wanyu/IteraTeR_v2 huggingface dataset:
Original:  In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them  while inhibitory neurons play only a stabilizing role for the network dynamics.
Revised:  In most models of feedforward networks, activity is exclusively driven by excitatory neurons and the wiring patterns between them , while inhibitory neurons play only a stabilizing role for the network dynamics.
Edit intent: fluency
Original:  In this paper, we present an improved definition of the Explicit Corridor Map (ECM) , a navigation mesh that allows efficient path planning and crowd simulation for disk-shaped characters of any radius.
Revised:  A navigation mesh is a data structure that allows efficient path planning. The Explicit Corridor Map (ECM) , a navigation mesh that allows efficient path planning and crowd simulation for disk-shaped characters of any radius.
Edit intent: clarity
Original:  In this paper, we present an improved definition of the Explicit Corridor Map (ECM) , a navigation mesh that allows efficient path planning and crowd simulation for disk-shaped characters of any radius.
Revised:  In this paper, we present an improved definition of the Explicit Corridor Map (ECM) is a navigation mesh that allows efficient path planning and crowd simulation for disk-shaped characters of any radius.
Edit intent: fluency
Original:  The ECM is a medial axis ( MA) annotated with nearest-obstacle information.
Revised:  In this paper, we formally extend the medial axis ( MA) annotated with nearest-obstacle information.
Edit intent: clarity
Original:  For an MLE with n obstacle points and k connections, the MA has size O(n) . We present an improved algorithm that constructs the MA and ECM in O(n \log n \log k) time  .
Revised:  For an MLE with n obstacle points and k connections, the MA has size O(n) , and we present an improved algorithm that constructs the MA and ECM in O(n \log n \log k) time .
Edit intent: coherence
Original: There has been increasing interest in the integrated information theory (IIT) of consciousness , which hypothesizes that consciousness emerges from integrated information processing by neuronal dynamics.
Revised: There has been increasing interest in the integrated information theory (IIT) ofconsciousness , which hypothesizes that consciousness emerges from integrated information processing by neuronal dynamics.
Edit intent: fluency
Original: There has been increasing interest in the integrated information theory (IIT) of consciousness , which hypothesizes that consciousness emerges from integrated information processing by neuronal dynamics.
Revised: There has been increasing interest in the integrated information theory (IIT) of consciousness , which hypothesizes that consciousness is integrated information withinneuronal dynamics.
Edit intent: clarity
Original:  However, the current formulation of IIT poses both practical and theoretical problems when we aim to empirically test the theory by computing integrated information from neuronal signals.
Revised:  However, the current formulation of IIT poses both practical andtheoretical problems when we aim to empirically test the theory by computing integrated information from neuronal signals.
Edit intent: fluency
Original:  However, the current formulation of IIT poses both practical and theoretical problems when we aim to empirically test the theory by computing integrated information from neuronal signals.
Revised:  However, the current formulation of IIT poses both practical and theoretical problems when we aim to empirically test the theory by computingintegrated information from neuronal signals.
Edit intent: fluency
Original:  For example, measuring integrated information requires observing all the elements at the same time , but this is practically rather difficult.
Revised:  For example, measuring integratedinformation requires observing all the elements at the same time , but this is practically rather difficult.
Edit intent: fluency
Original:  For example, measuring integrated information requires observing all the elements at the same time , but this is practically rather difficult.
Revised:  For example, measuring integrated information requires observing all the elements in the considered system at the sametime , but this is practically rather difficult.
Edit intent: clarity
Original:  In addition, the interpretation of spatial partition needed to compute integrated information becomes vague in continuous time-series variables due to a general property of nonlinear dynamical systems known as"embedding ".
Revised:  In addition, the interpretation of the spatialpartition needed to compute integrated information becomes vague in continuous time-series variables due to a general property of nonlinear dynamical systems known as"embedding ".
Edit intent: fluency
Original: Here we propose that some aspects of such problems are resolved by considering the topological dimensionality of shared attractor dynamics as an indicator of integrated information in continuous attractor dynamics.
Revised: Here we propose that some aspects of such problems are resolved byconsidering the topological dimensionality of shared attractor dynamics as an indicator of integrated information in continuous attractor dynamics.
Edit intent: fluency
Original: Here we propose that some aspects of such problems are resolved by considering the topological dimensionality of shared attractor dynamics as an indicator of integrated information in continuous attractor dynamics.
Revised: Here we propose that some aspects of such problems are resolved by considering the topological dimensionality of shared attractor dynamics as an indicatorof integrated information in continuous attractor dynamics.
