wanyu/IteraTeR_v2 huggingface dataset:
Original:  In contrast, X-rays allow access into centimeter-thick sampleswithout sectioning, providing an unique and largely untapped approach for producing large 3D mesoscale brain maps . Here we demonstrate the use of synchrotron X-ray microtomography (\muCT) for brain science and provide a much needed toolkit for analyzing the large datasets afforded by this approach .
Revised:  In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D mesoscale brain maps . Here we demonstrate the use of synchrotron X-ray microtomography (\muCT) for brain science and provide a much needed toolkit for analyzing the large datasets afforded by this approach .
Edit intent: clarity
Original:  In contrast, X-rays allow access into centimeter-thick sampleswithout sectioning, providing an unique and largely untapped approach for producing large 3D mesoscale brain maps . Here we demonstrate the use of synchrotron X-ray microtomography (\muCT) for brain science and provide a much needed toolkit for analyzing the large datasets afforded by this approach .
Revised:  In contrast, X-rays allow access into centimeter-thick sampleswithout sectioning, providing an unique and largely untapped approach for producing large 3D brain maps without sectioning . Here we demonstrate the use of synchrotron X-ray microtomography (\muCT) for brain science and provide a much needed toolkit for analyzing the large datasets afforded by this approach .
Edit intent: clarity
Original:  In contrast, X-rays allow access into centimeter-thick sampleswithout sectioning, providing an unique and largely untapped approach for producing large 3D mesoscale brain maps . Here we demonstrate the use of synchrotron X-ray microtomography (\muCT) for brain science and provide a much needed toolkit for analyzing the large datasets afforded by this approach .
Revised:  In contrast, X-rays allow access into centimeter-thick sampleswithout sectioning, providing an unique and largely untapped approach for producing large 3D mesoscale brain maps . Here we demonstrate the use of synchrotron X-ray microtomography (\muCT) for producing mesoscale (1~\mu m^3) resolution brain maps from millimeter-scale volumes of mouse brain .
Edit intent: clarity
Original:  We introduce \muCT methods for sample preparation, imaging, and analyzing the resultant neural structures.
Revised:  We introduce a pipeline for\muCT methods for sample preparation, imaging, and analyzing the resultant neural structures.
Edit intent: clarity
Original:  We applied our methods to produce dense micron-scale maps of the cells and blood vessels in a millimeter-scale volume of mouse brain with%DIFDELCMD < \muCT%%%  . Our results demonstrate that X-ray tomography promises rapid reconstructions over brain large volumes, in a way that is complementary to other brain mapping and connectomics efforts.
Revised:  We applied our methods to produce dense micron-scale maps of the cells and blood vessels in a millimeter-scale volume of mouse brain with%DIFDELCMD < \muCT%%% . Our results demonstrate that X-ray tomography promises rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts.
Edit intent: clarity
Original: We describe a new data structure for dynamic nearest neighbor queries in the plane with respect to a general family of distance functions that includes L_p-norms and additively weighted Euclidean distances , and for general (convex, pairwise disjoint) sites that have constant description complexity (  line segments, disks, etc.).
Revised: We describe a new data structure for dynamic nearest neighbor queries in the plane with respect to a general family of distance functions . These include L_p-norms and additively weighted Euclidean distances , and for general (convex, pairwise disjoint) sites that have constant description complexity ( line segments, disks, etc.).
Edit intent: clarity
Original: We describe a new data structure for dynamic nearest neighbor queries in the plane with respect to a general family of distance functions that includes L_p-norms and additively weighted Euclidean distances , and for general (convex, pairwise disjoint) sites that have constant description complexity (  line segments, disks, etc.).
Revised: We describe a new data structure for dynamic nearest neighbor queries in the plane with respect to a general family of distance functions that includes L_p-norms and additively weighted Euclidean distances . Our data structure supports general (convex, pairwise disjoint) sites that have constant description complexity ( line segments, disks, etc.).
Edit intent: coherence
Original: We describe a new data structure for dynamic nearest neighbor queries in the plane with respect to a general family of distance functions that includes L_p-norms and additively weighted Euclidean distances , and for general (convex, pairwise disjoint) sites that have constant description complexity (  line segments, disks, etc.).
Revised: We describe a new data structure for dynamic nearest neighbor queries in the plane with respect to a general family of distance functions that includes L_p-norms and additively weighted Euclidean distances , and for general (convex, pairwise disjoint) sites that have constant description complexity ( e.g., points, line segments, disks, etc.).
Edit intent: clarity
Original:  Our data structure has numerous applications , and in all of them  it gives faster algorithms, typically reducing an O(n^\varepsilon) factor in the  bounds to polylogarithmic.
