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Local cooperation in a stable environment may lead to over-optimization developing an always-old network, which ages slowly , and dies in an apoptosis-like process. | <clarity> Local cooperation in a stable environment may lead to over-optimization developing an always-old network, which ages slowly , and dies in an apoptosis-like process. | Local cooperation in a stable environment may lead to over-optimization , and forming an " always-old network, which ages slowly , and dies in an apoptosis-like process. | clarity | 0.98284435 | 0812.0325 | 1 |
Local cooperation in a stable environment may lead to over-optimization developing an always-old network, which ages slowly , and dies in an apoptosis-like process. | <fluency> Local cooperation in a stable environment may lead to over-optimization developing an always-old network, which ages slowly , and dies in an apoptosis-like process. | Local cooperation in a stable environment may lead to over-optimization developing an always-old " network, which ages slowly , and dies in an apoptosis-like process. | fluency | 0.9992994 | 0812.0325 | 1 |
Local cooperation in a stable environment may lead to over-optimization developing an always-old network, which ages slowly , and dies in an apoptosis-like process. | <meaning-changed> Local cooperation in a stable environment may lead to over-optimization developing an always-old network, which ages slowly , and dies in an apoptosis-like process. | Local cooperation in a stable environment may lead to over-optimization developing an always-old network, which accumulates damage , and dies in an apoptosis-like process. | meaning-changed | 0.99916196 | 0812.0325 | 1 |
Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise forever-young network in a necrosis-like process. | <clarity> Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise forever-young network in a necrosis-like process. | A rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise forever-young network in a necrosis-like process. | clarity | 0.5363393 | 0812.0325 | 1 |
Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise forever-young network in a necrosis-like process. | <meaning-changed> Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise forever-young network in a necrosis-like process. | Global cooperation by exploring a rapidly changing environment develops competition forming a "forever-young" network, which may suffer an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise forever-young network in a necrosis-like process. | meaning-changed | 0.99904996 | 0812.0325 | 1 |
Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise forever-young network in a necrosis-like process. | <clarity> Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, causing rapid degradation, ageing and death of an otherwise forever-young network in a necrosis-like process. | Global cooperation by exploring a rapidly changing environment may cause an occasional over-perturbation exhausting system-resources, and causing death in a necrosis-like process. | clarity | 0.99783725 | 0812.0325 | 1 |
Giving a number of examples we explain how local and global cooperation can both evoke and help successful ageing. | <meaning-changed> Giving a number of examples we explain how local and global cooperation can both evoke and help successful ageing. | Giving a number of examples we demonstrate how cooperation evokes the gradual accumulation of damage typical to ageing. | meaning-changed | 0.97763026 | 0812.0325 | 1 |
Finally, we show how various forms of cooperation and consequent ageing emerge as key elements in all major steps of evolution from the formation of protocells to the establishment of the globalized, modern human society . Thus, ageing emerges as a price of complexity, which is going hand-in-hand with cooperation enhancing each other in a successful community . | <coherence> Finally, we show how various forms of cooperation and consequent ageing emerge as key elements in all major steps of evolution from the formation of protocells to the establishment of the globalized, modern human society . Thus, ageing emerges as a price of complexity, which is going hand-in-hand with cooperation enhancing each other in a successful community . | Finally, we show how various forms of cooperation and consequent ageing emerge as key elements in all major steps of evolution from the formation of protocells to the establishment of the globalized, modern human society . | coherence | 0.9980533 | 0812.0325 | 1 |
Actin networks in certain URLanisms exhibit a complex pattern-forming dynamics that starts with the appearance of static `spots ' of actin on the cell cortex. | <fluency> Actin networks in certain URLanisms exhibit a complex pattern-forming dynamics that starts with the appearance of static `spots ' of actin on the cell cortex. | Actin networks in certain URLanisms exhibit a complex pattern-forming dynamics that starts with the appearance of static spots of actin on the cell cortex. | fluency | 0.99942183 | 0812.2066 | 1 |
Spots soon become mobile, and eventually give rise to , and coexist with, traveling waves of actin. | <meaning-changed> Spots soon become mobile, and eventually give rise to , and coexist with, traveling waves of actin. | Spots soon become mobile, executing persistent random walks, and eventually give rise to , and coexist with, traveling waves of actin. | meaning-changed | 0.9970107 | 0812.2066 | 1 |
