wanyu/IteraTeR_v2 huggingface dataset:
Original:  With this approach, we show how linear response is explicitly related to neuron dynamics with an example, the gIF model, introduced by M. Rudolph and A. Destexhe 91 . This illustrates the effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike statistics  .
Revised:  With this approach, we show how linear response is explicitly related to neuronal dynamics with an example, the gIF model, introduced by M. Rudolph and A. Destexhe 91 . This illustrates the effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike statistics .
Edit intent: fluency
Original:  With this approach, we show how linear response is explicitly related to neuron dynamics with an example, the gIF model, introduced by M. Rudolph and A. Destexhe 91 . This illustrates the effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike statistics  .
Revised:  With this approach, we show how linear response is explicitly related to neuron dynamics with an example, the gIF model, introduced by M. Rudolph and A. Destexhe 91 . This example illustrates the collective effect of the stimuli, intrinsic neuronal dynamics, and network connectivity on spike statistics .
Edit intent: clarity
Original:  We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of labeled , immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel E ectrophoresis and band detection on Autoradiograms (GEA)].
Revised:  We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of radiolabeled , immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel E ectrophoresis and band detection on Autoradiograms (GEA)].
Edit intent: clarity
Original:  We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of labeled , immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel E ectrophoresis and band detection on Autoradiograms (GEA)].
Revised:  We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of labeled , immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel E lectrophoresis and band detection on Autoradiograms (GEA)].
Edit intent: fluency
Original:  We show the preprocessing creates better separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering.
Revised:  We show the pre-processing creates more clearly separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering.
Edit intent: clarity
Original: In the recommendation literature, social connections have been successfully incorporated into the traditional recommendation methods, especially the most popular state-of-art one Matrix Factorization (MF)
Revised:  Matrix Factorization (MF)
Edit intent: coherence
Original:  . MF is based on the assumption that the user-item rating matrix is low rank and thus can be decomposed into two smaller matrices representing the user and item latent features . Recently, instead of assuming that the rating matrix is low rank, Local LOw Rank Matrix Approximation (LLORMA) has been proposed based on a novel assumption that the rating matrix is com- posed of a set of low-rank submatrices , termed local low rank.
Revised:  . MF is based on the assumption that the user-item rating matrix is low-rank. However, this assumption can be too restrictive to capture complex relationships and interactions among users and items . Recently, instead of assuming that the rating matrix is low rank, Local LOw Rank Matrix Approximation (LLORMA) has been proposed based on a novel assumption that the rating matrix is com- posed of a set of low-rank submatrices , termed local low rank.
Edit intent: coherence
Original:  . MF is based on the assumption that the user-item rating matrix is low rank and thus can be decomposed into two smaller matrices representing the user and item latent features . Recently, instead of assuming that the rating matrix is low rank, Local LOw Rank Matrix Approximation (LLORMA) has been proposed based on a novel assumption that the rating matrix is com- posed of a set of low-rank submatrices , termed local low rank.
Revised:  . MF is based on the assumption that the user-item rating matrix is low rank and thus can be decomposed into two smaller matrices representing the user and item latent features . Recently, Local LOw-Rank Matrix Approximation (LLORMA) has been proposed based on a novel assumption that the rating matrix is com- posed of a set of low-rank submatrices , termed local low rank.
Edit intent: coherence
Original:  . MF is based on the assumption that the user-item rating matrix is low rank and thus can be decomposed into two smaller matrices representing the user and item latent features . Recently, instead of assuming that the rating matrix is low rank, Local LOw Rank Matrix Approximation (LLORMA) has been proposed based on a novel assumption that the rating matrix is com- posed of a set of low-rank submatrices , termed local low rank.
Revised:  . MF is based on the assumption that the user-item rating matrix is low rank and thus can be decomposed into two smaller matrices representing the user and item latent features . Recently, instead of assuming that the rating matrix is low rank, Local LOw Rank Matrix Approximation (LLORMA) has been proposed based on a novel assumption that the rating matrix is composed of a number of low-rank submatrices , termed local low rank.
Edit intent: clarity
Original:  A population based searching method, called estimation of distribution algorithm (EDA)  is adopted to explore the model parameter space starting from a batch of random locations.
Revised:  A population based searching method, called estimation of distribution algorithm (EDA) , is adopted to explore the model parameter space starting from a batch of random locations.
Edit intent: fluency
Original: Holant problems are a family of counting problems on graphs, parameterised by sets of complex-valued functions of Boolean inputs.
Revised: Holant problems are a family of counting problems on graphs, parametrised by sets of complex-valued functions of Boolean inputs.
Edit intent: fluency
Original:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compare with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish more long lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate more with other successful scientists.
Revised:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compared with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish more long lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate more with other successful scientists.
Edit intent: fluency
Original:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compare with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish more long lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate more with other successful scientists.
Revised:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compare with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish longer lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate more with other successful scientists.
Edit intent: clarity
Original:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compare with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish more long lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate more with other successful scientists.
