wanyu/IteraTeR_v2 huggingface dataset:
Original:  There are two main challenges for fully automated HaN OARs segmentation: 1) challenge in segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and 2) training model with inconsistent data annotations with missing ground truth for some anatomical structures because of different RT planning.
Revised:  There are two main challenges in deep-learning-based HaN segmentation: a) segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and 2) training model with inconsistent data annotations with missing ground truth for some anatomical structures because of different RT planning.
Edit intent: clarity
Original:  There are two main challenges for fully automated HaN OARs segmentation: 1) challenge in segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and 2) training model with inconsistent data annotations with missing ground truth for some anatomical structures because of different RT planning.
Revised:  There are two main challenges for fully automated HaN OARs segmentation: 1) challenge in segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and b) training with inconsistent data annotations with missing ground truth for some anatomical structures because of different RT planning.
Edit intent: clarity
Original:  We collect 261 HaN CT images to train the AnatomyNet for segmenting nine anatomies .  Compared to previous state-of-the-art methods for each anatomy from the MICCAI 2015 competition, the AnatomyNet increases Dice similarity coefficient (DSC) by 3.3\% on average.
Revised:  We collect 261 HaN CT images to train the AnatomyNet for segmenting nine anatomies . Compared to previous state-of-the-art results from the MICCAI 2015 competition, the AnatomyNet increases Dice similarity coefficient (DSC) by 3.3\% on average.
Edit intent: clarity
Original:  We collect 261 HaN CT images to train the AnatomyNet for segmenting nine anatomies .  Compared to previous state-of-the-art methods for each anatomy from the MICCAI 2015 competition, the AnatomyNet increases Dice similarity coefficient (DSC) by 3.3\% on average.
Revised:  We collect 261 HaN CT images to train the AnatomyNet for segmenting nine anatomies . Compared to previous state-of-the-art methods for each anatomy from the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient (DSC) by 3.3\% on average.
Edit intent: fluency
Original:  We collect 261 HaN CT images to train the AnatomyNet for segmenting nine anatomies .  Compared to previous state-of-the-art methods for each anatomy from the MICCAI 2015 competition, the AnatomyNet increases Dice similarity coefficient (DSC) by 3.3\% on average.
Revised:  We collect 261 HaN CT images to train the AnatomyNet for segmenting nine anatomies . Compared to previous state-of-the-art methods for each anatomy from the MICCAI 2015 competition, the AnatomyNet increases Dice similarity coefficient by 3.3\% on average.
Edit intent: clarity
Original:  The proposed AnatomyNet takes only 0.12 seconds on average to segment a whole-volume HaN CT image of an average dimension of 178x302x225.
Revised:  AnatomyNet takes about 0.12 seconds on average to segment a whole-volume HaN CT image of an average dimension of 178x302x225.
Edit intent: clarity
Original:  The proposed AnatomyNet takes only 0.12 seconds on average to segment a whole-volume HaN CT image of an average dimension of 178x302x225.
Revised:  The proposed AnatomyNet takes only 0.12 seconds to fully segment a head and neck CT image of an average dimension of 178x302x225.
Edit intent: clarity
Original: In this paper, we develop a new nonparametric approach for estimating the risk-neutral density of asset price and reformulate its estimation into a double-constrained optimization problem.
Revised: We develop a new nonparametric approach for estimating the risk-neutral density of asset price and reformulate its estimation into a double-constrained optimization problem.
Edit intent: coherence
Original: In this paper, we develop a new nonparametric approach for estimating the risk-neutral density of asset price and reformulate its estimation into a double-constrained optimization problem.
Revised: In this paper, we develop a new nonparametric approach for estimating the risk-neutral density of asset prices and reformulate its estimation into a double-constrained optimization problem.
Edit intent: fluency
Original:  We implement our approach in R and evaluate it using the S\&P 500 market option prices from 1996 to 2015.
Revised:  We evaluate our approach using the S\&P 500 market option prices from 1996 to 2015.
