wanyu/IteraTeR_v2 huggingface dataset:
Original:  The SCube system implements an approach for segregation discovery on top of frequent itemset mining , by offering to the analyst a multi-dimensional (segregation ) data cube for exploratory data analysis.
Revised:  The SCube system implements an approach for segregation discovery on top of frequent itemset mining . It offers to the analyst a multi-dimensional (segregation ) data cube for exploratory data analysis.
Edit intent: coherence
Original:  The SCube system implements an approach for segregation discovery on top of frequent itemset mining , by offering to the analyst a multi-dimensional (segregation ) data cube for exploratory data analysis.
Revised:  The SCube system implements an approach for segregation discovery on top of frequent itemset mining , by offering to the analyst a multi-dimensional segregation data cube for exploratory data analysis.
Edit intent: clarity
Original: Community detection is one of the most popular research topics in the field of online social network analysis.
Revised: Community detection is of great importance for online social network analysis.
Edit intent: clarity
Original:  For instance, nowadays, the real world networks(e.g., Facebook, LinkedIn and Twitter ) are inherently evolving rapidly and expanding aggressively over time.
Revised:  For instance, real-world networks, such as Facebook, LinkedIn and Twitter ) are inherently evolving rapidly and expanding aggressively over time.
Edit intent: clarity
Original:  For instance, nowadays, the real world networks(e.g., Facebook, LinkedIn and Twitter ) are inherently evolving rapidly and expanding aggressively over time.
Revised:  For instance, nowadays, the real world networks(e.g., Facebook, LinkedIn and Twitter , are inherently growing rapidly and expanding aggressively over time.
Edit intent: clarity
Original:  It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the evolving networks.
Revised:  It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the dynamic networks.
Edit intent: clarity
Original:  We propose DynaMo, a novel dynamic modularity-based  community detection algorithm, aiming to detect communities in evolving social networks . DynaMo is an adaptive and incremental algorithm, which is designed for  maximizing the modularity gain while updating the community structure of evolving networks.
Revised:  We propose DynaMo, a novel modularity-based community detection algorithm, aiming to detect communities in evolving social networks . DynaMo is an adaptive and incremental algorithm, which is designed for maximizing the modularity gain while updating the community structure of evolving networks.
Edit intent: clarity
Original:  We propose DynaMo, a novel dynamic modularity-based  community detection algorithm, aiming to detect communities in evolving social networks . DynaMo is an adaptive and incremental algorithm, which is designed for  maximizing the modularity gain while updating the community structure of evolving networks.
Revised:  We propose DynaMo, a novel dynamic modularity-based community detection algorithm, aiming to detect communities in evolving social networks . DynaMo is an adaptive and incremental algorithm, which is designed for incrementally maximizing the modularity gain while updating the community structure of evolving networks.
Edit intent: clarity
Original:  We propose DynaMo, a novel dynamic modularity-based  community detection algorithm, aiming to detect communities in evolving social networks . DynaMo is an adaptive and incremental algorithm, which is designed for  maximizing the modularity gain while updating the community structure of evolving networks.
Revised:  We propose DynaMo, a novel dynamic modularity-based community detection algorithm, aiming to detect communities in evolving social networks . DynaMo is an adaptive and incremental algorithm, which is designed for maximizing the modularity gain while updating the community structure of dynamic networks.
Edit intent: clarity
Original:  The experimental results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of  effectiveness, and achieves (on average) 2 to 5 times  faster than Louvain algorithm.
Revised:  The experimental results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of effectiveness, and is 2 to 5 times faster than Louvain algorithm.
Edit intent: clarity
Original:  We show that  regular networks gradually disintegrate as environmental stress increases but disordered networks suddenly collapse at critical stress with hysteresis and vulnerability to perturbations.
Revised:  We show that , when regular networks gradually disintegrate as environmental stress increases but disordered networks suddenly collapse at critical stress with hysteresis and vulnerability to perturbations.
Edit intent: coherence
Original:  We show that  regular networks gradually disintegrate as environmental stress increases but disordered networks suddenly collapse at critical stress with hysteresis and vulnerability to perturbations.
Revised:  We show that regular networks gradually disintegrate as environmental stress increases , disordered networks can suddenly collapse at critical stress with hysteresis and vulnerability to perturbations.
Edit intent: clarity
Original:  On the other hand, the popularity of ride-hailing mobile apps facilitates the computerization and optimization of taxi service strategies, which  provide computer-assisted decisions of navigation and roaming for taxi drivers to locate potential customers.
Revised:  On the other hand, the popularity of ride-hailing mobile apps facilitates the computerization and optimization of taxi service strategies, which can provide computer-assisted decisions of navigation and roaming for taxi drivers to locate potential customers.
Edit intent: coherence
Original:  A big data analysis is provided using a large dataset of real-world taxi trips in New York City.
Revised:  A big data study is provided using a large dataset of real-world taxi trips in New York City.
