wanyu/IteraTeR_v2 huggingface dataset:
Original:  In particular, we undertake an axiomatic analysis of%DIFDELCMD < {\it %%% all possible ways to measure academic authorship for a given dataset of research articles and find that an egalitarian e-index is theonly method which satisfies the axioms of anonymity, monotonicity, and efficiency.
Revised:  In particular, we undertake an axiomatic analysis of%DIFDELCMD < {\it %%% all possible ways to measure academic authorship for a given dataset of research articles and find that an egalitarian e-index is the method which satisfies the axioms of anonymity, monotonicity, and efficiency.
Edit intent: fluency
Original:  In our setting, this egalitarian method } divides authorship of joint projects equally  and sums across all publications of an author.
Revised:  In our setting, this egalitarian method } divides authorship of joint projects equally among authors and sums across all publications of an author.
Edit intent: clarity
Original: In this paper we study the shifts, which are the shift-invariant and topologically closed sets of configurations over a fine alphabet in Z^d.
Revised: In this paper we study the shifts, which are the shift-invariant and topologically closed sets of configurations over a finite alphabet in Z^d.
Edit intent: clarity
Original:  Two classes of shifts play a prominent role in symbolic dynamics, in language theory and in the theory of computability: the shifts of finite type (obtained by forbidding a finite number of finite patterns) and the effective shifts (obtained by forbidding a recursively enumerable set of finite patterns).
Revised:  Two classes of shifts play a prominent role in symbolic dynamics, in language theory and in the theory of computability: the shifts of finite type (obtained by forbidding a finite number of finite patterns) and the effective shifts (obtained by forbidding a computably enumerable set of finite patterns).
Edit intent: clarity
Original: Jeandel. We prove a similar result for quasiperidic shifts and also show that there exists a quasiperidic shift of finite type for which Kolmogorov complexity of all patterns of size n\times n is \Omega(n) . Our results are based on a rather complex and subtle construction that we express in terms of tilings.
Revised: Jeandel. We prove a similar result for quasiperiodic shifts and also show that there exists a quasiperidic shift of finite type for which Kolmogorov complexity of all patterns of size n\times n is \Omega(n) . Our results are based on a rather complex and subtle construction that we express in terms of tilings.
Edit intent: fluency
Original: Jeandel. We prove a similar result for quasiperidic shifts and also show that there exists a quasiperidic shift of finite type for which Kolmogorov complexity of all patterns of size n\times n is \Omega(n) . Our results are based on a rather complex and subtle construction that we express in terms of tilings.
Revised: Jeandel. We prove a similar result for quasiperidic shifts and also show that there exists a quasiperiodic shift of finite type for which Kolmogorov complexity of all patterns of size n\times n is \Omega(n) . Our results are based on a rather complex and subtle construction that we express in terms of tilings.
Edit intent: fluency
Original:  Recent advances in neurotechnologies, many of which have been aided by computing's rapid progression over recent decades, are now reigniting this opportunity to bring neural computation insights into broader computing applications.
Revised:  This paper describes how recent advances in neurotechnologies, many of which have been aided by computing's rapid progression over recent decades, are now reigniting this opportunity to bring neural computation insights into broader computing applications.
Edit intent: clarity
Original:  Such a positive feedback has the potential to change the complexion of how computing sciences and neurosciences interact, and suggests that the next form of exponential scaling in computing may emerge from our progressive understanding of the brain.
Revised:  This paper will describe how this positive feedback has the potential to change the complexion of how computing sciences and neurosciences interact, and suggests that the next form of exponential scaling in computing may emerge from our progressive understanding of the brain.
Edit intent: clarity
Original: Collaborative filtering (CF) is one of the most efficient ways for recommender systems.
Revised: One of the most efficient ways for recommender systems.
Edit intent: coherence
Original:  Typically, CF-based algorithms analyze users ' preferences and items ' attributes using one of two types of feedback:\textit{explicit feedback (e.g., ratings given to item by users , like/dislike) orimplicit feedback (e. g., clicks, views, purchases).
Revised:  Typically, CF-based algorithms analyze users ' preferences and items ' attributes using one of two types of feedback:\textit{ (e.g., ratings given to item by users , like/dislike) orimplicit feedback (e. g., clicks, views, purchases).
Edit intent: fluency
Original:  Typically, CF-based algorithms analyze users ' preferences and items ' attributes using one of two types of feedback:\textit{explicit feedback (e.g., ratings given to item by users , like/dislike) orimplicit feedback (e. g., clicks, views, purchases).
Revised:  Typically, CF-based algorithms analyze users ' preferences and items ' attributes using one of two types of feedback:\textit{explicit feedback implicit feedback (e. g., clicks, views, purchases).
