wanyu/IteraTeR_v2 huggingface dataset:
Original:  Our simulation results show that the number of transmissions can be reduced by almost 85 \% in the sensor nodes with the highest workload , and we detail the impact of predicting and aggregating the transmissions according to the parameters that can be observed in average scenarios .
Revised:  Our simulation results show that the number of transmissions can be reduced by almost 85 \% in the sensor nodes with the highest workload . We also detail the impact of predicting and aggregating the transmissions according to the parameters that can be observed in average scenarios .
Edit intent: clarity
Original:  Our simulation results show that the number of transmissions can be reduced by almost 85 \% in the sensor nodes with the highest workload , and we detail the impact of predicting and aggregating the transmissions according to the parameters that can be observed in average scenarios .
Revised:  Our simulation results show that the number of transmissions can be reduced by almost 85 \% in the sensor nodes with the highest workload , and we detail the impact of predicting and aggregating transmissions according to the parameters that can be observed in average scenarios .
Edit intent: fluency
Original: In a recent letter, Carlos Vilchez-Rom\'an criticizes Bornmann et al. (2015) for using data which cannot be reproduced without access to an in-house version of the Web-of-Science (WoS) at the Max Planck Digital Libraries (MPDL, Munich).
Revised: In a recent letter, Carlos Vilchez-Roman criticizes Bornmann et al. (2015) for using data which cannot be reproduced without access to an in-house version of the Web-of-Science (WoS) at the Max Planck Digital Libraries (MPDL, Munich).
Edit intent: fluency
Original:  These wave size and duration distributions have been experimentally shown to display a stable power law profile, long-range correlations and 1/f^{ \beta } power spectrum in vivo and \textit{in vitro}.
Revised:  These waves' size and duration distributions have been experimentally shown to display a stable power law profile, long-range correlations and 1/f^{ \beta } power spectrum in vivo and \textit{in vitro}.
Edit intent: clarity
Original:  These wave size and duration distributions have been experimentally shown to display a stable power law profile, long-range correlations and 1/f^{ \beta } power spectrum in vivo and \textit{in vitro}.
Revised:  These wave size and duration distributions have been experimentally shown to display a stable power-law profile, long-range correlations and 1/f^{ \beta } power spectrum in vivo and \textit{in vitro}.
Edit intent: fluency
Original:  The phase transition point analytically determined by a mean field calculation is close to the expected experimental value of EPSP in the\textit{  cortex.
Revised:  The phase transition is analytically determined by a mean field calculation is close to the expected experimental value of EPSP in the\textit{ cortex.
Edit intent: clarity
Original:  The phase transition point analytically determined by a mean field calculation is close to the expected experimental value of EPSP in the\textit{  cortex.
Revised:  The phase transition point analytically determined by a mean-field approximation and is close to the expected experimental value of EPSP in the\textit{ cortex.
Edit intent: clarity
Original:  The phase transition point analytically determined by a mean field calculation is close to the expected experimental value of EPSP in the\textit{  cortex.
Revised:  The phase transition point analytically determined by a mean field calculation is close to the expected experimental value of the\textit{ cortex.
Edit intent: clarity
Original:  The phase transition is analytically determined by a mean-field approximation and is close to the expected experimental value of the\textit{excitatory postsynaptic potential in the cortex.
Revised:  The phase transition is analytically determined by a mean-field approximation \textit{excitatory postsynaptic potential in the cortex.
Edit intent: clarity
Original:  The phase transition is analytically determined by a mean-field approximation and is close to the expected experimental value of the\textit{excitatory postsynaptic potential in the cortex.
Revised:  The phase transition is analytically determined by a mean-field approximation and is close to the expected experimental value of the\textit{ in the cortex.
Edit intent: clarity
Original:  These benefits come with only a light increase in computational overhead during training (ranging from 5%DIFDELCMD < \\%%% %DIF < to 15%DIFDELCMD < \\%%% %DIF < in timing experiments) and a very modest increase in the number of model parameters.
Revised:  These benefits come with only a light increase in computational overhead during training %DIFDELCMD < \\%%% %DIF < to 15%DIFDELCMD < \\%%% %DIF < in timing experiments) and a very modest increase in the number of model parameters.