Edit intent: clarity
Original:  As an application, we use the proposed density estimator to price long-term variance swaps, and our prices match reasonably well with those of the variance future downloaded from the Chicago Board Options Exchange website.
Revised:  As an application, we use the proposed density estimator to price long-term variance swaps, and the model-implied prices match reasonably well with those of the variance future downloaded from the Chicago Board Options Exchange website.
Edit intent: clarity
Original:  As an application, we use the proposed density estimator to price long-term variance swaps, and our prices match reasonably well with those of the variance future downloaded from the Chicago Board Options Exchange website.
Revised:  As an application, we use the proposed density estimator to price long-term variance swaps, and our prices match reasonably well with those of the variance future downloaded from the CBOE website.
Edit intent: clarity
Original:  To accommodate the individual differences, we propose a novel unsupervised approach to align the EEG trials from different subjects in the Euclidean space to make them more consistent.
Revised:  To accommodate the individual differences, we propose a novel approach to align the EEG trials from different subjects in the Euclidean space to make them more consistent.
Edit intent: clarity
Original:  To accommodate the individual differences, we propose a novel unsupervised approach to align the EEG trials from different subjects in the Euclidean space to make them more consistent.
Revised:  To accommodate the individual differences, we propose a novel unsupervised approach to align EEG trials from different subjects in the Euclidean space to make them more consistent.
Edit intent: fluency
Original:  It has three desirable properties: 1) the aligned trial lie in the Euclidean space, which can be used by any Euclidean space signal processing and machine learning approach ;
Revised:  It has three desirable properties: 1) the aligned trial lie in the Euclidean space, and any signal processing, feature extraction and machine learning approach ;
Edit intent: clarity
Original:  2) it can be computed very efficiently ;
Revised:  2) its computational cost is very low ;
Edit intent: coherence
Original:  and, 3) it  does not need any labeled trials from the new subject.
Revised:  and, 3) it does not need any label information from the new subject.
Edit intent: clarity
Original:  Given these nearly tight bounds, it is perhaps surprising that no lower bound at all exists in terms of \nu---not even a bound of \Omega(\nu^{-1}) is known.
Revised:  Given these nearly tight bounds, it is perhaps surprising that no lower bound exists in terms of \nu---not even a bound of \Omega(\nu^{-1}) is known.
Edit intent: clarity
Original:  This shows that \nu need not factor into the space of an L_p sampler, which completely closes the complexity of the problem for this range of p.
Revised:  This shows that \nu need not factor into the space of an L_p sampler, which closes the complexity of the problem for this range of p.
Edit intent: clarity
Original:  Finally, we show improved upper and lower bounds for returning a (1%DIFDELCMD < \pm %%% \epsilon)relative error estimate of the frequency f_i of the sampled index i .
Revised:  %DIFDELCMD < \pm %%% \epsilon)relative error estimate of the frequency f_i of the sampled index i .
Edit intent: coherence
Original: We study a smooth matrix regression problem formulated to recover brain connectivity from data.
Revised: Recovering brain connectivity from data.
Edit intent: coherence
Original:  The performance of the method is evaluated on three test cases: a toy artificial dataset and two cortical problems employing data from the Allen Mouse Brain Connectivity Atlas.
Revised:  The performance of the method is evaluated on three examples: an artificial "toy" dataset and two cortical problems employing data from the Allen Mouse Brain Connectivity Atlas.
Edit intent: clarity
Original:  The performance of the method is evaluated on three test cases: a toy artificial dataset and two cortical problems employing data from the Allen Mouse Brain Connectivity Atlas.
Revised:  The performance of the method is evaluated on three test cases: a toy artificial dataset and two whole-cortex instances using data from the Allen Mouse Brain Connectivity Atlas.
Edit intent: clarity
Original:  We find numerically that the method is significantly faster than previous gradient-based methods and converges to the full rank solution as the rank increases .
Revised:  We find that the method is significantly faster than previous gradient-based methods and converges to the full rank solution as the rank increases .
