Decay of Scale-Free Property in Birth-and-Death Network

Size: px
Start display at page:

Download "Decay of Scale-Free Property in Birth-and-Death Network"

Transcription

1 Decay of Scale-Free Proery n Brh-and-Deah Newor Xaojun Zhang *, Zheng He 2, Lez Rayan-Bacchus 3 School of Maheacal Scences, 2 School of Manageen and Econocs, Unversy of Elecronc Scence and Technology of Chna, Chengdu, P. R. Chna, Unversy of Wncheser, Wncheser, SO22 5HT, UK Absrac: Ths aer exlores he decay of he scale-free roery for a nd of brh-and-deah evolvng newors, n whch a each e se, a node s added wh robably and conneced wh old nodes by referenal aachen; or a node s randoly deleed fro he newor wh robably q=-. Eloyng he SPR-based Marov chan ehod, we calculae he degree dsrbuon of he roosed newor and dscuss s al feaures. Our resuls show ha he node deleon robably q has a srong ac on he ower-law al. As q 0, he ower-law roery s dsnc, whle wh he ncreasng of q o /2, he scale-free characer s gradually dsaeared. Ths ay ly for he real brh-and-deah newor, durng he eergen and growng sages, snce he robably of node deleon s very sall, he ower-law al of he degree dsrbuon s aaren. However, when he newor s n he aured sage, he deleon robably q wll be ncreenally ncreasng, ang he scale-free feaure decay gradually. Key words: Brh-and-deah newor, degree dsrbuon, scale-free roery, Marov chan, ower-law al

2 Decay of Scale-Free Feaure n Brh-and-Deah Newor. Inroducon In he real world, nodes n nearly all socal and anageral newors are nellgen agens le eole or enerrses whch have her lfe-cycles and decson caables. Faced wh he rad changes of exernal or nernal envronen, hese agens wll ae dfferen sraeges, ang frequen o ener or ex o a newor. Recenly hs nd of brh-and-deah newors has caugh uch aenon. Several odels have been develoed o descrbe varous evolvng newors and nvesgae her generc feaures [-7]. To be a sgnfcan roery, scale-free feaure [8,9] has been dscussed n hese brh-and-deah newors [0-6]. For exale, Moreno e al. [0] used a fber-bundle odel o sudy he nsably of a large scale-free newor; Sarshar [] suded an ad hoc newor, showng ha he referenal aachens for new nodes do no lead o a suffcenly heavy-aled degree dsrbuon; Moore e al. [4] furher oned ou ha he exonen of owerlaw dsrbuon ay dverge as he growh rae vanshes. Alhough all hese odels deal wh he deleon of nodes n he newor, hey a he sae e add nodes a each e se o ee he newor sze growng or unchanged. However, uch leraure has shown ha he sze of an nellgen newors le nnovaon dffuson newor and ndusral cluser newor wll exhbs S-shaed cuulave curve [7-22], ha s, he oal nuber of nodes wll frs exerence very slow and long ncreasng rocess and suddenly grow very fas n a shor erod of e, hen ener no a coaravely sable sage, followed by gradually declnng hase. Consderng he S-shae rules n any nellgen newors, n hs aer we consruc a brh-and-deah newor odel wh referenal aachen as well as rando node deleon o exane he scale-free roery n her evolvng rocesses. Dfferen fro revous newor odels [,4-6], our odel allows for flucuaon n sze, granng addonal reals. Usng he SPR ehod [23], we rovde he exac soluons of degree dsrbuon. Our heorecal and sulaon resuls sugges ha he scale-free feaure wll gradually dsaear wh he ncreasng robably of node deleon. Ths fndng exlans why he scale-free newor s no as coon as we execed before n he real socey even hough here exss 2

3 referenal aachen evolvng rules n hese newors. 2. Newor Model As shown n Fgure, for any nellgen newors, durng he forave and growng erods, he robably of node addon s uch hgher han ha of node deleng, whle n he aury and declne erod, he robably of node deleon s gradually equal o and fnally exceed ha of node addon. Thus he newor sze of any evolvng nellgen newors wll exhb S-shaed evenually. aury erod Newor Sze forave erod growh erod Fg. Sech of newor sze for evolvng newor: S-shaed curve In order o exlore he ower-law roery n he newors wh S-shaed evolvng ah, n hs secon, we frs roose a odel o descrbe hs nd of newor, whch s characerzed by referenal aachen as well as rando node deleon. Ths brh-and-deah newor odel s as follows: () The nal newor s an solaed node; () A each un of e, add a new node o he newor wh robably q and connec wh old nodes by referenal aachen, ha s, he robably ha he new node connec wh old node deends on he degree of node,.e. Or randoly delee a node fro he newor wh robably Noe: q0 q 2. () 3

