wanyu/IteraTeR_v2 huggingface dataset:
Original:  We show that bursty behavior does not always hurt the diffusion, and depending on the features of the environment, having some (but not all) of the population being bursty significantly helps diffusion.
Revised:  We show that bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population being bursty significantly helps diffusion.
Edit intent: clarity
Original:  We show that bursty behavior does not always hurt the diffusion, and depending on the features of the environment, having some (but not all) of the population being bursty significantly helps diffusion.
Revised:  We show that bursty behavior does not always hurt the diffusion, and depending on the features of the environment, having some (but not all) of the population be bursty significantly helps diffusion.
Edit intent: fluency
Original:  Moreover, we prove that in a variety of settings maximizing diffusion requires heterogeneous activity patterns across agents and does not involve  any Poisson behavior.
Revised:  We prove that maximizing diffusion requires heterogeneous activity patterns across agents and does not involve any Poisson behavior.
Edit intent: clarity
Original:  Moreover, we prove that in a variety of settings maximizing diffusion requires heterogeneous activity patterns across agents and does not involve  any Poisson behavior.
Revised:  Moreover, we prove that in a variety of settings maximizing diffusion requires heterogeneous activity patterns across agents and does not involve any any Poisson behavior.
Edit intent: fluency
Original: Whether an idea, information, disease , or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions.
Revised: Whether an idea, information, infection , or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions.
Edit intent: clarity
Original:  We model diffusion on networks in which agents differ in their activity patterns .
Revised:  Here we show that, if people differ in their activity patterns .
Edit intent: coherence
Original:  We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any any Poisson behavior.
Revised:  We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any Poisson behavior.
Edit intent: fluency
Original:  With this work, we have taken a first step toward a semantic layout method for topical user profiles.
Revised:  With this work, we have taken a first step toward a semantic layout mode for topical user profiles.
Edit intent: clarity
Original:  We have developed a topical generalization approach which finds coherent groups of topics and and adds labels to them, based on their association with broader topics in the Wikipedia category graph.
Revised:  We have developed a topical generalization approach which finds coherent groups of topics and adds labels to them, based on their association with broader topics in the Wikipedia category graph.
Edit intent: fluency
Original:  The references cited therein were extracted and analyzed with regard to publications, which appeared prior to 1971 and are cited most frequently.
Revised:  The references cited therein were extracted and analyzed with regard to publications, which are cited most frequently.
Edit intent: clarity
Original:  We identified 25 highly-cited publications over all disciplines, which include fundamental early scientific works of the 19th century (with a weak connection to climate change) and some cornerstones of science ( with a stronger connection to climate change ).
Revised:  We identified 25 highly-cited publications across all disciplines, which include fundamental early scientific works of the 19th century (with a weak connection to climate change) and some cornerstones of science ( with a stronger connection to climate change ).
Edit intent: clarity
Original:  We identified 25 highly-cited publications over all disciplines, which include fundamental early scientific works of the 19th century (with a weak connection to climate change) and some cornerstones of science ( with a stronger connection to climate change ).
Revised:  We identified 25 highly-cited publications over all disciplines, which include fundamental early scientific works of the 19th century (with a weak connection to climate change) and some cornerstones of science with a stronger connection to climate change ).
Edit intent: fluency
Original:  The RPYS-CO focuses on the discovery of the greenhouse effect and the specific role of carbon dioxide.
Revised:  The RPYS-CO focuses on the discovery of the greenhouse effect and the role of carbon dioxide.
Edit intent: clarity
Original:  Using a stochastic gradient descent approach , we derive how  'tutor' circuits  should match plasticity mechanisms in 'student' circuits for efficient learning.
Revised:  Using stochastic gradient descent approach , we derive how 'tutor' circuits should match plasticity mechanisms in 'student' circuits for efficient learning.
Edit intent: fluency
Original:  Using a stochastic gradient descent approach , we derive how  'tutor' circuits  should match plasticity mechanisms in 'student' circuits for efficient learning.
Revised:  Using a stochastic gradient descent , we derive how 'tutor' circuits should match plasticity mechanisms in 'student' circuits for efficient learning.
Edit intent: clarity
Original:  Using a stochastic gradient descent approach , we derive how  'tutor' circuits  should match plasticity mechanisms in 'student' circuits for efficient learning.
Revised:  Using a stochastic gradient descent approach , we derive how the activity in 'tutor' circuits should match plasticity mechanisms in 'student' circuits for efficient learning.