Edit intent: fluency
Original:  In this formulation, the effects of unobserved nodes on the attractor dynamics can be reconstructed using a technique called delay-embedding , which allows us to determine the dimensionality of an embedded attractor from partial observations.
Revised:  In this formulation, theeffects of unobserved nodes on the attractor dynamics can be reconstructed using a technique called delay-embedding , which allows us to determine the dimensionality of an embedded attractor from partial observations.
Edit intent: fluency
Original:  In this formulation, the effects of unobserved nodes on the attractor dynamics can be reconstructed using a technique called delay-embedding , which allows us to determine the dimensionality of an embedded attractor from partial observations.
Revised:  In this formulation, the effects of unobserved nodes on the attractor dynamics can be reconstructed using atechnique called delay embedding , which allows us to determine the dimensionality of an embedded attractor from partial observations.
Edit intent: fluency
Original:  In this formulation, the effects of unobserved nodes on the attractor dynamics can be reconstructed using a technique called delay-embedding , which allows us to determine the dimensionality of an embedded attractor from partial observations.
Revised:  In this formulation, the effects of unobserved nodes on the attractor dynamics can be reconstructed using a technique called delay-embedding , which allows us to identify the dimensionality of an embedded attractor from partial observations.
Edit intent: clarity
Original:  In this formulation, the effects of unobserved nodes on the attractor dynamics can be reconstructed using a technique called delay-embedding , which allows us to determine the dimensionality of an embedded attractor from partial observations.
Revised:  In this formulation, the effects of unobserved nodes on the attractor dynamics can be reconstructed using a technique called delay-embedding , which allows us to determine the dimensionality of anembedded attractor from partial observations.
Edit intent: fluency
Original:  We propose that the topological dimensionality indicates a critical property of integrated information  as it is invariant to general coordinate transformations.
Revised:  We propose that the topologicaldimensionality represents a critical property of integrated information as it is invariant to general coordinate transformations.
Edit intent: clarity
Original:  We propose that the topological dimensionality indicates a critical property of integrated information  as it is invariant to general coordinate transformations.
Revised:  We propose that the topological dimensionality indicates a critical property of integrated information , as it is invariant to general coordinate transformations.
Edit intent: fluency
Original:  We propose that the topological dimensionality indicates a critical property of integrated information  as it is invariant to general coordinate transformations.
Revised:  We propose that the topological dimensionality indicates a critical property of integrated information as it is invariantto general coordinate transformations.
Edit intent: fluency
Original:  We illustrate this new framework with simple examples, and discuss how it fit together with recent findings based on neural recordings from awake and anesthetized animals.
Revised:  We illustrate this new framework with simpleexamples and discuss how it fit together with recent findings based on neural recordings from awake and anesthetized animals.
Edit intent: fluency
Original:  We illustrate this new framework with simple examples, and discuss how it fit together with recent findings based on neural recordings from awake and anesthetized animals.
Revised:  We illustrate this new framework with simple examples, and discuss how it fits together with recent findings based on neural recordings from awake and anesthetized animals.
Edit intent: fluency
Original:  We illustrate this new framework with simple examples, and discuss how it fit together with recent findings based on neural recordings from awake and anesthetized animals.
Revised:  We illustrate this new framework with simple examples, and discuss how it fit together with recent findings based on neuralrecordings from awake and anesthetized animals.
Edit intent: fluency
Original:  This topological approach extends the existing notions of IIT to continuous dynamical systems , and offers a much-needed framework for testing the theory with experimental data by substantially relaxing the conditions required for evaluating integrated information in real neural systems.
Revised:  This topological approach extendsthe existing notions of IIT to continuous dynamical systems , and offers a much-needed framework for testing the theory with experimental data by substantially relaxing the conditions required for evaluating integrated information in real neural systems.
Edit intent: fluency
Original:  This topological approach extends the existing notions of IIT to continuous dynamical systems , and offers a much-needed framework for testing the theory with experimental data by substantially relaxing the conditions required for evaluating integrated information in real neural systems.
Revised:  This topological approach extends the existing notions of IIT to continuous dynamical systems and offers a much-needed framework for testing the theory with experimental data by substantially relaxing the conditions required for evaluating integrated information in real neural systems.
Edit intent: fluency
Original:  This topological approach extends the existing notions of IIT to continuous dynamical systems , and offers a much-needed framework for testing the theory with experimental data by substantially relaxing the conditions required for evaluating integrated information in real neural systems.
