wanyu/IteraTeR_v2 huggingface dataset:
Original:  The new indicator appears highly correlated to traditional field normalized citations , however the shifts observed between the two measures are frequent and the number of outliers not at all negligible.
Revised:  The new indicator appears highly correlated to the traditional citation count , however the shifts observed between the two measures are frequent and the number of outliers not at all negligible.
Edit intent: clarity
Original:  The new indicator appears highly correlated to traditional field normalized citations , however the shifts observed between the two measures are frequent and the number of outliers not at all negligible.
Revised:  The new indicator appears highly correlated to traditional field normalized citations , however the shifts observed between the two measures are frequent and the number of outliers not negligible.
Edit intent: clarity
Original:  Moreover, the new indicator seems to show greater "sensitivity" when used in identification of the top-cited papers.
Revised:  Moreover, the new indicator shows greater "sensitivity" when used in identification of the top-cited papers.
Edit intent: coherence
Original:  Moreover, the new indicator seems to show greater "sensitivity" when used in identification of the top-cited papers.
Revised:  Moreover, the new indicator seems to show greater "sensitivity" when used to identify the highly-cited papers.
Edit intent: clarity
Original: Path analysis is a special class of models in structural equation modeling (SEM) where it describes causal relations among measured variables in a form of linear regression.
Revised: Path analysis is a model class of structural equation modeling (SEM) where it describes causal relations among measured variables in a form of linear regression.
Edit intent: clarity
Original: Path analysis is a special class of models in structural equation modeling (SEM) where it describes causal relations among measured variables in a form of linear regression.
Revised: Path analysis is a special class of models in structural equation modeling (SEM) , which it describes causal relations among measured variables in a form of linear regression.
Edit intent: clarity
Original:  This paper presents two estimation formulations  for confirmatory and exploratory SEM in path analysis problems where a zero pattern of the estimated path coefficient matrix explains a causality structure of the variables.
Revised:  This paper presents two estimation formulations for confirmatory and exploratory SEM , where a zero pattern of the estimated path coefficient matrix explains a causality structure of the variables.
Edit intent: clarity
Original:  This paper presents two estimation formulations  for confirmatory and exploratory SEM in path analysis problems where a zero pattern of the estimated path coefficient matrix explains a causality structure of the variables.
Revised:  This paper presents two estimation formulations for confirmatory and exploratory SEM in path analysis problems where a zero pattern of the estimated path coefficient matrix can explain a causality structure of the variables.
Edit intent: clarity
Original:  In confirmatory SEM, the original nonlinear equality constraints of model parameters are relaxed to an inequality, allowing us to transform the original problem into a convex problem .
Revised:  The original nonlinear equality constraints of model parameters are relaxed to an inequality, allowing us to transform the original problem into a convex problem .
Edit intent: coherence
Original:  In confirmatory SEM, the original nonlinear equality constraints of model parameters are relaxed to an inequality, allowing us to transform the original problem into a convex problem .
Revised:  In confirmatory SEM, the original nonlinear equality constraints of the model parameters were relaxed to an inequality, allowing us to transform the original problem into a convex problem .
Edit intent: fluency
Original:  In confirmatory SEM, the original nonlinear equality constraints of model parameters are relaxed to an inequality, allowing us to transform the original problem into a convex problem .
Revised:  In confirmatory SEM, the original nonlinear equality constraints of model parameters are relaxed to an inequality, allowing the transformation of the original problem into a convex problem .
Edit intent: clarity
Original:  In confirmatory SEM, the original nonlinear equality constraints of model parameters are relaxed to an inequality, allowing us to transform the original problem into a convex problem .
Revised:  In confirmatory SEM, the original nonlinear equality constraints of model parameters are relaxed to an inequality, allowing us to transform the original problem into a convex framework .
Edit intent: clarity
Original:  A regularized estimation formulation is proposed for exploratory SEM , where the objective function is added with an l1-type penalty of the path coefficient matrix.
Revised:  A regularized estimation formulation was then proposed for exploratory SEM , where the objective function is added with an l1-type penalty of the path coefficient matrix.
Edit intent: coherence
Original:  A regularized estimation formulation is proposed for exploratory SEM , where the objective function is added with an l1-type penalty of the path coefficient matrix.
Revised:  A regularized estimation formulation is proposed for exploratory SEM using an l1-type penalty of the path coefficient matrix.
Edit intent: clarity
Original:  Under a condition on problem parameters, we show that our optimal solution is low rank and provides an estimate of the path matrix of the original problem.
Revised:  Under a condition on problem parameters, our optimal solution is low rank and provides an estimate of the path matrix of the original problem.
