wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Clinical scans present unique challenges because, in contrast to research-quality scans, clinical scans are often sparse, missing up to 85 \% of the slices available in research scans. We build on a state-of-the-art learning-based registration method to improve the accuracy of sparse clinical image registration and demonstrate our method on a clinically-acquired MRI dataset of stroke patients . SparseVM registers 3D scans in under a second on a GPU, which is over 1000x faster than the most accurate clinical registration methods, without compromising accuracy.
Revised:  Clinical scans present unique challenges because, in contrast to research-quality scans, clinical scans are often sparse, missing up to 85 \% of the slices available in research-quality scans. Existing methods for registering these sparse images are either inaccurate or extremely slow. We present a learning-based registration method to improve the accuracy of sparse clinical image registration and demonstrate our method on a clinically-acquired MRI dataset of stroke patients . SparseVM registers 3D scans in under a second on a GPU, which is over 1000x faster than the most accurate clinical registration methods, without compromising accuracy.
Edit intent: coherence
Original:  We build on a state-of-the-art learning-based registration method to improve the accuracy of sparse clinical image registration and demonstrate our method on a clinically-acquired MRI dataset of stroke patients . SparseVM registers 3D scans in under a second on a GPU, which is over 1000x faster than the most accurate clinical registration methods, without compromising accuracy.
Revised:  We build on a state-of-the-art learning-based registration method , SparseVM, that is more accurate and orders of magnitude faster than the most accurate clinical registration methods. To our knowledge, it is the first method to use deep learning specifically tailored to registering clinical images. We demonstrate our method on a clinically-acquired MRI dataset of stroke patients . SparseVM registers 3D scans in under a second on a GPU, which is over 1000x faster than the most accurate clinical registration methods, without compromising accuracy.
Edit intent: meaning-changed
Original:  We build on a state-of-the-art learning-based registration method to improve the accuracy of sparse clinical image registration and demonstrate our method on a clinically-acquired MRI dataset of stroke patients . SparseVM registers 3D scans in under a second on a GPU, which is over 1000x faster than the most accurate clinical registration methods, without compromising accuracy. Because of this, SparseVM enables clinical analyses that were not previously possible. The code is publicly available at voxelmorph.mit.edu.
Revised:  We build on a state-of-the-art learning-based registration method to improve the accuracy of sparse clinical image registration and demonstrate our method on a clinically-acquired MRI dataset of stroke patients and on a simulated sparse MRI dataset. Our code is available as part of the VoxelMorph package at URL
Edit intent: meaning-changed
Original: We introduce the basic elements of a spatio-angular theory of fluorescence microscopy . We start by modeling an aplanatic microscope imaging an ensemble of in-focus fluorescent dipoles as a linear Hilbert-space operator, and we show that the operator takes a particularly convenient form when expressed in a basis of complex exponentials and spherical harmonics-a form we call the dipole spatio-angular transfer function.
Revised: We introduce the basic elements of a spatio-angular theory of fluorescence microscopy , providing a unified framework for analyzing systems that image single fluorescent dipoles and ensembles of overlapping dipoles that label biological molecules. We model an aplanatic microscope imaging an ensemble of in-focus fluorescent dipoles as a linear Hilbert-space operator, and we show that the operator takes a particularly convenient form when expressed in a basis of complex exponentials and spherical harmonics-a form we call the dipole spatio-angular transfer function.
Edit intent: meaning-changed
Original:  We demonstrate our formalism by analyzing a single-view fluorescence microscope without using the monopole or scalar approximations. We show that this imaging system has a spatio-angular band limit, and we exploit the band limit to perform efficient simulations.Notably, we show that information about the out-of-plane orientation of ensembles of in-focus fluorophores is recorded by paraxial fluorescence microscopes
Revised:  We discuss the implications of our analysis for all quantitative fluorescence microscopy studies and lay out a path towards a complete theory.
Edit intent: clarity
Original:  In the present study, we focused on the sharp transitions at \lambda\cong0.1 (\equiv \rm C1) with low values of \rho between the random motion phase and the one-way rotational motion phase.
