wanyu/IteraTeR_v2 huggingface dataset:
Original:  We then apply these results to a special family of TLNs, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrix is defined from just two continuous parameters and a directed graph .
Revised:  We apply these results to a special family of TLNs, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrix is defined from just two continuous parameters and a directed graph .
Edit intent: coherence
Original:  We then apply these results to a special family of TLNs, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrix is defined from just two continuous parameters and a directed graph .
Revised:  We then apply these results to a special family of TLNs, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrices are defined from directed graphs .
Edit intent: coherence
Original:  This leads us to a set of graph rules that enable us to determine fixed points for a CTLN that are independent of the parameters, and can be inferred directly from the underlying graph .
Revised:  This leads us to prove a series of graph rules that enable us to determine fixed points for a CTLN that are independent of the parameters, and can be inferred directly from the underlying graph .
Edit intent: clarity
Original:  This leads us to a set of graph rules that enable us to determine fixed points for a CTLN that are independent of the parameters, and can be inferred directly from the underlying graph .
Revised:  This leads us to a set of graph rules that enable one to determine fixed points for a CTLN that are independent of the parameters, and can be inferred directly from the underlying graph .
Edit intent: clarity
Original:  Our results thus provide the foundation for a kind of "graphical calculus" to infer features of the dynamics from a network's connectivity.
Revised:  Our results provide the foundation for a kind of "graphical calculus" to infer features of the dynamics from a network's connectivity.
Edit intent: coherence
Original: In this paper, we propose an attention-based classifier that predict multiple emotions of a given sentence.
Revised: In this paper, we propose an attention-based classifier that predicts multiple emotions of a given sentence.
Edit intent: fluency
Original:  Finally, we generalize this to an appearance estimation-and-segmentation task, where we map from an image showing multiple materials to multiple networks reproducing their appearance , as well as per-pixel segmentation .
Revised:  Finally, we show the example of an appearance estimation-and-segmentation task, where we map from an image showing multiple materials to multiple networks reproducing their appearance , as well as per-pixel segmentation .
Edit intent: clarity
Original:  Finally, we generalize this to an appearance estimation-and-segmentation task, where we map from an image showing multiple materials to multiple networks reproducing their appearance , as well as per-pixel segmentation .
Revised:  Finally, we generalize this to an appearance estimation-and-segmentation task, mapping from an image showing multiple materials to multiple networks reproducing their appearance , as well as per-pixel segmentation .
Edit intent: coherence
Original:  Finally, we generalize this to an appearance estimation-and-segmentation task, where we map from an image showing multiple materials to multiple networks reproducing their appearance , as well as per-pixel segmentation .
Revised:  Finally, we generalize this to an appearance estimation-and-segmentation task, where we map from an image showing multiple materials to multiple deep appearance maps .
Edit intent: clarity
Original: We propose a deep representation of appearance, i. e.  the relation of color, surface orientation, viewer position, material and illumination.
Revised: We propose a deep representation of appearance, i. e. , the relation of color, surface orientation, viewer position, material and illumination.
Edit intent: fluency
Original:  Previous approaches have used deep learning to extract classic appearance representations relating to reflectance model parameters (e. g. Phong) or illumination (e. g.  HDR environment maps).
Revised:  Previous approaches have useddeep learning to extract classic appearance representations relating to reflectance model parameters (e. g. Phong) or illumination (e. g. HDR environment maps).
Edit intent: fluency
Original:  Previous approaches have used deep learning to extract classic appearance representations relating to reflectance model parameters (e. g. Phong) or illumination (e. g.  HDR environment maps).
Revised:  Previous approaches have used deep learning to extract classic appearance representationsrelating to reflectance model parameters (e. g. Phong) or illumination (e. g. HDR environment maps).
Edit intent: fluency
Original:  Previous approaches have used deep learning to extract classic appearance representations relating to reflectance model parameters (e. g. Phong) or illumination (e. g.  HDR environment maps).
Revised:  Previous approaches have used deep learning to extract classic appearance representations relating to reflectance model parameters (e. g. , Phong) orillumination (e. g. HDR environment maps).
Edit intent: fluency
Original:  Previous approaches have used deep learning to extract classic appearance representations relating to reflectance model parameters (e. g. Phong) or illumination (e. g.  HDR environment maps).
Revised:  Previous approaches have used deep learning to extract classic appearance representations relating to reflectance model parameters (e. g. Phong) or illumination (e. g. , HDR environment maps).
Edit intent: fluency
Original:  We suggest to directly represent appearance itself as a network we call a deep appearance map (DAM).
Revised:  We suggest todirectly represent appearance itself as a network we call a deep appearance map (DAM).
Edit intent: fluency
Original:  We suggest to directly represent appearance itself as a network we call a deep appearance map (DAM).