Spots soon become mobile, and eventually give rise to , and coexist with, traveling waves of actin. | <clarity> Spots soon become mobile, and eventually give rise to , and coexist with, traveling waves of actin. | Spots soon become mobile, and eventually give rise to traveling waves of actin. | clarity | 0.9987086 | 0812.2066 | 1 |
Waves confer motility upon the cell by impinging on its periphery. | <clarity> Waves confer motility upon the cell by impinging on its periphery. | clarity | 0.9615986 | 0812.2066 | 1 |
|
Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . | <clarity> Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . | Here we describe a possible physical mechanism for this distinctive set of dynamic transformations , by equipping an excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . | clarity | 0.9676591 | 0812.2066 | 1 |
Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . | <clarity> Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . | Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion model with a variable describing the spatial orientation of its chief constituent . | clarity | 0.9988404 | 0812.2066 | 1 |
Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . | <clarity> Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . | Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a field describing the spatial orientation of its chief constituent . | clarity | 0.99333584 | 0812.2066 | 1 |
Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . We consider this constituent to be a caricature of polymerized actin, and regard its orientation as a measure of local actin fiber alignment. This spatial anisotropy, which is not possessed by conventional chemical species, profoundly affects localized structures to drive a transformation at fixed parameter values from static spots to moving spots to waves. | <clarity> Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent . We consider this constituent to be a caricature of polymerized actin, and regard its orientation as a measure of local actin fiber alignment. This spatial anisotropy, which is not possessed by conventional chemical species, profoundly affects localized structures to drive a transformation at fixed parameter values from static spots to moving spots to waves. | Here we describe a possible physical mechanism for this distinctive set of dynamic transformations . Starting from the observation that excitable reaction-diffusion models of chemical systems can describe both localized spots and traveling waves, we augment one such model with a variable describing the spatial orientation of its chief constituent (which we consider to be actin). The interplay of anisotropic actin growth and spatial inhibition drives a transformation at fixed parameter values from static spots to moving spots to waves. | clarity | 0.9954591 | 0812.2066 | 1 |
We extend the model of liquidity risk of Cetin et al. 5 to allow for price impacts. | <meaning-changed> We extend the model of liquidity risk of Cetin et al. 5 to allow for price impacts. | We extend a linear version of the liquidity risk model of Cetin et al. 5 to allow for price impacts. | meaning-changed | 0.9986842 | 0812.2440 | 1 |
We extend the model of liquidity risk of Cetin et al. 5 to allow for price impacts. | <meaning-changed> We extend the model of liquidity risk of Cetin et al. 5 to allow for price impacts. | We extend the model of liquidity risk of Cetin et al. (2004) to allow for price impacts. | meaning-changed | 0.999181 | 0812.2440 | 1 |
Starting from simple principles, we show that the impact of a trade on prices is directly proportional to the size of the transaction and the amount of liquidityof the asset. | <coherence> Starting from simple principles, we show that the impact of a trade on prices is directly proportional to the size of the transaction and the amount of liquidityof the asset. | We show that the impact of a trade on prices is directly proportional to the size of the transaction and the amount of liquidityof the asset. | coherence | 0.6760922 | 0812.2440 | 1 |
Starting from simple principles, we show that the impact of a trade on prices is directly proportional to the size of the transaction and the amount of liquidityof the asset. | <clarity> Starting from simple principles, we show that the impact of a trade on prices is directly proportional to the size of the transaction and the amount of liquidityof the asset. | Starting from simple principles, we show that the impact of a market order on prices depends on the size of the transaction and the amount of liquidityof the asset. | clarity | 0.99518716 | 0812.2440 | 1 |
Starting from simple principles, we show that the impact of a trade on prices is directly proportional to the size of the transaction and the amount of liquidityof the asset. This leads to a new characterization of self-financing trading strategies and a sufficient condition for no arbitrage. | <clarity> Starting from simple principles, we show that the impact of a trade on prices is directly proportional to the size of the transaction and the amount of liquidityof the asset. This leads to a new characterization of self-financing trading strategies and a sufficient condition for no arbitrage. | Starting from simple principles, we show that the impact of a trade on prices is directly proportional to the size of the transaction and the level of liquidity. We obtain a simple characterization of self-financing trading strategies and a sufficient condition for no arbitrage. | clarity | 0.99903274 | 0812.2440 | 1 |
