Universal tree structures in directed polymers and models of evolving populations

Size: px
Start display at page:

Download "Universal tree structures in directed polymers and models of evolving populations"

Transcription

1 PHYSICA REVIEW E 78, Universal tree structures in irecte polymers an moels of evolving populations Éric Brunet,* Bernar Derria, an Damien Simon aboratoire e Physique Statistique, École ormale Supérieure,, rue homon, 75 Paris Ceex 05, France Receive 0 June 008; publishe December 008 By measuring or calculating coalescence times for several moels of coalescence or evolution, with an without selection, we show that the ratios of these coalescence times become universal in the large size limit an we ientify a few universality classes. DOI: 0.0/PhysRevE PACS numbers: 05.0.a, k, Hc, 87..Kg Ranom trees appear in many contexts in biology, mathematics, an physics. In evolutionary biology, they represent the genealogies of reproucing populations. In physics, ranom trees appear in many systems such as iffusion limite aggregation DA, coarsening, river networks,, iagrams in perturbation theory, ultrametric structure of pure states in mean-fiel spin glasses,5, irecte polymers in a ranom meium 6,7, shocks in one-imensional turbulence 8 0, etc. From a mathematical point of view, one of the simplest examples of ranom trees is Kingman s coalescent,: it escribes the coalescence tree of particles, where each pair of particles has a probability t of coalescing into a single particle uring every infinitesimal time interval t. The ranom tree structures of Kingman s coalescent are ientical to the genealogies obtaine in simple mean fiel moels of neutral evolution such as the Wright-Fisher moel,. In such moels, each iniviual of a population of fixe size at a given generation gives birth to a ranom number of offspring an the population at the next generation is obtaine by choosing survivors at ranom among all these offspring. If one follows the evolution over a large enough number of generations for the initial conition to be forgotten, a steay state is reache where the statistics of the genealogical tree of a large population are ientical to those of Kingman s coalescent. Other ranom trees have been consiere in the mathematical literature, such as the coalescents 5 7, which generalize Kingman s coalescent an escribe a wier class of mean-fiel coalescence moels 8. Inthe coalescent, each subset of k particles among n particles has a probability n,k t of coalescing into a single particle uring an infinitesimal time t. As a set of n particles can be consiere as a subset of a larger set of n+ particles, the rates n,k have to satisfy some consistency relations: the coalescence of k particles in the subset of size n happens in two cases: either these k particles coalesce in the set of size n+ rate n+,k or they coalesce together with the n+-th particle rate n+,k+. Therefore n,k = n+,k + n+,k+. This recursion leas to the following general expression for the coalescence rates 5,6: n,k =0 x k x n k xx, where is some positive measure on the interval 0,. With these notations, Kingman s coalescent correspons to x = x. Another particular case, which has been stuie, in the context of spin glasses, is the Bolthausen-Sznitman coalescent 9 for which x=. Trees in the Kingman s coalescent an in the Bolthausen-Sznitman coalescent have ifferent statistical properties. In orer to compare ifferent moels of physical or biological systems which generate ranom trees an to try to ientify universality classes, we consier here simple quantities characteristic of these ranom tree structures. For a tree with a large number of en points, we efine T p as the istance one has to go up into the tree to fin the most recent common ancestor of p given points see Fig.. For moels of evolving populations, the istance T p is the age of the most recent common ancestor of p iniviuals chosen at ranom in the population. In general, it epens both on the generation at which these p iniviuals live, but also on the choice of the p iniviuals, even in the limit of very large trees. This ouble source of fluctuations for the T p is reminiscent of what happens in mean fiel spin glasses 5: as for the overlaps in Parisi s theory, the istribution of the T p remains broa even when the size of the population becomes very large 5,0. For a given moel, one can try to etermine averages T p or moments T p k of these times T p the averages are taken over all the branches of the tree, i.e., over all the population at a given generation, an over all the ranom trees, i.e., over all the generations in the language of moels of evolution. In recent works,, it was notice that for a large class of mean fiel moels of evolution with selection, the ratios of these average times T p take, for a large population, simple universal values inicating that the genealogical trees T T T *Eric.Brunet@lps.ens.fr Bernar.Derria@lps.ens.fr Damien.Simon@lps.ens.fr FIG.. The times T p are the ages of the most common ancestors of p iniviuals chosen at ranom /008/786/ The American Physical Society

2 BRUET, DERRIDA, AD SIMO are istribute accoring to the statistics of the Bolthausen- Sznitman coalescent. Therefore, at the mean-fiel level an for a large size of the population, two universality classes seem to emerge for moels of evolution: Kingman s trees in the case of neutral evolution for which T T =, T T =, T T =, T T = 6 9, an Bolthausen-Sznitman s trees in the case of selection T T = 5, T T = 5 8, T T =, T T =. The goal of the present work is to try to measure these coalescence ratios for other moels of evolution, in particular to analyze the effect of spatial fluctuations, an to argue that irecte polymers in a ranom meium are in the same universality classes as evolution moels in presence of selection. The paper is organize as follows. In Sec. I we consier, at the mean fiel level or in finite imension, coalescence moels which are equivalent, as we will see, to neutral moels of evolution. Above two imensions of space, the coalescence trees have the same statistics as in mean fiel with coalescence times given by Eq., whereas in one imension, they lea to a ifferent universality class for which we compute the ratios of coalescence times. In Sec. II, we consier the trees of optimal paths in the problem of irecte polymers in a ranom meium. Our numerical results will show that at the mean fiel level, the trees satisfy the Bolthausen-Sznitman statistics Eq., whereas the ratios of coalescence times vary with imension as expecte by the known universality classes of the problem. I. COAESCECE AD MODES OF EUTRA EVOUTIO A. Kingman s coalescent Kingman s coalescent, is a mean-fiel moel of coalescing particles: uring each infinitesimal time interval t every pair of particles has a probability of coalescing into a single particle. Therefore if one starts with p particles, there is a ranom waiting time p until a coalescence event occurs when these p particles become p particles. Then there is another ranom time p until a pair among these p particles coalesce an one is left with p particles, an so on. The times k are inepenent an istribute accoring to exponential istributions kk kk k k = exp 5 an the time T p for p particles chosen at ranom to coalesce is given by k T p = p + p This allows one to easily recover the values of Eq.. In fact the whole generating functions of the times T p can be calculate as PHYSICA REVIEW E 78, p e T kk p = k= kk. 7 In particular one can notice that the time T has an exponential istribution. B. Wright-Fisher moel The Wright-Fisher moel, is one of the simplest neutral moels of an evolving population. It escribes a population of constant size with nonoverlapping generations an asexual reprouction. At each generation, all the population is replace by new iniviuals with the following rule: each iniviual at a given generation has its parent ranomly chosen among the iniviuals at the previous generation. If one goes backwar in times, the lineage of an iniviual performs a ranom walk on a fully connecte graph of sites. Following the lineages of p iniviuals is the same as following p coalescing ranom walks on this fully connecte graph. Since the ranom walks are inepenent, the statistics of coalescence times can be easily calculate,: for p iniviuals chosen at ranom at generation g, the time T p is the age of their most recent common ancestor, i.e., T p is the number of time steps for the p ranom walkers on the fully connecte graph to coalesce. At each generation in the past, two istinct lineages have a probability / of merging, thus T scales as the size of the population. For fixe p, the probability that a pair of lineages coalesce is / whereas multiple coalescences occur with higher powers of / for large. One can then neglect these multiple coalescences an T p p + p + + +, 8 where the times k are istribute accoring to Eq. 5, implying that the statistics of the times T p are exactly the same as in Kingman s coalescent Eq.. C. Coalescing ranom walks in finite imension We are now going to look at coalescing ranom walks on an hypercube of = sites in imension with perioic bounary conitions. We consier the continuous time case, where uring infinitesimal time interval t, each walker on the hypercube has a probability t of hopping to each of its neighboring sites, an whenever two walkers occupy the same site, they instantaneously coalesce into a single walker. If T r is the coalescence time between two walkers at a istance r apart, its evolution is = T r t + T r with probability t, t + T r + e i with probability t, 9 where e i is one of the unit vectors on the hypercubic lattice. It is clear that the istance between the two walkers performs a ranom walk an that T is simply the first time that this istance vanishes. This is of course a very well known first passage problem,5 which can be solve easily it 060-

