wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this work , we introduce the DLight framework, which overcomes these challenges by utilizing a long short-term memory unit (LSTM) based deep neural network architecture to analyze the spatial dependency structure of whole-brain fMRI data. In order to maintain interpretability of the neuroimaging data, we adapt the layer-wise relevance propagation (LRP) method .
Revised:  In this work , we introduce the DLight framework, which overcomes these challenges by utilizing a long short-term memory unit (LSTM) based deep neural network architecture to analyze the spatial dependency structure of whole-brain functional Magnetic Resonance Imaging (fMRI) data. To decode a cognitive state (e.g., seeing the image of a house), DeepLight separates the fMRI volume into a sequence of axial brain slices, which is then sequentially processed by an LSTM. To maintain interpretability, DeepLight adapts the neuroimaging data, we adapt the layer-wise relevance propagation (LRP) method .
Edit intent: meaning-changed
Original:  Thereby, we enable the neuroscientist user to study the learned association of the LSTM between the data and the cognitive state of the individual. We demonstrate the versatility of DLight by applying it to a large fMRI dataset of the Human Connectome Project.
Revised:  Thereby, decomposing its decoding decision into the contributions of the single input voxels to this decision. Importantly, the decomposition is performed on the level of single fMRI volumes, enabling DeepLight to study the associations between cognitive state and brain activity on several levels of data granularity, from the level of the group down to the level of single time points. To demonstrate the versatility of DLight by applying it to a large fMRI dataset of the Human Connectome Project.
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Original:  We show that the decoding performance of our method scales better with large datasets, and moreover outperforms conventional decoding approaches , while still detecting physiologically appropriate brain areas for the cognitive statesclassified.
Revised:  We show that DeepLight outperforms conventional approaches of uni- and multivariate fMRI analysis in decoding the cognitive states and in identifying the physiologically appropriate brain areas for the cognitive statesclassified.
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Original:  We show that the decoding performance of our method scales better with large datasets, and moreover outperforms conventional decoding approaches , while still detecting physiologically appropriate brain areas for the cognitive statesclassified. We also demonstrate that DLight is able to detect these areas on several levels of data granularity (i.e., group, subject, trial, time point) .
Revised:  We show that the decoding performance of our method scales better with large datasets, and moreover outperforms conventional decoding approaches , while still detecting physiologically appropriate brain regions associated with these states. We further demonstrate DeepLight's ability to study the fine-grained temporo-spatial variability of brain activity over sequences of single fMRI samples .
Edit intent: clarity
Original:  Previous works showed that they act simultaneously, and can produce larger shifts in color matching when acting in synergy with a spatial pattern. At some point in an image, the color perceptually seems more similar to that of the adjacent locations , while being more dissimilar from that of remote neighbors.
Revised:  They have been previously shown to act synergistically, so that at some point in an image, the color perceptually seems more similar to that of the adjacent locations , while being more dissimilar from that of remote neighbors.
Edit intent: clarity
Original:  At some point in an image, the color perceptually seems more similar to that of the adjacent locations , while being more dissimilar from that of remote neighbors. The influence of neighbors hence reverses its nature above some characteristic scale .
Revised:  At some point in an image, the color perceptually seems more similar to that of the adjacent locations , while being more dissimilar from that of remote ones. Thus, their combined effects are enhanced in the presence of a spatial pattern, and can be measured as larger shifts in color matching experiments .
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Original:  Our model fully exploits the balance between attraction and repulsion in color space, combined at small or large scales in physical space.
Revised:  Our model supposes a hypercolumnar structure coding for colors in V1, and relies on the notion of color opponency introduced by Hering. The connectivity kernel of the neural field exploits the balance between attraction and repulsion in color space, combined at small or large scales in physical space.
Edit intent: meaning-changed
Original:  Our model fully exploits the balance between attraction and repulsion in color space, combined at small or large scales in physical space. For that purpose we rely on the opponent color theory introduced by Hering, and suppose a hypercolumnar structure coding for colors. At some neural mass, the pointwise influence of neighbors is spatially integrated to obtain the final effect that we call a color sensation. Alongside this neural field model, we describe the search for a color match in asymmetric matching experiments as a mathematical projector .
Revised:  Our model fully exploits the balance between attraction and repulsion in color and physical spaces, so as to reproduce the sign reversal in the influence of neighboring points. The color sensation at a point, defined from a steady state of the neural activities, is then extracted as a nonlinear percept conveyed by an assembly of neurons. It connects the cortical and perceptual levels, because we describe the search for a color match in asymmetric matching experiments as a mathematical projector .