Revised:  We suggest to directly represent appearance itself as a network we call aDeep Appearance Map (DAM).
Edit intent: fluency
Original:  This is a 4D generalization over 2D reflectance maps, which held the view direction fixed.
Revised:  This is a 4D generalizationover 2D reflectance maps, which held the view direction fixed.
Edit intent: fluency
Original:  Finally, we show the example of an appearance estimation-and-segmentation task, mapping from an image showing multiple materials to multiple deep appearance maps.
Revised:  Finally, we show the example of an appearance estimation-and-segmentation task, mapping from an image showingmultiple materials to multiple deep appearance maps.
Edit intent: fluency
Original:  These interactions can be abstracted as patterns of connections among distinct units - such as genes, proteins, individual organisms, or species - which form a hierarchy of biological organisation.
Revised:  These interactions can be abstracted as patterns of connections among distinct units such as genes, proteins, individual organisms, or species - which form a hierarchy of biological organisation.
Edit intent: fluency
Original:  These interactions can be abstracted as patterns of connections among distinct units - such as genes, proteins, individual organisms, or species - which form a hierarchy of biological organisation.
Revised:  These interactions can be abstracted as patterns of connections among distinct units - such as genes, proteins, individual organisms, or species which form a hierarchy of biological organisation.
Edit intent: fluency
Original:  This pervasive nestedness of networks can result in propagation of  within-level interactions  to units at higher or lower levels of organization.
Revised:  This pervasive "nestedness" of networks can result in propagation of within-level interactions to units at higher or lower levels of organization.
Edit intent: clarity
Original:  This pervasive nestedness of networks can result in propagation of  within-level interactions  to units at higher or lower levels of organization.
Revised:  This pervasive nestedness of networks can result in propagation of the effects from within-level interactions to units at higher or lower levels of organization.
Edit intent: clarity
Original: In this paper we consider the question what is the scientific and career performance of principal investigators ( PIs ) of publicly funded research projects compared to scientific performance of all researchers.
Revised: In this paper we consider the scientific and career performance of principal investigators ( PIs ) of publicly funded research projects compared to scientific performance of all researchers.
Edit intent: coherence
Original: In this paper we consider the question what is the scientific and career performance of principal investigators ( PIs ) of publicly funded research projects compared to scientific performance of all researchers.
Revised: In this paper we consider the question what is the scientific and career performance of principal investigators ( PI's ) of publicly funded research projects compared to scientific performance of all researchers.
Edit intent: fluency
Original:  Our study is based on high quality data about (i) research projects awarded in Slovenia in the period 1994--2016 (7508 projects with 2725 PIs in total) and (ii) about scientific productivity of all researchers in Slovenia that were active in the period 1970-2016 - there are 19598 such researchers in total, including the PIs .
Revised:  Our study is based on high quality data about (i) research projects awarded in Slovenia in the period 1994-2016 (7508 projects with 2725 PIs in total) and (ii) about scientific productivity of all researchers in Slovenia that were active in the period 1970-2016 - there are 19598 such researchers in total, including the PIs .
Edit intent: fluency
Original:  Our study is based on high quality data about (i) research projects awarded in Slovenia in the period 1994--2016 (7508 projects with 2725 PIs in total) and (ii) about scientific productivity of all researchers in Slovenia that were active in the period 1970-2016 - there are 19598 such researchers in total, including the PIs .
Revised:  Our study is based on high quality data about (i) research projects awarded in Slovenia in the period 1994--2016 (7508 projects with 2725 PI's in total) and (ii) about scientific productivity of all researchers in Slovenia that were active in the period 1970-2016 - there are 19598 such researchers in total, including the PIs .
Edit intent: fluency
Original:  Our study is based on high quality data about (i) research projects awarded in Slovenia in the period 1994--2016 (7508 projects with 2725 PIs in total) and (ii) about scientific productivity of all researchers in Slovenia that were active in the period 1970-2016 - there are 19598 such researchers in total, including the PIs .
Revised:  Our study is based on high quality data about (i) research projects awarded in Slovenia in the period 1994--2016 (7508 projects with 2725 PIs in total) and (ii) about scientific productivity of all researchers in Slovenia that were active in the period 1970-2016 - there are 19598 such researchers in total, including the PI's .
Edit intent: fluency
Original:  We compare average productivity, collaboration, internationality and interdisciplinarity of PIs and of all active researchers.
Revised:  We compare average productivity, collaboration, internationality and interdisciplinarity of PI's and of all active researchers.
Edit intent: fluency
Original:  Our analysis shows that for all four indicators the average performance of PIs is much higher compared to average performance of all active researchers.
Revised:  Our analysis shows that for all four indicators the average performance of PI's is much higher compared to average performance of all active researchers.
Edit intent: clarity
Original:  The results show that the PIs have on average longer and more fruitful career compared to all active researchers, with regards to all career indicators.