3 UIVERSA TREE STRUCTURES I DIRECTED reuces to the inversion of a aplacian: the generating function of T r satisfies for t an for r0 e T r = e t tet r +te T r+e i, i= 0 where enotes an average over all the ranom walks. At r=0, it satisfies the bounary conition e T 0 =. For r0, one can rewrite Eq. 0 as e T r +e T r+e i e T r =0 i= an this can be easily solve in Fourier space to give e T r = A n =0 n =0 in r exp + i= cos n i, where the constant A is fixe by the conition of Eq.. Starting with two particles at ranom positions on the lattice an averaging over these two positions leas to e T = + n0 This implies that T = n0 T = n0 n0 + + i= cos n i. i= cos n i, 5 i= i= cos n i cos n i. 6 For large, it is well known,5 that Eq. 5 gives for =, T ln for =, 7 for. On the other han, one can show that the secon term in the right-han sie of Eq. 6 grows as in imension an as in imension. Therefore for, the ratio T /T goes to when, as in the mean-fiel case Eq.. In fact it has been prove,6 that in an for large the whole genealogies of p iniviuals average over all their positions are given by the Kingman coalescent, up to the rescaling 7. This implies that the istribution of the time T is exponential an that the moments of all the coalescing times T p have their mean fie values above the upper critical imension = which is the well known upper critical imension of coalescing ranom walks 7. D. Coalescing ranom walks in one imension In imension, the two terms in the right-han sie of Eq. 6 are comparable, an the ratio T /T no longer converges to. In imension = the calculation of all the moments of the times T p is rather straightforwar. First one can easily solve Eq. for perioic bounary conitions with conition Eq. an one gets e T r = / r / r / /. 8 For large, this becomes a scaling function of an of r/ e T r cos r cos an, averaging over r, one gets PHYSICA REVIEW E 78, = e T tan, sin r + sin r sin 9 0 which shows that the istribution of T is no longer exponential. One can write own the equations satisfie by the generating functions of the times T p. For large an =O the solution is p sinrk / e T p r,...,r p, k= sin / where the r k are the istances between consecutive particles along the ring one has of course r + +r p =. In particular, for p=, r =r an r = r, one recovers Eq. 9. Averaging Eq. over all the positions of the p particles on the ring leas to e T ppp x sinx / x 0 sin p. / From Eq. one can then obtain all the moments of T p. For example, one has 060-

4 BRUET, DERRIDA, AD SIMO PHYSICA REVIEW E 78, p p + T p p +p + an one can show T T = 7 5, T T = 8 5, T T = 5, T T = 5, in contrast with Eqs. an. One coul repeat the calculations which lea to Eqs. 5 7 an Eq. for moels of coalescence on other lattices or with more general jumping rates. As long as the motion of the coalescing particles remains iffusive, one woul recover the same values Eq. or Eq. for the statistics of the trees. E. eutral evolution in finite imension One can try to generalize the Wright-Fisher moel to the finite-imensional case, for example, by consiering an hypercube with a finite population of fixe size m on each lattice site, an the case where each iniviual chooses its parent in the previous generation with a probability p on the same lattice site an with probability p on one of the neighboring sites. The stuy of the genealogies in this case is obviously the same problem as following the coalescences of the lineages which perform ranom walks on this lattice. Therefore in imension = an above, the trees are given by the statistics Eq. of Kingman s coalescent whereas in imension = they will be in the universality class Eq. of coalescing ranom walks in one imension. II. DIRECTED POYMERS I A RADOM MEDIUM Directe polymers in a ranom meium is one of the simplest examples of a strongly isorere system 7,8 0. It escribes irecte paths in a ranom energy lanscape. In its zero-temperature version, the problem reuces to fining the optimal path, i.e., the path of minimal energy in this ranom energy lanscape. The optimal paths starting at the same point but arriving at ifferent points give rise to a tree structure, that we try to characterize in this section by measuring the coalescence times T p. A irecte polymer in imension + is a line extening in one of the irections traitionnaly calle time, an which we represent as the vertical irection in Fig. an Fig. with some ranom excursions in the other transverse irections see Fig.. We consier here irecte polymers on a lattice which is infinite in the time irection but finite an perioic in the transverse irections. In each time section, there are = sites locate on a -imensional hypercube of linear size with perioic bounary conitions. Each site in a given time section is connecte to M = sites in the previous time section an it is also connecte to M other sites in the next time section. The way each site is connecte is shown for imension + in Fig.. In higher imensions, we generalize the lattice of Fig. in the following way: let x =x,x,...,x be the transverse coorinates of a given site; the x i are integers at even times an FIG.. eft a irecte polymer in imension +. The time irection is vertical. Right A irecte polymer arriving at A comes either from B or from C, whichever is more energetically favorable: In the example shown, the coalescence time of the irecte polymers arriving at B an C is. half-integers at o times, an the M = potential parent sites of x have coorinates x /,x /,...,x / in the previous time section. We consier also a mean-fiel version Fig., where there is no spatial structure in the transverse irections: A time section consists of a set of sites, an each of them is connecte to M sites chosen at ranom among the sites of the previous time section, where M might be any number between an. We assume that each link AB between two connecte sites A an B carries a ranom energy AB. The energy E of the polymer is then the sum of all the energies AB of the visite links. We choose an origin where the polymer starts, an for any given site A on the lattice, we call E A the minimal energy of the polymer over all the possible irecte paths connecting this origin to A. At zero temperature, the irecte polymer chooses the path which minimizes its energy an one has the simple recursion relation E A = mine B + AB,E C + AC,..., 5 where B,C,..., are the M potential parent sites of site A. For any pair of sites A an A in the same time section, we efine their coalescence time see Fig. as the number of up steps uring which the two optimal paths arriving at A an A iffer we suppose that the origin of the irecte polymers is at a remote enough time in the past for the paths to coalesce. In a similar way, we efine the coalescence times of any group of p ifferent sites as the maximal coalescence time of any pair within the p sites. All these quan- FIG.. Directe polymer in mean fiel. At a given time, each of the = sites is connecte to M = ranom sites at the previous time. B A C 060-

5 UIVERSA TREE STRUCTURES I DIRECTED T imensions + imensions + imensions (mean fiel) 0 0 FIG.. Average coalescence time T of two iniviuals for several moels of irecte polymers in imensions + an +, an in mean-fiel, as a function of the number of sites in each time section. The ata are compare to the preiction T / in otte lines for imensions + an +. ote that, by chance, two out of the four moels in imension + an two out of the three moels in imension + have nearly the same prefactor an their ata are unistinguishable. PHYSICA REVIEW E 78, tities epen on the chosen sites an on the realization of the isorer an, as in the previous section, we note by the average over the choice of sites an the isorer. In this section, we consier the average coalesence time T p an the average square of the coalesence time T p of p sites. We have simulate four moels in imension +; from top to bottom on Fig. : on the lattice of Fig. with a iscrete istribution of with values =0 or = with probabilities /, on the lattice of Fig. with a uniform istribution of in 0,, on the lattice of Fig., with negative values of, istribute accoring to =e, on a square lattice where each site is connecte to M = parents just above itself, on its right an on its left where takes positive values, with an exponentially ecreasing istribution: =e +. In imension +, we have simulate three moels all on the lattice with M = ancestors escribe above; from top to bottom on Fig. : with an exponentially increasing istribution =e +, with a uniform istribution of in 0, with an exponentially ecreasing istribution =e +. Finally, we have simulate two moels in mean-fiel with a uniform istribution of in 0, an either M = or M = ranom ancestors for each site M = is above M = in Fig.. Our ata for all these moels are plotte together with the same symbol for each imension to emphasize the universality of our results. To measure the T p s, the conceptualy simplest way is to upate a matrix containing for all pairs i, j of iniviuals the time T i, j of their most commun ancestor. Inee, for an arbitrary number p of iniviuals, one has T p i,...,i p =maxt i,i,t i,i,...,t i,i p, so that the matrix of the T s contains all the relevant information. Upating this matrix at each time step is easy: The T of two ifferent sites is one plus the T of their parents, an the T of a site with itself is zero. Because upating at each time step a matrix is time consuming, we use a more sophisticate metho, where we keep track of the genealogical tree of all the sites at a current time: there are of course sites at the current time, an at most noes, where a noe is a site from previous times which is the most recent common ancestor of two sites at the current time. At each time step, upating the whole tree takes a time linear in, an averaging the T p over all the choices of p iniviuals takes also a time linear in, as one simply has to recursively walk own the tree from its root an count for each noe the number of times it is the most recent commun ancestor of p sites in the current time. This algorithm is escribe in more etails in Ref.. For each ata point, we have run one long simulation an average our results over all the time steps once the steay state was reache. This is equivalent to averaging over many inepenent realizations if we run a simulation for a time much longer than the correlation time, which we estimate to be of the orer of magnitue of T. All of our simulations were at least times longer than T. In Fig., we plot the coalescence time T as a function of the system size. For irecte polymers on a lattice which is infinite both in the time irection an in the transverse irections, the transverse isplacement of the optimal path scales as t, where t is the length of the irecte polymer an is a universal exponent 7 equal to + =/ in imension + an in imension +. In our setup, with a finite lattice of linear size in the transverse irections, this scaling can only hol as long as tt corr with T corr == /. This time T corr is the correlation time on the scale of which the system forgets its initial conition. Moreover, if we consier several sites an the optimal paths arriving at these sites, these paths coalesce on a time scale of the orer of T corr, as can be seen in Fig.. In mean-fiel with a finite number M of potential ancestors per site, there is no notion of istance in the transverse irections, an the exponent is meaningless. We therefore expect a ifferent scaling. The problem of zero-temperature mean-fiel irecte polymers can be formulate as a noisy Fisher-Kolmogorov-Petrovsky-Pisconov Fisher-KPP like equation,. Recently, a phenomenological theory of coalescence trees in moels of Fisher-KPP fronts suggeste,,5 that the coalescence time in such moels shoul be of orer T corr ln. On Fig., one can see that the ata seem to have a slower growth than a power law, but the values of we simulate here are too small to check the ln preiction. Better simulations on a closely relate moel are presente in Ref., where the ln scaling appears clearly. We now turn to the ratios of coalescence times. Figure 5 shows the ratios T /T an T /T as a function of the system size for all the moels we stuy four moels in imension +, three in imension +, an two in meanfiel. umerically, all the symbols for a given imension overlap of large : these ratios seem to epen only on the imension, an not on the istribution of the bon energies, nor on the shape of the lattice. The results in meanfiel are compatible with the preiction that for an infinitely large system in the Fisher-KPP front equation class, the genealogical tree converges to a Bolthausen-Sznitman coalescent, with ratios given by Eq.. In imensions + an +, our numerical results inicate clearly that we have tree 060-5