Revised:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compare with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish more long lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate with successful scientists.
Edit intent: clarity
Original: Scientific collaborations shape novel ideas and new discoveries and help scientists to advance their scientific career through publishing high impact publications and grant proposals . Recent studies however show that gender inequality is still present in many scientific practices ranging from hiring to peer review processes and grant applications.
Revised: Scientific collaborations shape ideas as well as innovations and are both the substrate for, and the outcome of, academic careers . Recent studies however show that gender inequality is still present in many scientific practices ranging from hiring to peer review processes and grant applications.
Edit intent: clarity
Original: Scientific collaborations shape novel ideas and new discoveries and help scientists to advance their scientific career through publishing high impact publications and grant proposals . Recent studies however show that gender inequality is still present in many scientific practices ranging from hiring to peer review processes and grant applications.
Revised: Scientific collaborations shape novel ideas and new discoveries and help scientists to advance their scientific career through publishing high impact publications and grant proposals . Recent studies show that gender inequality is still present in many scientific practices ranging from hiring to peer review processes and grant applications.
Edit intent: coherence
Original: Scientific collaborations shape novel ideas and new discoveries and help scientists to advance their scientific career through publishing high impact publications and grant proposals . Recent studies however show that gender inequality is still present in many scientific practices ranging from hiring to peer review processes and grant applications.
Revised: Scientific collaborations shape novel ideas and new discoveries and help scientists to advance their scientific career through publishing high impact publications and grant proposals . Recent studies however show that gender inequality is still present in many scientific practices ranging from hiring to peer-review processes and grant applications.
Edit intent: fluency
Original:  While empirical findings highlight that collaborations impact success and gender inequality is present in science, we know little about gender-specific differences in collaboration patterns , how they change over time and how they impact scientific success.
Revised:  In this work, we investigate gender-specific differences in collaboration patterns , how they change over time and how they impact scientific success.
Edit intent: clarity
Original:  We investigate which collaboration patterns are related with scientific success and if these patterns are similar for male and female scientists .
Revised:  We explore how these patterns change over years and career ages and how they impact scientific success .
Edit intent: clarity
Original:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compared with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish longer lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate with successful scientists .
Revised:  Our results highlight that successful male and female scientists reveal the same collaboration patterns: compared with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish longer lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate with successful scientists .
Edit intent: clarity
Original:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compared with scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish longer lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate with successful scientists .
Revised:  Our results highlight that while subtle gender disparities in dropout rates, productivity and collaboration patterns exist, successful male and female scientists reveal the same collaboration patterns: compared to scientists in the same career age, they tend to collaborate with more colleagues than other scientists, establish longer lasting and repetitive collaborations, bring people together that have not been collaborating before and collaborate with successful scientists .
Edit intent: fluency
Original:  We investigate the new feature of forager greed ;
Revised:  We investigate the new feature of forager greed ;
Edit intent: fluency
Original:  Paradoxically, the lifetime of the forager can depend non-monotonically on greed, and the sense of the non-monotonicity is opposite in one and two dimensions.
Revised:  Paradoxically, the lifetime of the forager can depend non-monotonically on greed, and the sense of the non-monotonicity is opposite in one and two dimensions.
Edit intent: fluency
Original:  Even more unexpectedly, the forager lifetime in one dimension is substantially enhanced when the greed is negative ;
Revised:  Even more unexpectedly, the forager lifetime in one dimension is substantially enhanced when the greed is negative ;
Edit intent: fluency
Original:  Often, to preserve mathematical rigor, the crossbar itself is separated from the neuron capacitors  .
Revised:  Often, to preserve mathematical rigor, the crossbar itself has been separated from the neuron capacitors .
Edit intent: fluency
Original:  Often, to preserve mathematical rigor, the crossbar itself is separated from the neuron capacitors  .
Revised:  Often, to preserve mathematical rigor, the crossbar itself is separated from the neuron capacitors to preserve mathematical rigor .
Edit intent: fluency
Original:  In this work, we sought to simplify the design, removing extraneous components to consume significantly lower power at a minimal cost of accuracy.
Revised:  In this work, we sought to simplify the design, creating a fast circuit that consumed significantly lower power at a minimal cost of accuracy.
Edit intent: clarity
Original:  Compared to a previously proposed spiking model, our proposed hardware consumed 99\% less energy to do the same work at 21 times the throughput.
Revised:  Compared to a previously proposed spiking model, our proposed hardware consumed 99\% less energy to do the same work at 21x the throughput.
Edit intent: fluency
Original:  This work provides a means for extremely low-energy sparse coding in mobile devices, such as cellular phones, or for very sparse coding as is needed by self-driving cars or robotics that must integrate data from multiple, high-resolution sensors.
Edit intent: coherence
Original:  Alternatively, electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location.
Revised:  Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location.
Edit intent: coherence
Original:  We also illustrate that this automatically determined order only slightly increases with the problem dimension, compared to a linear increase of the number of eigenvalues inside the energy window.