Edit intent: clarity
Original:  Typically, CF-based algorithms analyze users ' preferences and items ' attributes using one of two types of feedback:\textit{explicit feedback (e.g., ratings given to item by users , like/dislike) orimplicit feedback (e. g., clicks, views, purchases).
Revised:  Typically, CF-based algorithms analyze users ' preferences and items ' attributes using one of two types of feedback:\textit{explicit feedback (e.g., ratings given to item by users , like/dislike) or (e. g., clicks, views, purchases).
Edit intent: fluency
Original:  This model is a combination ofmatrix factorization anditem embedding , a similar concept with word-embedding in natural language processing.
Revised:  matrix factorization anditem embedding , a similar concept with word-embedding in natural language processing.
Edit intent: clarity
Original:  This model is a combination ofmatrix factorization anditem embedding , a similar concept with word-embedding in natural language processing.
Revised:  This model is a combination of anditem embedding , a similar concept with word-embedding in natural language processing.
Edit intent: clarity
Original:  This model is a combination ofmatrix factorization anditem embedding , a similar concept with word-embedding in natural language processing.
Revised:  This model is a combination ofmatrix factorization item embedding , a similar concept with word-embedding in natural language processing.
Edit intent: fluency
Original:  This model is a combination ofmatrix factorization anditem embedding , a similar concept with word-embedding in natural language processing.
Revised:  This model is a combination ofmatrix factorization and , a similar concept with word-embedding in natural language processing.
Edit intent: fluency
Original:  This model is a combination ofmatrix factorization anditem embedding , a similar concept with word-embedding in natural language processing.
Revised:  This model is a combination ofmatrix factorization anditem embedding 
Edit intent: clarity
Original:  The experiments on three real-datasets (Movilens 1M ,Movielens 20M , andBookcrossing ) demonstrate that our method can efficiently predict ratings for items even if the ratings data is not available for them.
Revised:  The experiments on three Movilens 1M ,Movielens 20M , andBookcrossing ) demonstrate that our method can efficiently predict ratings for items even if the ratings data is not available for them.
Edit intent: clarity
Original:  The experiments on three real-datasets (Movilens 1M ,Movielens 20M , andBookcrossing ) demonstrate that our method can efficiently predict ratings for items even if the ratings data is not available for them.
Revised:  The experiments on three real-datasets (Movilens 1M , , andBookcrossing ) demonstrate that our method can efficiently predict ratings for items even if the ratings data is not available for them.
Edit intent: clarity
Original:  The experiments on three real-datasets (Movilens 1M ,Movielens 20M , andBookcrossing ) demonstrate that our method can efficiently predict ratings for items even if the ratings data is not available for them.
Revised:  The experiments on three real-datasets (Movilens 1M ,Movielens 20M Bookcrossing ) demonstrate that our method can efficiently predict ratings for items even if the ratings data is not available for them.
Edit intent: fluency
Original:  The experiments on three real-datasets (Movilens 1M ,Movielens 20M , andBookcrossing ) demonstrate that our method can efficiently predict ratings for items even if the ratings data is not available for them.
Revised:  The experiments on three real-datasets (Movilens 1M ,Movielens 20M , and ) demonstrate that our method can efficiently predict ratings for items even if the ratings data is not available for them.
Edit intent: clarity
Original:  The experimental results also show that our method outperforms competing methods on rating prediction task in general as well as for users and items which have few ratings .
Revised:  The experimental results also show that our method outperforms competing methods , especially when the data is very sparse .
Edit intent: clarity
Original: OpenStreetMap offers an underexplored source of worldwide geospatial data useful to urban researchers.
Revised: OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers.
Edit intent: clarity
Original:  It presents wide-ranging empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory,  urban design, and morphology such as structure, connectedness, density, centrality, and resilience.
Revised:  It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, urban design, and morphology such as structure, connectedness, density, centrality, and resilience.
Edit intent: clarity
Original:  In the past, street network data acquisition and processing has been challenging and ad hoc.
Revised:  In the past, street network data acquisition and processing have been challenging and ad hoc.
Edit intent: fluency
Original:  These street networks and measures data sets have been shared in a public repository for other researchers to use.
Revised:  These street networks and measures data have been shared in a public repository for other researchers to use.
Edit intent: clarity
Original:  In this work we give answer to some of them, extending the results obtained in C\'aceres-Carrillo-Perthame(2011), C\'aceres-Perthame(2014), C\'aceres-Schneider(2017) to a general network with two populations (excitatory and inhibitory), where there are transmission delays between the neurons and where the neurons remain in a refractory state for a time.
