wanyu/IteraTeR_v2 huggingface dataset:
Original:  This approach provides a full FreeSurfer alternative for volumetric analysis ( within 1 minute) and surface-based thickness analysis (within only around 1h runtime).
Revised:  This approach provides a full FreeSurfer alternative for volumetric analysis ( in under 1 minute) and surface-based thickness analysis (within only around 1h runtime).
Edit intent: clarity
Original: In spatial networks , vertices are arranged in some space and edges may cross.
Revised: In spatial networks vertices are arranged in some space and edges may cross.
Edit intent: fluency
Original:  Here we consider the particular case of arranging vertices in a 1-dimensional lattice , where edges may cross when drawn above the vertex sequence as it happens in linguistic and biological networks.
Revised:  When arranging vertices in a 1-dimensional lattice , where edges may cross when drawn above the vertex sequence as it happens in linguistic and biological networks.
Edit intent: coherence
Original:  Here we consider the particular case of arranging vertices in a 1-dimensional lattice , where edges may cross when drawn above the vertex sequence as it happens in linguistic and biological networks.
Revised:  Here we consider the particular case of arranging vertices in a 1-dimensional lattice edges may cross when drawn above the vertex sequence as it happens in linguistic and biological networks.
Edit intent: clarity
Original:  Here we investigate the  distribution of edge crossings under the null hypothesis of a uniformly random arrangement of the vertices.
Revised:  Here we investigate the distribution of edge crossings in random arrangements of the vertices.
Edit intent: clarity
Original:  We generalize the existing formula for the expectation of this number in  trees to any network and derive a general expression for the variance of the number of crossings  relying on a novel characterization of the algebraic structure of that variance in an arbitrary space.
Revised:  We generalize the existing formula for the expectation of this number in trees to any network and derive an expression for the variance of the number of crossings relying on a novel characterization of the algebraic structure of that variance in an arbitrary space.
Edit intent: fluency
Original:  We generalize the existing formula for the expectation of this number in  trees to any network and derive a general expression for the variance of the number of crossings  relying on a novel characterization of the algebraic structure of that variance in an arbitrary space.
Revised:  We generalize the existing formula for the expectation of this number in trees to any network and derive a general expression for the variance of the number of crossings in an arbitrary layout relying on a novel characterization of the algebraic structure of that variance in an arbitrary space.
Edit intent: clarity
Original:   First, we show when it is possible how to construct geodesic triangles which either have one, two, or three interior right angles .
Revised:  First, we show how to construct geodesic triangles which either have one, two, or three interior right angles .
Edit intent: coherence
Original:   First, we show when it is possible how to construct geodesic triangles which either have one, two, or three interior right angles .
Revised:  First, we show when it is possible how to construct geodesic triangles with either one, two, or three interior right angles .
Edit intent: fluency
Original:  Second, we report a construction of triples of points for which the dual Pythagorean theorems hold simultaneously at a point, yielding two dual pairs of dual geodesics with right angles at that point.
Revised:  Second, we report a construction of triples of points for which the dual Pythagorean theorems hold simultaneously at a point, yielding two dual pairs of dual-type geodesics with right angles at that point.
Edit intent: clarity
Original:  Given their numerous built-in sensors , smartphones are able to capture detailed and continuous observations on activities of daily living .
Revised:  Given their numerous built-in sensors enable continuous measurement of activities of daily living .
Edit intent: clarity
Original:  In this paper , we summarize  existing approaches to smartphone-based HAR.
Revised:  In this review , we summarize existing approaches to smartphone-based HAR.
Edit intent: clarity
Original:  In this paper , we summarize  existing approaches to smartphone-based HAR.
Revised:  In this paper , we summarize the existing approaches to smartphone-based HAR.
Edit intent: fluency
Original:  We systematically screened the literature on Scopus, PubMed, and Web of Science in the areas of data acquisition, data preprocessing, feature extraction, and activity classification .
Revised:  Methods: We systematically searched Scopus, PubMed, and Web of Science in the areas of data acquisition, data preprocessing, feature extraction, and activity classification .