Edit intent: coherence
Original:  Under a condition on problem parameters, we show that our optimal solution is low rank and provides an estimate of the path matrix of the original problem.
Revised:  Under a condition on problem parameters, we show that our optimal solution is low rank and provides a useful solution to the original problem.
Edit intent: clarity
Original:  To solve our estimation problems in a convex framework, we apply alternating direction method of multiplier (ADMM) which is shown to be suitable for a large-scale implementation.
Revised:  Proximal algorithms were applied to solve our convex programs in a large-scale implementation.
Edit intent: clarity
Original:  The performance of our approach is demonstrated in both simulated and real data sets, and with a comparison of existing methods.
Revised:  The performance of this approach was demonstrated in both simulated and real data sets, and with a comparison of existing methods.
Edit intent: clarity
Original: Alzheimer Disease (AD) is the most common form of dementia affecting the elderly population worldwide.
Revised: Alzheimer 's disease (AD) is the most common form of dementia affecting the elderly population worldwide.
Edit intent: fluency
Original:   472 recordings were obtained during clinical routine at our sleep center and were subdivided into two subgroups of 220 (HMC-S) and 252 (HMC-M) recordings each, attending to the procedure followed by the clinical expert during the visual review (semi-automatic or purely manual, respectively).
Revised:  Of them, 472 recordings were obtained during clinical routine at our sleep center and were subdivided into two subgroups of 220 (HMC-S) and 252 (HMC-M) recordings each, attending to the procedure followed by the clinical expert during the visual review (semi-automatic or purely manual, respectively).
Edit intent: coherence
Original:  Results: Event-by-event epoch-based validation resulted in an overall Cohen kappa agreement K = 0.600 (HMC-S), 0.559 (HMC-M), and 0.573 ( SHHS2 ). Estimated inter-scorer variability on the datasets was, respectively, K = 0.594, 0.561 and 0.543.
Revised:  Results: Event-by-event epoch-based validation resulted in an overall Cohen kappa agreement K = 0.600 (HMC-S), 0.559 (HMC-M), and 0.573 ( SHHS-2 ). Estimated inter-scorer variability on the datasets was, respectively, K = 0.594, 0.561 and 0.543.
Edit intent: fluency
Original:  Analyses of the corresponding Arousal Index scores showed associated automatic-human repeatability indices ranging in 0.693-0.771 (HMC-S), 0.646-0.791 (HMC-M), and 0.759-0.791 ( SHHS2 ). Conclusions: Large-scale validation of our automatic EEG arousal detector on different databases has shown robust performance and good generalization results comparable to the expected levels of human agreement.
Revised:  Analyses of the corresponding Arousal Index scores showed associated automatic-human repeatability indices ranging in 0.693-0.771 (HMC-S), 0.646-0.791 (HMC-M), and 0.759-0.791 ( SHHS-2 ). Conclusions: Large-scale validation of our automatic EEG arousal detector on different databases has shown robust performance and good generalization results comparable to the expected levels of human agreement.
Edit intent: fluency
Original: Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when  is available.
Revised: Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available.
Edit intent: coherence
Original:  For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field for scientific collaboration networks . Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as  number of triangles, or the assortativity of node metadata.
Revised:  For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network . Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as number of triangles, or the assortativity of node metadata.
Edit intent: clarity
Original:  For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field for scientific collaboration networks . Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as  number of triangles, or the assortativity of node metadata.
Revised:  For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field for scientific collaboration networks . Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata.
Edit intent: fluency
Original:  We introduce the concept of multi-hop assortativity, that captures the similarity of node situated at the extremities of a randomly selected path of a given length.
Revised:  We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length.
Edit intent: fluency
Original:  We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of fingerprints to characterize networks.
Revised:  We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of 'fingerprints' to characterize networks.
Edit intent: fluency
Original:  Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task 
Revised:  Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task .
Edit intent: fluency
Original: The relative fluctuations of observables in discrete stochastic systems are bounded  by the mean dynamical activity in the system, quantified by the mean number of jumps.
Revised: Relative fluctuations of observables in discrete stochastic systems are bounded by the mean dynamical activity in the system, quantified by the mean number of jumps.
Edit intent: fluency
Original:  In the paradigmatic simple systems we analyze, the thermodynamic constraint is more relevant close to equilibrium while the kinetic constraint is the limiting factor of the precision of a current in regimes far from equilibrium.
Revised:  The thermodynamic constraint is more relevant close to equilibrium while the kinetic constraint is the limiting factor of the precision of a current in regimes far from equilibrium.
Edit intent: coherence
Original:  In the paradigmatic simple systems we analyze, the thermodynamic constraint is more relevant close to equilibrium while the kinetic constraint is the limiting factor of the precision of a current in regimes far from equilibrium.