Edit intent: clarity
Original:  These benefits come with only a light increase in computational overhead during training (ranging from 5%DIFDELCMD < \\%%% %DIF < to 15%DIFDELCMD < \\%%% %DIF < in timing experiments) and a very modest increase in the number of model parameters.
Revised:  These benefits come with only a light increase in computational overhead during training (ranging from 5%DIFDELCMD < \\%%% %DIF < to %DIFDELCMD < \\%%% %DIF < in timing experiments) and a very modest increase in the number of model parameters.
Edit intent: clarity
Original: Computational models of complex systems are usually elaborate and sensitive to implementation details, characteristics which often affect model verification and validation.
Revised: Computational models of complex systems are usually elaborate and sensitive to implementation details, characteristics which often affect their verification and validation.
Edit intent: clarity
Original:  Model replication is a possible solution to this problem, as it bypasses the biases associated with the language or toolkit used to develop the original model, promoting model verification , model validation, and improved modelunderstanding.
Revised:  Model replication is a possible solution to this issue. It avoids biases associated with the language or toolkit used to develop the original model, promoting model verification , model validation, and improved modelunderstanding.
Edit intent: clarity
Original:  We introduce a convolutional neural network that operates directly on graphs , allowing end-to-end learning of the feature pipeline.
Revised:  We introduce a convolutional neural network that operates directly on graphs . These networks allow end-to-end learning of the feature pipeline.
Edit intent: coherence
Original:  To address this limitation, We introduce a three stage pipeline: pruning,  quantization and Huffman encoding , that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy.
Revised:  To address this limitation, We introduce a three stage pipeline: pruning, quantization and Huffman coding , that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy.
Edit intent: fluency
Original:  Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman encoding . After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids.
Revised:  Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding . After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids.
Edit intent: fluency
Original:  Pruning , reduces the number of connections by 9x to 13x ;
Revised:  Pruning reduces the number of connections by 9x to 13x ;
Edit intent: fluency
Original:  Pruning , reduces the number of connections by 9x to 13x ;
Revised:  Pruning , reduces the number of connections by 9x to 13x .
Edit intent: fluency
Original:  On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x  from 240MB to 6.9MB, without loss of accuracy.
Revised:  On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x , from 240MB to 6.9MB, without loss of accuracy.
Edit intent: fluency
Original:  Our method reduced the size of VGG16 by 49x  from 552MB to 11.3MB, again with no loss of accuracy.
Revised:  Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy.
Edit intent: fluency
Original:  Our method reduced the size of VGG16 by 49x  from 552MB to 11.3MB, again with no loss of accuracy.
Revised:  Our method reduced the size of VGG16 by 49x , from 552MB to 11.3MB, again with no loss of accuracy.
Edit intent: fluency
Original:  Pruning  reduces the number of connections by 9x to 13x . Quantization then reduces the number of bits that represent each connection from 32 to 5.
Revised:  Pruning , reduces the number of connections by 9x to 13x . Quantization then reduces the number of bits that represent each connection from 32 to 5.
Edit intent: fluency
Original:  Pruning  reduces the number of connections by 9x to 13x . Quantization then reduces the number of bits that represent each connection from 32 to 5.
Revised:  Pruning reduces the number of connections by 9x to 13x ; Quantization then reduces the number of bits that represent each connection from 32 to 5.
Edit intent: fluency
Original:  Our method reduced the size of VGG-16 by 49x , from 552MB to 11.3MB, again with no loss of accuracy.
Revised:  Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy.
Edit intent: fluency
Original:  Here, we consider the neural network of the %DIFDELCMD < {\it %%% C.elegans soil worm, organized into six interconnected communities, where neurons obey chaotic bursting dynamics.
Revised:  Here, we consider the neural network of the %DIFDELCMD < {\it %%% C.elegans soil worm, organized into six interconnected communities, where neurons obey chaotic bursting dynamics.
Edit intent: fluency
Original:  We perform the same analysis for networks that share common features with the %DIFDELCMD < {\it %%% C.elegans neural network.
Revised:  We perform the same analysis for networks that share common features with the %DIFDELCMD < {\it %%% C.elegans neural network.
Edit intent: fluency
Original: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Revised: The overwhelming majority of neural field and mass modelsuse current-based synapses unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Edit intent: fluency
Original: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Revised: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 %DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Edit intent: clarity
Original: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Revised: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Edit intent: coherence
Original: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Revised: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 %DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Edit intent: coherence
Original: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Revised: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Edit intent: coherence
Original: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 , the functional effects on the neuronal dynamics have not been systematically analysed and compared to that of models with current-based synapses .