Edit intent: clarity
Original: We obtain structural results for non-Markovian optimal stopping problems in discrete time  when the decision maker is risk averse and has partial information about the stochastic sequences generating the costs .
Revised: We obtain structural results for non-Markovian optimal switching problems in discrete time when the decision maker is risk averse and has partial information about the stochastic sequences generating the costs .
Edit intent: clarity
Original:  An example illustrates the interaction between partial information and risk aversion in the special case of optimal stopping problems .
Revised:  An example illustrates the interaction between delayed or missing observations and risk sensitivity .
Edit intent: clarity
Original:  An example illustrates the interaction between delayed or missing observations and risk sensitivity .
Revised:  An example application to hydropower planning is provided .
Edit intent: clarity
Original:  MetaGenRL distills the experiences of many complex agents to meta-learn a low-complexity neural objective function that affects how future individuals will learn.
Revised:  MetaGenRL distills the experiences of many complex agents to meta-learn a low-complexity neural objective function that decides how future individuals will learn.
Edit intent: clarity
Original:  Based on such a fact, we design a k-hop Collaborate Game Model, which means that an activity initiated by a user can only influence those users whose distance are within k-hop from the initiator in OSNs. Besides, we introduce the problem of Revenue Maximization under k-hop Collaborate Game (RMKCG), which identifies a limited number of initiators in order to obtain benefits as much as possible.
Revised:  Based on such a fact, we design a k-hop Collaborate Game Model, which means that an activity initiated by a user can only influence those users whose distance are within k-hop from this initiator in OSNs. Besides, we introduce the problem of Revenue Maximization under k-hop Collaborate Game (RMKCG), which identifies a limited number of initiators in order to obtain benefits as much as possible.
Edit intent: clarity
Original:  Based on such a fact, we design a k-hop Collaborate Game Model, which means that an activity initiated by a user can only influence those users whose distance are within k-hop from the initiator in OSNs. Besides, we introduce the problem of Revenue Maximization under k-hop Collaborate Game (RMKCG), which identifies a limited number of initiators in order to obtain benefits as much as possible.
Revised:  Based on such a fact, we design a k-hop Collaborate Game Model, which means that an activity initiated by a user can only influence those users whose distance are within k-hop from the initiator in OSNs. Besides, we introduce the problem of Revenue Maximization under k-hop Collaborate Game (RMKCG), which identifies a limited number of initiators in order to obtain revenue as much as possible.
Edit intent: clarity
Original:  We do not know how many followers would be generated for an activity in advance, thus, we need to adopt an adaptive strategy, where the decision who is the next potential initiator depends on the results of past decisions.
Revised:  We do not know how many followers would be generated from an activity in advance, thus, we need to adopt an adaptive strategy, where the decision who is the next potential initiator depends on the results of past decisions.
Edit intent: clarity
Original:  But in some special cases, we prove it is adaptive submodular and an adaptive greedy strategy can obtain a (1-1/e)-approximation by adaptive submodularity theory.
Revised:  But in some special cases, it is adaptive submodular and an adaptive greedy strategy can obtain a (1-1/e)-approximation by adaptive submodularity theory.
Edit intent: coherence
Original:  They collected all information received by two sensors between a communication event and  the previous communication events for each of them  into one information packet, and considered the number of sensors a packet eventually reaches.
Revised:  They collected all information received by two sensors between a communication event and after the previous communication events for each of them into one information packet, and considered the number of sensors a packet eventually reaches.
Edit intent: clarity
Original:  Then they defined the capacity of an RCMSN to be the ratio of the average number of sensors the packets reach and the total number of sensors .
Revised:  Then they defined the capacity is defined as the ratio of the average number of sensors the packets reach and the total number of sensors .
Edit intent: clarity
Original:  Then they defined the capacity of an RCMSN to be the ratio of the average number of sensors the packets reach and the total number of sensors .
Revised:  Then they defined the capacity of an RCMSN to be the ratio of the average number of sensors reached by the packets and the total number of sensors .