Revised:  In this work we give answer to some of them, extending the results obtained in C\'aceres-Carrillo-Perthame(2011), C\'aceres-Perthame(2014), C\'aceres-Schneider(2017) to a general network with two populations (excitatory and inhibitory), with transmission delays between the neurons and where the neurons remain in a refractory state for a time.
Edit intent: clarity
Original:  More precisely, we have studied the number of steady states in terms of the model parameters, its long time behaviour for small connectivity parameters, and the blow-up phenomenon when there is no transmission delay between excitatory neurons .
Revised:  More precisely, we have studied the number of steady states in terms of the model parameters, the long time behaviour for small connectivity parameters, and the blow-up phenomenon when there is no transmission delay between excitatory neurons .
Edit intent: clarity
Original:  More precisely, we have studied the number of steady states in terms of the model parameters, its long time behaviour for small connectivity parameters, and the blow-up phenomenon when there is no transmission delay between excitatory neurons .
Revised:  More precisely, we have studied the number of steady states in terms of the model parameters, its long time behaviour for small connectivity parameters, and the blow-up phenomena, and the importance of transmission delays between excitatory neurons .
Edit intent: clarity
Original:  Moreover , we have presented a numerical resolutor for this model, based on high order  WENO schemes and an explicit third order TVD Runge-Kutta method, which produce a powerful numerical solver to describe the wide range of phenomena in the network: blow-up, asynchronous/synchronous solutions , instability/stability of the steady states .. .
Revised:  Moreover , we have presented a numerical resolutor for this model, based on high order WENO schemes and an explicit third order TVD Runge-Kutta method, in order to describe the wide range of phenomena in the network: blow-up, asynchronous/synchronous solutions , instability/stability of the steady states .. .
Edit intent: coherence
Original:  Moreover , we have presented a numerical resolutor for this model, based on high order  WENO schemes and an explicit third order TVD Runge-Kutta method, which produce a powerful numerical solver to describe the wide range of phenomena in the network: blow-up, asynchronous/synchronous solutions , instability/stability of the steady states .. .
Revised:  Moreover , we have presented a numerical resolutor for this model, based on high order WENO schemes and an explicit third order TVD Runge-Kutta method, which produce a powerful numerical solver to describe the wide range of phenomena exhibited by the network: blow-up, asynchronous/synchronous solutions , instability/stability of the steady states .. .
Edit intent: clarity
Original:  Moreover , we have presented a numerical resolutor for this model, based on high order  WENO schemes and an explicit third order TVD Runge-Kutta method, which produce a powerful numerical solver to describe the wide range of phenomena in the network: blow-up, asynchronous/synchronous solutions , instability/stability of the steady states .. .
Revised:  Moreover , we have presented a numerical resolutor for this model, based on high order WENO schemes and an explicit third order TVD Runge-Kutta method, which produce a powerful numerical solver to describe the wide range of phenomena in the network: blow-up, asynchronous/synchronous solutions and instability/stability of the steady states .. .
Edit intent: fluency
Original:  Finally, three classical Polya processes, one computational and two analytical, are proposed to statistically approximate the contagion process of each node, showing a good fit for a range of system parameters  .
Revised:  Three classical Polya processes, one computational and two analytical, are proposed to statistically approximate the contagion process of each node, showing a good fit for a range of system parameters .
Edit intent: coherence
Original:  Enforcement of stability can be particularly challenging in scenarios where a setup is composed of multiple components, such as differently refined FDTD grids , macromodels, advanced boundary conditions, and others.
Revised:  Stability enforcement can be particularly challenging in scenarios where a setup is composed of multiple components, such as differently refined FDTD grids , macromodels, advanced boundary conditions, and others.
Edit intent: clarity
Original:  Enforcement of stability can be particularly challenging in scenarios where a setup is composed of multiple components, such as differently refined FDTD grids , macromodels, advanced boundary conditions, and others.
Revised:  Enforcement of stability can be particularly challenging in scenarios where a setup is composed of multiple components, such as grids of different resolution, advanced boundary conditions, and others.
Edit intent: clarity
Original:  In a general class of edge-independent networks, we demonstrate that graph matchability is almost surely lost when matching the networks directly , and is almost perfectly recovered when first centering the networks using Universal Singular Value Thresholding before matching.
Revised:  In a general class of edge-independent networks, we demonstrate that graph matchability can be lost with high probability when matching the networks directly , and is almost perfectly recovered when first centering the networks using Universal Singular Value Thresholding before matching.
Edit intent: clarity
Original:  In a general class of edge-independent networks, we demonstrate that graph matchability is almost surely lost when matching the networks directly , and is almost perfectly recovered when first centering the networks using Universal Singular Value Thresholding before matching.