Edit intent: clarity
Original:  Our methodology is to first model the computerized taxi service strategy by Markov Decision Process (MDP), and then devise the optimized taxi service strategy based on NYC taxi trip dataset.
Revised:  Our methodology is to first model the computerized taxi service strategy by Markov Decision Process (MDP), and then obtain the optimized taxi service strategy based on NYC taxi trip dataset.
Edit intent: clarity
Original:  Here, we compare the two methods using the energetic norm of local fields, which has the clear physical interpretation as being the error in the homogenized properties.
Revised:  Here, we systematically compare these two methods using the energetic norm of local fields, which has the clear physical interpretation as being the error in the homogenized properties.
Edit intent: clarity
Original:  Here, we compare the two methods using the energetic norm of local fields, which has the clear physical interpretation as being the error in the homogenized properties.
Revised:  Here, we compare the two methods using the energetic norm of local fields, which has the clear physical interpretation as being the error in the homogenised properties.
Edit intent: fluency
Original:  We show that the methods' effectiveness rely on the smoothness (regularity) of the solution and thus on the material coefficients.
Revised:  We show that the methods' effectiveness relies on the smoothness (regularity) of the solution and thus on the material coefficients.
Edit intent: fluency
Original:  FFTH profits from a good conditioning of  linear system, independent of the number of degrees of freedom, but generally needs more degrees of freedom  .
Revised:  FFTH profits from a good conditioning of the linear system, independent of the number of degrees of freedom, but generally needs more degrees of freedom .
Edit intent: fluency
Original:  In particular, the lasso and elastic net linear regression models employ a double-exponential distribution in their prior , which results in some maximum-likelihood regression coefficients being identically zero.
Revised:  In particular, the lasso and elastic net linear regression models impose a double-exponential distribution in their prior , which results in some maximum-likelihood regression coefficients being identically zero.
Edit intent: clarity
Original:  Because of their ability to simultaneously perform parameter estimation and variable selection, these models have become enormously popular .
Revised:  Because of their ability to simultaneously perform parameter estimation and variable selection, allowing the inference of robust models from large data sets .
Edit intent: clarity
Original:  Here, through the use of the Fourier transform , these integrals are expressed as complex-valued oscillatory integrals over "regression frequencies".
Revised:  Here, the Fourier transform is used to express these integrals as complex-valued oscillatory integrals over "regression frequencies".
Edit intent: clarity
Original:  Use of this approximation leads to highly accurate numerical estimates for the expectation values and marginal posterior distributions of the regression coefficients, thus allowing for Bayesian inference of much higher dimensional models than previously possible.
Revised:  Use of this approximation leads to highly accurate numerical estimates for the expectation values and marginal posterior distributions of the regression coefficients, and allows for Bayesian inference of much higher dimensional models than previously possible.
Edit intent: coherence
Original: Information needs to be appropriately encoded to be reliably transmitted over a physical media.
Revised: Information needs to be appropriately encoded to be reliably transmitted over physical media.
Edit intent: fluency
Original:  Even though it is well-know that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance as a function of the network parameters and external stimuli is still one of the great mysteries in Neuroscience .
Revised:  Even though it is well-known that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance as a function of the network parameters and external stimuli is still one of the great mysteries in Neuroscience .
Edit intent: fluency
Original:  Even though it is well-know that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance as a function of the network parameters and external stimuli is still one of the great mysteries in Neuroscience .
Revised:  Even though it is well-know that neurons exchange information using a pool of several protocols of spatio-temporal encodings, the suitability of each code and their performance as a function of the network parameters and external stimuli is still one of the great mysteries in Neuroscience .
Edit intent: fluency
Original:  Even though it is well-know that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance as a function of the network parameters and external stimuli is still one of the great mysteries in Neuroscience .
Revised:  Even though it is well-know that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance as a function of network parameters and external stimuli is still one of the great mysteries in Neuroscience .
Edit intent: fluency
Original:  Even though it is well-know that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance as a function of the network parameters and external stimuli is still one of the great mysteries in Neuroscience .
Revised:  Even though it is well-know that neurons exchange information using a pool of several protocols of spatial-temporal encodings, the suitability of each code and their performance as a function of the network parameters and external stimuli is still one of the great mysteries in neuroscience .
Edit intent: fluency
Original:  This paper sheds light into this problem considering small networks of chemically and electrically coupled Hindmarsh-Rose spiking neurons.
Revised:  This paper sheds light on this by modeling small-size networks of chemically and electrically coupled Hindmarsh-Rose spiking neurons.
Edit intent: clarity
Original:  We focus on the mathematical fundamental aspects of a class of temporal and firing-rate codes that result from the neurons' action-potentials and phases, and quantify their performance by measuring the Mutual Information Rate, aka the rate of information exchange.