Edit intent: clarity
Original:  Finally, we provide some "rule-of-thumb" warmup schedules, and we demonstrate that simple untuned warmup of Adam performs more-or-less identically to RAdam in typical practical settings.
Revised:  We then provide some "rule-of-thumb" warmup schedules, and we demonstrate that simple untuned warmup of Adam performs more-or-less identically to RAdam in typical practical settings.
Edit intent: coherence
Original: Adaptive optimization algorithms such as Adam (Kingma Ba, 2014) are widely used in deep learning.
Revised: Adaptive optimization algorithms such as Adam Ba, 2014) are widely used in deep learning.
Edit intent: fluency
Original: Adaptive optimization algorithms such as Adam (Kingma Ba, 2014) are widely used in deep learning.
Revised: Adaptive optimization algorithms such as Adam (Kingma are widely used in deep learning.
Edit intent: coherence
Original:  Motivated by the difficulty of choosing and tuning warmup schedules, Liu et al. (2020) propose automatic variance rectification of Adam's adaptive learning rate, claiming that this rectified approach ("RAdam") surpasses the vanilla Adam algorithm and reduces the need for expensive tuning of Adam with warmup.
Revised:  Motivated by the difficulty of choosing and tuning warmup schedules, recent work proposes automatic variance rectification of Adam's adaptive learning rate, claiming that this rectified approach ("RAdam") surpasses the vanilla Adam algorithm and reduces the need for expensive tuning of Adam with warmup.
Edit intent: clarity
Original:  If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to explain to stackholders how it was created.
Revised:  If data are not tracked properly during the lifecycle, it becomes unfeasible to recreate a ML model from scratch or to explain to stakeholders how it was created.
Edit intent: fluency
Original:  Therefore, the current study aimed at the comparative evaluation of the Epionics SPINE system, a portable and cost-effective device for measuring sagittal lumbar movement during functional activities.
Revised:  The current study therefore aimed at the comparative evaluation of the Epionics SPINE system, a portable and cost-effective device for measuring sagittal lumbar movement during functional activities.
Edit intent: coherence
Original:  Therefore, the current study aimed at the comparative evaluation of the Epionics SPINE system, a portable and cost-effective device for measuring sagittal lumbar movement during functional activities.
Revised:  Therefore, the current study aimed at the comparative evaluation of the Epionics SPINE system, a portable device for measuring sagittal lumbar movement during functional activities.
Edit intent: clarity
Original:  Therefore, the current study aimed at the comparative evaluation of the Epionics SPINE system, a portable and cost-effective device for measuring sagittal lumbar movement during functional activities.
Revised:  Therefore, the current study aimed at the comparative evaluation of the Epionics SPINE system, a portable and cost-effective device for measuring sagittal lumbar back motion during functional activities.
Edit intent: clarity
Original:  Our methods are illustrated on datasets of  asymmetric stochastic blockmodel networks  and planar shapes viewed as metric spaces.
Revised:  Our methods are illustrated on datasets of asymmetric stochastic blockmodel networks , and planar shapes viewed as metric spaces.
Edit intent: fluency
Original: We study the vertex classification problem on a graph in which the vertices are in k  (k\geq 2) different groups, or communities, and edges are only allowed between vertices in distinct groups .
Revised: We study the vertex classification problem on a graph whose vertices are in k (k\geq 2) different groups, or communities, and edges are only allowed between vertices in distinct groups .
Edit intent: clarity
Original: We study the vertex classification problem on a graph in which the vertices are in k  (k\geq 2) different groups, or communities, and edges are only allowed between vertices in distinct groups .
Revised: We study the vertex classification problem on a graph in which the vertices are in k \ (k\geq 2) different groups, or communities, and edges are only allowed between vertices in distinct groups .
Edit intent: fluency
Original: We study the vertex classification problem on a graph in which the vertices are in k  (k\geq 2) different groups, or communities, and edges are only allowed between vertices in distinct groups .
Revised: We study the vertex classification problem on a graph in which the vertices are in k (k\geq 2) different communities, edges are only allowed between vertices in distinct groups .