We show that, with the use of volatility swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated . | <meaning-changed> We show that, with the use of volatility swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated . | We consider a stochastic volatility model in which the volatility is partly correlated with the liquidity process and show that, with the use of volatility swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated . | meaning-changed | 0.9995171 | 0812.2440 | 1 |
We show that, with the use of volatility swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated . | <fluency> We show that, with the use of volatility swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated . | We show that, with the use of variance swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated . | fluency | 0.9987459 | 0812.2440 | 1 |
We show that, with the use of volatility swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated . | <meaning-changed> We show that, with the use of volatility swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated . | We show that, with the use of volatility swaps, contingent claims whose payoffs depend on the value of the asset can be approximately replicated in this setting . | meaning-changed | 0.98023754 | 0812.2440 | 1 |
The replicating costs of such payoffs are obtained from the solutions of BSDEs with | <meaning-changed> The replicating costs of such payoffs are obtained from the solutions of BSDEs with | The replicating costs of such payoffs are obtained from the solutions of BSDEs with quadratic growth and analytical properties of these solutions are investigated. | meaning-changed | 0.9995097 | 0812.2440 | 1 |
In this paper we develop an algorithm to calculate prices and Greeks of barrier options driven by a class of additive processes. | <fluency> In this paper we develop an algorithm to calculate prices and Greeks of barrier options driven by a class of additive processes. | In this paper we develop an algorithm to calculate the prices and Greeks of barrier options driven by a class of additive processes. | fluency | 0.99839145 | 0812.3117 | 1 |
In this paper we develop an algorithm to calculate prices and Greeks of barrier options driven by a class of additive processes. Additive processes are time-inhomogeneous Levy processes, or equivalently, processes with independent but inhomogeneous increments . | <meaning-changed> In this paper we develop an algorithm to calculate prices and Greeks of barrier options driven by a class of additive processes. Additive processes are time-inhomogeneous Levy processes, or equivalently, processes with independent but inhomogeneous increments . | In this paper we develop an algorithm to calculate prices and Greeks of barrier options in a hyper-exponential additive model with piecewise constant parameters . | meaning-changed | 0.9988128 | 0812.3117 | 1 |
We obtain an explicit semi-analytical expression for the first-passage probability of an additive process with hyper-exponential jumps . | <clarity> We obtain an explicit semi-analytical expression for the first-passage probability of an additive process with hyper-exponential jumps . | We obtain an explicit semi-analytical expression for the first-passage probability . | clarity | 0.99844307 | 0812.3117 | 1 |
The solution rests on a randomization and an explicit matrix Wiener- Hopf factorization. | <fluency> The solution rests on a randomization and an explicit matrix Wiener- Hopf factorization. | The solution rests on a randomization and an explicit matrix Wiener-Hopf factorization. | fluency | 0.9994235 | 0812.3117 | 1 |
Employing this result we derive explicit expressions for the Laplace(-Fourier) transforms of prices and Greeks of digital and barrier options. | <fluency> Employing this result we derive explicit expressions for the Laplace(-Fourier) transforms of prices and Greeks of digital and barrier options. | Employing this result we derive explicit expressions for the Laplace-Fourier transforms of the prices and Greeks of digital and barrier options. | fluency | 0.999141 | 0812.3117 | 1 |
Employing this result we derive explicit expressions for the Laplace(-Fourier) transforms of prices and Greeks of digital and barrier options. | <clarity> Employing this result we derive explicit expressions for the Laplace(-Fourier) transforms of prices and Greeks of digital and barrier options. | Employing this result we derive explicit expressions for the Laplace(-Fourier) transforms of prices and Greeks of barrier options. | clarity | 0.9989542 | 0812.3117 | 1 |
As numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable. | <fluency> As numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable. | As a numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable. | fluency | 0.99936634 | 0812.3117 | 1 |
As numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable. | <clarity> As numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable. | As numerical illustration, the prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable. | clarity | 0.9891061 | 0812.3117 | 1 |
As numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable. | <meaning-changed> As numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated . Comparison with Monte Carlo simulation results shows that the method is fast, accurate, and stable. | As numerical illustration, the model is simultaneously calibrated to Stoxx50E call options at four different maturities and subsequently prices and Greeks of down-and-in digital and down-and-in call options are calculated for a set of parameters obtained by a simultaneous calibration to Stoxx50E call options across strikes and four different maturities. By comparing the results with Monte-Carlo simulations, we show that the method is fast, accurate, and stable. | meaning-changed | 0.99937767 | 0812.3117 | 1 |
] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | <meaning-changed> ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | This work generalises the model of ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | meaning-changed | 0.99933475 | 0812.3867 | 1 |
] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | <meaning-changed> ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | Phys. Rev. Lett., 101, 118104] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | meaning-changed | 0.999246 | 0812.3867 | 1 |
] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | <meaning-changed> ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | ] to a broader class of linear feedback systems. We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | meaning-changed | 0.9993475 | 0812.3867 | 1 |
] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | <meaning-changed> ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | ] We present a complete analytical solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | meaning-changed | 0.9992617 | 0812.3867 | 1 |
] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | <meaning-changed> ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, and for the flip time (persistence ) distributions for this model switch . | ] We present a general solution for the steady-state statistics of the number of enzyme molecules in the on and off states, for the general case where the enzyme can mediate flipping in either direction. For this general case we also solve for the flip time distribution, making a connection to first passage and persistence problems in statistical physics . | meaning-changed | 0.99924123 | 0812.3867 | 1 |
We also show that this model can exhibit long-lived temporal correlations, thus providing a primitive form of cellular memory. | <meaning-changed> We also show that this model can exhibit long-lived temporal correlations, thus providing a primitive form of cellular memory. | The occurrence of such a peak is analysed as a function of the parameter space. We present a new relation between the flip time distributions measured for two relevant choices of initial condition. We also introduce a new correlation measure to show that this model can exhibit long-lived temporal correlations, thus providing a primitive form of cellular memory. | meaning-changed | 0.9992908 | 0812.3867 | 1 |
Sorting permutations by reversals and/or transpositions is an important genome rearrangement problem in computational molecular biology. From theoretical point of view, finding efficient algorithms for this problem and its variations are quite challenging. In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | <clarity> Sorting permutations by reversals and/or transpositions is an important genome rearrangement problem in computational molecular biology. From theoretical point of view, finding efficient algorithms for this problem and its variations are quite challenging. In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | In this paper we study several variations of the the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | clarity | 0.999041 | 0812.3933 | 1 |
In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | <meaning-changed> In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | In this paper we considerpancake flipping problem the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | meaning-changed | 0.9993894 | 0812.3933 | 1 |
In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | <meaning-changed> In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | In this paper we consider, which is also well known as the problem of the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | meaning-changed | 0.9993777 | 0812.3933 | 1 |
In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | <meaning-changed> In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | In this paper we considersorting by prefix reversals the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | meaning-changed | 0.81601954 | 0812.3933 | 1 |
In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | <meaning-changed> In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | In this paper we consider. We consider the variations in the sorting process by adding with prefix reversals other similar operations such as prefix transpositions and prefix transreversals. These type of sorting problems have applications in interconnection networks and computational biology. We first study the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | meaning-changed | 0.99951184 | 0812.3933 | 1 |
In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | <meaning-changed> In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. | In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions and present a 3-approximation algorithm for this problem. Then we give a 2-approximation algorithm for sorting by prefix reversals and prefix transreversals. We also provide a 3-approximation algorithm for sorting by prefix reversals and prefix transpositions where the operations are always applied at the unsorted suffix of the given permutation. | meaning-changed | 0.9922025 | 0812.3933 | 1 |
In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. For this problem we first present a 3-approximation algorithm, which performs close to 2 in practice. | <coherence> In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the given permutation. For this problem we first present a 3-approximation algorithm, which performs close to 2 in practice. | In this paper we consider the problem of sorting unsigned permutations by prefix reversals and prefix transpositions , where a prefix reversal or a prefix transposition is applied always at the unsorted suffix of the permutation. | coherence | 0.99381244 | 0812.3933 | 1 |