Revised:  In this study, we focused on the sharp transitions at \lambda\cong0.1 (\equiv \rm C1) with low values of \rho between the random motion phase and the one-way rotational motion phase.
Edit intent: fluency
Original:  a positive predictive value of 92.57\% and sensitivity of 88.94\% for the category S, and a positive predictive value of 99.50\% and sensitivity of 99.94\% for the category V for the inter-patient scheme.) 
Revised:  a positive predictive value of 92.57\% and sensitivity of 88.94\% for the category S, and a positive predictive value of 99.50\% and sensitivity of 99.94\% for the category V for the inter-patient scheme.) . The source code is available at URL
Edit intent: meaning-changed
Original:  We unexpectedly find that symmetric sawtooth potentials maximize time asymmetry, while earlier work examining ratchet models of molecular machines required asymmetric sawtooth potentials -- a distinction related to differences between autonomous and deterministic driving .
Revised:  For systems driven over a sawtooth potential according to a protocol, we find that symmetric sawtooth potentials maximize time asymmetry, while earlier work examining ratchet models of molecular machines required asymmetric sawtooth potentials -- a distinction related to differences between autonomous and deterministic driving .
Edit intent: clarity
Original:  We unexpectedly find that symmetric sawtooth potentials maximize time asymmetry, while earlier work examining ratchet models of molecular machines required asymmetric sawtooth potentials -- a distinction related to differences between autonomous and deterministic driving .
Revised:  We unexpectedly find that symmetric sawtooths maximize time asymmetry, while earlier work examining ratchet models of molecular machines required asymmetric sawtooth potentials -- a distinction related to differences between autonomous and deterministic driving .
Edit intent: fluency
Original:  We unexpectedly find that symmetric sawtooth potentials maximize time asymmetry, while earlier work examining ratchet models of molecular machines required asymmetric sawtooth potentials -- a distinction related to differences between autonomous and deterministic driving .
Revised:  We unexpectedly find that symmetric sawtooth potentials maximize time asymmetry, while earlier work examining ratchet models of molecular machines required asymmetric sawtooth potentials to achieve directed behavior. This distinction arises because deterministically driven machines are externally provided with direction, while autonomous machines must generate directed behavior .
Edit intent: clarity
Original:  The difficulties then faced are computing a number of expectations induced by the Taylor expansion in a closed-form manner. We establish a fast calibration scheme under the assumption that the parameters are piecewise-constant .
Revised:  The difficulties then faced are computing a number of expectations induced by the Taylor expansion . Under the assumption of piecewise-constant parameters, we derive explicit pricing formulas and devise a fast calibration scheme under the assumption that the parameters are piecewise-constant .
Edit intent: meaning-changed
Original:  An interesting phenomenon that the growing trend of  lifespan differential at the beginning of the studied interval does not persist, i.e. that it reverts to stagnation or even decline is revealed for a large majority of countries in the dataset.
Revised:  An interesting phenomenon that the growing trend of the observed lifespan differential at the beginning of the studied interval does not persist, i.e. that it reverts to stagnation or even decline is revealed for a large majority of countries in the dataset.
Edit intent: meaning-changed
Original:  Finally, a novel method of calculating  lifespan differential of a population from mortality indicators of its subpopulations is introduced and applied to the analysis of  lifespan differential in Israel from 1990 to 2000.
Revised:  Finally, a novel method of calculating the observed lifespan differential of a population from mortality indicators of its subpopulations is introduced and applied to the analysis of lifespan differential in Israel from 1990 to 2000.
Edit intent: meaning-changed
Original:  Finally, a novel method of calculating  lifespan differential of a population from mortality indicators of its subpopulations is introduced and applied to the analysis of  lifespan differential in Israel from 1990 to 2000.
Revised:  Finally, a novel method of calculating lifespan differential of a population from mortality indicators of its subpopulations is introduced and applied to the analysis of the observed lifespan differential in Israel from 1990 to 2000.
