wanyu/IteraTeR_full_sent huggingface dataset:
Original:  The latent roles or membership of the actors as determined by these dynamic links will also exhibit rich temporal phenomena, assuming a distinct role at one point while leaning more towards a second role at an another point. To capture this dynamic mixed membership in rewiring networks, we propose a state space mixed membership stochastic blockmodel which embeds an actor into a latent space and track its mixed membership in the latent space across time .
Revised:  In this paper we propose a model-based approach to analyze what we will refer to as the dynamic tomography of such time-evolving networks. Our approach offers an intuitive but powerful tool to infer the semantic underpinnings of each actor, such as its social roles or biological functions, underlying the observed network topologies. Our model builds on earlier work on a mixed membership stochastic blockmodel which embeds an actor into a latent space and track its mixed membership in the latent space across time .
Edit intent: clarity
Original:  To capture this dynamic mixed membership in rewiring networks, we propose a state space mixed membership stochastic blockmodel which embeds an actor into a latent space and track its mixed membership in the latent space across time . We derived efficient approximate learning and inference algorithms for our model, and applied the learned models to analyze a social network between monks , and a rewiring gene interaction network of Drosophila melanogaster collected during its full life cycle.
Revised:  To capture this dynamic mixed membership in rewiring networks, we propose a state space mixed membership stochastic blockmodel for static networks, and the state-space model for tracking object trajectory. It overcomes a major limitation of many current network inference techniques, which assume that each actor plays a unique and invariant role that accounts for all its interactions with other actors; instead, our method models the role of each actor as a time-evolving mixed membership vector that allows actors to behave differently over time and carry out different roles/functions when interacting with different peers, which is closer to reality. We present an efficient algorithm for approximate inference and learning using our model; and we applied our model to analyze a social network between monks , and a rewiring gene interaction network of Drosophila melanogaster collected during its full life cycle.
Edit intent: meaning-changed
Original:  We derived efficient approximate learning and inference algorithms for our model, and applied the learned models to analyze a social network between monks , and a rewiring gene interaction network of Drosophila melanogaster collected during its full life cycle.
Revised:  We derived efficient approximate learning and inference algorithms for our model, and applied the learned models to analyze a social network between monks (i.e., the Sampson's network), a dynamic email communication network between the Enron employees, and a rewiring gene interaction network of Drosophila melanogaster collected during its full life cycle.
Edit intent: meaning-changed
Original:  We derived efficient approximate learning and inference algorithms for our model, and applied the learned models to analyze a social network between monks , and a rewiring gene interaction network of Drosophila melanogaster collected during its full life cycle.
Revised:  We derived efficient approximate learning and inference algorithms for our model, and applied the learned models to analyze a social network between monks , and a rewiring gene interaction network of fruit fly collected during its full life cycle.
Edit intent: meaning-changed
Original: Motivation: Recent advances in experimental techniques have generated large amounts of protein interaction data, producing networks containing large numbers of cellular proteins. Mathematically sound and robust foundations are needed for extensive, context-specific exploration of networks, integrating knowledge from different specializations and facilitating biological discovery.
Revised: In our previous publication, a framework for information flow in interaction networks based on random walks with damping was formulated with two fundamental modes: emitting and absorbing. While many other network analysis methods based on random walks or equivalent notions have been developed before and after our earlier work, one can show that they can all be mapped to one of the two modes. In addition to these two fundamental modes, a major strength of our earlier formalism was its accommodation of context-specific exploration of networks, integrating knowledge from different specializations and facilitating biological discovery.
Edit intent: meaning-changed
Original:  Mathematically sound and robust foundations are needed for extensive, context-specific exploration of networks, integrating knowledge from different specializations and facilitating biological discovery. Results: Extending our earlier work, we present a theoretical construct, based on random walks, for modelling of information channels between selected points in interaction networks. The software implementation, called ITM Probe, can be used as network exploration and hypothesis forming tool.