Edit intent: clarity
Original:  We further describe a reinforcement learning framework with which the tutor can build its teaching signal.
Revised:  We further describe a reinforcement learning framework through which the tutor can build its teaching signal.
Edit intent: clarity
Original:  We show that mismatching the tutor signal and  plasticity mechanism can impair or abolish learning.
Revised:  We show that mismatches between the tutor signal and plasticity mechanism can impair or abolish learning.
Edit intent: clarity
Original:  We show that mismatching the tutor signal and  plasticity mechanism can impair or abolish learning.
Revised:  We show that mismatching the tutor signal and the plasticity mechanism can impair or abolish learning.
Edit intent: fluency
Original:  We show that mismatching the tutor signal and  plasticity mechanism can impair or abolish learning.
Revised:  We show that mismatching the tutor signal and plasticity mechanism can impair learning.
Edit intent: clarity
Original:  If any point of the plane is contained in at most k elements of F (with k sufficiently large), then we show that the elements of F can be colored with at most k+1 colors so that intersecting pseudo-disks are assigned distinct colors.
Revised:  If any point of the plane is contained in at most k elements of F (with k sufficiently large), then we show that the elements of F can be colored with at most k+1 colors so that intersecting Jordan regions are assigned distinct colors.
Edit intent: clarity
Original: Topic Modeling finds human-readable structures in large sets of unstructured SE data.
Revised: Topic Modeling finds human-readable structures in unstructured textual data.
Edit intent: clarity
Original:  A widely used topic modeler is Latent Dirichlet Allocation .
Revised:  A widely used topic modeler is Latent Dirichlet allocation .
Edit intent: fluency
Original:  When run on SE data , LDA suffers from "order effects" i.e. different topics be generated if the training data was shuffledinto a different order . Such order effects introduce a systematic error for any study that uses topics to make conclusions.
Revised:  When run on different datasets , LDA suffers from "order effects" i.e. different topics be generated if the training data was shuffledinto a different order . Such order effects introduce a systematic error for any study that uses topics to make conclusions.
Edit intent: clarity
Original:  When run on SE data , LDA suffers from "order effects" i.e. different topics be generated if the training data was shuffledinto a different order . Such order effects introduce a systematic error for any study that uses topics to make conclusions.
Revised:  When run on SE data , LDA suffers from "order effects" i.e. different topics are generated if the training data was shuffledinto a different order . Such order effects introduce a systematic error for any study that uses topics to make conclusions.
Edit intent: fluency
Original:  When run on SE data , LDA suffers from "order effects" i.e. different topics be generated if the training data was shuffledinto a different order . Such order effects introduce a systematic error for any study that uses topics to make conclusions.
Revised:  When run on SE data , LDA suffers from "order effects" i.e. different topics be generated if the order of training data is shuffled . Such order effects introduce a systematic error for any study that uses topics to make conclusions.
Edit intent: clarity
Original:  This paper introduces LDADE, a Search-Based SE tool that tunes LDA's parameters using DE (Differential Evolution).
Revised:  This paper introduces LDADE, a search-based software engineering tool that tunes LDA's parameters using DE (Differential Evolution).
Edit intent: clarity
Original:  LDADE has been tested on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands of SE papers, and software defect reports from NASA.
Revised:  LDADE is evaluated on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands of SE papers, and software defect reports from NASA.
Edit intent: clarity
Original:  LDADE has been tested on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands of SE papers, and software defect reports from NASA.
Revised:  LDADE has been tested on data from a programmer information exchange site (Stackoverflow), title and abstract text of thousands ofSoftware Engineering (SE) papers, and software defect reports from NASA.
Edit intent: clarity
Original:  across different platforms (Linux, Macintosh) and for different kinds of LDAs ( the traditional VEMmethod ,or using Gibbs sampling).
Revised:  across different platforms (Linux, Macintosh) and for different kinds of LDAs ( VEM ,or using Gibbs sampling).
Edit intent: clarity
Original:  Various "no-flattening theorems" show when these efficient deep networks cannot be accurately approximated by shallow ones without efficiency loss - even for linear networks .
Revised:  We prove various "no-flattening theorems" show when these efficient deep networks cannot be accurately approximated by shallow ones without efficiency loss - even for linear networks .
Edit intent: clarity
Original:  Various "no-flattening theorems" show when these efficient deep networks cannot be accurately approximated by shallow ones without efficiency loss - even for linear networks .