4 (a) In order o connec he node whch degree s 0, n Eq.(), we use. (b) For he convenence of research, here we assue he low-bound of he newor sze n0, ha s, f he nuber of nodes n he newor s n 0 a e, hen a e, we only add a new node o he newor wh robably newor. and connec o he old node n he (c) If a e, a new node s added o he newor and he newor sze s less han, hen he new node s conneced wh all old nodes. (d) If a e, a node s deleed, hen all he edges ncden o he reoved node are also reoved fro he newor. Thus he degree of s neghbors decreases by one. 3. Exac Soluon of he Degree Dsrbuon Accordng o he sochasc rocess rules (SPR) ehod n [23],we use n, o descrbe he sae of node v, where n s he nuber of nodes n he newor ha conans v, and s he degree of node v. Le NK be he sae of node v a e, The sochasc rocess NK, 0 s an nhoogeneous Marov chan, and he sae sace,,,0. Le E n n n P be he one-se ranson robably arx of NK, 0 a e. n,, n2, 2 P (2) Usng SPR, he one-se ranson robably arx P roduces wo cases: A. Add a node and ln o he old nodes by referenal aachen. () The one-se ranson robably whch, urns o n, or n, n s gven by n P NK,,, n, NK n,, n,0 n n n n P NK,,, n, n NK n,, n,0 n n n n n () The one-se ranson robably whch, urns o n, s gven by n (3) (4) 4

5 P NK n NK n,, n,, n, PNK n, n n n * PNK n,, n,0 n (5) () The one-se ranson robably whch urns o s gven by n, n, P NK n NK n,, n,, n, PNK n, n * PNK n,, n,0 n n P NK,,, n, NK n,, n,0 n n n n B. Delee a node randoly (6) (7) (v) The one-se ranson robably whch urns o s gven by P NK,,, n, NK n, q, n n n n P NK n,0 NK n,0 q, n,0,,0 (9) n n (v) The one-se ranson robably whch, urns o n, s gven by n, n, n (8) Le n P NK,,, n, NK n, q, n n n n P 0 and P be he robably vecors of (0) NK 0 and NK resecvely, ha s, n, P P NK n () he nal robably vecor P 0 sasfes P NK 0,0 (2) We have P P P (3) Cobnng Eq.(3) and Eqs. (3-0),we ay derve he sae ransfer equaons for he roosed brh-and-deah newor(see Aendx). Le K be he average degree dsrbuon a e. Then we have 5

6 Thus he seady sae degree dsrbuon Noe n he case of,, (4) P K P NK P K [9,24,25] s gven by l P K l P NK, (5) 0 q 2, n l l P NK, (6) n So s deerned by he las ers of each equaon n he sae ransfer equaons (32-36). Snce l * P NK n, 2 2 q (7) n ang he ls of boh sdes n Eqs. (32-36), we ay oban he followng dsrbuon equaons 0 q 2 2 q 2q q q q q q 2q (8) r r rq r r q r r 2 2 Case : q=0 By Eq.(8), he degree dsrbuon can be obaned drecly as follows: (9) 6

7 Case 2: 0<q</2 In hs case, we consruc he robably generang funcon Fro Eq. (8), we have Gx x, G (20) 0 x q x G x x Gx x 2 2 (2) (a) If 2 q,fro Eq. (2), we have x c c 2 Gx G x 2 q x x 2 q x x (22) where c 2 2 q. The soluon of Eq. (22) under he condon G s as follows G x 2 x 2 q x c 2 q 2 c x c q d (23) c so we have 2 q 2 c 0 c 2 q 0 G0 d (24) 2 q Slar ehod can be used o oban he followng resuls. (b) If 2 q, he soluon of Eq. (2) under he condon G and G x (c) If 2 2 x 2 c c 2 q c x c c s as follows: d (25) q x 2 0 G0 c 0 c q, usng Eq.(2), we have 2 q d (26) 2 q c x Gx Gx x q x q x 2 2 (27) The soluon of Eq. (27) under he condon G s as follows: 7