Revised:  In the paradigmatic simple systems we analyze, the thermodynamic constraint is more relevant close to equilibrium while the kinetic constraint is the limiting factor of the precision of a observables in regimes far from equilibrium.
Edit intent: clarity
Original:  This is confirmed by an example of molecular motor dynamics.
Revised:  This is visualized for paradigmatic simple systems and with an example of molecular motor dynamics.
Edit intent: coherence
Original: A set family F is said to satisfy the (p,q) property if among any p sets in F, some q have a non-empty intersection.
Revised: A family of sets F is said to satisfy the (p,q) property if among any p sets in F, some q have a non-empty intersection.
Edit intent: clarity
Original:  This bound, obtained by an explicit family of lines,  is tight for all families that have a bounded VC-dimension.
Revised:  This bound, obtained by an explicit family of lines, and is tight for all families that have a bounded VC-dimension.
Edit intent: coherence
Original:  We then generalize the bound to c_2(p,q) for any q \geq 3 with a construction based on a subset of the (2q-2)-dimensional grid.
Revised:  We then generalize the bound to c_2(p,q) for any q \geq 3.
Edit intent: clarity
Original:  Furthermore, under a structural sparsity assumption, we first illustrate an implicit regularization phenomenon where the standard stochastic gradient algorithm with zeroth-order information adapts to the sparsity of the problem at hand by just varying the step-size . Next, we propose a truncated stochastic gradient algorithm with zeroth-order information, whose rate  depends only poly-logarithmically on the dimensionality  .
Revised:  Furthermore, under a structural sparsity assumption, we first illustrate an implicit regularization phenomenon where the standard stochastic gradient algorithm with zeroth-order information adapts to the sparsity of the problem at hand by just varying the step-size and (ii) propose a truncated stochastic gradient algorithm with zeroth-order information, whose rate depends only poly-logarithmically on the dimensionality .
Edit intent: coherence
Original:  Furthermore, under a structural sparsity assumption, we first illustrate an implicit regularization phenomenon where the standard stochastic gradient algorithm with zeroth-order information adapts to the sparsity of the problem at hand by just varying the step-size . Next, we propose a truncated stochastic gradient algorithm with zeroth-order information, whose rate  depends only poly-logarithmically on the dimensionality  .
Revised:  Furthermore, under a structural sparsity assumption, we first illustrate an implicit regularization phenomenon where the standard stochastic gradient algorithm with zeroth-order information adapts to the sparsity of the problem at hand by just varying the step-size . Next, we propose a truncated stochastic gradient algorithm with zeroth-order information, whose rate of convergence depends only poly-logarithmically on the dimensionality .
Edit intent: clarity
Original:  The ill-posedness is rectified by two complimentary strategies.
Revised:  These issues are addressed by two complimentary strategies.
Edit intent: clarity
Original: Sample entropy (SampEn) has been accepted as an alternate, and sometimes a replacement, measure to approximate entropy ( ApEn) for characterizing temporal complexity of time series .
Revised: Approximate entropy ( ApEn) for characterizing temporal complexity of time series .
Edit intent: clarity
Original:  In this study, we first show using simulations that ApEn and SampEn are related to the Hurst exponent through their tolerance and embedding dimension  parameters.
Revised:  In this study, we first show , using simulations, that ApEn and SampEn are related to the Hurst exponent through their tolerance and embedding dimension parameters.
Edit intent: fluency
Original:  We then propose a modification to ApEn and SampEn called Range entropy or RangeEn.
Revised:  We then propose a modification to ApEn and SampEn called range entropy or RangeEn.
Edit intent: fluency
Original:  RangeEn measures represent a better robustness to nonstationary changes in signals and a more linear link to the Hurst exponent  compared to ApEn and SampEn.
Revised:  We show that RangeEn is more robust to nonstationary signal changes, and it has a more linear link to the Hurst exponent compared to ApEn and SampEn.
Edit intent: clarity
Original:  RangeEn measures represent a better robustness to nonstationary changes in signals and a more linear link to the Hurst exponent  compared to ApEn and SampEn.
Revised:  RangeEn measures represent a better robustness to nonstationary changes in signals and a more linear relationship with the Hurst exponent compared to ApEn and SampEn.
Edit intent: clarity
Original:  RangeEn measures represent a better robustness to nonstationary changes in signals and a more linear link to the Hurst exponent  compared to ApEn and SampEn.
Revised:  RangeEn measures represent a better robustness to nonstationary changes in signals and a more linear link to the Hurst exponent , compared to ApEn and SampEn.
Edit intent: fluency
Original:  They are always bounded in the tolerance r-plane between 0 (maximum entropy) and 1 (minimum entropy) and need no signal amplitude correction.