Revised:  Various "no-flattening theorems" showing when such efficient deep networks cannot be accurately approximated by shallow ones without efficiency loss - even for linear networks .
Edit intent: clarity
Original: We show how the success of deep learning depends not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can  be approximated through "cheap learning" with exponentially fewer parameters than generic ones , because they have simplifying properties tracing back to the laws of physics.
Revised: We show how the success of deep learning could depend not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can be approximated through "cheap learning" with exponentially fewer parameters than generic ones , because they have simplifying properties tracing back to the laws of physics.
Edit intent: clarity
Original: We show how the success of deep learning depends not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can  be approximated through "cheap learning" with exponentially fewer parameters than generic ones , because they have simplifying properties tracing back to the laws of physics.
Revised: We show how the success of deep learning depends not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can frequently be approximated through "cheap learning" with exponentially fewer parameters than generic ones , because they have simplifying properties tracing back to the laws of physics.
Edit intent: clarity
Original:  The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality  and polynomial log-probability , and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings .
Revised:  The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality , and polynomial log-probability , and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings .
Edit intent: fluency
Original:  The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality  and polynomial log-probability , and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings .
Revised:  The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality and polynomial log-probability translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings .
Edit intent: clarity
Original:  The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality  and polynomial log-probability , and we explore how these properties translate into exceptionally simple neural networks approximating both natural phenomena such as images and abstract representations thereof such as drawings .
Revised:  The exceptional simplicity of physics-based functions hinges on properties such as symmetry, locality, compositionality and polynomial log-probability , and we explore how these properties translate into exceptionally simple neural networks .
Edit intent: clarity
Original:  We formalize these claims using information theory and discuss the relation to renormalization group procedures .
Revised:  We formalize these claims using information theory and discuss the relation to the renormalization group .
Edit intent: clarity
Original:  We prove various "no-flattening theorems" showing when such efficient deep networks cannot be accurately approximated by shallow ones without efficiency loss : flattening even linear functions can be costly, and flattening polynomials is exponentially expensive;
Revised:  We prove various "no-flattening theorems" showing when efficient linear deep networks cannot be accurately approximated by shallow ones without efficiency loss : flattening even linear functions can be costly, and flattening polynomials is exponentially expensive;
Edit intent: clarity
Original: We consider a fundamental problem concerning the deployment of a wireless robotic network: to fulfill performance requirements with respect to end to end throughput, error rates, etc., a "sufficient" number of robotic relays must be deployed to ensure that links are of acceptable quality.
Revised: We consider a fundamental problem concerning the deployment of a wireless robotic network: to fulfill various end-to-end performance requirements, a "sufficient" number of robotic relays must be deployed to ensure that links are of acceptable quality.
Edit intent: clarity
Original:  We employ the properties of Carries Sense Multiple Access (CSMA) based wireless communication (without RTS/CTS mechanism) to formulate an upper bound on the spacing between any transmitter-receiver pair, which directly translates to a lower bound on the number of robots to deploy to fulfill the target network performance requirements.
Revised:  We use the properties of Carries Sense Multiple Access (CSMA) based wireless communication (without RTS/CTS mechanism) to formulate an upper bound on the spacing between any transmitter-receiver pair, which directly translates to a lower bound on the number of robots to deploy to fulfill the target network performance requirements.
Edit intent: fluency
Original:  We employ the properties of Carries Sense Multiple Access (CSMA) based wireless communication (without RTS/CTS mechanism) to formulate an upper bound on the spacing between any transmitter-receiver pair, which directly translates to a lower bound on the number of robots to deploy to fulfill the target network performance requirements.
Revised:  We employ the properties of Carrier Sense Multiple Access (CSMA) based wireless communication (without RTS/CTS mechanism) to formulate an upper bound on the spacing between any transmitter-receiver pair, which directly translates to a lower bound on the number of robots to deploy to fulfill the target network performance requirements.
Edit intent: fluency
Original:  We employ the properties of Carries Sense Multiple Access (CSMA) based wireless communication (without RTS/CTS mechanism) to formulate an upper bound on the spacing between any transmitter-receiver pair, which directly translates to a lower bound on the number of robots to deploy to fulfill the target network performance requirements.
Revised:  We employ the properties of Carries Sense Multiple Access (CSMA) based wireless communication to derive an upper bound on the spacing between any transmitter-receiver pair, which directly translates to a lower bound on the number of robots to deploy to fulfill the target network performance requirements.