8 q x q e G x e d x (28) x So G e e d (29) 0 q q 0 0 Cobnng (a)-(c) and Eq.(8), we can oban he degree dsrbuon as follows: q q 0 c 2 2 c q 2 2 q d, 0, 2 c q 0 c 2q, 0, 2 e e d q 2 q d, 0, 2 c q (30) 0 c 2 q a 0, where he sequence aj, j 0,,2, sasfes a0 q 2 a 2 j j jqa 2 j a 2 j a j j 0, j q qa a a q (3) 4. Decay of Scale-Free Proery Ths secon dscusses how he scale-free feaure changes wh he robably of node deleon. Fgures 2-4 exhb he changes of degree dsrbuon al wh he decreasng of node deleon robably q, where x-axs and y-axs reresens he degree of a node and s robably resecvely. As shown n Fg. 2, n he case of q 0, he degree dsrbuon al s a sragh lne, showng a srong scale-free roery. 8

9 Π () q=0, =2 q=0, =4 q=0, =6 q=0, = Fg.2 The degree dsrbuon for q=0 Fg.3 exhbs he change of degree dsrbuon wh q for dfferen. As shown n Fgs. 3.a- 3.d, wh he ncreasng q, he degree dsrbuon gradually bend downwards, showng he dsaear of scale-free. In addon, we also exlore he al shae of degree dsrbuon n he case q 0.5 he al of degree dsrbuons gradually exhb exonenal dsrbuon wh he ncreasng of q. Π () q=0.,=2 0-8 q=0.,=4 q=0.,=6 0-0 q=0.,= Fg. 3.a: The degree dsrbuons for q=0. Π () q=0.2,=2 0-8 q=0.2,=4 q=0.2,=6 0-0 q=0.2,= Fg. 3.b: The degree dsrbuons q=0.2 Π () q=0.3,=2 q=0.3,=4 q=0.3,=6 q=0.3,= Fg 3.c: The degree dsrbuons for q=0.3 Π () q=0.4,=2 0-8 q=0.4,=4 q=0.4,=6 q=0.4,= Fg. 3.d: The degree dsrbuons for q=0.4 9

10 Fg.3: The degree dsrbuons for q</2 Fg.4 llusraes he degree dsrbuon changes wh q n he case of =4. Fro Fg.4 we can fnd wh he ncreasng of q, he al of he degree dsrbuon gradually curve downward, showng he decay of he scale-free characerscs Π () =4,q=0 =4,q=0. =4,q=0.2 =4,q=0.3 =4,q= Fg.4 The degree dsrbuons for dfferen q (=4) Cobnng Fgs.-4, we ay fnd ha he scale-free feaure ay only aears n he forave and fas growng hases whle durng he aured or declnng sages, wll show an aaren decay of scale-free feaure wh he ncreasng robably of node deleon. 5. Concluson Ths aer dscusses he decay of scale-free feaure whch s oular n any nellgen newors. We frs roose a brh-and-deah newor odel wh referenal aachen and hen calculae s degree dsrbuons for varous node deleon robables. Sulaon resuls exhb ha coared o referenal aachen, he node deleon robably q also has srong ac on he scale-free roery. In he case q 0, he ower law roery s dsnc. However wh he ncreasng of q, he ower-law dsrbuon s gradually dsaeared. Ths sudy shows ha dese he referenal aachen for an evolvng newor ee unchanged, n real world, he scale-free feaure ay also gradually decay due o he deah of nodes n hs evolvng newor. Consderng he S-shaed newor sze for any evolvng newor, our fndngs ay ly ha durng he 0