6 BRUET, DERRIDA, AD SIMO T T statistics ifferent from the Bolthausen-Sznitman coalescent, an also ifferent from the Kingman coalescent for which T /T woul be / asineq.. On Fig. 6, we show the ratios T /T an T /T. Here, the situation is less clear: the symbols for the ifferent moels o not superpose an the ratios o not seem to have converge in particular, the mean-fiel ratios are rather far from the preiction. For some reason we o not unerstan, it seems that the T p /T nee much larger values of to converge to their final values than the T p /T.We alreay observe a similar phenomenon on an exactly solvable relate moel. We also measure the ratios T /T, where T is the age of the most recent common ancestor of the whole population an foun these ratios to be close to.9 in imensions + an +, while it iverges in meanfiel. A. ong tail istributions T T FIG. 5. Ratios of coalescence times for irecte polymers at zero temperature as a function of the size of the system in, from top to bottom, imension +, imension +, an meanfiel. The otte line represents the preiction Eq. for meanfiel in the limit of infinite size. eft ratios T /T. Right ratios T /T T.95.9 T T T FIG. 6. Ratios of moments of the coalescence times for irecte polymers at zero temperature as a function of the size of the system in, from top to bottom, imension +, imension +, an meanfiel. The otte line represents the preiction Eq. for meanfiel in the limit of infinite size. eft ratios T /T. Right ratios T /T. T T an T T T T In the irecte polymer problem, it is known that the scaling regime is moifie when the istribution of the energies of the bons ecays as a power law for large negative : when c with c 7, the irecte polymer in imension + has an anomalous scaling 7,6 an the exponent epens on. We have measure the coalescence times in imension + for a istribution of energies given by = size = 00 size = α A +, T T FIG. 7. Ratios T /T an T /T as a function of the exponent appearing in the noise Eq. 6, for two ifferent system sizes in imension +. 6 with an A such that is normalize, for sizes =00 an =00. The ratios T /T an T /T are presente in Fig. 7. We observe that, for large, these ratios converge towars the universal values shown on Fig. 5, while for +, they seem to converge close to, respectively,. an.5. As we expect T to scale like /, it is possible to obtain a rough estimate of the exponent from the only two atapoints at sizes =00 an =00. This estimate is shown in Fig. 8. ν PHYSICA REVIEW E 78, α FIG. 8. Estimate of the exponent of the irecte polymer as a function of the exponent appearing in the istribution of in Eq. 6. This exponent has been evaluate from the formula ln/lnt =00/T =00. The universal value + =/ for istributions ecaying fast enough is also shown

7 UIVERSA TREE STRUCTURES I DIRECTED T.8 T =6 = The exponent seems to converge towar the universal value + =/ for large, while it seems to be for. As with previous numerical stuies 7, our results are not precise enough to etermine accurately the critical c above which =/. B. Discrete istributions =6 =6 = =6 We are now going to iscuss the case where the energies of the bons take iscrete values. In this case, it may happen in Eq. 5 that there are several paths coming from ifferent potential parent sites in the previous time section with the same minimal energy, an the question is, of course, which path shoul be selecte as the parent site. The simplest iea is to choose ranomly at each time step with equal probabilities one of the parent site with the lowest energy. With this proceure, we have run numerical simulations in imension + for several sizes with a binary noise for the energies of the bons =0 with probability p, 7 with probability p, for several values of p. Our results for the ratio T /T as a function of p are shown in Fig. 9 as otte lines. As p varies, we observe a crossover between two values: for small p, T /T.6 as for irecte polymers in imension + when the istribution of energies is continuous see Fig. 5 an, for large p, T /T. which correspons to the coalescence of ranom walks in imension, as in Eq.. The crossover between the two regions becomes sharper as increases, which suggests a phase transition. The critical value of p is very consistent with the known threshol 0.67 for irecte percolation on the same lattice 7. Thus, the system behaves similar to the neutral moel when the =0 bons percolate. p FIG. 9. Ratios T /T as a function of p for the istribution of of Eq. 7 in imension +. The ashe lines correspon to the simplest proceure of choosing with equal probabilities one of the potential parent sites realizing the minimal energy, an the plain lines represent the results using the weights which correspons to the T 0 + limit of finite temperature irecte polymers. The vertical otte line inicates the irecte percolation threshol on the same lattice. PHYSICA REVIEW E 78, Instea of choosing with equal probabilities which bon the polymer follows when they are energetically equivalent, there is an alternative proceure which correspons to taking the limit T 0 + in the problem of irecte polymers at a finite temperature T. At finite temperature, we keep track for each site A of the partition function Z A of a polymer arriving on A. Assuming that the site A has M = potential parent sites B an C, we have the recursion Eq. 5 Z A = Z A B + Z A C, 8 where Z A B =Z B exp AB is the partition function of a irecte polymer arriving on A via the site B an where =/T. The probability that a polymer reaching A comes from B is given by probability the polymer comes from B = Z A B. 9 Z A At very low temperature, the partition function is ominate by the lowest energy paths Z e E, 0 where E is the minimal energy an the number of ways that this energy E can be obtaine, so that Eq. 8 reas, at low temperature, A e E A B e E A B + C e E A C, where E A B =E B + AB is the minimal energy of the path arriving at A through B. IfE A B E A C, then the first term in the right-han sie of Eq. ominates an we obtain E A =E A B an A = B. Furthermore, from Eq. 9, the chosen path comes from B. On the other han, if E A B =E A C, both terms in Eq. have the same orer of magnitue an we obtain E A =E A B =E A C an A = B + C. Then, from Eq. 9, the probability that the irecte polymer comes from B is B / A. In this way, we not only choose the optimal energy but we also keep track of entropy effects. We have run numerical simulations with the same parameters as above but with this new proceure. The ratios T /T are shown on Fig. 9 in plain lines. For small values of p, both proceures yiel the same results. For larger p, however, the ifference is striking, an the phase transition seems to have isappeare: on both sies of the percolation threshol the ata seem to be in the same universality class as they converge to.6. III. COCUSIO In this paper, we have presente analytical an numerical results showing the existence of universality classes in the tree structures which appear in several moels of evolution an in irecte polymers see Table I for a summary. Without selection, the genealogies of neutral moels like the Wright-Fisher moel or coalescing ranom walks are escribe above the critical imension c = by the Kingman coalescent. For = the universality class is ifferent: we have obtaine the istribution Eq. of the ages T p of the most recent common ancestor of p iniviuals. For irecte polymers in a ranom meium at zero temperature, the same coalescence times T p have been measure 060-7