Edit intent: clarity
Original:  The observation is the weighted adjacency matrix, perturbed by a  Gaussian Orthogonal Ensemble (GOE), or Gaussian Unitary Ensemble (GUE) matrix.
Revised:  The observation is a weighted adjacency matrix, perturbed by a Gaussian Orthogonal Ensemble (GOE), or Gaussian Unitary Ensemble (GUE) matrix.
Edit intent: fluency
Original:  Different from the standard symmetric%DIFDELCMD < \ZZ%%% _2-synchronization in which there are 2 communities with equal number of vertices, we do not assume that the number of vertices in each group is the same.
Revised:  %DIFDELCMD < \ZZ%%% _2-synchronization in which there are 2 communities with equal number of vertices, we do not assume that the number of vertices in each group is the same.
Edit intent: coherence
Original:  Different from the standard symmetric%DIFDELCMD < \ZZ%%% _2-synchronization in which there are 2 communities with equal number of vertices, we do not assume that the number of vertices in each group is the same.
Revised:  Different from the standard symmetric%DIFDELCMD < \ZZ%%% 
Edit intent: coherence
Original:  For the exact recovery of the maximum likelihood estimation (MLE) with various weighted adjacency matrix , we prove a sharp phase transition result with respect to the intensity \sigma of the Gaussian perturbation.
Revised:  For the exact recovery of the maximum likelihood estimation (MLE) with various weighted adjacency matrices , we prove a sharp phase transition result with respect to the intensity \sigma of the Gaussian perturbation.
Edit intent: fluency
Original:  For the exact recovery of the maximum likelihood estimation (MLE) with various weighted adjacency matrix , we prove a sharp phase transition result with respect to the intensity \sigma of the Gaussian perturbation.
Revised:  For the exact recovery of the maximum likelihood estimation (MLE) with various weighted adjacency matrix , we prove sharp thresholds of the intensity \sigma of the Gaussian perturbation.
Edit intent: clarity
Original:  Surprisingly, the threshold, or critical value, of \sigma does not depend on whether or not the sample space for MLE is restricted to the classifications such that the number of vertices in each group is the same as the true value.
Revised:  Surprisingly, these thresholds of \sigma do not depend on whether or not the sample space for MLE is restricted to the classifications such that the number of vertices in each group is the same as the true value.
Edit intent: clarity
Original:  Surprisingly, the threshold, or critical value, of \sigma does not depend on whether or not the sample space for MLE is restricted to the classifications such that the number of vertices in each group is the same as the true value.
Revised:  Surprisingly, the threshold, or critical value, of \sigma does not depend on whether the sample space for MLE is restricted to the classifications such that the number of vertices in each group is the same as the true value.
Edit intent: clarity
Original:  Surprisingly, the threshold, or critical value, of \sigma does not depend on whether or not the sample space for MLE is restricted to the classifications such that the number of vertices in each group is the same as the true value.
Revised:  Surprisingly, the threshold, or critical value, of \sigma does not depend on whether or not the sample space for MLE is restricted to such classifications that the number of vertices in each group is the same as the true value.
Edit intent: clarity
Original:  Surprisingly, the threshold, or critical value, of \sigma does not depend on whether or not the sample space for MLE is restricted to the classifications such that the number of vertices in each group is the same as the true value.
Revised:  Surprisingly, the threshold, or critical value, of \sigma does not depend on whether or not the sample space for MLE is restricted to the classifications such that the number of vertices in each group is equal to the true value.
Edit intent: clarity
Original:  In contrast to the well-studied \ZZ_2 -sychronization , a new complex version of the semi-definite programming (SDP) is designed to efficiently implement the community detection problem when the number of communities k is greater than 2, and a common region  for \sigma such that SDP exactly recovers the true classification is obtained , which is independent of k .
Revised:  In contrast to the \ZZ_2 -sychronization , a new complex version of the semi-definite programming (SDP) is designed to efficiently implement the community detection problem when the number of communities k is greater than 2, and a common region for \sigma such that SDP exactly recovers the true classification is obtained , which is independent of k .