11 eergen and growng sage, snce he robably of node deleon s very sall, he ower law of degree dsrbuon al s aaren. However, when he newor s n he aured sage, he deleon robably q wll be ncreenally ncreasng, ang he scale-free feaure decay gradually. Acnowledgens Ths research s fnancally suored by he Naonal Naural Scence Foundaon of Chna (No ) and he Chna Scholarsh Councl. Aendx: Sae ransfer equaons P qp qp qp 2 2,0,0 2,0 2, P 2,0 qp 3,0 qp 3, P qp qp,0 2,0 2, 2 P 2 2,0 qp 3,0 qp 3, PKV, P,0 * PKV, PKV n, np,0 n nqp qp n P n,0 n, n n * P KV n,,0 2P qp 2qP P P 2, 3, 3,2,0,0 (32) P qp 2qP P, 2, 2,2,0 PKV, 2 P 2, qp 3, 2qP 3,2 P,0 *, P KV 2PKV, P, * PKV, PKV n, np, n qp, 2qP,2 P n n n n,0 *, P KV n 2PKV n, n P n, n * PKV n,

12 (33) 2 P qp qp,,, P, 2 P, 0 P 2qP qp P, 2, 2,, 2 PKV, 2 3 * PKV, P qp qp P PKV, P, * PKV, 2, 3, 3,, 2 PKV n, n * PKV n, PKV n, * PKV n, np n qp qp P n, n, n, n, 2 n P n, (34) P qp qp P, 2, 2,, P, 0 P KV 2 P 2qP qp P, * PKV, 2, 3, 3,, PKV, P * PKV, P,, 0 PKV n, np n qp qp P,,,, n n n n * P KV n, PKV n, n2 n P n, P n, n * PKV n, 0 (35) 2

13 2 2, * PKV r, r P KV r r P, qp 2, r r r r r qp r 2, r P r, r r P KV r r P qp r qp P, * PKV r, r2, r r3, r r3, r r, r r PKV r, r * PKV r, r P r, r r P KV n np n rqp,, r qp, n r n r n r P n, r r PKV n, n P n, r n * PKV n,, * PKV n, (36) References:. Peer Hole, and Beo Jun K.Verex overload breadown n evolvng newors. Phys. Rev. E 65, 06609(2002) 2. Newan M.E.J., Par J. Why socal newor are dfferen fro oher yes of newor. Phys. Rev. E 68, 03622(2003) 3. J. L. Slaer, B. D. Hughes, K. A. Landan, Evolvng oral newors, Phys. Rev. E 73, 066 (2006). 4. Saldaña, J. Connuu forals for odelng growng newors wh deleon of nodes, Phys. Rev. E 75, (2007). 5. E. Ben-Na and P. L. Kravsy, Addon deleon newors, J. Phys. A: Mah. Theor. 40, 8607(2007) 6. Garca-Dongo J. L., Juher D., Saldaña.J. Degree correlaons n growng newors wh deleon of nodes. Physca D 237, 640(2008) 7. Ca, K., Dong, Z., Lu, K., and Wu, X. Phase ranson on he degree sequence of a rando grah rocess wh verex coyng and deleon. Sochasc Processes and her Alcaons 2, 885(20) 8. A. L. Barábas and R. Alber, Eergence of scalng n rando newors, Scence 286, 5439 (999). 9. A. L. Barabás, R. Alber and H. Jeong, Mean-feld heory for scale-free rando newors, Physca A 272, 73 (999). 0. Y. Moreno, J. B. Góez and A. F. Pacheco. Insably of scale-free newors under node-breang avalanches Eurohys. Le., 58 (4), 630 (2002). N. Sarshar and V. Roychowdhury. Scale-free and sable srucures n colex ad hoc newors, Phys. Rev. E 69, 0260 (2004). 2. Vo D. P. Servedo and Gudo Caldarell. Verex nrnsc fness: How o roduce arbrary scale-free newors. Phys. Rev. E 70, (2004) 3. Lazaros K. Gallos, Reuven Cohen, Panos Argyras,Arn Bunde, and Shloo Havln. Sably and Toology of Scale-Free Newors under Aac and Defense Sraeges. Phys. Rev. Le. 94, 8870 (2005) 3