Revised:  This topological approach extends the existing notions of IIT to continuous dynamical systems , and offers a much-neededframework for testing the theory with experimental data by substantially relaxing the conditions required for evaluating integrated information in real neural systems.
Edit intent: fluency
Original:  This topological approach extends the existing notions of IIT to continuous dynamical systems , and offers a much-needed framework for testing the theory with experimental data by substantially relaxing the conditions required for evaluating integrated information in real neural systems.
Revised:  This topological approach extends the existing notions of IIT to continuous dynamical systems , and offers a much-needed framework for testing the theory with experimental data by substantially relaxing theconditions required for evaluating integrated information in real neural systems.
Edit intent: fluency
Original: In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest  or size of an item's inventory.
Revised: In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest (POI) or size of an item's inventory.
Edit intent: clarity
Original:  To close this gap, we extend two state-of-the art latent factor recommendation approaches: probabilistic matrix factorization (PMF) and geographical matrix factorization (GeoMF),  to optimize for both prediction accuracy and expected item usage that respects the capacity constraints.
Revised:  To close this gap, we extend two state-of-the art latent factor recommendation approaches: probabilistic matrix factorization (PMF) , geographical matrix factorization (GeoMF), to optimize for both prediction accuracy and expected item usage that respects the capacity constraints.
Edit intent: fluency
Original:  To close this gap, we extend two state-of-the art latent factor recommendation approaches: probabilistic matrix factorization (PMF) and geographical matrix factorization (GeoMF),  to optimize for both prediction accuracy and expected item usage that respects the capacity constraints.
Revised:  To close this gap, we extend two state-of-the art latent factor recommendation approaches: probabilistic matrix factorization (PMF) and geographical matrix factorization (GeoMF), to optimize for both recommendation accuracy and expected item usage that respects the capacity constraints.
Edit intent: fluency
Original:  Our experimental results in public datasets, both for the domain of item recommendation and Point-of-Interest recommendation, highlight the benefit of our method for the setting of recommendation under capacity constraints.
Revised:  Our experimental results in real-world datasets, both for the domain of item recommendation and Point-of-Interest recommendation, highlight the benefit of our method for the setting of recommendation under capacity constraints.
Edit intent: clarity
Original:  Our experimental results in public datasets, both for the domain of item recommendation and Point-of-Interest recommendation, highlight the benefit of our method for the setting of recommendation under capacity constraints.
Revised:  Our experimental results in public datasets, both for the domain of item recommendation and POI recommendation, highlight the benefit of our method for the setting of recommendation under capacity constraints.
Edit intent: fluency
Original: This paper studies randomized approximation algorithm for a variant of the set cover problem called %DIFDELCMD < {\em %%% minimum submodular cost partial multi-cover (SCPMC) . In apartial set cover problem , the goal is to find a minimum cost subcollection of sets covering at least a required fraction of elements.
Revised: This paper studies randomized approximation algorithm for a variant of the set cover problem called %DIFDELCMD < {\em %%% minimum submodular cost partial multi-cover (SCPMC) partial set cover problem , the goal is to find a minimum cost subcollection of sets covering at least a required fraction of elements.
Edit intent: coherence
Original: This paper studies randomized approximation algorithm for a variant of the set cover problem called %DIFDELCMD < {\em %%% minimum submodular cost partial multi-cover (SCPMC) . In apartial set cover problem , the goal is to find a minimum cost subcollection of sets covering at least a required fraction of elements.
Revised: This paper studies randomized approximation algorithm for a variant of the set cover problem called %DIFDELCMD < {\em %%% minimum submodular cost partial multi-cover (SCPMC) . In a , the goal is to find a minimum cost subcollection of sets covering at least a required fraction of elements.
Edit intent: fluency
Original:  In a%DIFDELCMD < {\em %%% multi-cover problem ,  each element e has a covering requirement r_e  , and the  goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by \mathcal S' if e belongs to at least r_e sets of \mathcal S'.
Revised:  In a%DIFDELCMD < {\em %%% , each element e has a covering requirement r_e , and the goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by \mathcal S' if e belongs to at least r_e sets of \mathcal S'.
Edit intent: clarity
Original:  In a%DIFDELCMD < {\em %%% multi-cover problem ,  each element e has a covering requirement r_e  , and the  goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by \mathcal S' if e belongs to at least r_e sets of \mathcal S'.
Revised:  In a%DIFDELCMD < {\em %%% multi-cover problem , in which each element e has a covering requirement r_e , and the goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by \mathcal S' if e belongs to at least r_e sets of \mathcal S'.