Revised:  In a general class of edge-independent networks, we demonstrate that graph matchability is almost surely lost when matching the networks directly , and is almost perfectly recovered by centering the networks using Universal Singular Value Thresholding before matching.
Edit intent: clarity
Original: The driving force behind convolutional networks - the most successful deep learning architecture to date, is their expressive power.
Revised: The driving force behind convolutional networks ~--~ the most successful deep learning architecture to date, is their expressive power.
Edit intent: fluency
Original:  In this paper we provide a high-level overview of a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various architectural features in convolutional networks (depth, width, convolution strides and more).
Revised:  In this paper we present a high-level overview of a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various architectural features in convolutional networks (depth, width, convolution strides and more).
Edit intent: clarity
Original: The driving force behind convolutional networks ~--~ the most successful deep learning architecture to date, is their expressive power.
Revised: The driving force behind convolutional networks - the most successful deep learning architecture to date, is their expressive power.
Edit intent: fluency
Original:  In this paper we present a high-level overview of a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various architectural features in convolutional networks (depth, width, convolution strides and more).
Revised:  In this paper we overview a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various architectural features in convolutional networks (depth, width, convolution strides and more).
Edit intent: clarity
Original:  In this paper we present a high-level overview of a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various architectural features in convolutional networks (depth, width, convolution strides and more).
Revised:  In this paper we present a high-level overview of a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various convolutional network architectural features (depth, width, convolution strides and more).
Edit intent: fluency
Original:  In this paper we present a high-level overview of a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various architectural features in convolutional networks (depth, width, convolution strides and more).
Revised:  In this paper we present a high-level overview of a series of works written by the authors, that through an equivalence to hierarchical tensor decompositions, analyze the expressive efficiency and inductive bias of various architectural features in convolutional networks (depth, width, strides and more).
Edit intent: fluency
Original: We present a new method for the separation of superimposed, independent, auto-correlated com- ponents from noisy multi-channel measurement.
Revised: We present a new method for the separation of superimposed, independent, auto-correlated components from noisy multi-channel measurement.
Edit intent: fluency
Original:  Independent posterior samples can be provided, permitting error estimates on all de- sired quantities.
Revised:  Independent posterior samples can be provided, permitting error estimates on all desired quantities.
Edit intent: clarity
Original: Indirect hex-dominant meshing relies on the detection of adjacent tetrahedra that may be combined to form hexahedra.
Revised: Indirect hex-dominant meshing methods rely on the detection of adjacent tetrahedra that may be combined to form hexahedra.
Edit intent: clarity
Original:  The new method does not rely on a predefined set of patterns of tetrahedral decompositions of hexahedra but allows to look for all combinations of eight vertices whose connectivity matches the connectivity of a hexahedron.
Revised:  The new method first computes all combinations of eight vertices whose connectivity matches the connectivity of a hexahedron.
Edit intent: clarity
Original:  The subset of tetrahedra of triangulating each potential hexahedron is determined once its eight vertices are computed.
Revised:  The subset of tetrahedra triangulating each potential hexahedron is determined once its eight vertices are computed.
Edit intent: clarity
Original:  The new method first computes all combinations of eight  vertices whose connectivity  matches the connectivity of a hexahedron  .
Revised:  First, all combinations of eight vertices whose connectivity matches the connectivity of a hexahedron .
Edit intent: coherence
Original: Recent outbreaks of Ebola, N1H1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic.
Revised: Recent outbreaks of Ebola, H1N1 and other infectious diseases have shown that the assumptions underlying the established theory of epidemics management are too idealistic.
Edit intent: fluency
Original:  We then propose efficient analytical and approximate methods for computing these measures on both undirected and directed networks.
Revised:  We propose efficient analytical and approximate methods for computing these measures on both undirected and directed networks.
Edit intent: coherence
Original:  Finally, we describe a simple method inspired by shell modulus centrality, called general degree, which improves simple degree centrality and could prove to be a useful tool for network science.
Revised:  Finally, we describe a simple method inspired by shell modulus centrality, called general degree, which proved to be a useful tool for network science.
Edit intent: clarity
Original:  We see a profit increase of over 60%DIFDELCMD < \\%%% %DIF < of our proposed method compared to other schemes in certain simulation scenarios.
Revised:  We see a profit increase of over 60%DIFDELCMD < \\%%% %DIF < of our proposed method compared to other schemes in 
Edit intent: clarity
Original: Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th Generation (5G) cellular systems.
Revised: Due to its potential for multi-gigabit and low latency wireless links, millimeter wave (mmWave) technology is expected to play a central role in 5th generation cellular systems.
Edit intent: clarity