Revised:  The results show that the PI's have on average longer and more fruitful career compared to all active researchers, with regards to all career indicators.
Edit intent: clarity
Original:   On long run, the PIs leading the research programs (the most prestigious grants) on average demonstrate the best scientific performance.
Revised:  On long run, the PI's leading the research programs (the most prestigious grants) on average demonstrate the best scientific performance.
Edit intent: fluency
Original:  We find out that they are well connected and that PIs are well distributed across these networks forming their backbones.
Revised:  We find out that they are well connected and that PI's are well distributed across these networks forming their backbones.
Edit intent: fluency
Original:   We believe that our study shed new light to the relations between the public funding of the science and the scientific output  .
Revised:  We believe that our study sheds new light to the relations between the public funding of the science and the scientific output .
Edit intent: fluency
Original:  We also conducted three large-scale, wide-ranging perceptual studies on the perception of harmonic colors and harmonization algorithms 
Revised:  We also conducted three large-scale, wide-ranging perceptual studies on the perception of harmonic colors and harmonization algorithms .
Edit intent: fluency
Original:  These observed trends are deeply rooted in the spatial patterns of society, and require geographical extensions in network models of social influence . improved understanding of the ways in which new technologies and new ideas diffuse across locations can provide us with new insights into the spread of information, technological progress and economic development.
Revised:  These observed trends are deeply rooted in the spatial patterns of society, and require geographical extensions in network models of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development.
Edit intent: clarity
Original:  In this work, we uncover the role of geography in the spatial diffusion of innovations via a unique dataset from iWiW, which is an online social network (OSN) in Hungary .
Revised:  In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary .
Edit intent: clarity
Original:  First, we clarify the motivation for the topological approach by presenting a novel formulation of the underlying inference problem.
Revised:  First, we present a novel formulation of the underlying inference problem.
Edit intent: coherence
Original:  We then develop a theory for the estimation of novelty profiles using barcodes.
Revised:  We then study the problem of obtaining a lower bound on the novelty profile using barcodes.
Edit intent: clarity
Original:  To establish these results, we describe the topology of the Vietoris-Rips filtrations arising from evolutionary histories indexed by galled trees.
Revised:  To establish these results, we describe the topology of the Vietoris--Rips filtrations arising from evolutionary histories indexed by galled trees.
Edit intent: fluency
Original: This paper extends the algorithm of Benner, Heinkenschloss, Saak, and Weichelt: An inexact low-rank Newton-ADI method for large-scale algebraic Riccati equations, Applied Numerical Mathematics Vol.  108 (2016), pp. 125-142, URL to Riccati equations associated with Hessenberg index-2 Differential Algebraic Equation (DAE) systems.
Revised: This paper extends the algorithm of Benner, Heinkenschloss, Saak, and Weichelt: An inexact low-rank Newton-ADI method for large-scale algebraic Riccati equations, Applied Numerical Mathematics Vol. ~ 108 (2016), pp. 125-142, URL to Riccati equations associated with Hessenberg index-2 Differential Algebraic Equation (DAE) systems.
Edit intent: fluency
Original: This paper extends the algorithm of Benner, Heinkenschloss, Saak, and Weichelt: An inexact low-rank Newton-ADI method for large-scale algebraic Riccati equations, Applied Numerical Mathematics Vol.  108 (2016), pp. 125-142, URL to Riccati equations associated with Hessenberg index-2 Differential Algebraic Equation (DAE) systems.
Revised: This paper extends the algorithm of Benner, Heinkenschloss, Saak, and Weichelt: An inexact low-rank Newton-ADI method for large-scale algebraic Riccati equations, Applied Numerical Mathematics Vol. 108 (2016), pp. 125-142, URL to Riccati equations associated with Hessenberg index-2 Differential Algebratic Equation (DAE) systems.
Edit intent: fluency
Original:  Challenges in the numerical solution of the Riccati equation arise from the large-scale of the underlying systems , the algebraic constraint in the DAE system , and the fact that matrices arising in some subproblems may only be marginally stable .
Revised:  Challenges in the numerical solution of the Riccati equation arise from the large-scale of the underlying systems and the algebraic constraint in the DAE system , and the fact that matrices arising in some subproblems may only be marginally stable .
Edit intent: fluency
Original:  Challenges in the numerical solution of the Riccati equation arise from the large-scale of the underlying systems , the algebraic constraint in the DAE system , and the fact that matrices arising in some subproblems may only be marginally stable .
Revised:  Challenges in the numerical solution of the Riccati equation arise from the large-scale of the underlying systems , the algebraic constraint in the DAE system .
Edit intent: coherence
Original:  A main ingredient in the extension to the DAE case is the projection onto the manifold of the algebraic constraints.