Edit intent: clarity
Original: Building on the model proposed in Lillicrap et. al. we show that deep networks can be trained using biologically plausible Hebbian rules yielding similar performance to ordinary back-propagation  .
Revised: Building on the model proposed in Lillicrap et. al. we show that deep networks can be trained using biologically plausible Hebbian rules yielding similar performance to ordinary back-propagation on challenging image datasets .
Edit intent: clarity
Original:  With fixed feedback weights the performance degrades quickly as the depth of the network increases  , when they are updated the performance is comparable to regular back-propagation .
Revised:  With fixed feedback weights the performance degrades quickly as the depth of the network increases . If the feedforward and feedback weights are initialized with the same values, as proposed in Zipser and Rumelhart (1990), they remain the same throughout training thus precisely implementing back-propagation. We show that even when the weights are initialized differently and at random, and the algorithm is no longer performing back-propagation , when they are updated the performance is comparable to regular back-propagation .
Edit intent: meaning-changed
Original: Here I introduce an extension to demixed principal component analysis (dPCA), a linear dimensionality reduction technique for analyzing the activity of neural populations, to the case of nonlinear dimensions. This is accomplished using kernel methods, resulting in kernel demixed principal component analysis (kdPCA) . This extension resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
Revised: Here I introduce an extension to demixed principal component analysis (dPCA), a linear dimensionality reduction technique for analyzing the activity of neural populations, to the case of nonlinear components. This extension, kernel demixed principal component analysis (kdPCA) . This extension resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
Edit intent: coherence
Original:  This is accomplished using kernel methods, resulting in kernel demixed principal component analysis (kdPCA) . This extension resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
Revised:  This is accomplished using kernel methods, resulting in kernel demixed principal component analysis (kdPCA) , relies on kernel least-squares regression techniques, and it resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
Edit intent: meaning-changed
Original:  These simulations demonstrate that nonlinear interactions can impede the ability of dPCA to demix neural activity corresponding to experimental parameters , but kdPCA can still recover interpretable components  .
Revised:  These simulations demonstrate that neurally relevant nonlinearities - such as stimulus-dependent gain and rotations - impede the ability of dPCA to demix neural activity corresponding to experimental parameters , but kdPCA can still recover interpretable components .
Edit intent: meaning-changed
Original:  Additionally, I compare kdPCA and dPCA to a neural population  from rat orbitofrontal cortex during an odor classification task in recovering decision-related activity  .
Revised:  Additionally, I compare kdPCA and dPCA to a neural population from rat orbitofrontal cortex during an odor classification task in recovering decision-related activity . The components recovered by kdPCA achieve better generalization and demixing performance compared to dPCA by accounting for a nonlinear interaction between stimulus and decision in the neural activity. In conclusion, simple nonlinear interactions inhibit the ability of linear dimensionality reduction techniques to recover interpretable demixed components in neural data, but this problem can be tackled by nonlinear dimensionality reduction approaches like kdPCA .
Edit intent: meaning-changed
Original: Here I introduce an extension to demixed principal component analysis (dPCA), a  linear dimensionality reduction technique for analyzing the activity of neural populations, to the case of nonlinear components .
Revised: Many studies of neural activity in behaving animals aim to discover interpretable low-dimensional structure in large-scale neural population recordings. One approach to this problem is demixed principal component analysis (dPCA), a linear dimensionality reduction technique for analyzing the activity of neural populations, to the case of nonlinear components .
Edit intent: meaning-changed
Original: Here I introduce an extension to demixed principal component analysis (dPCA), a  linear dimensionality reduction technique for analyzing the activity of neural populations, to the case of nonlinear components . This extension, kernel demixed principal component analysis (kdPCA) , relies on kernel least-squares regression techniques, and it resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
Revised: Here I introduce an extension to demixed principal component analysis (dPCA), a linear dimensionality reduction technique to find components that depend on particular experimental parameters. Here, I introduce kernel dPCA (kdPCA) , relies on kernel least-squares regression techniques, and it resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
Edit intent: meaning-changed
Original:  This extension, kernel demixed principal component analysis (kdPCA) , relies on kernel least-squares regression techniques, and it resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
Revised:  This extension, kernel demixed principal component analysis (kdPCA) as a nonlinear extension of dPCA by applying kernel least-squares regression techniques, and it resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear.
