, we derive a novel maximum entropy model of people's internal representations of the transitions between stimuli .
Edit intent: clarity
Original:  Additionally, our model asserts that human expectations should depend critically on the different topological scales in a transition network, a prediction that we subsequently test and validate in a novel experiment. Generally, our results highlight the important role of mental errors in shaping abstract representations , and directly inspire new physically-motivated models of human behavior .
Revised:  Together, these results suggest that mental errors influence our abstract representations of the world in significant and predictable ways, with direct implications for the study and design of optimally learnable information sources .
Edit intent: clarity
Original:  Our theoretical results naturally suggest a numerical algorithm to explicitly obtain the solution to quadratic ordinary differential equations of fractional type, that turns out to be quite promising in terms of computational performance. In particular, we introduce a hybrid numerical scheme, we test its precision by comparing our results with the (few) benchmark available in the literature, based on the Adams method , and we numerically study the convergence of our procedure .
Revised:  Our theoretical results naturally suggest a numerical algorithm to explicitly obtain the solution to quadratic ordinary differential equations of fractional type, that turns out to be extremely fast and stable. When applied to option pricing, our method largely outperforms the only available alternative in the literature, based on the Adams method , and we numerically study the convergence of our procedure .
Edit intent: meaning-changed
Original: Decoding the energy pathway by which a DNA unravels is the holy grail of biologists and physicists alike. The underlying physics is an intriguing competition of entropy ordering and disordering in the bound and unbound base-pairs of the intermediate bubbles. We re-examine this in a controlled denaturation study with picoliter solutions of DNA molecules confined in a resonating microfluidic cantilever.
Revised: A double-stranded DNA unravels thermally through intermediate denatured bubble segments. Intrinsically, fluctuations ensue at the bubble boundaries from non-equilibrium (NE) energy exchanges with the environment. However, such details gets obscured by large population kinetics at the macroscale, associating equilibrium pathway to the unravelling landscape. In this work, we capture evidence of fluctuation energetics with picoliter samples in a microfluidic cantilever.
Edit intent: clarity
Original:  To our surprise, a unique entropy fluctuation interplay of the denaturation bubbles is revealed, apparent in a two-stage transition profile. The dynamic dissipation at resonance , which we record, faithfully captures the base-pair binding-unbinding energy landscape, revealing the singular entropy fluctuation characteristic. We attribute the characteristic to the onset of an energy fluctuation scale observable only at the small volume scales employed. The fluctuations apparently favor entropy ordering over disordering at biologically relevant T< 42C in a non-equilibrium transition pathway.
Revised:  We exploit nanomechanical resonance to measure the NE energy exchanges through dissipation, revealing a crucial pre-melting transition at T~ 42C in a non-equilibrium transition pathway.
Edit intent: clarity
Original:  The fluctuations apparently favor entropy ordering over disordering at biologically relevant T< 42C in a non-equilibrium transition pathway. The observed ordering energetics suggests the probability of an intermediate collapsed-bubble conformation releasing energy, sufficient to unbind the bubble ends.
Revised:  The fluctuations apparently favor entropy ordering over disordering at biologically relevant T< 42C . This signifies that unravelling possibly proceeds via intermediate collapsed-bubble conformation releasing energy, sufficient to unbind the bubble ends.
Edit intent: clarity
Original:  The observed ordering energetics suggests the probability of an intermediate collapsed-bubble conformation releasing energy, sufficient to unbind the bubble ends. An alternate energy pathway thus surfaces complementing molecular zipper and bubble state cooperativity transitions. Fluctuation theorem supports our argument, which we further validate from DNA solutions across varying oligomer lengths, base pair moieties, and concentrations. This new information of fluctuation driven ordering energetics can contribute to new insights on DNA transcription, the integral basis of life. The nanomechanical dynamic dissipation paradigm of investigating picoliter bio-samples as introduced offers a new method for investigating other interesting challenges such as folding of proteins and cell proliferation, which also evolve through similar energetics of non-equilibrium processes .