Revised:  A main ingredient in the extension to the DAE case is the projection onto the manifold described by the algebraic constraints.
Edit intent: clarity
Original:  Because of the novel nature of the methods , we focus on an interesting problem of community structures in brain network of the CA1 region of the hippocampus of anesthetized rats to study the pre-ictal and post-ictal states.
Revised:  Because of the CA1 region of the hippocampus of anesthetized rats to study the pre-ictal and post-ictal states.
Edit intent: coherence
Original:  Our implementation produces accurate counter-examples when properties are violated and  , with sufficient problem structure, can scale to analyze systems with up to a billion real-valued state variables.
Revised:  Our implementation produces accurate counter-examples when properties are violated and , with sufficient problem structure, is shown to be able to analyze a system with a billion real-valued state variables.
Edit intent: clarity
Original:  With further computation, methods exist to reason about richer models that have inputs, nonlinear differential equations, and hybrid dynamics.
Revised:  With further computation, methods exist to reason about richer models with inputs, nonlinear differential equations, and hybrid dynamics.
Edit intent: fluency
Original:  As such, the scalability of affine systems verification is a prerequisite to the scalability of analysis methods for more complex systems.
Revised:  As such, the scalability of affine systems verification is a prerequisite to scalable analysis for more complex systems.
Edit intent: clarity
Original:  One benefit of affine systems is that their reachable states can be written in terms of the matrix exponential, and safety checking can be performed at specific time steps with linear programming.
Revised:  One benefit of affine systems can be written in terms of the matrix exponential, and safety checking can be performed at specific time steps with linear programming.
Edit intent: clarity
Original:  We overcome these two problems by combining several methods that leverage common problem structure.
Revised:  We overcome these challenges by combining several methods that leverage common problem structure.
Edit intent: clarity
Original:  Memory demands can be reduced by taking advantage of both initial states that are not full-dimensional , and safety properties (outputs) that only need a few linear projections of the state variables.
Revised:  Memory is reduced by exploiting initial states that are not full-dimensional , and safety properties (outputs) that only need a few linear projections of the state variables.
Edit intent: clarity
Original:  Memory demands can be reduced by taking advantage of both initial states that are not full-dimensional , and safety properties (outputs) that only need a few linear projections of the state variables.
Revised:  Memory demands can be reduced by taking advantage of both initial states that are not full-dimensional and safety properties (outputs) that only need a few linear projections of the state variables.
Edit intent: fluency
Original:  Memory demands can be reduced by taking advantage of both initial states that are not full-dimensional , and safety properties (outputs) that only need a few linear projections of the state variables.
Revised:  Memory demands can be reduced by taking advantage of both initial states that are not full-dimensional , and safety properties (outputs) over a few linear projections of the state variables.
Edit intent: clarity
Original:  Since large systems often have sparse dynamics, we use fast Krylov-subspace simulation methods based on the Arnoldi or Lanczos iterations.
Revised:  Since large systems often have sparse dynamics, we use Krylov-subspace simulation methods based on the Arnoldi or Lanczos iterations.
Edit intent: clarity
Original:  Since large systems often have sparse dynamics, we use fast Krylov-subspace simulation methods based on the Arnoldi or Lanczos iterations.
Revised:  Since large systems often have sparse dynamics, we use fast Krylov-subspace simulation approaches based on the Arnoldi or Lanczos iterations.
Edit intent: clarity
Original:  Our implementation produces accurate counter-examples when properties are violated and  in the extreme case , with sufficient problem structure, is shown to be able to analyze a system with a billion real-valued state variables.
Revised:  Our method produces accurate counter-examples when properties are violated and in the extreme case , with sufficient problem structure, is shown to be able to analyze a system with a billion real-valued state variables.
Edit intent: clarity
Original:  Our implementation produces accurate counter-examples when properties are violated and  in the extreme case , with sufficient problem structure, is shown to be able to analyze a system with a billion real-valued state variables.
Revised:  Our implementation produces accurate counter-examples when properties are violated and , in the extreme case , with sufficient problem structure, is shown to be able to analyze a system with a billion real-valued state variables.
Edit intent: fluency
Original:  Our implementation produces accurate counter-examples when properties are violated and  in the extreme case , with sufficient problem structure, is shown to be able to analyze a system with a billion real-valued state variables.
Revised:  Our implementation produces accurate counter-examples when properties are violated and in the extreme case with sufficient problem structure, is shown to be able to analyze a system with a billion real-valued state variables.
Edit intent: fluency
Original:  Our implementation produces accurate counter-examples when properties are violated and  in the extreme case , with sufficient problem structure, is shown to be able to analyze a system with a billion real-valued state variables.
Revised:  Our implementation produces accurate counter-examples when properties are violated and in the extreme case , with sufficient problem structure, can analyze a system with a billion real-valued state variables.
Edit intent: clarity