We further analyze the problem in more practical way and consider the variation where a certain number of prefix reversals are forced to happen in the sorting process. Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. | <clarity> We further analyze the problem in more practical way and consider the variation where a certain number of prefix reversals are forced to happen in the sorting process. Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. | We further analyze the problem in more practical way and {b(\pi)+r}), where r is the } number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. | clarity | 0.8080419 | 0812.3933 | 1 |
Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. | <meaning-changed> Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. | Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } show quantitatively how approximation ratios of our algorithms improve with the increase of number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. | meaning-changed | 0.99954873 | 0812.3933 | 1 |
Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. Again, in this variation our algorithm performs close to 1.6 in practice . | <clarity> Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } number of prefix reversals that must happen in the sorting, when possible, and b(\pi)\geq 2r is the number of breakpoints in the given permutation. Again, in this variation our algorithm performs close to 1.6 in practice . | Here we achieve an improved approximation ratio of (3-\frac{3r{b(\pi)+r}), where r is the } number of prefix reversals applied by optimal algorithms. Finally, we present experimental results to support our analysis . | clarity | 0.7462266 | 0812.3933 | 1 |
The system-level dynamics of biomolecular interactions can be difficult to specify and simulate using methods that involve explicit specification of a chemical reaction network. | <clarity> The system-level dynamics of biomolecular interactions can be difficult to specify and simulate using methods that involve explicit specification of a chemical reaction network. | The system-level dynamics of biomolecular interactions can be difficult to simulate using methods that involve explicit specification of a chemical reaction network. | clarity | 0.9347395 | 0812.4619 | 1 |
The system-level dynamics of biomolecular interactions can be difficult to specify and simulate using methods that involve explicit specification of a chemical reaction network. | <clarity> The system-level dynamics of biomolecular interactions can be difficult to specify and simulate using methods that involve explicit specification of a chemical reaction network. | The system-level dynamics of biomolecular interactions can be difficult to specify and simulate using methods that require explicit specification of a chemical reaction network. | clarity | 0.99807763 | 0812.4619 | 1 |
Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . | <meaning-changed> Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . | Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . | meaning-changed | 0.99941874 | 0812.4619 | 1 |
Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . | <clarity> Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . | Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . The method has a computational cost independent of reaction network size . | clarity | 0.99803275 | 0812.4619 | 1 |
Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . The method is based on sampling a set of chemical transformation classes that characterize the interactions in a system. | <coherence> Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size . The method is based on sampling a set of chemical transformation classes that characterize the interactions in a system. | Here, we present a stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , which has a computational cost independent of reaction network size , and it is based on sampling a set of chemical transformation classes that characterize the interactions in a system. | coherence | 0.99811184 | 0812.4619 | 1 |
We apply the method to simulate multivalent ligand-receptor interaction systems . Simulation results reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models . | <meaning-changed> We apply the method to simulate multivalent ligand-receptor interaction systems . Simulation results reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models . | We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models . | meaning-changed | 0.9957289 | 0812.4619 | 1 |
We apply the method to simulate multivalent ligand-receptor interaction systems . Simulation results reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models . | <meaning-changed> We apply the method to simulate multivalent ligand-receptor interaction systems . Simulation results reveal insights into ligand-receptor binding kinetics that are not available from previously developed equilibrium models . | We apply the method to simulate multivalent ligand-receptor interaction systems . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | meaning-changed | 0.99916923 | 0812.4619 | 1 |