8 BRUET, DERRIDA, AD SIMO PHYSICA REVIEW E 78, TABE I. Universal ratios an orer of magnitues of coalescence times for moels of evolution with an without selection, an irecte polymers in a ranom meium. We coul not reach large enough system sizes to give a reliable numerical preiction for the ratios T p /T p. T T T T T T T T T T T Kingman coalescent 6 Coalescing ranom walks in 9 eutral moels of evolution in eutral moel in = ln eutral moel in = Bolthausen-Sznitman s coalescent 5 5 Moels of evolution with selection ln 8 or irecte polymers at zero temperature in mean fiel Moels of evolution with selection in imension or irecte polymers at zero temperature in imension ?? / Moels of evolution with selection in imension or irecte polymers at zero temperature in imension ?? / numerically. In the mean fiel case, their values are compatible with Bolthausen-Sznitman s coalescent, which is alreay known to appear in spin glasses 9 an in branching ranom walks with a selection mechanism keeping the size constant,. In low imension at least = an =, the coalescence times belong to ifferent universality classes. It woul be interesting to preict analytically the values of T /T an T /T measure numerically in Fig. 5 for fast ecaying istributions of as well as the ones obtaine in Fig. 7 for power-law istributions of with exponent +. In the mean-fiel case, it woul also be interesting to know if the replica metho can be use in orer to etermine the coalescence times. The simulations presente in this paper eal only with irecte polymers at T = 0. Directe polymers exhibit a phase transition for as the temperature increases 8. We expect the tree statistics to change at T c from the universality class of irecte polymers at zero temperature to the universality class of coalescing ranom walks. The construction of the minimal energy path for irecte polymers can be relate to spatial moels in presence of selection. In population ynamics, selection can be taken into account through a parameter, calle the fitness or the aaptability, which characterizes the ability of an iniviual to survive an reprouce 9. Iniviuals with a higher fitness have a higher probability of having a escenance. This parameter is transmitte from parents to offspring up to fluctuations ue to mutations. An analogy can be rawn between the minimal energy of a irecte polymer arriving on a site, an minus the fitness of an iniviual living on a site. In presence of local selection, a spatial moel of population coul therefore be formulate as follows: on each site there woul be one or a finite number m of iniviuals; at each generation, each iniviual woul branch into k offspring with mutate fitnesses. These offspring iffuse an, uner the effect of selection, only the best or the m best iniviuals on each site woul be kept. Because of the similarity of such spatial moels of population ynamics in presence of selection with the irecte polymers, we expect these moels to belong to the same universality classes. We performe preliminary simulations on such a spatial moel of evolution with selection in imension + with m=5 iniviuals per site. Our results for the ratios T /T an T /T coincie with those of irecte polymers

9 UIVERSA TREE STRUCTURES I DIRECTED T. A. Witten an. M. Saner, Phys. Rev. ett. 7, M. Takayasu an H. Takayasu, onequilibrium Statistical Mechanics in One Dimension Cambrige University Press, Cambrige, 997, Chap. 9. M. Cieplak, A. Giacometti, A. Maritan, A. Rinalo, I. Roriguez-Iturbe, an J. Banavar, J. Stat. Phys. 9, 998. E. Bolthausen an A.-S. Sznitman, Commun. Math. Phys. 97, M. Mézar, G. Parisi, an M. Virasoro, Spin Glass Theory an Beyon Worl Scientific, Singapore, M. Karar, G. Parisi, an Y.-C. Zhang, Phys. Rev. ett. 56, T. Halpin-Healy an Y.-C. Zhang, Phys. Rep. 5, C. Girau, Commun. Math. Phys., Z.-S. She, E. Aurell, an U. Frisch, Commun. Math. Phys. 8, Y. G. Sinai, Commun. Math. Phys. 8, J. Kingman, Stochastic Proc. Appl., J. Kingman, J. Appl. Probab. 9A, 798. S. Wright, Genetics 6, 979. R. Fisher, The Genetical Theory of atural Selection Clarenon Press, Oxfor, S. Sagitov, J. Appl. Probab. 6, J. Pitman, Ann. Probab. 7, J. Schweinsberg, Electron. J. Probab. 5, M. Birkner, J. Blath, M. Capalo, A. M. Etherige, M. Möhle, J. Schweinsberg, an A. Wakolbinger, Electron. J. Probab. 0, E. Bolthausen an A.-S. Sznitman, Commun. Math. Phys. PHYSICA REVIEW E 78, , B. Derria an. Peliti, Bull. Math. Biol. 5, É. Brunet, B. Derria, A. H. Mueller, an S. Munier, Phys. Rev. E 76, É. Brunet, B. Derria, A. H. Mueller, an S. Munier, Europhys. ett. 76, 006. V. imic an A. Sturm, Electron. J. Probab., E. W. Montroll, Proc. Symp. Appl. Math. 6, E. W. Montroll an G. H. Weiss, J. Math. Phys. 6, J. T. Cox, Ann. Probab. 7, Peliti, J. Phys. A 9, M. Karar, ucl. Phys. B 90, T. Emig an M. Karar, ucl. Phys. B 60, M. Karar an Y.-C. Zhang, Phys. Rev. ett. 58, É. Brunet an B. Derria, Phys. Rev. E 70, R. A. Fisher, Proc. Annu. Symp. Eugen. Soc. 7, A. Kolmogorov, I. Petrovsky, an. Piscounov, Bull. Univ. État Moscou A, 97. É. Brunet an B. Derria, Phys. Rev. E 56, É. Brunet, B. Derria, A. H. Mueller, an S. Munier, Phys. Rev. E 7, Y.-C. Zhang, Physica A 70, I. Jensen, J. Phys. A 9, C. Monthus an T. Garel, Eur. Phys. J. B 5, R. E. Snyer, Ecology 8, M. Kloster an C. Tang, Phys. Rev. ett. 9, M. Kloster, Phys. Rev. ett. 95, T. Antal, K. B. Blagoev, S. A. Trugman, an S. Rener, J. Theor. Biol. 8, 007. B. Derria an D. Simon, Europhys. ett. 78,

Generalization of the persistent random walk to dimensions greater than 1

Generalization of the persistent random walk to dimensions greater than 1 PHYSICAL REVIEW E VOLUME 58, NUMBER 6 DECEMBER 1998 Generalization of the persistent ranom walk to imensions greater than 1 Marián Boguñá, Josep M. Porrà, an Jaume Masoliver Departament e Física Fonamental,

More information

Thermal conductivity of graded composites: Numerical simulations and an effective medium approximation

Thermal conductivity of graded composites: Numerical simulations and an effective medium approximation JOURNAL OF MATERIALS SCIENCE 34 (999)5497 5503 Thermal conuctivity of grae composites: Numerical simulations an an effective meium approximation P. M. HUI Department of Physics, The Chinese University

More information

ensembles When working with density operators, we can use this connection to define a generalized Bloch vector: v x Tr x, v y Tr y

ensembles When working with density operators, we can use this connection to define a generalized Bloch vector: v x Tr x, v y Tr y Ph195a lecture notes, 1/3/01 Density operators for spin- 1 ensembles So far in our iscussion of spin- 1 systems, we have restricte our attention to the case of pure states an Hamiltonian evolution. Toay

More information

6 General properties of an autonomous system of two first order ODE

6 General properties of an autonomous system of two first order ODE 6 General properties of an autonomous system of two first orer ODE Here we embark on stuying the autonomous system of two first orer ifferential equations of the form ẋ 1 = f 1 (, x 2 ), ẋ 2 = f 2 (, x

More information

Table of Common Derivatives By David Abraham

Table of Common Derivatives By David Abraham Prouct an Quotient Rules: Table of Common Derivatives By Davi Abraham [ f ( g( ] = [ f ( ] g( + f ( [ g( ] f ( = g( [ f ( ] g( g( f ( [ g( ] Trigonometric Functions: sin( = cos( cos( = sin( tan( = sec

More information

Math 342 Partial Differential Equations «Viktor Grigoryan

Math 342 Partial Differential Equations «Viktor Grigoryan Math 342 Partial Differential Equations «Viktor Grigoryan 6 Wave equation: solution In this lecture we will solve the wave equation on the entire real line x R. This correspons to a string of infinite

More information

05 The Continuum Limit and the Wave Equation

05 The Continuum Limit and the Wave Equation Utah State University DigitalCommons@USU Founations of Wave Phenomena Physics, Department of 1-1-2004 05 The Continuum Limit an the Wave Equation Charles G. Torre Department of Physics, Utah State University,

More information

Lower Bounds for the Smoothed Number of Pareto optimal Solutions

Lower Bounds for the Smoothed Number of Pareto optimal Solutions Lower Bouns for the Smoothe Number of Pareto optimal Solutions Tobias Brunsch an Heiko Röglin Department of Computer Science, University of Bonn, Germany brunsch@cs.uni-bonn.e, heiko@roeglin.org Abstract.