Revised:  Our data structure has numerous applications . In all of them it gives faster algorithms, typically reducing an O(n^\varepsilon) factor in the bounds to polylogarithmic.
Edit intent: coherence
Original:  Our data structure has numerous applications , and in all of them  it gives faster algorithms, typically reducing an O(n^\varepsilon) factor in the  bounds to polylogarithmic.
Revised:  Our data structure has numerous applications , and in all of them , it gives faster algorithms, typically reducing an O(n^\varepsilon) factor in the bounds to polylogarithmic.
Edit intent: fluency
Original:  To further demonstrate its power , we give here two new applications: an efficient construction of a spanner in a disk intersection graph, and a data structure for efficient connectivity queries in a dynamic disk graph.
Revised:  In addition , we give here two new applications: an efficient construction of a spanner in a disk intersection graph, and a data structure for efficient connectivity queries in a dynamic disk graph.
Edit intent: coherence
Original:  Consider an undirected graph G=(V,E) with n (non-isolated) vertices, and define (for s > 0) \mu_s = 1{n}\cdotv \in V d^s_v. Our aim is to estimate \mu_s within a multiplicative error of (1+ \varepsilon ) (for a given approximation parameter \varepsilon >0) in sublinear time.
Revised:  Consider an undirected graph G=(V,E) with n vertices, and define (for s > 0) \mu_s = 1{n}\cdotv \in V d^s_v. Our aim is to estimate \mu_s within a multiplicative error of (1+ \varepsilon ) (for a given approximation parameter \varepsilon >0) in sublinear time.
Edit intent: clarity
Original:  Consider an undirected graph G=(V,E) with n (non-isolated) vertices, and define (for s > 0) \mu_s = 1{n}\cdotv \in V d^s_v. Our aim is to estimate \mu_s within a multiplicative error of (1+ \varepsilon ) (for a given approximation parameter \varepsilon >0) in sublinear time.
Revised:  Consider an undirected graph G=(V,E) with n (non-isolated) vertices, and define (for s > 0) \mu_s = 1{n}\cdotv \in V d^s_v. Our aim is to estimate \mu_s within a multiplicative error of (1+ \epsilon ) (for a given approximation parameter \varepsilon >0) in sublinear time.
Edit intent: fluency
Original:  Consider an undirected graph G=(V,E) with n (non-isolated) vertices, and define (for s > 0) \mu_s = 1{n}\cdotv \in V d^s_v. Our aim is to estimate \mu_s within a multiplicative error of (1+ \varepsilon ) (for a given approximation parameter \varepsilon >0) in sublinear time.
Revised:  Consider an undirected graph G=(V,E) with n (non-isolated) vertices, and define (for s > 0) \mu_s = 1{n}\cdotv \in V d^s_v. Our aim is to estimate \mu_s within a multiplicative error of (1+ \varepsilon ) (for a given approximation parameter \epsilon >0) in sublinear time.
Edit intent: fluency
Original:  We consider the sparse graph model that allows access to: uniform random vertices, queries to the degree of any vertex, and queries to a random neighbor of any vertex.
Revised:  We consider the sparse graph model that allows access to: uniform random vertices, queries for the degree of any vertex, and queries to a random neighbor of any vertex.
Edit intent: fluency
Original:  We consider the sparse graph model that allows access to: uniform random vertices, queries to the degree of any vertex, and queries to a random neighbor of any vertex.
Revised:  We consider the sparse graph model that allows access to: uniform random vertices, queries to the degree of any vertex, and queries for a neighbor of any vertex.
Edit intent: clarity
Original:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 case (the average degree)  ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an  \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Revised:  (\sqrt{n s=1 case (the average degree) ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Edit intent: coherence
Original:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 case (the average degree)  ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an  \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Revised:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 (the average degree) ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Edit intent: fluency
Original:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 case (the average degree)  ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an  \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Revised:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 case (the average degree) , ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Edit intent: fluency
Original:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 case (the average degree)  ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an  \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Revised:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 case (the average degree) ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an algorithms that performs \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Edit intent: clarity
Original:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 case (the average degree)  ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an  \widetilde{O}(n^{1-1/(s+1)}) algorithm .
Revised:  Feige (SICOMP 06) and Goldreich-Ron (RSA 08) proved (\sqrt{n s=1 case (the average degree) ) queries suffice for any constant \epsilon (Feige, SICOMP 06 and Goldreich-Ron, RSA 08)} . Gonen-Ron-Shavitt (SIDMA 11) extended this result to all integral s > 0, by designing an \widetilde{O}(n^{1-1/(s+1)}) queries .
Edit intent: clarity
Original:  More importantly, the running time of this algorithm is connected to the \emph{arboricity of G: the minimum number of forests into which E can be partitioned .