Revised:  Mathematically sound and robust foundations are needed for extensive, context-specific directed information flow that yielded plausible and meaningful biological interpretation of protein functions and pathways. However, the directed flow from origins to destinations was induced via a potential function that was heuristic. Here, with a theoretically sound approach called the channel mode, we extend our earlier work for directed information flow. This is achieved by our newly constructed nonheuristic potential function that facilitates a purely probabilistic interpretation of the channel mode. For each network node, the channel mode combines the solutions of emitting and absorbing modes in the same context, producing what we call a channel tensor. The entries of the channel tensor at each node can be interpreted as the amount of flow passing through that node from an origin to a destination. Similarly to our earlier model, the channel mode encompasses damping as a free parameter that controls the locality of information flow.
Edit intent: meaning-changed
Original: This article  presents differential equations and solution methods for the functions of the form A(z) = F^{-1}(G(z)), where F and G are cumulative distribution functions.
Revised: This article Revised working paper V 1.1 presents differential equations and solution methods for the functions of the form A(z) = F^{-1}(G(z)), where F and G are cumulative distribution functions.
Edit intent: meaning-changed
Original:  The method developed here may also be regarded as providing  analytical and numerical bases for doing a more precise form of Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Revised:  The method may also be regarded as providing analytical and numerical bases for doing a more precise form of Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Edit intent: clarity
Original:  The method developed here may also be regarded as providing  analytical and numerical bases for doing a more precise form of Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Revised:  The method developed here may also be regarded as providing both analytical and numerical bases for doing a more precise form of Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Edit intent: clarity
Original:  The method developed here may also be regarded as providing  analytical and numerical bases for doing a more precise form of Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Revised:  The method developed here may also be regarded as providing analytical and numerical bases for doing more precise Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Edit intent: clarity
Original:  The method developed here may also be regarded as providing  analytical and numerical bases for doing a more precise form of Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Revised:  The method developed here may also be regarded as providing analytical and numerical bases for doing a more precise form of Cornish-Fisher transformations . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Edit intent: clarity
Original:  The method developed here may also be regarded as providing  analytical and numerical bases for doing a more precise form of Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic and variance gamma .
Revised:  The method developed here may also be regarded as providing analytical and numerical bases for doing a more precise form of Cornish-Fisher expansion . Examples are given of equations for converting normal samples to Student t, and converting exponential to hyperbolic , variance gamma and normal. In the case of the normal distribution, the change of variables employed allows the sampling to take place to good accuracy based on a single rational approximation over a very wide range of the sample space. The avoidance of any branching statement may be of use in optimal GPU computations .
Edit intent: meaning-changed
Original: This article Revised working paper V 1.1 presents differential equations and solution methods for the functions of the form A(z) = F^{-1}(G(z)), where F and G are cumulative distribution functions.
Revised: This article presents differential equations and solution methods for the functions of the form A(z) = F^{-1}(G(z)), where F and G are cumulative distribution functions.
Edit intent: coherence
Original:  The avoidance of any branching statement is of use in optimal GPU computations, and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the precision characteristics of well-known methods  .
Revised:  The avoidance of any branching statement is of use in optimal GPU computations, and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the precision characteristics of well-known methods . Comparisons are made on Nvidia Quadro and GTX 285 and 480 GPU cards .
Edit intent: meaning-changed
Original:  The avoidance of any branching statement is of use in optimal GPU computations {\it  , and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the  precision characteristics of well-known methods.
Revised:  The avoidance of any branching statement is of use in optimal GPU computations as it avoids the effect of{\it , and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the precision characteristics of well-known methods.
Edit intent: meaning-changed
Original:  The avoidance of any branching statement is of use in optimal GPU computations {\it  , and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the  precision characteristics of well-known methods.
Revised:  The avoidance of any branching statement is of use in optimal GPU computations {\it warp divergence , and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the precision characteristics of well-known methods.
Edit intent: meaning-changed
Original:  The avoidance of any branching statement is of use in optimal GPU computations {\it  , and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the  precision characteristics of well-known methods.
Revised:  The avoidance of any branching statement is of use in optimal GPU computations {\it , and we give examples of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the precision characteristics of well-known methods.
Edit intent: fluency
Original:  The avoidance of any branching statement is of use in optimal GPU computations {\it  , and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the  precision characteristics of well-known methods.