More information

Qubit channels that achieve capacity with two states

Qubit channels that achieve capacity with two states Qubit channels that achieve capacity with two states Dominic W. Berry Department of Physics, The University of Queenslan, Brisbane, Queenslan 4072, Australia Receive 22 December 2004; publishe 22 March

More information

DEGREE DISTRIBUTION OF SHORTEST PATH TREES AND BIAS OF NETWORK SAMPLING ALGORITHMS

DEGREE DISTRIBUTION OF SHORTEST PATH TREES AND BIAS OF NETWORK SAMPLING ALGORITHMS DEGREE DISTRIBUTION OF SHORTEST PATH TREES AND BIAS OF NETWORK SAMPLING ALGORITHMS SHANKAR BHAMIDI 1, JESSE GOODMAN 2, REMCO VAN DER HOFSTAD 3, AND JÚLIA KOMJÁTHY3 Abstract. In this article, we explicitly

More information

SYNCHRONOUS SEQUENTIAL CIRCUITS

SYNCHRONOUS SEQUENTIAL CIRCUITS CHAPTER SYNCHRONOUS SEUENTIAL CIRCUITS Registers an counters, two very common synchronous sequential circuits, are introuce in this chapter. Register is a igital circuit for storing information. Contents

More information

A new proof of the sharpness of the phase transition for Bernoulli percolation on Z d

A new proof of the sharpness of the phase transition for Bernoulli percolation on Z d A new proof of the sharpness of the phase transition for Bernoulli percolation on Z Hugo Duminil-Copin an Vincent Tassion October 8, 205 Abstract We provie a new proof of the sharpness of the phase transition

More information

Quantum Mechanics in Three Dimensions

Quantum Mechanics in Three Dimensions Physics 342 Lecture 20 Quantum Mechanics in Three Dimensions Lecture 20 Physics 342 Quantum Mechanics I Monay, March 24th, 2008 We begin our spherical solutions with the simplest possible case zero potential.

More information

J. Stat. Mech. (2013) P01006

J. Stat. Mech. (2013) P01006 Journal of Statistical Mechanics: Theory and Experiment Genealogies in simple models of evolution Éric Brunet and Bernard Derrida Laboratoire de Physique Statistique, École ormale Supérieure, UPMC, Université

More information

Model for Dopant and Impurity Segregation During Vapor Phase Growth

Model for Dopant and Impurity Segregation During Vapor Phase Growth Mat. Res. Soc. Symp. Proc. Vol. 648, P3.11.1-7 2001 Materials Research Society Moel for Dopant an Impurity Segregation During Vapor Phase Growth Craig B. Arnol an Michael J. Aziz Division of Engineering

More information

The derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x)

The derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x) Y. D. Chong (2016) MH2801: Complex Methos for the Sciences 1. Derivatives The erivative of a function f(x) is another function, efine in terms of a limiting expression: f (x) f (x) lim x δx 0 f(x + δx)

More information

Lie symmetry and Mei conservation law of continuum system

Lie symmetry and Mei conservation law of continuum system Chin. Phys. B Vol. 20, No. 2 20 020 Lie symmetry an Mei conservation law of continuum system Shi Shen-Yang an Fu Jing-Li Department of Physics, Zhejiang Sci-Tech University, Hangzhou 3008, China Receive

More information

The Exact Form and General Integrating Factors

The Exact Form and General Integrating Factors 7 The Exact Form an General Integrating Factors In the previous chapters, we ve seen how separable an linear ifferential equations can be solve using methos for converting them to forms that can be easily

More information

Schrödinger s equation.

Schrödinger s equation. Physics 342 Lecture 5 Schröinger s Equation Lecture 5 Physics 342 Quantum Mechanics I Wenesay, February 3r, 2010 Toay we iscuss Schröinger s equation an show that it supports the basic interpretation of

More information

Survey Sampling. 1 Design-based Inference. Kosuke Imai Department of Politics, Princeton University. February 19, 2013

Survey Sampling. 1 Design-based Inference. Kosuke Imai Department of Politics, Princeton University. February 19, 2013 Survey Sampling Kosuke Imai Department of Politics, Princeton University February 19, 2013 Survey sampling is one of the most commonly use ata collection methos for social scientists. We begin by escribing

More information

Dot trajectories in the superposition of random screens: analysis and synthesis

Dot trajectories in the superposition of random screens: analysis and synthesis 1472 J. Opt. Soc. Am. A/ Vol. 21, No. 8/ August 2004 Isaac Amiror Dot trajectories in the superposition of ranom screens: analysis an synthesis Isaac Amiror Laboratoire e Systèmes Périphériques, Ecole

More information

arxiv: v1 [hep-lat] 19 Nov 2013

arxiv: v1 [hep-lat] 19 Nov 2013 HU-EP-13/69 SFB/CPP-13-98 DESY 13-225 Applicability of Quasi-Monte Carlo for lattice systems arxiv:1311.4726v1 [hep-lat] 19 ov 2013, a,b Tobias Hartung, c Karl Jansen, b Hernan Leovey, Anreas Griewank

More information

Lagrangian and Hamiltonian Mechanics

Lagrangian and Hamiltonian Mechanics Lagrangian an Hamiltonian Mechanics.G. Simpson, Ph.. epartment of Physical Sciences an Engineering Prince George s Community College ecember 5, 007 Introuction In this course we have been stuying classical

More information

arxiv:hep-th/ v1 3 Feb 1993

arxiv:hep-th/ v1 3 Feb 1993 NBI-HE-9-89 PAR LPTHE 9-49 FTUAM 9-44 November 99 Matrix moel calculations beyon the spherical limit arxiv:hep-th/93004v 3 Feb 993 J. Ambjørn The Niels Bohr Institute Blegamsvej 7, DK-00 Copenhagen Ø,

More information

Chapter 4. Electrostatics of Macroscopic Media

Chapter 4. Electrostatics of Macroscopic Media Chapter 4. Electrostatics of Macroscopic Meia 4.1 Multipole Expansion Approximate potentials at large istances 3 x' x' (x') x x' x x Fig 4.1 We consier the potential in the far-fiel region (see Fig. 4.1

More information

Delocalization of boundary states in disordered topological insulators

Delocalization of boundary states in disordered topological insulators Journal of Physics A: Mathematical an Theoretical J. Phys. A: Math. Theor. 48 (05) FT0 (pp) oi:0.088/75-83/48//ft0 Fast Track Communication Delocalization of bounary states in isorere topological insulators

More information

arxiv: v1 [cond-mat.stat-mech] 9 Jan 2012

arxiv: v1 [cond-mat.stat-mech] 9 Jan 2012 arxiv:1201.1836v1 [con-mat.stat-mech] 9 Jan 2012 Externally riven one-imensional Ising moel Amir Aghamohammai a 1, Cina Aghamohammai b 2, & Mohamma Khorrami a 3 a Department of Physics, Alzahra University,

More information

Separation of Variables

Separation of Variables Physics 342 Lecture 1 Separation of Variables Lecture 1 Physics 342 Quantum Mechanics I Monay, January 25th, 2010 There are three basic mathematical tools we nee, an then we can begin working on the physical

More information

Lecture XII. where Φ is called the potential function. Let us introduce spherical coordinates defined through the relations

Lecture XII. where Φ is called the potential function. Let us introduce spherical coordinates defined through the relations Lecture XII Abstract We introuce the Laplace equation in spherical coorinates an apply the metho of separation of variables to solve it. This will generate three linear orinary secon orer ifferential equations:

More information

θ x = f ( x,t) could be written as

θ x = f ( x,t) could be written as 9. Higher orer PDEs as systems of first-orer PDEs. Hyperbolic systems. For PDEs, as for ODEs, we may reuce the orer by efining new epenent variables. For example, in the case of the wave equation, (1)

More information

APPROXIMATE SOLUTION FOR TRANSIENT HEAT TRANSFER IN STATIC TURBULENT HE II. B. Baudouy. CEA/Saclay, DSM/DAPNIA/STCM Gif-sur-Yvette Cedex, France

APPROXIMATE SOLUTION FOR TRANSIENT HEAT TRANSFER IN STATIC TURBULENT HE II. B. Baudouy. CEA/Saclay, DSM/DAPNIA/STCM Gif-sur-Yvette Cedex, France APPROXIMAE SOLUION FOR RANSIEN HEA RANSFER IN SAIC URBULEN HE II B. Bauouy CEA/Saclay, DSM/DAPNIA/SCM 91191 Gif-sur-Yvette Ceex, France ABSRAC Analytical solution in one imension of the heat iffusion equation

More information

THE VAN KAMPEN EXPANSION FOR LINKED DUFFING LINEAR OSCILLATORS EXCITED BY COLORED NOISE

THE VAN KAMPEN EXPANSION FOR LINKED DUFFING LINEAR OSCILLATORS EXCITED BY COLORED NOISE Journal of Soun an Vibration (1996) 191(3), 397 414 THE VAN KAMPEN EXPANSION FOR LINKED DUFFING LINEAR OSCILLATORS EXCITED BY COLORED NOISE E. M. WEINSTEIN Galaxy Scientific Corporation, 2500 English Creek

More information

arxiv:cond-mat/ v1 12 Oct 2005

arxiv:cond-mat/ v1 12 Oct 2005 Creation an estruction of a spin gap in weakly couple quarter-fille laers B. Eegger,, H.G. Evertz, an R.M. Noack Institut für Theoretische Physik, Technische Universität Graz, A-8 Graz, Austria Institut

More information

Math Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors

Math Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors Math 18.02 Notes on ifferentials, the Chain Rule, graients, irectional erivative, an normal vectors Tangent plane an linear approximation We efine the partial erivatives of f( xy, ) as follows: f f( x+

More information

Computing Exact Confidence Coefficients of Simultaneous Confidence Intervals for Multinomial Proportions and their Functions

Computing Exact Confidence Coefficients of Simultaneous Confidence Intervals for Multinomial Proportions and their Functions Working Paper 2013:5 Department of Statistics Computing Exact Confience Coefficients of Simultaneous Confience Intervals for Multinomial Proportions an their Functions Shaobo Jin Working Paper 2013:5

More information

PARETO GENEALOGIES ARISING FROM A POISSON BRANCHING EVOLUTION MODEL WITH SELECTION

PARETO GENEALOGIES ARISING FROM A POISSON BRANCHING EVOLUTION MODEL WITH SELECTION PARETO GEEALOGIES ARISIG FROM A POISSO BRACHIG EVOLUTIO MODEL WITH SELECTIO THIERRY E. HUILLET Abstract. We stuy a class of coalescents erive from a sampling proceure out of i.i.. Paretoα ranom variables,

More information

Analytic Scaling Formulas for Crossed Laser Acceleration in Vacuum

Analytic Scaling Formulas for Crossed Laser Acceleration in Vacuum October 6, 4 ARDB Note Analytic Scaling Formulas for Crosse Laser Acceleration in Vacuum Robert J. Noble Stanfor Linear Accelerator Center, Stanfor University 575 San Hill Roa, Menlo Park, California 945

More information

arxiv: v2 [cond-mat.stat-mech] 11 Nov 2016

arxiv: v2 [cond-mat.stat-mech] 11 Nov 2016 Noname manuscript No. (will be inserte by the eitor) Scaling properties of the number of ranom sequential asorption iterations neee to generate saturate ranom packing arxiv:607.06668v2 [con-mat.stat-mech]

More information

Scalar Field Theory in the AdS/CFT Correspondence Revisited

Scalar Field Theory in the AdS/CFT Correspondence Revisited Scalar Fiel Theory in the AS/CFT Corresponence Revisite arxiv:hep-th/9907079v4 Feb 000 Pablo Minces an Victor. Rivelles Universiae e São Paulo, Instituto e Física Caixa Postal 66.38 - CEP 0535-970 - São

More information

6. Friction and viscosity in gasses

6. Friction and viscosity in gasses IR2 6. Friction an viscosity in gasses 6.1 Introuction Similar to fluis, also for laminar flowing gases Newtons s friction law hols true (see experiment IR1). Using Newton s law the viscosity of air uner

More information

Calculus of Variations

Calculus of Variations Calculus of Variations Lagrangian formalism is the main tool of theoretical classical mechanics. Calculus of Variations is a part of Mathematics which Lagrangian formalism is base on. In this section,

More information

Role of parameters in the stochastic dynamics of a stick-slip oscillator

Role of parameters in the stochastic dynamics of a stick-slip oscillator Proceeing Series of the Brazilian Society of Applie an Computational Mathematics, v. 6, n. 1, 218. Trabalho apresentao no XXXVII CNMAC, S.J. os Campos - SP, 217. Proceeing Series of the Brazilian Society

More information

1 dx. where is a large constant, i.e., 1, (7.6) and Px is of the order of unity. Indeed, if px is given by (7.5), the inequality (7.

1 dx. where is a large constant, i.e., 1, (7.6) and Px is of the order of unity. Indeed, if px is given by (7.5), the inequality (7. Lectures Nine an Ten The WKB Approximation The WKB metho is a powerful tool to obtain solutions for many physical problems It is generally applicable to problems of wave propagation in which the frequency

More information

18 EVEN MORE CALCULUS

18 EVEN MORE CALCULUS 8 EVEN MORE CALCULUS Chapter 8 Even More Calculus Objectives After stuing this chapter you shoul be able to ifferentiate an integrate basic trigonometric functions; unerstan how to calculate rates of change;

More information

The total derivative. Chapter Lagrangian and Eulerian approaches

The total derivative. Chapter Lagrangian and Eulerian approaches Chapter 5 The total erivative 51 Lagrangian an Eulerian approaches The representation of a flui through scalar or vector fiels means that each physical quantity uner consieration is escribe as a function

More information

12.11 Laplace s Equation in Cylindrical and

12.11 Laplace s Equation in Cylindrical and SEC. 2. Laplace s Equation in Cylinrical an Spherical Coorinates. Potential 593 2. Laplace s Equation in Cylinrical an Spherical Coorinates. Potential One of the most important PDEs in physics an engineering

More information

Lectures - Week 10 Introduction to Ordinary Differential Equations (ODES) First Order Linear ODEs

Lectures - Week 10 Introduction to Ordinary Differential Equations (ODES) First Order Linear ODEs Lectures - Week 10 Introuction to Orinary Differential Equations (ODES) First Orer Linear ODEs When stuying ODEs we are consiering functions of one inepenent variable, e.g., f(x), where x is the inepenent

More information

Chaos, Solitons and Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena

Chaos, Solitons and Fractals Nonlinear Science, and Nonequilibrium and Complex Phenomena Chaos, Solitons an Fractals (7 64 73 Contents lists available at ScienceDirect Chaos, Solitons an Fractals onlinear Science, an onequilibrium an Complex Phenomena journal homepage: www.elsevier.com/locate/chaos

More information

1 Heisenberg Representation

1 Heisenberg Representation 1 Heisenberg Representation What we have been ealing with so far is calle the Schröinger representation. In this representation, operators are constants an all the time epenence is carrie by the states.