Revised:  The observed ordering energetics suggests the probability of an intermediate collapsed-bubble conformation releasing energy, sufficient to unbind bubble ends, assisting further unbinding. Fluctuation theorem explains the observations opening further avenues to investigate fluctuation kinetics in other biological phenomena that also proceed through similar NE energetics .
Edit intent: clarity
Original:  In part, this is because we lack an ideal observer model of segmentation to guide constrained experiments. On the other hand, despite recent progress in machine learning, modern algorithms still fall short of human segmentation performance. Our goal here is two-fold (i) propose a model to probe human visual segmentation mechanisms and (ii) develop an efficient algorithm for image segmentation. To this aim , we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
Revised:  In part, this is because we lack segmentation models consistent with experimental and theoretical knowledge in visual neuroscience. Biological sensory systems have been shown to approximate probabilistic inference to interpret their inputs. This requires a generative model that captures both the statistics of the sensory inputs and expectations about the causes of those inputs. Following this hypothesis , we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
Edit intent: coherence
Original:  To this aim , we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
Revised:  To this aim , we propose a probabilistic generative model of visual segmentation that for the first time combines 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
Edit intent: clarity
Original:  To this aim , we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
Revised:  To this aim , we propose a novel probabilistic generative model of visual segmentation that combines knowledge about 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
Edit intent: clarity
Original:  To this aim , we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1 ) knowledge about the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
Revised:  To this aim , we propose a novel probabilistic generative model of visual segmentation that for the first time combines 1 ) the sensitivity of neurons in the visual cortex to statistical regularities in natural images;
Edit intent: clarity
Original:  and 2) non-parametric Bayesian priors over segmentation maps (ie partitions of the visual space). We provide an algorithm for learning and inference , validate it on synthetic data , and illustrate how the two components of our model improve segmentation of natural images .
Revised:  and 2) the preference of humans to form contiguous partitions of visual space. We develop an efficient algorithm for training and inference based on expectation-maximization and validate it on synthetic data , and illustrate how the two components of our model improve segmentation of natural images .
Edit intent: meaning-changed
Original:  We provide an algorithm for learning and inference , validate it on synthetic data , and illustrate how the two components of our model improve segmentation of natural images . We then show that the posterior distribution over segmentations captures well the variability across human subjects , indicating that our model provides a viable approach to probe human visual segmentation.
Revised:  We provide an algorithm for learning and inference , validate it on synthetic data . Importantly, with the appropriate choice of the prior, we derive an intuitive closed--form update rule for assigning pixels to segments: at each iteration, the pixel assignment probabilities to segments is the sum of the evidence (i.e. local pixel statistics) and prior (i.e. the assignments of neighboring pixels) weighted by their relative uncertainty. The model performs competitively on natural images from the Berkeley Segmentation Dataset (BSD), and we illustrate how the likelihood and prior components improve segmentation relative to traditional mixture models. Furthermore, our model explains some variability across human subjects , indicating that our model provides a viable approach to probe human visual segmentation.
Edit intent: meaning-changed
Original:  Based on consistent covariance estimation, our method allows for constructing confidence intervals and sta- tistical tests for single or low-dimensional components of beta_0 . In Monte-Carlo simulations we analyze the finite sample behavior of our estimator.
Revised:  Based on consistent covariance estimation, our method allows for constructing confidence intervals and statistical tests for single or low-dimensional components of beta_0 . In Monte-Carlo simulations we analyze the finite sample behavior of our estimator.
Edit intent: fluency
Original:  Based on consistent covariance estimation, our method allows for constructing confidence intervals and sta- tistical tests for single or low-dimensional components of beta_0 . In Monte-Carlo simulations we analyze the finite sample behavior of our estimator.
Revised:  Based on consistent covariance estimation, our method allows for constructing confidence intervals and sta- tistical tests for single or low-dimensional components of \beta^0 . In Monte-Carlo simulations we analyze the finite sample behavior of our estimator.
Edit intent: meaning-changed
Original:  The grids URLanized in modules with spatial periods  clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure  .
Revised:  The grids URLanized in modules with spatial periods , or scales, clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure .
Edit intent: meaning-changed
Original:  The grids URLanized in modules with spatial periods  clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure  .
Revised:  The grids URLanized in modules with spatial periods clustered around discrete values separated by ratios in the range 1.3-1.8 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure .
Edit intent: clarity
Original:  The grids URLanized in modules with spatial periods  clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure  .
Revised:  The grids URLanized in modules with spatial periods clustered around discrete values separated by constant ratios reported in the range 1.2--2.0 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure .
Edit intent: meaning-changed
Original:  The grids URLanized in modules with spatial periods  clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure  .
Revised:  The grids URLanized in modules with spatial periods clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism that produces this modular structure .
Edit intent: clarity
Original:  The grids URLanized in modules with spatial periods  clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure  .
Revised:  The grids URLanized in modules with spatial periods clustered around discrete values separated by constant ratios reported in the range 1.3-1.8 . We propose a mechanism for dynamical URLanization in the mEC that can produce this modular structure through dynamical URLanization in the MEC .
Edit intent: clarity
Original:  We show that grid cells will instead form a hierarchy of discrete modules if a continuous increase in inhibition distance along the dorso-ventral axis of the mEC is accompanied by excitatory interactions along this axis.
Revised:  We show that grid cells will instead form a hierarchy of discrete modules if a smooth increase in inhibition distance along the dorso-ventral axis of the mEC is accompanied by excitatory interactions along this axis.
Edit intent: clarity
Original:  We show that grid cells will instead form a hierarchy of discrete modules if a continuous increase in inhibition distance along the dorso-ventral axis of the mEC is accompanied by excitatory interactions along this axis.
Revised:  We show that grid cells will instead form a hierarchy of discrete modules if a continuous increase in inhibition distance along its dorso-ventral axis of the mEC is accompanied by excitatory interactions along this axis.
Edit intent: clarity
Original:  Moreover, constant scale ratios between successive modules arise through geometric relationships between triangular grids , whose lattice constants are separated by \sqrt{3 \approx 1.7, \sqrt{7}/2 \approx 1.3, or other ratios}  .
Revised:  Moreover, constant scale ratios between successive modules arise through geometric relationships between triangular grids \approx 1.7, \sqrt{7}/2 \approx 1.3, or other ratios} .
Edit intent: clarity
Original:  Moreover, constant scale ratios between successive modules arise through geometric relationships between triangular grids , whose lattice constants are separated by \sqrt{3 \approx 1.7, \sqrt{7}/2 \approx 1.3, or other ratios}  .
Revised:  Moreover, constant scale ratios between successive modules arise through geometric relationships between triangular grids , whose lattice constants are separated by \sqrt{3 \approx 1.7, \sqrt{7}/2 \approx 1.3, or other ratios} and have values that fall within the observed range .
Edit intent: meaning-changed
Original:  In this paper, we prove that Schneiders' pyramid has no hexahedral meshes with fewer than 12 interior vertices and 14 hexahedra, and introduce a valid mesh with 66 hexahedra.
Revised:  In this paper, we prove that Schneiders' pyramid has no hexahedral meshes with fewer than 18 interior vertices and 14 hexahedra, and introduce a valid mesh with 66 hexahedra.
Edit intent: meaning-changed
Original:  In this paper, we prove that Schneiders' pyramid has no hexahedral meshes with fewer than 12 interior vertices and 14 hexahedra, and introduce a valid mesh with 66 hexahedra.
Revised:  In this paper, we prove that Schneiders' pyramid has no hexahedral meshes with fewer than 12 interior vertices and 17 hexahedra, and introduce a valid mesh with 66 hexahedra.
Edit intent: meaning-changed
Original:  In this paper, we prove that Schneiders' pyramid has no hexahedral meshes with fewer than 12 interior vertices and 14 hexahedra, and introduce a valid mesh with 66 hexahedra.
Revised:  In this paper, we prove that Schneiders' pyramid has no hexahedral meshes with fewer than 12 interior vertices and 14 hexahedra, and introduce a valid mesh with 44 hexahedra.