Edit intent: clarity
Original:  In contrast to the well-studied \ZZ_2 -sychronization , a new complex version of the semi-definite programming (SDP) is designed to efficiently implement the community detection problem when the number of communities k is greater than 2, and a common region  for \sigma such that SDP exactly recovers the true classification is obtained , which is independent of k .
Revised:  In contrast to the well-studied \ZZ_2 -synchronization , a new complex version of the semi-definite programming (SDP) is designed to efficiently implement the community detection problem when the number of communities k is greater than 2, and a common region for \sigma such that SDP exactly recovers the true classification is obtained , which is independent of k .
Edit intent: fluency
Original:  In contrast to the well-studied \ZZ_2 -sychronization , a new complex version of the semi-definite programming (SDP) is designed to efficiently implement the community detection problem when the number of communities k is greater than 2, and a common region  for \sigma such that SDP exactly recovers the true classification is obtained , which is independent of k .
Revised:  In contrast to the well-studied \ZZ_2 -sychronization , a new complex version of the semi-definite programming (SDP) is designed to efficiently implement the community detection problem when the number of communities k is greater than 2, and a common region (independent of k) for \sigma such that SDP exactly recovers the true classification is obtained , which is independent of k .
Edit intent: clarity
Original:  However, querying with BigSI still requires probing Bloom filters (or sets of bitslices) which scales linearly with the number of datasets.
Revised:  However, querying with BIGSI still requires probing Bloom filters (or sets of bitslices) which scales linearly with the number of datasets.
Edit intent: fluency
Original:  However, querying with BigSI still requires probing Bloom filters (or sets of bitslices) which scales linearly with the number of datasets.
Revised:  However, querying with BigSI still requires probing Bloom Filters (or sets of bitslices) which scales linearly with the number of datasets.
Edit intent: fluency
Original:  In this paper, we propose RAMBO ( Repeated and Merged Bloom Filter ) where the number of Bloom filter probes is significantly less than BigSI due to sub-linear scaling for the same false-positive rate.
Revised:  In this paper, we propose RAMBO ( Repeated and Merged Bloom Filter ) where the number of Bloom filter probes is significantly less than BIGSI due to sub-linear scaling for the same false-positive rate.
Edit intent: fluency
Original:  RAMBO provides a significant improvement over BigSI in terms of query time when evaluated on real genome datasets.
Revised:  RAMBO provides a significant improvement over BIGSI in terms of query time when evaluated on real genome datasets.
Edit intent: fluency
Original:  Furthermore , due to sub-linear scaling  , the larger the size and number of datasets, the bigger the gains are with RAMBO over BigSI .
Revised:  Furthermore , due to sub-linear scaling , the larger the size and number of datasets, the bigger the gains are with RAMBO over BIGSI .
Edit intent: fluency
Original:  Several recent attempts have been made to tame the computational burden of read classification and sequence search on these ultra large-scale datasets, including both raw reads and assembled genomes .
Revised:  Given terabytes of genetic sequences from raw reads and assembled genomes .
Edit intent: clarity
Original:  Our idea is theoretically sound and inspired by the count-min sketch data structure, a popular streaming algorithm.
Revised:  Our idea is theoretically sound and inspired by the Count-Min Sketch data structure, a popular streaming algorithm.
Edit intent: fluency
Original:  RAMBO provides a significant improvement over BIGSI in terms of query time when evaluated on real genome datasets.
Revised:  RAMBO provides a significant improvement over state of the art methods in terms of query time when evaluated on real genome datasets.
Edit intent: clarity
Original:  RAMBO provides a significant improvement over BIGSI in terms of query time when evaluated on real genome datasets.
Revised:  RAMBO provides a significant improvement over BIGSI in terms of query time when evaluated on real genomic datasets.
Edit intent: fluency
Original:  Due to the sub-linear scaling of query time  , the larger the size and number of datasets, the bigger the  gains are with RAMBO over  BIGSI.
Revised:  Due to the sub-linear scaling of query time , the larger the size and number of datasets, the bigger the query time gains are with RAMBO over BIGSI.
Edit intent: fluency