Edit intent: clarity
Original:  We only focus on SINR based performance requirements due to its wide applicabilityand its direct relation with most other standard performance metrics . Next, we show that  by exploiting the geometrical structure of a network, such as linearity in the case of flow-based robotic router networks , the spacing bound can be significantly improved .
Revised:  We only focus on SINR based performance requirements due to their wide applicability . Next, we show that by exploiting the geometrical structure of a network, such as linearity in the case of flow-based robotic router networks , the spacing bound can be significantly improved .
Edit intent: clarity
Original:  We only focus on SINR based performance requirements due to its wide applicabilityand its direct relation with most other standard performance metrics . Next, we show that  by exploiting the geometrical structure of a network, such as linearity in the case of flow-based robotic router networks , the spacing bound can be significantly improved .
Revised:  We only focus on SINR based performance requirements due to its wide applicabilityand its direct relation with most other standard performance metrics . Next, we show that the bound can be improved by exploiting the geometrical structure of a network, such as linearity in the case of flow-based robotic router networks , the spacing bound can be significantly improved .
Edit intent: fluency
Original:  We only focus on SINR based performance requirements due to its wide applicabilityand its direct relation with most other standard performance metrics . Next, we show that  by exploiting the geometrical structure of a network, such as linearity in the case of flow-based robotic router networks , the spacing bound can be significantly improved .
Revised:  We only focus on SINR based performance requirements due to its wide applicabilityand its direct relation with most other standard performance metrics . Next, we show that by exploiting the geometrical structure of a network, such as linearity in the case of flow-based robotic router networks .
Edit intent: coherence
Original:  Via a set of simulation experiments, we demonstrate and validate our proposed bounds  .
Revised:  We demonstrate and validate our proposed bounds .
Edit intent: clarity
Original:  Via a set of simulation experiments, we demonstrate and validate our proposed bounds  .
Revised:  Via a set of simulation experiments, we demonstrate and validate our proposed bounds through simulations .
Edit intent: clarity
Original:  For the case of K_{2,n completely characterize and enumerate drawings in which the order of the edges around each vertex is the same for vertices on the same side of the bipartition.
Revised:  For the case of K_{2,n completely characterize and enumerate such drawings of K_{k,n in which the order of the edges around each vertex is the same for vertices on the same side of the bipartition.
Edit intent: clarity
Original:  We also investigate straight-line drawings of K_{ 3 ,n %DIFDELCMD < \MBLOCKRIGHTBRACE %%% and relate the realizability problem to that of stretchability of tropical pseudoline arrangements .
Revised:  Finally, we investigate drawings of K_{ 3 ,n %DIFDELCMD < \MBLOCKRIGHTBRACE %%% and relate the realizability problem to that of stretchability of tropical pseudoline arrangements .
Edit intent: clarity
Original:  We also investigate straight-line drawings of K_{ 3 ,n %DIFDELCMD < \MBLOCKRIGHTBRACE %%% and relate the realizability problem to that of stretchability of tropical pseudoline arrangements .
Revised:  We also investigate straight-line drawings of K_{ 3 ,n %DIFDELCMD < \MBLOCKRIGHTBRACE %%% using straight lines and pseudolines, and consider the complexity of the corresponding realizability problems .
Edit intent: clarity
Original: Using n uniform random points inside an arbitrary tetrahedron , we give an algorithm to estimate its vertices .
Revised: In this paper , we give an algorithm to estimate its vertices .
Edit intent: clarity
Original: Using n uniform random points inside an arbitrary tetrahedron , we give an algorithm to estimate its vertices .
Revised: Using n uniform random points inside an arbitrary tetrahedron , we give an algorithm to infer the positions of the vertices of an unknown tetrahedron, given a sample of points which are uniformly distributed within the tetrahedron .
Edit intent: clarity
Original:  The accuracy of the algorithm is proved by applying a Monte Carlo simulation technique .
Revised:  The accuracy of the algorithm is demonstrated using some numerical experiments .
Edit intent: clarity
Original:  A new metric, which combines PageRank centrality with the explicit requirement that paper score is not biased by paper age, outperforms the others in identifying the Milestone Letters short after they are published .
Revised:  A new metric, which combines PageRank centrality with the explicit requirement that paper score is not biased by paper age, is the best-performing metric overall in identifying the Milestone Letters short after they are published .
Edit intent: clarity