Edit intent: clarity
Original:  In this study, mice spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using this modality in combination with biophysical modeling to reveal the relationship between microstructural metrics and clinical disability.
Revised:  In this study, mouse spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using this modality in combination with biophysical modeling to reveal the relationship between microstructural metrics and clinical disability.
Edit intent: fluency
Original:  In this study, mice spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using this modality in combination with biophysical modeling to reveal the relationship between microstructural metrics and clinical disability.
Revised:  In this study, mice spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using DKI in combination with biophysical modeling to reveal the relationship between microstructural metrics and clinical disability.
Edit intent: meaning-changed
Original:  In this study, mice spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using this modality in combination with biophysical modeling to reveal the relationship between microstructural metrics and clinical disability.
Revised:  In this study, mice spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using this modality in combination with biophysical modeling to study the relationship between microstructural metrics and clinical disability.
Edit intent: clarity
Original:  In this study, mice spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using this modality in combination with biophysical modeling to reveal the relationship between microstructural metrics and clinical disability. Thirteen EAE spinal cords of variable disability were extracted, dissected and subsequently scanned in a high-field MRI scanner along with 5 control specimen.
Revised:  In this study, mice spinal cords from the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis (MS) were investigated for the first time using this modality in combination with biophysical modeling to reveal the relationship between microstructural metrics and degree of animal dysfunction. Thirteen spinal cords were extracted from animals of variable disability were extracted, dissected and subsequently scanned in a high-field MRI scanner along with 5 control specimen.
Edit intent: clarity
Original:  Our results show strong relationship between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity, similar to what was found in other hypomyelinating MS models and in patients.
Revised:  Our results unveil a strong relationship between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity, similar to what was found in other hypomyelinating MS models and in patients.
Edit intent: clarity
Original:  Our results show strong relationship between disability and mean of the kurtosis tensor, radial kurtosis, radial diffusivity, similar to what was found in other hypomyelinating MS models and in patients. However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Revised:  Our results show strong relationship between disability and measured microstructural parameters in normal appearing white matter and gray matter. The changes we found in biophysical modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Edit intent: clarity
Original:  However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Revised:  However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity were clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Edit intent: fluency
Original:  However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Revised:  However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies employing other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Edit intent: fluency
Original:  However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Revised:  However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. In conclusion, our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Edit intent: coherence
Original:  However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes in normal appearing white matter and gray matter correlating with clinical disability.
Revised:  However, changes in WM-modeling parameters and in particular in extra-axonal axial diffusivity are clearly different from previous studies using other animal models of MS. Our data suggest that DKI and microstructural modeling can provide a unique contrast capable of detecting EAE-specific changes correlating with clinical disability.
Edit intent: clarity
Original:  Tree-based networks can be viewed as a natural generalization of rooted binary phylogenetic trees because they are merely trees with additional arcs, and in defining those networks, a certain kind of spanning trees called subdivision trees plays an essential role .
Revised:  Tree-based networks can be viewed as a natural generalization of rooted binary phylogenetic trees because they are merely trees with additional arcs, and in defining those networks, a certain kind called "subdivision trees" play an essential role .