The system-level dynamics of biomolecular interactions can be difficult to simulate using methods that require explicit specification of a chemical reaction network. | <meaning-changed> The system-level dynamics of biomolecular interactions can be difficult to simulate using methods that require explicit specification of a chemical reaction network. | The system-level dynamics of multivalent biomolecular interactions can be difficult to simulate using methods that require explicit specification of a chemical reaction network. | meaning-changed | 0.9993129 | 0812.4619 | 3 |
The system-level dynamics of biomolecular interactions can be difficult to simulate using methods that require explicit specification of a chemical reaction network. | <meaning-changed> The system-level dynamics of biomolecular interactions can be difficult to simulate using methods that require explicit specification of a chemical reaction network. | The system-level dynamics of biomolecular interactions can be simulated using a rule-based kinetic Monte Carlo method in which a rejection sampling strategy is used to generate reaction events. This method becomes inefficient when simulating aggregation processes with large biomolecular complexes. | meaning-changed | 0.99935704 | 0812.4619 | 3 |
Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . | <clarity> Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . | Here, we present a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . | clarity | 0.98670655 | 0812.4619 | 3 |
Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . | <clarity> Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . | Here, we present and evaluate a rejection-free method for determining the kinetics of multivalent biomolecular interactions . | clarity | 0.999 | 0812.4619 | 3 |
Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . The method has a computational cost independent of reaction network size, and it is based on sampling a set of chemical transformation classes defined by formal rules that characterize the interactions in a system. We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | <coherence> Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions . The method has a computational cost independent of reaction network size, and it is based on sampling a set of chemical transformation classes defined by formal rules that characterize the interactions in a system. We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | Here, we present and evaluate a rejection-free stochastic simulation method for determining the kinetics of multivalent biomolecular interactions , and we apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | coherence | 0.9049511 | 0812.4619 | 3 |
We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | <clarity> We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | We apply the method to simulate simple models for ligand-receptor interactions . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | clarity | 0.9264331 | 0812.4619 | 3 |
We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | <clarity> We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that performance of the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | clarity | 0.9952099 | 0812.4619 | 3 |
We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | <meaning-changed> We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is equal to or better than that of the rejection method over wide parameter ranges than a related method that relies on rejection sampling . | meaning-changed | 0.9187268 | 0812.4619 | 3 |
We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | <meaning-changed> We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges than a related method that relies on rejection sampling . | We apply the method to simulate interactions of an m-valent ligand with an n-valent cell-surface receptor . Simulation results show that the rejection-free method is more efficient over wide parameter ranges , and the rejection-free method is more efficient for simulating systems in which aggregation is extensive. The rejection-free method reported here should be useful for simulating a variety of systems in which multisite molecular interactions yield large molecular aggregates . | meaning-changed | 0.99886394 | 0812.4619 | 3 |
RNA polymerase (RNAP) is like a mobile molecular workshop that polymerizes a RNA molecule by adding monomeric subunits one by one, while moving step by step on the DNA template itself. | <coherence> RNA polymerase (RNAP) is like a mobile molecular workshop that polymerizes a RNA molecule by adding monomeric subunits one by one, while moving step by step on the DNA template itself. | RNA polymerase (RNAP) is a mobile molecular workshop that polymerizes a RNA molecule by adding monomeric subunits one by one, while moving step by step on the DNA template itself. | coherence | 0.9960855 | 0812.4692 | 1 |
Here we develop a theoretical model by incorporating their steric interactions and mechanochemical cycles which explicitly captures the cyclical shape changes of each motor. | <meaning-changed> Here we develop a theoretical model by incorporating their steric interactions and mechanochemical cycles which explicitly captures the cyclical shape changes of each motor. | Here we develop a theoretical model by incorporating the steric interactions of the RNAPs and their mechanochemical cycles which explicitly captures the cyclical shape changes of each motor. | meaning-changed | 0.99739933 | 0812.4692 | 1 |
In principle, our predictions can be tested by carrying out {\it in-vitro} experiments . | <meaning-changed> In principle, our predictions can be tested by carrying out {\it in-vitro} experiments . | In principle, our predictions can be tested by carrying out {\it in-vitro} experiments which we suggest here . | meaning-changed | 0.9993591 | 0812.4692 | 1 |