Revised:  The avoidance of any branching statement is of use in optimal GPU computations {\it , and we give example of branch-free normal quantiles that offer performance improvements in a GPU environment, while retaining the best precision characteristics of well-known methods.
Edit intent: clarity
Original:  Comparisons are made on Nvidia Quadro and GTX 285 and 480 GPU cards .
Revised:  Comparisons are made on Nvidia Quadro and GTX 285 and 480 , and Tesla C2050 GPUs. We argue that in single-precision mode, the change-of-variables approach offers performance competitive with the fastest existing scheme while substantially improving precision, and that in double-precision mode, this approach offers the most GPU-optimal Gaussian quantile yet, and without compromise on precision for Monte Carlo applications, working twice as fast as the CUDA 4 library function with increased precision .
Edit intent: meaning-changed
Original:  We present a unifying approach with a refined version of the DIR theorem, where we identify the reciprocal of the maturity date as the maximal order that long-term rates at earlier dates can dominate long-term rates at later dates.
Revised:  We present a refined version of the DIR theorem, where we identify the reciprocal of the maturity date as the maximal order that long-term rates at earlier dates can dominate long-term rates at later dates.
Edit intent: clarity
Original: We formalize a cumulative interest compliant conservation requirement for pure financial products proposed by Wesseling and van den Bergh to make financial issues relating to these products amenable to mathematical analysis.
Revised: We develop an algebraic framework for the description and analysis of financial behaviours, that is, behaviours that consist of transferring certain amounts of money at planned times. To a large extent, analysis of financial products amounts to analysis of such behaviours. We formalize the cumulative interest compliant conservation requirement for pure financial products proposed by Wesseling and van den Bergh to make financial issues relating to these products amenable to mathematical analysis.
Edit intent: meaning-changed
Original: We formalize a cumulative interest compliant conservation requirement for pure financial products proposed by Wesseling and van den Bergh to make financial issues relating to these products amenable to mathematical analysis. The formalization is given in a timed extension of tuplix calculus and abstracts from the idiosyncrasies of time measurement .
Revised: We formalize a cumulative interest compliant conservation requirement for pure financial products proposed by Wesseling and van den Bergh by an equation in the framework developed and define a notion of financial product behaviour using this formalization .
Edit intent: meaning-changed
Original:  Integrating out the latter, we derive the Fokker-Planck equation for the probability distribution of the former. This has the form of diffusion equation with time-dependent diffusion coefficient, resulting in an anomalous diffusion. The diffusion exponent can not be predicted using a simple scaling argument, and anomalous scaling appears as well .
Revised:  Integrating out the latter, we derive the long time behavior of the mean square displacement. Anomalous diffusion is found where the diffusion exponent can not be predicted using a simple scaling argument, and anomalous scaling appears as well .
Edit intent: meaning-changed
Original:  While the distribution of links in the dependency network of this system is scale-free for the intermediate nodes, the richest nodes  deviate from this trend, and exhibit a nonlinearity-induced saturation effect.
Revised:  While the frequency distributions of links in the dependency network of this system is scale-free for the intermediate nodes, the richest nodes deviate from this trend, and exhibit a nonlinearity-induced saturation effect.
Edit intent: meaning-changed
Original:  While the distribution of links in the dependency network of this system is scale-free for the intermediate nodes, the richest nodes  deviate from this trend, and exhibit a nonlinearity-induced saturation effect.
Revised:  While the distribution of links in both the in-directed and out-directed dependency networks of this system is scale-free for the intermediate nodes, the richest nodes deviate from this trend, and exhibit a nonlinearity-induced saturation effect.
Edit intent: meaning-changed
Original:  While the distribution of links in the dependency network of this system is scale-free for the intermediate nodes, the richest nodes  deviate from this trend, and exhibit a nonlinearity-induced saturation effect.
Revised:  While the distribution of links in the dependency network of this system follow Zipf's law for the intermediate nodes, the richest nodes deviate from this trend, and exhibit a nonlinearity-induced saturation effect.
Edit intent: meaning-changed
Original:  While the distribution of links in the dependency network of this system is scale-free for the intermediate nodes, the richest nodes  deviate from this trend, and exhibit a nonlinearity-induced saturation effect.