Revised:  More importantly, the running time of this algorithm is connected to the \emph{ of G: the minimum number of forests into which E can be partitioned .
Edit intent: clarity
Original:  Thus, for the class of bounded arboricity graphs (which includes planar graphs, all minor closed families , and preferential attachment graphs), we can estimate the average degree in \widetilde{O}(1) queries, and can estimate the variance of the degree distribution in \widetilde{O}(\sqrt{n}) queries.
Revised:  Thus, for the class of bounded arboricity graphs (which includes all minor closed families , and preferential attachment graphs), we can estimate the average degree in \widetilde{O}(1) queries, and can estimate the variance of the degree distribution in \widetilde{O}(\sqrt{n}) queries.
Edit intent: clarity
Original:  Thus, for the class of bounded arboricity graphs (which includes planar graphs, all minor closed families , and preferential attachment graphs), we can estimate the average degree in \widetilde{O}(1) queries, and can estimate the variance of the degree distribution in \widetilde{O}(\sqrt{n}) queries.
Revised:  Thus, for the class of bounded arboricity graphs (which includes planar graphs, all minor closed families and preferential attachment graphs), we can estimate the average degree in \widetilde{O}(1) queries, and can estimate the variance of the degree distribution in \widetilde{O}(\sqrt{n}) queries.
Edit intent: fluency
Original:  Our key insight is that a certain estimator for \mu_s has significantly smaller variance when G does not have large dense subgraphs.
Revised:  Our key insight is in designing an estimator for \mu_s has significantly smaller variance when G does not have large dense subgraphs.
Edit intent: clarity
Original:  Our key insight is that a certain estimator for \mu_s has significantly smaller variance when G does not have large dense subgraphs.
Revised:  Our key insight is that a certain estimator for \mu_s that has low variance when G does not have large dense subgraphs.
Edit intent: clarity
Original:  The latter define output by majority vote among "yes" and "no" voters.
Revised:  The CRNs using the latter output convention define output by majority vote among "yes" and "no" voters.
Edit intent: clarity
Original: Many modern datasets are becoming ever more unwieldy as data volumes grow.
Revised: Datasets representing the world around us are becoming ever more unwieldy as data volumes grow.
Edit intent: clarity
Original:  In an effort to facilitate analysis of these data sets , computationally intensive calculations are increasingly being performed on specialised remote servers , before the reduced data are transferred to the consumer.
Revised:  In an effort to facilitate analysis of these datasets , computationally intensive calculations are increasingly being performed on specialised remote servers , before the reduced data are transferred to the consumer.
Edit intent: clarity
Original:  In an effort to facilitate analysis of these data sets , computationally intensive calculations are increasingly being performed on specialised remote servers , before the reduced data are transferred to the consumer.
Revised:  In an effort to facilitate analysis of these data sets , computationally intensive calculations are increasingly being performed on specialised remote servers before the reduced data are transferred to the consumer.
Edit intent: fluency
Original:  Due to bandwidth limitations, this often means data are displayed as simple 2D data visualisations, such as scatter or image plots .
Revised:  Due to bandwidth limitations, this often means data are displayed as simple 2D data visualisations, such as scatter plots or images .
Edit intent: fluency
Original:  Instead it is higher at the island center and scales up with island size, which we confirmed experimentally using laser ablation assays and immunofluorescence.
Revised:  Instead , these parameters are higher at the island center and scales up with island size, which we confirmed experimentally using laser ablation assays and immunofluorescence.
Edit intent: clarity
Original:  Yet, capturing the geometry or complete shapes of everyday objects using scanning devices ( eg. Kinect) still comes with several challenges that result in noise or even incomplete shapes.
Revised:  Yet, capturing the geometry or complete shapes of everyday objects using scanning devices ( e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes.
Edit intent: fluency
Original:  Furthermore, deep learning research argues ~\mbox{ Vincent08 Thus, we investigate a full convolutional volumetric denoising auto encoder that is trained in a unsupervised fashion.
Revised:  Thus, we investigate a full convolutional volumetric denoising auto encoder that is trained in a unsupervised fashion.
Edit intent: coherence
Original:  Furthermore, deep learning research argues ~\mbox{ Vincent08 Thus, we investigate a full convolutional volumetric denoising auto encoder that is trained in a unsupervised fashion.
Revised:  Furthermore, deep learning research argues ~\mbox{ Vincent08 Thus, we propose a full convolutional volumetric denoising auto encoder that is trained in a unsupervised fashion.
Edit intent: clarity
Original:  Furthermore, deep learning research argues ~\mbox{ Vincent08 Thus, we investigate a full convolutional volumetric denoising auto encoder that is trained in a unsupervised fashion.