In this paper we investigate the problem of optimal dividend distribution in the presence of regime shifts. | <coherence> In this paper we investigate the problem of optimal dividend distribution in the presence of regime shifts. | We investigate the problem of optimal dividend distribution in the presence of regime shifts. | coherence | 0.8307754 | 0812.4978 | 1 |
In this paper we investigate the problem of optimal dividend distribution in the presence of regime shifts. | <meaning-changed> In this paper we investigate the problem of optimal dividend distribution in the presence of regime shifts. | In this paper we investigate the problem of optimal dividend distribution for a company in the presence of regime shifts. | meaning-changed | 0.99927276 | 0812.4978 | 1 |
We consider a company whose cumulative net revenues evolve as a drifted Brownian motion modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the current state of the chain. | <meaning-changed> We consider a company whose cumulative net revenues evolve as a drifted Brownian motion modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the current state of the chain. | We consider a company whose cumulative net revenues evolve as a Brownian motion with positive drift that is modulated by a finite state Markov chain, and model the discount rate as a deterministic function of the current state of the chain. | meaning-changed | 0.9989976 | 0812.4978 | 1 |
The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero. | <meaning-changed> The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero. | In this setting the objective of the company is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero. | meaning-changed | 0.9973193 | 0812.4978 | 1 |
The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero. | <clarity> The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero. | The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which is taken to be the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero. | clarity | 0.9986518 | 0812.4978 | 1 |
The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero. | <clarity> The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) hit zero. | The objective is to maximize the expected cumulative discounted dividend payments until the moment of bankruptcy, which occurs the first time that the cash reserves (the cumulative net revenues minus cumulative dividend payments) are zero. | clarity | 0.99700314 | 0812.4978 | 1 |
We show that, if the drift is positive in each regime , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and explicitly characterize the value function as the fixed point of a contraction. | <clarity> We show that, if the drift is positive in each regime , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and explicitly characterize the value function as the fixed point of a contraction. | We show that, if the drift is positive in each state , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and explicitly characterize the value function as the fixed point of a contraction. | clarity | 0.9982572 | 0812.4978 | 1 |
We show that, if the drift is positive in each regime , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and explicitly characterize the value function as the fixed point of a contraction. | <clarity> We show that, if the drift is positive in each regime , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and explicitly characterize the value function as the fixed point of a contraction. | We show that, if the drift is positive in each regime , it is optimal to adopt a barrier strategy at certain positive regime-dependent levels, and provide an explicit characterization of the value function as the fixed point of a contraction. | clarity | 0.9979892 | 0812.4978 | 1 |
In the case that the drift is small and negative in some regime , the optimal strategy takes a different form, which we explicitly identify in the case that there are two regimes. | <clarity> In the case that the drift is small and negative in some regime , the optimal strategy takes a different form, which we explicitly identify in the case that there are two regimes. | In the case that the drift is small and negative in one state , the optimal strategy takes a different form, which we explicitly identify in the case that there are two regimes. | clarity | 0.99754614 | 0812.4978 | 1 |
In the case that the drift is small and negative in some regime , the optimal strategy takes a different form, which we explicitly identify in the case that there are two regimes. | <coherence> In the case that the drift is small and negative in some regime , the optimal strategy takes a different form, which we explicitly identify in the case that there are two regimes. | In the case that the drift is small and negative in some regime , the optimal strategy takes a different form, which we explicitly identify if there are two regimes. | coherence | 0.9771359 | 0812.4978 | 1 |
While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done towards estimating time-varying networks from time series of entity attributes. | <fluency> While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done towards estimating time-varying networks from time series of entity attributes. | While there is a rich literature in estimating a static or temporally invariant network from observation data, little has been done toward estimating time-varying networks from time series of entity attributes. | fluency | 0.99679154 | 0812.5087 | 1 |
In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. | <fluency> In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. | In this paper we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. | fluency | 0.9986576 | 0812.5087 | 1 |