More information

Acute sets in Euclidean spaces

Acute sets in Euclidean spaces Acute sets in Eucliean spaces Viktor Harangi April, 011 Abstract A finite set H in R is calle an acute set if any angle etermine by three points of H is acute. We examine the maximal carinality α() of

More information

Chapter 2 Lagrangian Modeling

Chapter 2 Lagrangian Modeling Chapter 2 Lagrangian Moeling The basic laws of physics are use to moel every system whether it is electrical, mechanical, hyraulic, or any other energy omain. In mechanics, Newton s laws of motion provie

More information

The maximum sustainable yield of Allee dynamic system

The maximum sustainable yield of Allee dynamic system Ecological Moelling 154 (2002) 1 7 www.elsevier.com/locate/ecolmoel The maximum sustainable yiel of Allee ynamic system Zhen-Shan Lin a, *, Bai-Lian Li b a Department of Geography, Nanjing Normal Uni ersity,

More information

Quantum Search on the Spatial Grid

Quantum Search on the Spatial Grid Quantum Search on the Spatial Gri Matthew D. Falk MIT 2012, 550 Memorial Drive, Cambrige, MA 02139 (Date: December 11, 2012) This paper explores Quantum Search on the two imensional spatial gri. Recent

More information

Vertical shear plus horizontal stretching as a route to mixing

Vertical shear plus horizontal stretching as a route to mixing Vertical shear plus horizontal stretching as a route to mixing Peter H. Haynes Department of Applie Mathematics an Theoretical Physics, University of Cambrige, UK Abstract. The combine effect of vertical

More information

arxiv:nlin/ v1 [nlin.cd] 21 Mar 2002

arxiv:nlin/ v1 [nlin.cd] 21 Mar 2002 Entropy prouction of iffusion in spatially perioic eterministic systems arxiv:nlin/0203046v [nlin.cd] 2 Mar 2002 J. R. Dorfman, P. Gaspar, 2 an T. Gilbert 3 Department of Physics an Institute for Physical

More information

Agmon Kolmogorov Inequalities on l 2 (Z d )

Agmon Kolmogorov Inequalities on l 2 (Z d ) Journal of Mathematics Research; Vol. 6, No. ; 04 ISSN 96-9795 E-ISSN 96-9809 Publishe by Canaian Center of Science an Eucation Agmon Kolmogorov Inequalities on l (Z ) Arman Sahovic Mathematics Department,

More information

Assignment 1. g i (x 1,..., x n ) dx i = 0. i=1

Assignment 1. g i (x 1,..., x n ) dx i = 0. i=1 Assignment 1 Golstein 1.4 The equations of motion for the rolling isk are special cases of general linear ifferential equations of constraint of the form g i (x 1,..., x n x i = 0. i=1 A constraint conition

More information

19 Eigenvalues, Eigenvectors, Ordinary Differential Equations, and Control

19 Eigenvalues, Eigenvectors, Ordinary Differential Equations, and Control 19 Eigenvalues, Eigenvectors, Orinary Differential Equations, an Control This section introuces eigenvalues an eigenvectors of a matrix, an iscusses the role of the eigenvalues in etermining the behavior

More information

Sharp Thresholds. Zachary Hamaker. March 15, 2010

Sharp Thresholds. Zachary Hamaker. March 15, 2010 Sharp Threshols Zachary Hamaker March 15, 2010 Abstract The Kolmogorov Zero-One law states that for tail events on infinite-imensional probability spaces, the probability must be either zero or one. Behavior

More information

Switching Time Optimization in Discretized Hybrid Dynamical Systems

Switching Time Optimization in Discretized Hybrid Dynamical Systems Switching Time Optimization in Discretize Hybri Dynamical Systems Kathrin Flaßkamp, To Murphey, an Sina Ober-Blöbaum Abstract Switching time optimization (STO) arises in systems that have a finite set

More information

Linear First-Order Equations

Linear First-Order Equations 5 Linear First-Orer Equations Linear first-orer ifferential equations make up another important class of ifferential equations that commonly arise in applications an are relatively easy to solve (in theory)

More information

Convective heat transfer

Convective heat transfer CHAPTER VIII Convective heat transfer The previous two chapters on issipative fluis were evote to flows ominate either by viscous effects (Chap. VI) or by convective motion (Chap. VII). In either case,

More information

Applications of First Order Equations

Applications of First Order Equations Applications of First Orer Equations Viscous Friction Consier a small mass that has been roppe into a thin vertical tube of viscous flui lie oil. The mass falls, ue to the force of gravity, but falls more

More information

Energy behaviour of the Boris method for charged-particle dynamics

Energy behaviour of the Boris method for charged-particle dynamics Version of 25 April 218 Energy behaviour of the Boris metho for charge-particle ynamics Ernst Hairer 1, Christian Lubich 2 Abstract The Boris algorithm is a wiely use numerical integrator for the motion

More information

Chapter 6: Energy-Momentum Tensors

Chapter 6: Energy-Momentum Tensors 49 Chapter 6: Energy-Momentum Tensors This chapter outlines the general theory of energy an momentum conservation in terms of energy-momentum tensors, then applies these ieas to the case of Bohm's moel.

More information

Thermal Modulation of Rayleigh-Benard Convection

Thermal Modulation of Rayleigh-Benard Convection Thermal Moulation of Rayleigh-Benar Convection B. S. Bhaauria Department of Mathematics an Statistics, Jai Narain Vyas University, Johpur, Inia-3400 Reprint requests to Dr. B. S.; E-mail: bsbhaauria@reiffmail.com

More information

On a limit theorem for non-stationary branching processes.

On a limit theorem for non-stationary branching processes. On a limit theorem for non-stationary branching processes. TETSUYA HATTORI an HIROSHI WATANABE 0. Introuction. The purpose of this paper is to give a limit theorem for a certain class of iscrete-time multi-type

More information

Integration Review. May 11, 2013

Integration Review. May 11, 2013 Integration Review May 11, 2013 Goals: Review the funamental theorem of calculus. Review u-substitution. Review integration by parts. Do lots of integration eamples. 1 Funamental Theorem of Calculus In

More information

Conservation Laws. Chapter Conservation of Energy

Conservation Laws. Chapter Conservation of Energy 20 Chapter 3 Conservation Laws In orer to check the physical consistency of the above set of equations governing Maxwell-Lorentz electroynamics [(2.10) an (2.12) or (1.65) an (1.68)], we examine the action

More information

A Model of Electron-Positron Pair Formation

A Model of Electron-Positron Pair Formation Volume PROGRESS IN PHYSICS January, 8 A Moel of Electron-Positron Pair Formation Bo Lehnert Alfvén Laboratory, Royal Institute of Technology, S-44 Stockholm, Sween E-mail: Bo.Lehnert@ee.kth.se The elementary

More information

Lower bounds on Locality Sensitive Hashing

Lower bounds on Locality Sensitive Hashing Lower bouns on Locality Sensitive Hashing Rajeev Motwani Assaf Naor Rina Panigrahy Abstract Given a metric space (X, X ), c 1, r > 0, an p, q [0, 1], a istribution over mappings H : X N is calle a (r,

More information

Chapter 2 Governing Equations

Chapter 2 Governing Equations Chapter 2 Governing Equations In the present an the subsequent chapters, we shall, either irectly or inirectly, be concerne with the bounary-layer flow of an incompressible viscous flui without any involvement

More information

Lecture 2 Lagrangian formulation of classical mechanics Mechanics

Lecture 2 Lagrangian formulation of classical mechanics Mechanics Lecture Lagrangian formulation of classical mechanics 70.00 Mechanics Principle of stationary action MATH-GA To specify a motion uniquely in classical mechanics, it suffices to give, at some time t 0,

More information

Number of wireless sensors needed to detect a wildfire

Number of wireless sensors needed to detect a wildfire Number of wireless sensors neee to etect a wilfire Pablo I. Fierens Instituto Tecnológico e Buenos Aires (ITBA) Physics an Mathematics Department Av. Maero 399, Buenos Aires, (C1106ACD) Argentina pfierens@itba.eu.ar

More information

Convergence of Random Walks

Convergence of Random Walks Chapter 16 Convergence of Ranom Walks This lecture examines the convergence of ranom walks to the Wiener process. This is very important both physically an statistically, an illustrates the utility of

More information

M. KURZYŃSKI*, K. PALACZ, AND P. CHEŁMINIAK

M. KURZYŃSKI*, K. PALACZ, AND P. CHEŁMINIAK Proc. Natl. Aca. Sci. USA Vol. 95, pp. 11685 11690, September 1998 Biophysics Time course of reactions controlle an gate by intramolecular ynamics of proteins: Preictions of the moel of ranom walk on fractal

More information

The effect of nonvertical shear on turbulence in a stably stratified medium

The effect of nonvertical shear on turbulence in a stably stratified medium The effect of nonvertical shear on turbulence in a stably stratifie meium Frank G. Jacobitz an Sutanu Sarkar Citation: Physics of Fluis (1994-present) 10, 1158 (1998); oi: 10.1063/1.869640 View online:

More information

ELECTRON DIFFRACTION

ELECTRON DIFFRACTION ELECTRON DIFFRACTION Electrons : wave or quanta? Measurement of wavelength an momentum of electrons. Introuction Electrons isplay both wave an particle properties. What is the relationship between the

More information

Generalizing Kronecker Graphs in order to Model Searchable Networks

Generalizing Kronecker Graphs in order to Model Searchable Networks Generalizing Kronecker Graphs in orer to Moel Searchable Networks Elizabeth Boine, Babak Hassibi, Aam Wierman California Institute of Technology Pasaena, CA 925 Email: {eaboine, hassibi, aamw}@caltecheu

More information

RFSS: Lecture 4 Alpha Decay

RFSS: Lecture 4 Alpha Decay RFSS: Lecture 4 Alpha Decay Reaings Nuclear an Raiochemistry: Chapter 3 Moern Nuclear Chemistry: Chapter 7 Energetics of Alpha Decay Geiger Nuttall base theory Theory of Alpha Decay Hinrance Factors Different

More information

Homework 7 Due 18 November at 6:00 pm

Homework 7 Due 18 November at 6:00 pm Homework 7 Due 18 November at 6:00 pm 1. Maxwell s Equations Quasi-statics o a An air core, N turn, cylinrical solenoi of length an raius a, carries a current I Io cos t. a. Using Ampere s Law, etermine

More information

Equilibrium in Queues Under Unknown Service Times and Service Value

Equilibrium in Queues Under Unknown Service Times and Service Value University of Pennsylvania ScholarlyCommons Finance Papers Wharton Faculty Research 1-2014 Equilibrium in Queues Uner Unknown Service Times an Service Value Laurens Debo Senthil K. Veeraraghavan University

More information

Lecture Introduction. 2 Examples of Measure Concentration. 3 The Johnson-Lindenstrauss Lemma. CS-621 Theory Gems November 28, 2012

Lecture Introduction. 2 Examples of Measure Concentration. 3 The Johnson-Lindenstrauss Lemma. CS-621 Theory Gems November 28, 2012 CS-6 Theory Gems November 8, 0 Lecture Lecturer: Alesaner Mąry Scribes: Alhussein Fawzi, Dorina Thanou Introuction Toay, we will briefly iscuss an important technique in probability theory measure concentration

More information

Unified kinetic model of dopant segregation during vapor-phase growth

Unified kinetic model of dopant segregation during vapor-phase growth PHYSICAL REVIEW B 72, 195419 2005 Unifie kinetic moel of opant segregation uring vapor-phase growth Craig B. Arnol 1, * an Michael J. Aziz 2 1 Department of Mechanical an Aerospace Engineering an Princeton

More information

V q.. REASONING The potential V created by a point charge q at a spot that is located at a

V q.. REASONING The potential V created by a point charge q at a spot that is located at a 8. REASONING The electric potential at a istance r from a point charge q is given by Equation 9.6 as kq / r. The total electric potential at location P ue to the four point charges is the algebraic sum

More information

A. Incorrect! The letter t does not appear in the expression of the given integral

A. Incorrect! The letter t does not appear in the expression of the given integral AP Physics C - Problem Drill 1: The Funamental Theorem of Calculus Question No. 1 of 1 Instruction: (1) Rea the problem statement an answer choices carefully () Work the problems on paper as neee (3) Question

More information

inflow outflow Part I. Regular tasks for MAE598/494 Task 1

inflow outflow Part I. Regular tasks for MAE598/494 Task 1 MAE 494/598, Fall 2016 Project #1 (Regular tasks = 20 points) Har copy of report is ue at the start of class on the ue ate. The rules on collaboration will be release separately. Please always follow the

More information

Laplace s Equation in Cylindrical Coordinates and Bessel s Equation (II)

Laplace s Equation in Cylindrical Coordinates and Bessel s Equation (II) Laplace s Equation in Cylinrical Coorinates an Bessel s Equation (II Qualitative properties of Bessel functions of first an secon kin In the last lecture we foun the expression for the general solution

More information

Non-Equilibrium Continuum Physics TA session #10 TA: Yohai Bar Sinai Dislocations

Non-Equilibrium Continuum Physics TA session #10 TA: Yohai Bar Sinai Dislocations Non-Equilibrium Continuum Physics TA session #0 TA: Yohai Bar Sinai 0.06.206 Dislocations References There are countless books about islocations. The ones that I recommen are Theory of islocations, Hirth

More information

The frenetic origin of negative differential response

The frenetic origin of negative differential response The frenetic origin of negative ifferential response Pieter Baerts, Urna Basu, Christian Maes, an Soghra Safaveri Instituut voor Theoretische Fysica, KU Leuven, Belgium The Green-Kubo formula for linear

More information

Vectors in two dimensions

Vectors in two dimensions Vectors in two imensions Until now, we have been working in one imension only The main reason for this is to become familiar with the main physical ieas like Newton s secon law, without the aitional complication

More information

On colour-blind distinguishing colour pallets in regular graphs

On colour-blind distinguishing colour pallets in regular graphs J Comb Optim (2014 28:348 357 DOI 10.1007/s10878-012-9556-x On colour-blin istinguishing colour pallets in regular graphs Jakub Przybyło Publishe online: 25 October 2012 The Author(s 2012. This article

More information

Physics 505 Electricity and Magnetism Fall 2003 Prof. G. Raithel. Problem Set 3. 2 (x x ) 2 + (y y ) 2 + (z + z ) 2

Physics 505 Electricity and Magnetism Fall 2003 Prof. G. Raithel. Problem Set 3. 2 (x x ) 2 + (y y ) 2 + (z + z ) 2 Physics 505 Electricity an Magnetism Fall 003 Prof. G. Raithel Problem Set 3 Problem.7 5 Points a): Green s function: Using cartesian coorinates x = (x, y, z), it is G(x, x ) = 1 (x x ) + (y y ) + (z z

More information

Leaving Randomness to Nature: d-dimensional Product Codes through the lens of Generalized-LDPC codes

Leaving Randomness to Nature: d-dimensional Product Codes through the lens of Generalized-LDPC codes Leaving Ranomness to Nature: -Dimensional Prouct Coes through the lens of Generalize-LDPC coes Tavor Baharav, Kannan Ramchanran Dept. of Electrical Engineering an Computer Sciences, U.C. Berkeley {tavorb,

More information

Markov Chains in Continuous Time

Markov Chains in Continuous Time Chapter 23 Markov Chains in Continuous Time Previously we looke at Markov chains, where the transitions betweenstatesoccurreatspecifietime- steps. That it, we mae time (a continuous variable) avance in

More information

Discrete Mathematics

Discrete Mathematics Discrete Mathematics 309 (009) 86 869 Contents lists available at ScienceDirect Discrete Mathematics journal homepage: wwwelseviercom/locate/isc Profile vectors in the lattice of subspaces Dániel Gerbner

More information

PDE Notes, Lecture #11

PDE Notes, Lecture #11 PDE Notes, Lecture # from Professor Jalal Shatah s Lectures Febuary 9th, 2009 Sobolev Spaces Recall that for u L loc we can efine the weak erivative Du by Du, φ := udφ φ C0 If v L loc such that Du, φ =

More information

Time-of-Arrival Estimation in Non-Line-Of-Sight Environments

Time-of-Arrival Estimation in Non-Line-Of-Sight Environments 2 Conference on Information Sciences an Systems, The Johns Hopkins University, March 2, 2 Time-of-Arrival Estimation in Non-Line-Of-Sight Environments Sinan Gezici, Hisashi Kobayashi an H. Vincent Poor

More information

Average value of position for the anharmonic oscillator: Classical versus quantum results

Average value of position for the anharmonic oscillator: Classical versus quantum results verage value of position for the anharmonic oscillator: Classical versus quantum results R. W. Robinett Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 682 Receive

More information

MATH 56A: STOCHASTIC PROCESSES CHAPTER 3

MATH 56A: STOCHASTIC PROCESSES CHAPTER 3 MATH 56A: STOCHASTIC PROCESSES CHAPTER 3 Plan for rest of semester (1) st week (8/31, 9/6, 9/7) Chap 0: Diff eq s an linear recursion (2) n week (9/11...) Chap 1: Finite Markov chains (3) r week (9/18...)

More information