Revised:  While the distribution of links in the dependency network of this system is scale-free for the intermediate nodes, the richest nodes , as well as the weakest nodes, deviate from this trend, and exhibit a nonlinearity-induced saturation effect.
Edit intent: meaning-changed
Original:  While the distribution of links in the dependency network of this system is scale-free for the intermediate nodes, the richest nodes  deviate from this trend, and exhibit a nonlinearity-induced saturation effect. This also distinguishes the two directed networks of incoming and outgoing links from each other.
Revised:  While the distribution of links in the dependency network of this system is scale-free for the intermediate nodes, the richest nodes deviate from this trend, and exhibit a saturation behaviour arising from the finiteness of semantic possibilities in the network. To preserve uniqueness of operations in the network, the nodes obey an "exclusion principle", with no two nodes being exactly alike in their functionality. The parameters related to finite-size behaviour make a quantitative distinction between the two directed networks of incoming and outgoing links from each other.
Edit intent: meaning-changed
Original:  This also distinguishes the two directed networks of incoming and outgoing links from each other. The initial condition for a dynamic model, evolving towards the steady dependency distribution , determines the saturation properties of the mature scale-free network .
Revised:  This also distinguishes the two directed networks of incoming and outgoing links . Dynamic evolution, over two generations of free software releases, shows that the saturation properties of the in-directed and out-directed networks are oppositely affected. For the out-degree distribution, whose top nodes form the foundation of the entire network, the initial condition for a dynamic model, evolving towards the steady dependency distribution , determines the saturation properties of the mature scale-free network .
Edit intent: meaning-changed
Original:  While the degree distributions of links in both the in-directed and out-directed dependency networks follow Zipf's law for the intermediate nodes, the most richly linked nodes , as well as the most poorly linked nodes , deviate from this trend and exhibit finite-size effects.
Revised:  The degree distributions of links in both the in-directed and out-directed dependency networks follow Zipf's law for the intermediate nodes, the most richly linked nodes , as well as the most poorly linked nodes , deviate from this trend and exhibit finite-size effects.
Edit intent: coherence
Original:  While the degree distributions of links in both the in-directed and out-directed dependency networks follow Zipf's law for the intermediate nodes, the most richly linked nodes , as well as the most poorly linked nodes , deviate from this trend and exhibit finite-size effects.
Revised:  While the degree distributions of links in both the in-directed and out-directed dependency networks follow Zipf's law for the intermediate nodes, but the heavily linked nodes and the poorly linked nodes , deviate from this trend and exhibit finite-size effects.
Edit intent: clarity
Original:  While the degree distributions of links in both the in-directed and out-directed dependency networks follow Zipf's law for the intermediate nodes, the most richly linked nodes , as well as the most poorly linked nodes , deviate from this trend and exhibit finite-size effects.
Revised:  While the degree distributions of links in both the in-directed and out-directed dependency networks follow Zipf's law for the intermediate nodes, the most richly linked nodes , as well as the most poorly linked nodes deviate from this trend and exhibit finite-size effects.
Edit intent: fluency
Original:  We present the Pareto optimal sets for an example sensor network when delay, robustness and energy are considered as performance criteria for the network .
Revised:  The resulting framework is a complex multiobjective optimization problem that can be efficiently solved through existing multiobjective search techniques. In this contribution, we present the Pareto optimal sets for an example sensor network when delay, robustness and energy are considered as performance criteria for the network .
Edit intent: meaning-changed
Original: We present a methodology to extract the backbone of complex networks in which the weight and direction of links, as well as non-topological state variables associated with nodesplay a crucial role. This methodology can be applied in general to networks in which mass or energy is flowing along the links.
Revised: We present a methodology to extract the backbone of complex networks in which the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links.
Edit intent: clarity
Original:   In particular, we construct AY martingales with a given terminal law and this allows us to rediscover the AY solution to the Skorokhod embedding problem.
Revised:  We establish relations between Average Value at Risk, Drawdown function, Hardy-Littlewood transform and its generalised inverse. In particular, we construct AY martingales with a given terminal law and this allows us to rediscover the AY solution to the Skorokhod embedding problem.
Edit intent: meaning-changed