Revised:  We also illustrate that this automatically determined order only slightly increases with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window.
Edit intent: fluency
Original: It was shown in C. elegans that life-long treatments with low level of the prooxidant paraquat increased ROS , which then acted as the signal to slow down aging by activating the intrinsic apoptosis pathway.
Revised: The view that ROS slow down aging by activating the intrinsic apoptosis pathway.
Edit intent: coherence
Original:  Analogous to how online algorithms receive their input piece-by-piece, our model presents one of the players Bob his input piece-by-piece, and has the players Alice and Bob cooperate to compute a result it presents Bob with the next piece  .
Revised:  Analogous to how online algorithms receive their input piece-by-piece, our model presents one of the players , Bob, his input piece-by-piece, and has the players Alice and Bob cooperate to compute a result it presents Bob with the next piece .
Edit intent: clarity
Original:  Analogous to how online algorithms receive their input piece-by-piece, our model presents one of the players Bob his input piece-by-piece, and has the players Alice and Bob cooperate to compute a result it presents Bob with the next piece  .
Revised:  Analogous to how online algorithms receive their input piece-by-piece, our model presents one of the players Bob his input piece-by-piece, and has the players Alice and Bob cooperate to compute a result each time before the next piece .
Edit intent: clarity
Original:  This model has a closer and more natural correspondence to dynamic data structures than the classic communication models do  and hence presents a new perspective on data structures.
Revised:  This model has a closer and more natural correspondence to dynamic data structures than classic communication models do and hence presents a new perspective on data structures.
Edit intent: fluency
Original:  This model has a closer and more natural correspondence to dynamic data structures than the classic communication models do  and hence presents a new perspective on data structures.
Revised:  This model has a closer and more natural correspondence to dynamic data structures than the classic communication models do , and hence presents a new perspective on data structures.
Edit intent: fluency
Original:  We first present a  lower bound for the online set intersection problem in the online communication model, demonstrating a general approach for proving online communication lower bounds.
Revised:  We first present a lower bound for the online set intersection problem in the online communication model, demonstrating a general approach for proving online communication lower bounds.
Edit intent: fluency
Original:  Then we apply the online communication model to  data structure lower bounds by studying the Group Range Problem, a dynamic data structure problem.
Revised:  We then apply the online communication model to data structure lower bounds by studying the Group Range Problem, a dynamic data structure problem.
Edit intent: coherence
Original:  Then we apply the online communication model to  data structure lower bounds by studying the Group Range Problem, a dynamic data structure problem.
Revised:  Then we apply the online communication model to prove data structure lower bounds by studying the Group Range Problem, a dynamic data structure problem.
Edit intent: coherence
Original:  Then we apply the online communication model to  data structure lower bounds by studying the Group Range Problem, a dynamic data structure problem.
Revised:  Then we apply the online communication model to data structure lower bounds for two dynamic data structure problem.
Edit intent: clarity
Original:  This problem admits an O(\log n)-time worst-case data structure.
Revised:  This problem admits an O(\log n)-time data structure.
Edit intent: clarity
Original: We investigate monotone circuits with local oracles ( K., 2016 ) , i.e., circuits containing additional inputs y_i = y_i(x) that can perform unstructured computations on the input string x. Let \mu \in [0,1] be the locality of the circuit, a parameter that bounds the combined strength of the oracle functions y_i(x), and U_{n,k}, V_{n,k} \subseteq \{0,1\}^m be the classical sets of positive and negative inputs considered for k-clique in the approximation method ( Razborov, 1985 ).
Revised: We investigate monotone circuits with local oracles K., 2016 ) , i.e., circuits containing additional inputs y_i = y_i(x) that can perform unstructured computations on the input string x. Let \mu \in [0,1] be the locality of the circuit, a parameter that bounds the combined strength of the oracle functions y_i(x), and U_{n,k}, V_{n,k} \subseteq \{0,1\}^m be the classical sets of positive and negative inputs considered for k-clique in the approximation method ( Razborov, 1985 ).
Edit intent: fluency
Original: We investigate monotone circuits with local oracles ( K., 2016 ) , i.e., circuits containing additional inputs y_i = y_i(x) that can perform unstructured computations on the input string x. Let \mu \in [0,1] be the locality of the circuit, a parameter that bounds the combined strength of the oracle functions y_i(x), and U_{n,k}, V_{n,k} \subseteq \{0,1\}^m be the classical sets of positive and negative inputs considered for k-clique in the approximation method ( Razborov, 1985 ).
Revised: We investigate monotone circuits with local oracles ( K., 2016 , i.e., circuits containing additional inputs y_i = y_i(x) that can perform unstructured computations on the input string x. Let \mu \in [0,1] be the locality of the circuit, a parameter that bounds the combined strength of the oracle functions y_i(x), and U_{n,k}, V_{n,k} \subseteq \{0,1\}^m be the classical sets of positive and negative inputs considered for k-clique in the approximation method ( Razborov, 1985 ).
Edit intent: fluency