Revised: The overwhelming majority of neural field and mass modelsuse current-based synapses 1 unlike spiking models which typically use conductance-based synapses%DIFDELCMD < [%%% 2 . Although neural field models that employ conductance-based synapses have been studied%DIFDELCMD < [%%% 3, 9 .
Edit intent: coherence
Original:  This shortcoming is particularly apparent with respect to epileptic dynamics, where neural field models of epilepsy typically describe the transition to seizure-like activity as a bifurcation 10%DIFDELCMD < ]%%% . This letter examines and compares the differences between conductance-based synapses and current-based synapses by constructing a neural field model that encapsulates both through a homotopic mapping.
Revised:  10%DIFDELCMD < ]%%% . This letter examines and compares the differences between conductance-based synapses and current-based synapses by constructing a neural field model that encapsulates both through a homotopic mapping.
Edit intent: clarity
Original:  This shortcoming is particularly apparent with respect to epileptic dynamics, where neural field models of epilepsy typically describe the transition to seizure-like activity as a bifurcation 10%DIFDELCMD < ]%%% . This letter examines and compares the differences between conductance-based synapses and current-based synapses by constructing a neural field model that encapsulates both through a homotopic mapping.
Revised:  This shortcoming is particularly apparent with respect to epileptic dynamics, where neural field models of epilepsy typically describe the transition to seizure-like activity as a bifurcation %DIFDELCMD < ]%%% . This letter examines and compares the differences between conductance-based synapses and current-based synapses by constructing a neural field model that encapsulates both through a homotopic mapping.
Edit intent: fluency
Original:  This article is for demographers but even more importantly it is for teachers, lecturers and university professors who might not be aware of the long-lasting and serious problems with the Demographic Transition Theory, making it obsolete.
Edit intent: coherence
Original:  Vaccines against the seasonal influenza disease are of variable effectiveness , for example being fairly low in the 2014/2015 Northern hemisphere season .
Revised:  Vaccines against the seasonal influenza disease are of variable effectiveness .
Edit intent: coherence
Original:  We illustrate how the current A/Texas/50/2012 vaccine is not expected to be protective against the A/California/02/2014 strain that has emerged in the population , consistent with recent observations .
Revised:  We illustrate how the current A/Texas/50/2012 vaccine against the A/California/02/2014 strain that has emerged in the population , consistent with recent observations .
Edit intent: clarity
Original:  We illustrate how the current A/Texas/50/2012 vaccine is not expected to be protective against the A/California/02/2014 strain that has emerged in the population , consistent with recent observations .
Revised:  We illustrate how the current A/Texas/50/2012 vaccine is not expected to be protective against the A/California/02/2014 strain that emerged in the population , consistent with recent observations .
Edit intent: clarity
Original:  In addition, we used multidimensional scaling to cluster the A/ H3N2 hemagglutinin from GenBank to find that there is a transition underway from the A/California/02/ 2014 to the A/New Mexico/11/2014 strain, suggesting the latter may be an appropriate vaccine component for next season .
Revised:  In addition, we used multidimensional scaling to cluster the A/ H3N2 hemagglutinin from GenBank to find that there is a transition underway from the A/California/02/ 2014-like strains .
Edit intent: clarity
Original: H3N2 Influenza A is a serious disease which can lead to hospitalization and which causes significant morbidity and mortality .
Revised:  Influenza A is a serious disease which can lead to hospitalization and which causes significant morbidity and mortality .
Edit intent: coherence
Original: H3N2 Influenza A is a serious disease which can lead to hospitalization and which causes significant morbidity and mortality .
Revised: H3N2 Influenza A is a serious disease that causes significant morbidity and mortality .
Edit intent: coherence
Original:  We illustrate how the effectiveness of the 2014/2015 A/Texas/50/2012  vaccine against the A/California/02/2014  strain that emerged in the population can be estimated via p_{\rm epitope .
Revised:  We illustrate how the effectiveness of the 2014/2015 A/Texas/50/2012 vaccine against the A/California/02/2014 strain that emerged in the population can be estimated via pepitope .