Edit intent: clarity
Original:  In a%DIFDELCMD < {\em %%% multi-cover problem ,  each element e has a covering requirement r_e  , and the  goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by \mathcal S' if e belongs to at least r_e sets of \mathcal S'.
Revised:  In a%DIFDELCMD < {\em %%% multi-cover problem , each element e has a covering requirement r_e , and the goal is to find a minimum cost sub-collection of sets which fully covers all elements , where an element e is fully covered by \mathcal S' if e belongs to at least r_e sets of \mathcal S'.
Edit intent: fluency
Original:  In a%DIFDELCMD < {\em %%% multi-cover problem ,  each element e has a covering requirement r_e  , and the  goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by \mathcal S' if e belongs to at least r_e sets of \mathcal S'.
Revised:  In a%DIFDELCMD < {\em %%% multi-cover problem , each element e has a covering requirement r_e , and the goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by sub-collection S' if e belongs to at least r_e sets of \mathcal S'.
Edit intent: clarity
Original:  In a%DIFDELCMD < {\em %%% multi-cover problem ,  each element e has a covering requirement r_e  , and the  goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by \mathcal S' if e belongs to at least r_e sets of \mathcal S'.
Revised:  In a%DIFDELCMD < {\em %%% multi-cover problem , each element e has a covering requirement r_e , and the goal is to find a minimum cost subcollection of sets \mathcal S' which fully covers all elements , where an element e is fully covered by \mathcal S' if and only if it belongs to at least r_e sets of \mathcal S'.
Edit intent: clarity
Original:  In aminimum submodular cost set cover problem (SCSC), the objective function is submodular and the constraint is a set cover.
Revised:  minimum submodular cost set cover problem (SCSC), the objective function is submodular and the constraint is a set cover.
Edit intent: fluency
Original:  In aminimum submodular cost set cover problem (SCSC), the objective function is submodular and the constraint is a set cover.
Revised:  In a (SCSC), the objective function is submodular and the constraint is a set cover.
Edit intent: clarity
Original:  In this paper, assuming that the maximum covering requirement r_{\max}=\max_e r_e is a constant and the objection function is nonnegative, monotone increasing , and submodular, we give the first randomized  algorithm for SCPMC achieving performance ratio O(b  ) with a high probability, where b=\max_e\binom{f}{r_{e}} and f is the maximum number of sets containing a common element.
Revised:  In this paper, assuming that the maximum covering requirement r_{\max}=\max_e r_e is a constant and the cost function is nonnegative, monotone increasing , and submodular, we give the first randomized algorithm for SCPMC achieving performance ratio O(b ) with a high probability, where b=\max_e\binom{f}{r_{e}} and f is the maximum number of sets containing a common element.
Edit intent: fluency
Original:  In this paper, assuming that the maximum covering requirement r_{\max}=\max_e r_e is a constant and the objection function is nonnegative, monotone increasing , and submodular, we give the first randomized  algorithm for SCPMC achieving performance ratio O(b  ) with a high probability, where b=\max_e\binom{f}{r_{e}} and f is the maximum number of sets containing a common element.
Revised:  In this paper, assuming that the maximum covering requirement r_{\max}=\max_e r_e is a constant and the objection function is nonnegative, monotone nondecreasing , and submodular, we give the first randomized algorithm for SCPMC achieving performance ratio O(b ) with a high probability, where b=\max_e\binom{f}{r_{e}} and f is the maximum number of sets containing a common element.
Edit intent: fluency
Original:  While cons-free programs have previously been used by several au thors to characterise complexity classes, the work on non-deterministic programs has almost exclusively considered programs of data order 0. Previous work has shown that adding explicit non-determinism to cons-free programs taking data of order 0 does not increase expressivity;
Revised:  While cons-free programs have previously been used by several authors to characterise complexity classes, the work on non-deterministic programs has almost exclusively considered programs of data order 0. Previous work has shown that adding explicit non-determinism to cons-free programs taking data of order 0 does not increase expressivity;
Edit intent: fluency
Original:  In this paper, we address the problem of optimizing information propagation in uncertain networks given a constraint budget of edges.
Revised:  In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges.
Edit intent: fluency
Original:  To compute the expected information flow to a source vertex, we employ a novel data structurecalled Component Tree , that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independent of the remaining parts of the network.
Revised:  To compute the expected information flow to a source vertex, we employ a novel data structurecalled Component Tree , that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independent of the remaining network.
Edit intent: clarity
Original:  For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this Component Tree .
Revised:  For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this data structure .
Edit intent: clarity