Revised:  RangeEn is bounded in the tolerance r-plane between 0 (maximum entropy) and 1 (minimum entropy) and need no signal amplitude correction.
Edit intent: clarity
Original:  They are always bounded in the tolerance r-plane between 0 (maximum entropy) and 1 (minimum entropy) and need no signal amplitude correction.
Revised:  They are always bounded in the tolerance r-plane between 0 (maximum entropy) and 1 (minimum entropy) and it has no need for signal amplitude correction.
Edit intent: clarity
Original: Various O(\log n) approximations are known for the Set Cover problem, where n is the number of elements.
Revised: Various O(\log n) are known for the Set Cover problem, where n is the number of elements.
Edit intent: clarity
Original:  Here, the elements are partitioned into r color classes , and we are required to cover at least k_t elements from each color class C_t, using the minimum number of sets.
Revised:  Here, the elements are partitioned into r color classes , and we are required to cover at least k_t elements from each color class C_t, using the minimum number of sets.
Edit intent: fluency
Original:  We give a randomized LP rounding algorithm that is an O(\beta + \log r) approximation for the Partition Set Cover problem.
Revised:  We give a randomized LP-rounding algorithm that is an O(\beta + \log r) approximation for the Partition Set Cover problem.
Edit intent: fluency
Original:  As a corollary, we obtain improved approximation guarantees for various set systems , for which \beta is known to be sublogarithmic in n  .
Revised:  As a corollary, we obtain improved approximation guarantees for various set systems for which \beta is known to be sublogarithmic in n .
Edit intent: fluency
Original:  Updated C source code is available at URL  Second, R generates random integers between 1 and m by multiplying random floats by m, taking the floor, and adding 1 to the result.
Revised:  Updated C source code is available at URL . Second, R generates random integers between 1 and m by multiplying random floats by m, taking the floor, and adding 1 to the result.
Edit intent: fluency
Original: We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts  artificial neural networks vs. biological neuron networks  . Generative topic models infer topic-word distributions, taking no or only little context into account.
Revised: We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts " artificial neural networks vs. biological neuron networks . Generative topic models infer topic-word distributions, taking no or only little context into account.
Edit intent: fluency
Original: We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts  artificial neural networks vs. biological neuron networks  . Generative topic models infer topic-word distributions, taking no or only little context into account.
Revised: We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts artificial neural networks " vs. " biological neuron networks . Generative topic models infer topic-word distributions, taking no or only little context into account.
Edit intent: fluency
Original: We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts  artificial neural networks vs. biological neuron networks  . Generative topic models infer topic-word distributions, taking no or only little context into account.
Revised: We address two challenges in topic models: (1) Context information around words helps in determining their actual meaning, e.g., "networks" used in the contexts artificial neural networks vs. biological neuron networks " . Generative topic models infer topic-word distributions, taking no or only little context into account.
Edit intent: fluency
Original:  We prove the first such upper bounds and tightly bound the optimal efficiency in terms of the minimum ratio of speeds s = \frac{v_{\max}{v_{\min}} }  { and the number of agents k.
Revised:  We prove the first such upper bounds and tightly bound the optimal efficiency in terms of the minimum ratio of speeds s = }{v_{\min}} } { and the number of agents k.
Edit intent: coherence
Original:  We prove the first such upper bounds and tightly bound the optimal efficiency in terms of the minimum ratio of speeds s = \frac{v_{\max}{v_{\min}} }  { and the number of agents k.
Revised:  We prove the first such upper bounds and tightly bound the optimal efficiency in terms of the minimum ratio of speeds s = \frac{v_{\max}{v_{\min}} } {v_{\min and the number of agents k.
Edit intent: fluency
Original:  Our bounds of \alpha \leq 1{1 + 1{s} and \alpha \leq 1 - 1{2k} imply that in order to achieve efficiency 1 - \epsilon, at least k \geq \Omega(\epsilon^{-2}) agents with a speed ratio of s \geq \Omega(\epsilon^{-1}) are necessary.
Revised:  } and \alpha \leq 1 - 1{2k} imply that in order to achieve efficiency 1 - \epsilon, at least k \geq \Omega(\epsilon^{-2}) agents with a speed ratio of s \geq \Omega(\epsilon^{-1}) are necessary.
Edit intent: clarity
Original:  Vertices represents cell states, say phenotypes \mic{genotypes } or physical locations, while possible transitions are acquiring a mutation or cell migration.
Revised:  Vertices represents cell states, say \mic{genotypes } or physical locations, while possible transitions are acquiring a mutation or cell migration.
Edit intent: clarity