Revised:  Furthermore, deep learning research argues ~\mbox{ Vincent08 Thus, we investigate a full convolutional volumetric auto encoder that is trained in a unsupervised fashion.
Edit intent: fluency
Original:  It outperforms prior work on recognition as well as more challenging tasks like denoising and shape completion.
Revised:  It outperforms prior work on challenging tasks like denoising and shape completion.
Edit intent: clarity
Original:  While a large portion of users faithfully express their opinions , some malicious users add noisy ratings in order to change the overall ratings of a specific group of items.
Revised:  While a large portion of users faithfully express their opinions . However, they are vulnerable to malicious users biasing their inputs in order to change the overall ratings of a specific group of items.
Edit intent: clarity
Original:  Although the robustness of different recommendation systems has been extensively studied, designing a robust recommendation system remains a significant challenge as detecting malicious users is computationally expensive .
Revised:  Although the robustness of different recommendation systems has been extensively studied, designing an efficient system that is immune to manipulation remains a significant challenge as detecting malicious users is computationally expensive .
Edit intent: clarity
Original:  Although the robustness of different recommendation systems has been extensively studied, designing a robust recommendation system remains a significant challenge as detecting malicious users is computationally expensive .
Revised:  Although the robustness of different recommendation systems has been extensively studied, designing a robust recommendation system remains a significant challenge .
Edit intent: coherence
Original:  Social networks and information or disease spreading processes are paradigmatic examples of this situation.
Revised:  Information or disease spreading processes are paradigmatic examples of this situation.
Edit intent: clarity
Original:  Social networks and information or disease spreading processes are paradigmatic examples of this situation.
Revised:  Social networks and information or disease spreading in networks are paradigmatic examples of this situation.
Edit intent: clarity
Original:  Besides the burstiness, several other correlations may appear in the network dynamics.
Revised:  Besides burstiness, several other correlations may appear in the network dynamics.
Edit intent: fluency
Original:  Besides the burstiness, several other correlations may appear in the network dynamics.
Revised:  Besides the burstiness, several correlations may appear in the network dynamics.
Edit intent: clarity
Original:  When they occur together, our results show that the direction and intensity of the final outcome depends on the combination in a non trivial way.
Revised:  When they occur together, our results show that the direction and intensity of the final outcome depends on the competition in a non trivial way.
Edit intent: clarity
Original:  The timing of job selection has significant impact on delivery time and stability for NJ while it has no impact for FJ. Based on these findings we introduce a methodological approach for decision- making support to set up and operate such a system, taking into account the trade-off between monetary cost and service quality.
Revised:  The timing of job selection has significant impact on delivery time and stability for NJ while it has no impact for FJ. Based on these findings we introduce a methodological approach for decision-making support to set up and operate such a system, taking into account the trade-off between monetary cost and service quality.
Edit intent: fluency
Original:  The approach includes three time horizons: long-term decisions on the number of depots to deploy in the service area, mid- term decisions on the number of vehicles to use, and short-term decisions on the policy to operate the vehicles.
Revised:  The approach includes three time horizons: long-term decisions on the number of depots to deploy in the service area, mid-term decisions on the number of vehicles to use, and short-term decisions on the policy to operate the vehicles.
Edit intent: fluency
Original: NMDA receptors are ion channels activated by the neurotransmitter glutamate in the mammalian brain and are important in synaptic function and plasticity, but also influence neuronal excitability and mediate dendritic action potentials (NMDA spikes) .
Revised: NMDA receptors are ion channels activated by the neurotransmitter glutamate in the mammalian brain and are important in synaptic function and plasticity, but also influence neuronal excitability .
Edit intent: coherence
Original:  Using patch-clamp, single NMDA receptor currents were recorded from spiny stellate neurons of cortical layer 4 and pyramidal neurons of layer 2/ 3, and both cell types were found to contain high-conductance as well as low-conductance NMDA receptors.
Revised:  Pharmacological data imply the presence of low-conductance NMDA receptors in spiny stellate neurons of cortical layer 4 and pyramidal neurons of layer 2/ 3, and both cell types were found to contain high-conductance as well as low-conductance NMDA receptors.
Edit intent: clarity
Original: The Massive MIMO (multiple-input multiple-output) technology has a great potential to manage the rapid growth of wireless data traffic.
Revised: The Massive MIMO (multiple-input multiple-output) technology has great potential to manage the rapid growth of wireless data traffic.
Edit intent: fluency
Original:  Massive MIMO achieve tremendous spectral efficiency by spatial multiplexing of many tens of user equipments (UEs).
Revised:  Massive MIMO achieves tremendous spectral efficiency by spatial multiplexing of many tens of user equipments (UEs).
Edit intent: fluency