In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. | <fluency> In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. | In this paper , we present two new machine learning methods for estimating time-varying networks, which both build on a temporally smoothed l_1-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently using generic solvers scalable to large networks. | fluency | 0.9986394 | 0812.5087 | 1 |
For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | <clarity> For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | For real data sets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | clarity | 0.99815696 | 0812.5087 | 1 |
For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | <fluency> For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | For real datasets , we reverse engineer the latent sequence of temporally rewiring political networks between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | fluency | 0.99939764 | 0812.5087 | 1 |
For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | <fluency> For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US Senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | fluency | 0.99936134 | 0812.5087 | 1 |
For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | <meaning-changed> For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving regulatory networks underlying 588 genes across the life cycle of Drosophila melanogaster from microarray time course. | meaning-changed | 0.9984452 | 0812.5087 | 1 |
For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | <clarity> For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of from microarray time course. | clarity | 0.997537 | 0812.5087 | 1 |
For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | <meaning-changed> For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster from microarray time course. | For real datasets , we reverse engineer the latent sequence of temporally rewiring political network between Senators from the US senate voting records and the latent evolving gene network which contains more than 4000 genes from the life cycle of Drosophila melanogaster Drosophila melanogaster from the microarray time course. | meaning-changed | 0.9718066 | 0812.5087 | 1 |
In a dynamic social or biological environment, the interactions between the underlying actors can undergo large and systematic changes. | <clarity> In a dynamic social or biological environment, the interactions between the underlying actors can undergo large and systematic changes. | In a dynamic social or biological environment, the interactions between the actors can undergo large and systematic changes. | clarity | 0.99847907 | 0901.0135 | 1 |
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 . | <clarity> 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 . | 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 . | clarity | 0.99356395 | 0901.0135 | 1 |
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. | <meaning-changed> 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. | 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. | meaning-changed | 0.99875426 | 0901.0135 | 1 |
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. | <meaning-changed> 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. | 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. | meaning-changed | 0.9993944 | 0901.0135 | 1 |
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. | <meaning-changed> 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. | 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. | meaning-changed | 0.99909115 | 0901.0135 | 1 |
In both cases, our model reveals interesting patterns of the dynamic roles of the actors. | <clarity> In both cases, our model reveals interesting patterns of the dynamic roles of the actors. | In all cases, our model reveals interesting patterns of the dynamic roles of the actors. | clarity | 0.9974147 | 0901.0135 | 1 |
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. | <meaning-changed> 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. | 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. | meaning-changed | 0.9991936 | 0901.0287 | 1 |
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. | <meaning-changed> 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. | 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. | meaning-changed | 0.999315 | 0901.0287 | 1 |
Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of ITM Probe.Availability: www.ncbi.nlm.nih.gov/CBBresearch/qmbp/itm_probe | <clarity> Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of ITM Probe.Availability: www.ncbi.nlm.nih.gov/CBBresearch/qmbp/itm_probe | Through examples involving the yeast pheromone response pathway, we illustrate the versatility and stability of our new framework. | clarity | 0.9975758 | 0901.0287 | 1 |
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. | <meaning-changed> 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. | 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. | meaning-changed | 0.9994467 | 0901.0638 | 1 |
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 . | <clarity> 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 . | 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 . | clarity | 0.9991542 | 0901.0638 | 1 |
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 . | <clarity> 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 . | 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 . | clarity | 0.98665744 | 0901.0638 | 1 |