Edit intent: clarity
Original:  Comparative analysis is carried out on a stylized non Lipschitz MV-SDE  and the neuron network model proposed in J. Baladron et al., Mean-field description and propagation of chaos in networks of Hodgkin-Huxley and FitzHugh-Nagumo neurons, The Journal of Mathematical Neuroscience, 2 (2012) .
Revised:  Comparative analysis is carried out on a stylized non Lipschitz MV-SDE and the neuron network model proposed in .
Edit intent: clarity
Original:  We provide numerical tests illustrating particle corruption effect where one single particle diverging can `corrupt' the whole system.
Revised:  We provide numerical tests illustrating particle corruption effect where one single particle diverging can ``corrupt'' the whole particle system.
Edit intent: fluency
Original:  We provide numerical tests illustrating particle corruption effect where one single particle diverging can ``corrupt'' the whole particle system.
Revised:  We provide numerical tests illustrating particle corruption effect where one single particle diverging can "corrupt" the whole particle system.
Edit intent: fluency
Original:  Comparative analysis is carried out on a stylized non Lipschitz MV-SDE from \mbox{ gomes2019mean0pt and the neuron network model proposed in \mbox{ BaladronFasoliFaugerasEtAl2012}0pt }  . We provide numerical tests illustrating particle corruption effect where one single particle diverging can "corrupt" the whole particle system.
Revised:  A comparative analysis is carried out on a stylized non Lipschitz MV-SDE from \mbox{ gomes2019mean0pt and the neuron network model proposed in \mbox{ BaladronFasoliFaugerasEtAl2012}0pt } . We provide numerical tests illustrating particle corruption effect where one single particle diverging can "corrupt" the whole particle system.
Edit intent: fluency
Original:  Comparative analysis is carried out on a stylized non Lipschitz MV-SDE from \mbox{ gomes2019mean0pt and the neuron network model proposed in \mbox{ BaladronFasoliFaugerasEtAl2012}0pt }  . We provide numerical tests illustrating particle corruption effect where one single particle diverging can "corrupt" the whole particle system.
Revised:  Comparative analysis is carried out on a stylized non-Lipschitz MV-SDE from \mbox{ gomes2019mean0pt and the neuron network model proposed in \mbox{ BaladronFasoliFaugerasEtAl2012}0pt } . We provide numerical tests illustrating particle corruption effect where one single particle diverging can "corrupt" the whole particle system.
Edit intent: fluency
Original:  Comparative analysis is carried out on a stylized non Lipschitz MV-SDE from \mbox{ gomes2019mean0pt and the neuron network model proposed in \mbox{ BaladronFasoliFaugerasEtAl2012}0pt }  . We provide numerical tests illustrating particle corruption effect where one single particle diverging can "corrupt" the whole particle system.
Revised:  Comparative analysis is carried out on a stylized non Lipschitz MV-SDE 0pt and the neuron network model proposed in \mbox{ BaladronFasoliFaugerasEtAl2012}0pt } . We provide numerical tests illustrating particle corruption effect where one single particle diverging can "corrupt" the whole particle system.
Edit intent: clarity
Original:  Comparative analysis is carried out on a stylized non Lipschitz MV-SDE from \mbox{ gomes2019mean0pt and the neuron network model proposed in \mbox{ BaladronFasoliFaugerasEtAl2012}0pt }  . We provide numerical tests illustrating particle corruption effect where one single particle diverging can "corrupt" the whole particle system.
Revised:  Comparative analysis is carried out on a stylized non Lipschitz MV-SDE from \mbox{ gomes2019mean0pt and the neuron network model proposed in \mbox{ BaladronFasoliFaugerasEtAl2012}0pt } . We provide numerical tests illustrating "particle corruption" effect where one single particle diverging can "corrupt" the whole particle system.
Edit intent: fluency
Original:  However, using curvature measures to detect anomalies in networks is not yet very common due to several reasons such as lack of preferred geometric interpretation of networks and lack of experimental evidences that may lead to specific desired curvature properties .
Revised:  However, using curvature measures to detect anomalies in networks is not yet very common .
Edit intent: coherence
Original:  To this end, our goal in this paper to formulate and analyze curvature analysis methods to provide the foundations of systematic approaches to find critical components and anomaly detection in networks.