Edit intent: meaning-changed
Original:  This extension, kernel demixed principal component analysis (kdPCA) , relies on kernel least-squares regression techniques, and it resembles kernel-based extensions to standard principal component analysis and canonical correlation analysis. kdPCA includes dPCA as a special case when the kernel is linear. I present simulated examples of high-dimensional neural activity generated from low-dimensional trajectories and compare the results of kdPCA to dPCA .
Revised:  This extension, kernel demixed principal component analysis (kdPCA) , relies on kernel least-squares regression to the demixing problem. I consider simulated examples of high-dimensional neural activity generated from low-dimensional trajectories and compare the results of kdPCA to dPCA .
Edit intent: clarity
Original:  However, kdPCA can still recover interpretable components from such data. Additionally, I apply kdPCA to a neural population previously analyzed by dPCA from rat orbitofrontal cortex during an odor classification task in recovering decision-related activity. The components recovered by kdPCA achieve better generalization and demixing performance compared to dPCA by accounting for a nonlinear interaction between stimulus and decision in the neural activity. In conclusion, simple nonlinear interactions inhibit the ability of linear dimensionality reduction techniques to recover interpretable demixed components in neural data, but this problem can be tackled by nonlinear dimensionality reduction approaches like kdPCA .
Revised:  However, kdPCA can still recover interpretable components in these examples. Finally, I demonstrate kdPCA using two examples of neural populations recorded during perceptual decision-making tasks .
Edit intent: clarity
Original:  The cuPC algorithm has two variants, cuPC-E and cuPC-S, which parallelize conditional independence tests over the pairs of variables under the tests, and over the conditional sets, respectively. In particular, cuPC-E offers two degrees of parallelization by performing tests of multiple pairs of variables and also the tests of each pair in parallel. In the other hand, cuPC-S reuses the results of computations of a test for a given conditional set in other tests on the same conditional set. Experiment results on GTX 1080 GPU show two to three orders of magnitude speedup.
Revised:  The cuPC algorithm has two variants, cuPC-E and cuPC-S, which parallelize PC in two different ways. Experimental results show significant speedup.
Edit intent: clarity
Original:  For instance, in one of the most challenging benchmarks, cuPC-S reduces the runtime from about 73 hours to about one minute and achieves a significant speedup factor of about 4000 X .
Revised:  For instance, in one of the most challenging datasets, the runtime is reduced from more than 11 hours to about one minute and achieves a significant speedup factor of about 4000 X .
Edit intent: meaning-changed
Original:  For instance, in one of the most challenging benchmarks, cuPC-S reduces the runtime from about 73 hours to about one minute and achieves a significant speedup factor of about 4000 X .
Revised:  For instance, in one of the most challenging benchmarks, cuPC-S reduces the runtime from about 73 hours to about 4 seconds. On average, cuPC-E and cuPC-S achieve 500 X and 1300 X speedup, respectively, compared to serial implementation on CPU .
Edit intent: meaning-changed
Original:  The proposed solution has two variants, cuPC-E and cuPC-S, which parallelize PC in two different ways  . Experimental results show significant speedup .
Revised:  The proposed solution has two variants, cuPC-E and cuPC-S, which parallelize PC in two different ways . Experimental results show the scalability of the proposed algorithms with respect to the number of variables, the number of samples, and different graph densities .
Edit intent: meaning-changed
Original:  }  Second, we study algorithms for determining the order type of a point set in terms of the the number of coordinate bits they require to know.
Revised:  } A simple method to produce a random order type is to take the order type of a random point set. We conjecture that many probability distributions on order types defined in this way are heavily concentrated and therefore sample inefficiently the space of order types. We present two results on this question. First, we study experimentally the bias in the order types of n random points chosen uniformly and independently in a square, for n up to 16. Second, we study algorithms for determining the order type of a point set in terms of the the number of coordinate bits they require to know.