Edit intent: clarity
Original:  Tree-based networks can be viewed as a natural generalization of rooted binary phylogenetic trees because they are merely trees with additional arcs, and in defining those networks, a certain kind of spanning trees called subdivision trees plays an essential role . In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, in linear delay)  : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree  if there exists any (decision/search problem);
Revised:  Tree-based networks can be viewed as a natural generalization of rooted binary phylogenetic trees because they are merely trees with additional arcs, and in defining those networks, a certain kind of spanning trees called subdivision trees plays an essential role and there are many important computational problems about them, whose time complexity is still unclear. Against this backdrop, the present paper aims to provide a graph theoretical framework for solving different problems on subdivision trees in a simple and unified manner. To this end, we focus on a structure called the maximal zig-zag trail decomposition that is inherent in any rooted binary phylogenetic network N and prove a structure theorem that characterizes the collection of all subdivision trees of N. Our theorem does not only imply and unify various results in the literature but also yield linear time (for enumeration, in linear delay) : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree if there exists any (decision/search problem);
Edit intent: meaning-changed
Original:  In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, in linear delay)  : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree  if there exists any (decision/search problem);
Revised:  In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, linear delay) : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree if there exists any (decision/search problem);
Edit intent: coherence
Original:  In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, in linear delay)  : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree  if there exists any (decision/search problem);
Revised:  In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, in linear delay) algorithms for the following problems : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree if there exists any (decision/search problem);
Edit intent: meaning-changed
Original:  In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, in linear delay)  : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree  if there exists any (decision/search problem);
Revised:  In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, in linear delay) : given a rooted binary phylogenetic network N, 1) determine whether or not N has a subdivision tree if there exists any (decision/search problem);
Edit intent: coherence
Original:  In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, in linear delay)  : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree  if there exists any (decision/search problem);
Revised:  In this paper, we provide a structural characterization of tree-based networks that furnishes efficient algorithms for solving the following problems in linear time (for enumeration, in linear delay) : given a rooted binary phylogenetic network N, 1) determine whether or not N is tree-based and find a subdivision tree and find one if there exists any (decision/search problem);
Edit intent: coherence
Original:  This, unfortunately, was not the case for the geometry processing community, where many visual recognition problems are defined on arbitrary surfaces(2D-manifolds). Technical difficulties hindering the generalization of CNNs to arbitrary-shaped manifold are rooted in the lacks of such key elements including the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain.
Revised:  This, unfortunately, was not the case for the geometry processing community, where many visual recognition problems are defined on curved surfaces, and the generalization of CNNs to arbitrary-shaped manifold are rooted in the lacks of such key elements including the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain.
Edit intent: clarity
Original:  Technical difficulties hindering the generalization of CNNs to arbitrary-shaped manifold are rooted in the lacks of such key elements including the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain.
Revised:  Technical difficulties hindering the generalization of CNNs is not quite trivial. The difficulties are rooted in the lacks of such key elements including the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain.
Edit intent: meaning-changed
Original:  Technical difficulties hindering the generalization of CNNs to arbitrary-shaped manifold are rooted in the lacks of such key elements including the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain.
Revised:  Technical difficulties hindering the generalization of CNNs to arbitrary-shaped manifold are rooted in the lack of key ingredients such as the canonical grid-like representation, the notion of consistent orientation, and a compatible local topology across the domain.
Edit intent: clarity
Original:  To this end, in this paper, we propose a novel mathematical formulation to extend CNNs onto two-dimensional (2D) manifold domains. More specifically, we approximate a tensor field defined over a manifold using orthogonal basis functions, called Zernike polynomials, on local tangent spaces. We prove that the convolution of two functions can be represented as a simple dot product between Zernike polynomial coefficients .
Revised:  To this end, in this paper, we prove that the convolution of two functions can be represented as a simple dot product between Zernike polynomial coefficients .
Edit intent: coherence
Original:  We prove that the convolution of two functions can be represented as a simple dot product between Zernike polynomial coefficients . We also prove that a rotation of a convolution kernel equates to a set of 2 by 2 rotation matrices applied to Zernike polynomial coefficients, which can be critical in manifold domains .
Revised:  We prove that the convolution of two functions can be represented as a simple dot product between Zernike polynomial coefficients ; and the rotation of a convolution kernel equates to a set of 2 by 2 rotation matrices applied to Zernike polynomial coefficients, which can be critical in manifold domains .
Edit intent: coherence
Original:  We also prove that a rotation of a convolution kernel equates to a set of 2 by 2 rotation matrices applied to Zernike polynomial coefficients, which can be critical in manifold domains .
Revised:  We also prove that a rotation of a convolution kernel is essentially a set of 2 by 2 rotation matrices applied to Zernike polynomial coefficients, which can be critical in manifold domains .
Edit intent: clarity
Original:  We also prove that a rotation of a convolution kernel equates to a set of 2 by 2 rotation matrices applied to Zernike polynomial coefficients, which can be critical in manifold domains .
Revised:  We also prove that a rotation of a convolution kernel equates to a set of 2-by-2 rotation matrices applied to Zernike polynomial coefficients, which can be critical in manifold domains .
Edit intent: fluency
Original:  Our networks contain leaky integrate-and-fire neurons (positive dissipation) as well as neurons with inverted sign of the leak current (negative dissipation) .
Revised:  We study inhibitory networks of concave leaky (LIF) and convex "anti-leaky" (XIF) integrate-and-fire neurons (positive dissipation) as well as neurons with inverted sign of the leak current (negative dissipation) .
Edit intent: meaning-changed