Edit intent: meaning-changed
Original: We introduce groupICA, a novel independent component analysis (ICA ) algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise  confounding.
Revised: We introduce coroICA, confounding-robust independent component analysis (ICA ) algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise confounding.
Edit intent: meaning-changed
Original: We introduce groupICA, a novel independent component analysis (ICA ) algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise  confounding.
Revised: We introduce groupICA, a novel independent component analysis , a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise confounding.
Edit intent: clarity
Original: We introduce groupICA, a novel independent component analysis (ICA ) algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise  confounding.
Revised: We introduce groupICA, a novel independent component analysis (ICA ) algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden group-wise stationary confounding.
Edit intent: meaning-changed
Original:  It extends the ordinary ICA model in a theoretically sound and explicit way to incorporate group-wise (or environment-wise) structure in data and hence provides a justified alternative to the use of ICA on data blindly pooled across groups .
Revised:  It extends the ordinary ICA model in a theoretically sound and explicit way to incorporate group-wise (or environment-wise) confounding. We show that our general noise model allows to perform ICA in settings where other noisy ICA procedures fail. Additionally, it can be used for applications with grouped data by adjusting for different stationary noise within each group. We show that the noise model has a natural relation to causality and explain how it can be applied in the context of causal inference .
Edit intent: meaning-changed
Original:  Finally, we illustrate the performance and robustness of our method on simulated data and run experiments on publicly available EEG datasets demonstrating the applicability to real-world scenarios  .
Revised:  Finally, we illustrate the performance and robustness of our method on simulated data and run experiments on publicly available EEG datasets demonstrating the applicability to real-world scenarios by experiments on publicly available Antarctic ice core data as well as two EEG data sets .
Edit intent: meaning-changed
Original:  These risk measures are defined on a special space of financial positions, which is called the variable exponent Bochner-Lebesgue space. This space is denoted by L^{p(\cdot)} where the variable exponent p(\cdot) is no longer a given real number like the space L^{p}, but a random variable, which reflects the possible volatility of the financial markets .
Revised:  These risk measures are defined on a special space L^{p(\cdot)} where the variable exponent p(\cdot) is no longer a given real number like the space L^{p}, but a random variable, which reflects the possible volatility of the financial markets .
Edit intent: clarity
Original:  This space is denoted by L^{p(\cdot)} where the variable exponent p(\cdot) is no longer a given real number like the space L^{p}, but a random variable, which reflects the possible volatility of the financial markets . This space was studied in detail by Cheng and Xu (2013) and we will defined several well-known risk measures on it .
Revised:  This space is denoted by L^{p(\cdot)} where the variable exponent p(\cdot) is no longer a given real number like the space L^{p}, but a random variable, which reflects the possible volatility of the financial markets .
Edit intent: coherence
Original:  To address how these shape changes affect cell migration and cell speed we model motility using deformable, 2D cross-sections of cells in which adhesion is incorporated independently of the viscous frictional forces between cell and surface .
Revised:  To address how these shape changes affect cell migration and cell speed we model motility using deformable, 2D cross-sections of cells in which adhesion and frictional forces between cell and surface .
Edit intent: clarity
Original:  To address how these shape changes affect cell migration and cell speed we model motility using deformable, 2D cross-sections of cells in which adhesion is incorporated independently of the viscous frictional forces between cell and surface .
Revised:  To address how these shape changes affect cell migration and cell speed we model motility using deformable, 2D cross-sections of cells in which adhesion is incorporated independently of the viscous frictional forces between cell and substrate can be varied separately .
Edit intent: meaning-changed
Original:  The estimator's limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed along with a procedure for constructing uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination to Italian social security records,
Revised:  For quadratic forms of increasing rank, the limiting distribution can be represented by a linear combination of normal and non-central \chi^2 random variables . Consistent variance estimators are proposed along with a procedure for constructing uniformly valid confidence intervals . Applying a two-way fixed effects model of wage determination to Italian social security records,
Edit intent: meaning-changed