Revised:  To this end, a main goal in this paper to formulate and analyze curvature analysis methods to provide the foundations of systematic approaches to find critical components and anomaly detection in networks.
Edit intent: clarity
Original:  To this end, our goal in this paper to formulate and analyze curvature analysis methods to provide the foundations of systematic approaches to find critical components and anomaly detection in networks.
Revised:  To this end, our goal in this paper to formulate and analyze curvature analysis methods to provide the foundations of systematic approaches to find critical components and detect anomalies in networks.
Edit intent: fluency
Original:  and (ii ) geometric curvatures%DIFDELCMD < } %%% based on defining k-complex-based Forman's combinatorial Ricci curvature for elementary components,
Revised:  and (ii %DIFDELCMD < } %%% based on defining k-complex-based Forman's combinatorial Ricci curvature for elementary components,
Edit intent: clarity
Original:  and using Euler characteristic of the complex that is topologically associated with the given graph . Based on these measures, we precisely formulate several computational problems related to anomaly detection in static or dynamic networks, and provide non-trivial computational complexity results (e. g. , exact or approximation algorithms, approximation hardness) for these problems .
Revised:  and using Euler characteristic of the complex that is topologically associated with the given graph . Based on these measures, we precisely formulate several computational problems related to anomaly detection in static or dynamic networks, and provide non-trivial computational complexity results for these problems. This paper must not be viewed as delivering the final word on appropriateness and suitability of specific curvature measures. Instead, it is our hope that this paper will stimulate and motivate further theoretical or empirical research concerning the exciting interplay between notions of curvatures from network and non-network domains, a much desired goal in our opinion .
Edit intent: coherence
Original: Anomaly detection problems (also called change-point detection problems) have been studied in data mining, statistics and computer science over the last several decades (mostly in non-network context) in applications such as medical condition monitoring , weather change detection and speech recognition .
Revised: Anomaly detection problems (also called change-point detection problems) have been studied in data mining, statistics and computer science over the last several decades in applications such as medical condition monitoring , weather change detection and speech recognition .
Edit intent: coherence
Original: Anomaly detection problems (also called change-point detection problems) have been studied in data mining, statistics and computer science over the last several decades (mostly in non-network context) in applications such as medical condition monitoring , weather change detection and speech recognition .
Revised: Anomaly detection problems (also called change-point detection problems) have been studied in data mining, statistics and computer science over the last several decades (mostly in non-network context) in applications such as medical condition monitoring and weather change detection and speech recognition .
Edit intent: fluency
Original: Anomaly detection problems (also called change-point detection problems) have been studied in data mining, statistics and computer science over the last several decades (mostly in non-network context) in applications such as medical condition monitoring , weather change detection and speech recognition .
Revised: Anomaly detection problems (also called change-point detection problems) have been studied in data mining, statistics and computer science over the last several decades (mostly in non-network context) in applications such as medical condition monitoring , weather change detection .
Edit intent: coherence
Original: We use techniques from compressive sensing to design a local clustering algorithm by treating the cluster indicator vector as a sparse solution to a linear system whose coefficient matrix is the graph Laplacian.
Revised: We use techniques from compressive sensing to design a cluster extraction algorithm by treating the cluster indicator vector as a sparse solution to a linear system whose coefficient matrix is the graph Laplacian.
Edit intent: clarity
Original: We use techniques from compressive sensing to design a local clustering algorithm by treating the cluster indicator vector as a sparse solution to a linear system whose coefficient matrix is the graph Laplacian.
Revised: We use techniques from compressive sensing to design a local clustering algorithm by treating the cluster indicator vector as a sparse solution to a linear system involving the graph Laplacian.
Edit intent: clarity
Original:  We formulate an abstract notion of cardinality estimators, which captures this aggregation requirement: one can merge sketches without losing precision.
Revised:  We formulate an abstract notion of cardinality estimators, that captures this aggregation requirement: one can merge sketches without losing precision.