Edit intent: meaning-changed
Original:  We give an algorithm that requires on average 4n \log  2 n+O(n) bits to determine the order type of P, and show that any algorithm requires at least 4n \log  2 n -- O(n \log\log n) bits.
Revised:  We give an algorithm that requires on average 4n \log \_ 2 n+O(n) bits to determine the order type of P, and show that any algorithm requires at least 4n \log 2 n -- O(n \log\log n) bits.
Edit intent: meaning-changed
Original:  We give an algorithm that requires on average 4n \log  2 n+O(n) bits to determine the order type of P, and show that any algorithm requires at least 4n \log  2 n -- O(n \log\log n) bits.
Revised:  We give an algorithm that requires on average 4n \log 2 n+O(n) bits to determine the order type of P, and show that any algorithm requires at least 4n \log \_ 2 n -- O(n \log\log n) bits.
Edit intent: meaning-changed
Original:  We give an algorithm that requires on average 4n \log  2 n+O(n) bits to determine the order type of P, and show that any algorithm requires at least 4n \log  2 n -- O(n \log\log n) bits.
Revised:  We give an algorithm that requires on average 4n \log 2 n+O(n) bits to determine the order type of P, and show that any algorithm requires at least 4n \log 2 n - O(n \log\log n) bits.
Edit intent: meaning-changed
Original:  The complexity of our algorithms is 2^{( d /\varepsilon) ^{O(d) } n \log^4 n + 2^{O(d)} n \log^9 n, making a significant improvement over the state-of-the-art algorithm which runs in time n^{(d/\varepsilon)^{O(d)}}.
Revised:  The complexity of our algorithms is 2^{( \log(1 /\varepsilon) ^{O(d) } n \log^4 n + 2^{O(d)} n \log^9 n, making a significant improvement over the state-of-the-art algorithm which runs in time n^{(d/\varepsilon)^{O(d)}}.
Edit intent: meaning-changed
Original:  The complexity of our algorithms is 2^{( d /\varepsilon) ^{O(d) } n \log^4 n + 2^{O(d)} n \log^9 n, making a significant improvement over the state-of-the-art algorithm which runs in time n^{(d/\varepsilon)^{O(d)}}.
Revised:  The complexity of our algorithms is 2^{( d /\varepsilon) /\varepsilon)^{O(d^2) } n \log^4 n + 2^{O(d)} n \log^9 n, making a significant improvement over the state-of-the-art algorithm which runs in time n^{(d/\varepsilon)^{O(d)}}.
Edit intent: meaning-changed
Original:  The complexity of our algorithms is 2^{(\log(1/ \varepsilon \eps )/ \varepsilon \eps )^{O(d^2)}} n \log^4 n + 2^{O(d)} n \log^9 n, making a significant improvement over the state-of-the-art algorithm which runs in time n^{(d/ \varepsilon \eps )^{O(d)}}  .
Revised:  The complexity of our algorithms is 2^{(\log(1/ \eps )/ \varepsilon \eps )^{O(d^2)}} n \log^4 n + 2^{O(d)} n \log^9 n, making a significant improvement over the state-of-the-art algorithm which runs in time n^{(d/ \varepsilon \eps )^{O(d)}} .
Edit intent: fluency
Original:  The complexity of our algorithms is 2^{(\log(1/ \varepsilon \eps )/ \varepsilon \eps )^{O(d^2)}} n \log^4 n + 2^{O(d)} n \log^9 n, making a significant improvement over the state-of-the-art algorithm which runs in time n^{(d/ \varepsilon \eps )^{O(d)}}  .
Revised:  The complexity of our algorithms is 2^{(\log(1/ \varepsilon \eps )/ \eps )^{O(d^2)}} n \log^4 n + 2^{O(d)} n \log^9 n, making a significant improvement over the state-of-the-art algorithm which runs in time n^{(d/ \varepsilon \eps )^{O(d)}} .
Edit intent: fluency