Edit intent: fluency
Original:  In this work, we generalize the concept of s-shellability to bishellability, where the former implies the latter in the sense that every s-shellable drawing is, for any b \leq s-2, also \mbox{b-bishellable .
Revised:  In this work, we generalize the concept of s-shellability to bishellability, where the former implies the latter in the sense that every s-shellable drawing is, for any b \leq s-2, also b-bishellable .
Edit intent: coherence
Original:  Our main result is that (\lfloor n{2} \rfloor\!-\!2)-bishellability also guarantees, with a simpler proof than for \mbox{s-shellability , that a drawing has at least H(n) crossings.
Revised:  Our main result is that (\lfloor n{2} \rfloor\!-\!2)-bishellability also guarantees, with a simpler proof than for s-shellability , that a drawing has at least H(n) crossings.
Edit intent: clarity
Original: The Harary-Hill conjecture, still open after more than 50 years, asserts that the crossing number of the complete graph K_n is %DIFDELCMD < \[ H(n) = \frac 1 4 %DIFDELCMD < \left\lfloor\mathstrut n{\mathstrut 2}\right\rfloor %DIFDELCMD < \left\lfloor\mathstrut n-1{\mathstrut 2}\right\rfloor %DIFDELCMD < \left\lfloor\mathstrut n-2{\mathstrut 2}\right\rfloor %DIFDELCMD < \left\lfloor\mathstrut n-3{\mathstrut 2}\right\rfloor\,.
Revised: The Harary--Hill conjecture, still open after more than 50 years, asserts that the crossing number of the complete graph K_n is %DIFDELCMD < \[ H(n) = \frac 1 4 %DIFDELCMD < \left\lfloor\mathstrut n{\mathstrut 2}\right\rfloor %DIFDELCMD < \left\lfloor\mathstrut n-1{\mathstrut 2}\right\rfloor %DIFDELCMD < \left\lfloor\mathstrut n-2{\mathstrut 2}\right\rfloor %DIFDELCMD < \left\lfloor\mathstrut n-3{\mathstrut 2}\right\rfloor\,.
Edit intent: fluency
Original: \] %%% \left\lfloor{\mathstrut 2}}\right\rfloor \left\lfloor{\mathstrut 2}}\right\rfloor \left\lfloor{\mathstrut 2}}\right\rfloor \left\lfloor{\mathstrut 2}}\right \rfloor \'Abrego et al.
Revised: \] %%% \left\lfloor{\mathstrut 2}}\right\rfloor \left\lfloor{\mathstrut 2}}\right\rfloor \left\lfloor{\mathstrut 2}}\right\rfloor \left\lfloor{\mathstrut 2}}\right \rfloor. \'Abrego et al.
Edit intent: fluency
Original:  ~\mbox{ shell1 introduced the notion of shellability of a drawing D of K_n.
Revised:  introduced the notion of shellability of a drawing D of K_n.
Edit intent: clarity
Original: Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ~ ethnic minorities.
Revised: Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ~ ethnic minorities.
Edit intent: fluency
Original: Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ~ ethnic minorities.
Revised: Discrimination discovery from data is an important task aiming at identifying patterns of illegal and unethical discriminatory activities against protected-by-law groups, e.g., ethnic minorities.
Edit intent: fluency
Original:  Following Suppes' \emph{probabilistic causation theory , we define a method to extract, from a dataset of historical decision records, the causal structures existing among the attributes in the data.
Revised:  Following Suppes' \emph{probabilistic causation theory , we define a method to extract, from a dataset of historical decision records, the causal structures existing among the attributes in the data.
Edit intent: fluency
Original:  The result is a type of constrained Bayesian network, which we dub Suppes-Bayes Causal Network (%DIFDELCMD < \SBCN%%%  ). Next, we develop a toolkit of methods based on random walks on top of the %DIFDELCMD < \SBCN%%%  , addressing different anti-discrimination legal concepts, such as direct and indirect discrimination, group and individual discrimination, genuine requirement, and favoritism.
Revised:  The result is a type of constrained Bayesian network, which we dub (%DIFDELCMD < \SBCN%%% ). Next, we develop a toolkit of methods based on random walks on top of the %DIFDELCMD < \SBCN%%% , addressing different anti-discrimination legal concepts, such as direct and indirect discrimination, group and individual discrimination, genuine requirement, and favoritism.
Edit intent: coherence