Revised:  We focus on a class of temporal and firing-rate codes that result from the neurons' action-potentials and phases, and quantify their performance by measuring the Mutual Information Rate, aka the rate of information exchange.
Edit intent: clarity
Original:  We focus on the mathematical fundamental aspects of a class of temporal and firing-rate codes that result from the neurons' action-potentials and phases, and quantify their performance by measuring the Mutual Information Rate, aka the rate of information exchange.
Revised:  We focus on the mathematical fundamental aspects of a class of temporal and firing-rate codes that result from neurons' membrane-potentials and phases, and quantify their performance by measuring the Mutual Information Rate, aka the rate of information exchange.
Edit intent: clarity
Original:  We focus on the mathematical fundamental aspects of a class of temporal and firing-rate codes that result from the neurons' action-potentials and phases, and quantify their performance by measuring the Mutual Information Rate, aka the rate of information exchange.
Revised:  We focus on the mathematical fundamental aspects of a class of temporal and firing-rate codes that result from the neurons' action-potentials and phases, and quantify numerically their performance estimating the Mutual Information Rate, aka the rate of information exchange.
Edit intent: clarity
Original:  We show that pairs of neurons that have the largest rate of information exchange using the interspike interval and firing-rate codes are not adjacent in the network, whereas the spiking-time and phase codes promote large exchange of information rate from adjacent neurons.
Revised:  We show that pairs of neurons that have the largest rate of information exchange using the interspike-intervals and firing-rate codes are not adjacent in the network, whereas the spiking-time and phase codes promote large exchange of information rate from adjacent neurons.
Edit intent: fluency
Original:  We show that pairs of neurons that have the largest rate of information exchange using the interspike interval and firing-rate codes are not adjacent in the network, whereas the spiking-time and phase codes promote large exchange of information rate from adjacent neurons.
Revised:  We show that pairs of neurons that have the largest rate of information exchange using the interspike interval and firing-rate codes are not adjacent in the network, whereas spike-timings and phase codes promote large exchange of information rate from adjacent neurons.
Edit intent: clarity
Original:   would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Revised:  it would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Edit intent: fluency
Original:   would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Revised:  would suggest that small microcircuits would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Edit intent: clarity
Original:   would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Revised:  would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spike-timings and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Edit intent: clarity
Original:   would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Revised:  would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where there would be typically pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Edit intent: fluency
Original:   would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Revised:  would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Edit intent: fluency
Original:   would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, the most efficient codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Revised:  would suggest that small microcircuits of fully connected neurons, also known as cliques, would preferably exchange information using temporal codes ( spiking-time and phase codes), whereas on the macroscopic scale, where typically there will be pairs of neurons that are not directly connected due to the brain's sparsity, firing-rate and interspike-intervals codes would be the firing rate and interspike interval codes, with the latter being closely related to the firing rate code .
Edit intent: clarity
Original:  With the aim to provide an all-atom representation in an explicit solvent to the predictions of our SPlit and conQueR (SPQR) coarse-grained model of RNA, we  introduced a backmapping procedure which enforces the predicted structure into an atomistic one by means of steered Molecular Dynamics.
Revised:  With the aim to provide an all-atom representation in an explicit solvent to the predictions of our SPlit and conQueR (SPQR) coarse-grained model of RNA, we recently introduced a backmapping procedure which enforces the predicted structure into an atomistic one by means of steered Molecular Dynamics.
Edit intent: clarity
Original: Models of molecular computing generally embed computation in kinetics--the specific time evolution of a chemical system.
Revised: The promise of chemical computation lies in controlling systems incompatible with traditional electronic micro-controllers, with applications in synthetic biology and nano-scale manufacturing. Computation is typically embedded in kinetics---the specific time evolution of a chemical system.
Edit intent: clarity
Original:  The TBN model together with automated verification tools will help inform strategies for leak and error reduction in  strand displacement cascades and algorithmic tile assembly.
Revised:  The TBN model together with automated verification tools will help inform strategies for error reduction in strand displacement cascades and algorithmic tile assembly.
Edit intent: clarity
Original:  The Thermodynamic Binding Network (TBN) model was introduced to formally study how the thermodynamic equilibrium can be made consistent with the desired computation, and it idealizes binding interactions subject to enthalpy and entropy tradeoffs .
Revised:  The thermodynamic binding network (TBN) model was introduced to formally study how the thermodynamic equilibrium can be made consistent with the desired computation, and it idealizes binding interactions subject to enthalpy and entropy tradeoffs .
Edit intent: fluency
Original:  The Thermodynamic Binding Network (TBN) model was introduced to formally study how the thermodynamic equilibrium can be made consistent with the desired computation, and it idealizes binding interactions subject to enthalpy and entropy tradeoffs .
Revised:  The Thermodynamic Binding Network (TBN) model was introduced to formally study how the thermodynamic equilibrium can be made consistent with the desired computation, and it idealizes tradeoffs between configurational entropy and binding .
Edit intent: clarity