Edit intent: fluency
Original:  We conclude that sketches from cardinality estimators should be considered as sensitive as raw data , and propose risk mitigation strategies for their real-world applications.
Revised:  We conclude that sketches from cardinality estimators should be considered as sensitive as raw data . We also propose risk mitigation strategies for their real-world applications.
Edit intent: clarity
Original: We analyze the uncertainties in the minimum norm solution of full-rank regression problems, arising from Gaussian linear models, computed by randomized (row-wise sampling and, more generally, sketching) algorithms.
Revised: For full-rank regression problems, arising from Gaussian linear models, computed by randomized (row-wise sampling and, more generally, sketching) algorithms.
Edit intent: coherence
Original:  From a probabilistic perspective, our expressions for the total expectation and variance  with regard to both model- and algorithm-induced uncertainties , are exact, hold for general sketching matrices , and make no assumptions on the rank of the sketched matrix.
Revised:  From a probabilistic perspective, our expressions for the total expectation and variance with regard to both model- and algorithm-induced uncertainties -- are exact; hold for general sketching matrices , and make no assumptions on the rank of the sketched matrix.
Edit intent: fluency
Original:  From a probabilistic perspective, our expressions for the total expectation and variance  with regard to both model- and algorithm-induced uncertainties , are exact, hold for general sketching matrices , and make no assumptions on the rank of the sketched matrix.
Revised:  From a probabilistic perspective, our expressions for the total expectation and variance with regard to both model- and algorithm-induced uncertainties , are exact, hold for general sketching matrices ; and make no assumptions on the rank of the sketched matrix.
Edit intent: fluency
Original:  The relative differences between the total bias and variance on the one hand, and the model bias and variance on the other hand, are governed by two factors: (i) the expected rank deficiency of the sketched matrix, and (ii) the expected difference between projectors associated with the original and the sketched problems.
Revised:  The relative differences between the total bias and variance on the one hand, and the model bias and variance on the other hand, are governed by two factors: (i) the expected rank deficiency of the sketched matrix, and (ii) the expected difference between projectors onto the spaces of the original and the sketched problems.
Edit intent: clarity
Original: Chemical clock reactions are characterised by a relatively long induction period followed by a rapid `switchover' during which the concentration of a clock chemical rises rapidly.
Revised: Chemical clock reactions are characterised by a relatively long induction period followed by a rapid `switchover' during which the concentration of a clock chemical rises rapidly.
Edit intent: fluency
Original:  This formula is tested via `kitchen sink chemistry' experiments, and is found to enable a good single-parameter fit to experimental series varying in initial concentrations of both iodine and vitamin C.
Revised:  This formula is tested via `kitchen sink chemistry' experiments, and is found to enable a good fit to experimental series varying in initial concentrations of both iodine and vitamin C.
Edit intent: clarity
Original:  Application to large-scale finite-sum minimization based on sub-sampled Hessian is discussed and analyzed in both a deterministic and probabilistic manner  .
Revised:  Application to large-scale finite-sum minimization based on subsampled Hessian is discussed and analyzed in both a deterministic and probabilistic manner .
Edit intent: fluency
Original: In this paper, we study the problem of computing the effective diffusivity for a particle moving in chaotic flows.
Revised: In this paper, we study the problem of computing the effective diffusivity for particles moving in chaotic flows.
Edit intent: fluency
Original:  By exploring the ergodicity of the solution process, we prove the convergence analysis of our method in computing the effective diffusivity over infinite time.
Revised:  By exploring the ergodicity of the solution process, we prove the convergence analysis of our method in computing effective diffusivity over infinite time.
Edit intent: fluency
Original:  We present numerical results to demonstrate the accuracy and efficiency of the proposed method in computing effective diffusivity for several chaotic flows, especially the Arnold-Beltrami-Childress (ABC) flow and the Kolmogorov flow in three-dimensional space.
Revised:  We present numerical results to verify the accuracy and efficiency of the proposed method in computing effective diffusivity for several chaotic flows, especially the Arnold-Beltrami-Childress (ABC) flow and the Kolmogorov flow in three-dimensional space.
Edit intent: clarity