14 4. C. Moore, G. Ghoshal, M.E.J. Newan, Exac soluons for odels of evolvng newors wh addon and deleon of nodes, Phys. Rev. E 74, 0362 (2006). 5. F. Chung and L. Lu, Coulng Onlne and Offlne Analyses for Rando Power Law Grahs, Inerne Maheacs, 409, C. Cooer, A. Freze, and J. Vera, Rando Deleon n a Scale-Free Rando Grah Process, Inerne Maheacs, 463, S. E. Kngsland. The refracory odel: The logsc curve and he hsory of oulaon ecology, Quarerly Revew of Bology 57, 29 (982). 8. P.Young, Technologcal growh curves: A Coeon of forecasng odels, Technologcal Forecasng and Socal Change 44, 375 (993). 9. F. M. Bass. A new roduc growh odel for consuer durables. Manageen Scence, 5,25(969) 20. V.Mahajan, E. Muller, and F.M. Bass. Dffuson of new roducs: Ercal generalzaons and anageral uses. Mareng Scence 4 (3): G79(995). 2. J. A. Noron, F. M. Bass. A dffuson heory odel of adoon and subsuon for successve generaons of hgh echnology roducs. Manageen Scence,33, 069 (987) 22. V. Mahajan, E. Muller, F. M. Bass. new roduc dffuson odels n areng: A revew and drecons for research. Journal of areng, 54():(990) 23. X. J. Zhang, Z. S. He, Z. He, R. B. Lez, SPR-based Marov chan ehod for degree dsrbuon of evolvng newors, Physca A 39, 3350 (202). 24. P.L. Kravsy, S. Redner, F. Leyvraz, Connecvy of Growng Rando Newors, Phys. Rev. Le. 85, 4629(2000). 25. S.N. Dorogovsev, J.F.F. Mendes, A.N. Sauhn, Srucure of Growng Newors wh Preferenal Lnng, Phys. Rev. Le. 85, 4633(2000). 4

EP2200 Queuing theory and teletraffic systems. 3rd lecture Markov chains Birth-death process - Poisson process. Viktoria Fodor KTH EES

EP2200 Queuing theory and teletraffic systems. 3rd lecture Markov chains Birth-death process - Poisson process. Viktoria Fodor KTH EES EP Queung heory and eleraffc sysems 3rd lecure Marov chans Brh-deah rocess - Posson rocess Vora Fodor KTH EES Oulne for oday Marov rocesses Connuous-me Marov-chans Grah and marx reresenaon Transen and

More information

The Degree Distribution of Random Birth-and-Death Network with Network Size Decline

The Degree Distribution of Random Birth-and-Death Network with Network Size Decline The Degree Dstrbuton of Random Brth-and-Death etwork wth etwork Sze Declne Xaojun Zhang *, Hulan Yang School of Mathematcal Scences, Unversty of Electronc Scence and Technology of Chna, Chengdu 673, P.R.

More information

グラフィカルモデルによる推論 確率伝搬法 (2) Kenji Fukumizu The Institute of Statistical Mathematics 計算推論科学概論 II (2010 年度, 後期 )

グラフィカルモデルによる推論 確率伝搬法 (2) Kenji Fukumizu The Institute of Statistical Mathematics 計算推論科学概論 II (2010 年度, 後期 ) グラフィカルモデルによる推論 確率伝搬法 Kenj Fukuzu he Insue of Sascal Maheacs 計算推論科学概論 II 年度 後期 Inference on Hdden Markov Model Inference on Hdden Markov Model Revew: HMM odel : hdden sae fne Inference Coue... for any Naïve

More information

Normal Random Variable and its discriminant functions

Normal Random Variable and its discriminant functions Noral Rando Varable and s dscrnan funcons Oulne Noral Rando Varable Properes Dscrnan funcons Why Noral Rando Varables? Analycally racable Works well when observaon coes for a corruped snle prooype 3 The

More information

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

Let s treat the problem of the response of a system to an applied external force. Again,

Let s treat the problem of the response of a system to an applied external force. Again, Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem

More information

FI 3103 Quantum Physics

FI 3103 Quantum Physics /9/4 FI 33 Quanum Physcs Aleander A. Iskandar Physcs of Magnesm and Phooncs Research Grou Insu Teknolog Bandung Basc Conces n Quanum Physcs Probably and Eecaon Value Hesenberg Uncerany Prncle Wave Funcon

More information

A New Generalized Gronwall-Bellman Type Inequality

A New Generalized Gronwall-Bellman Type Inequality 22 Inernaonal Conference on Image, Vson and Comung (ICIVC 22) IPCSIT vol. 5 (22) (22) IACSIT Press, Sngaore DOI:.7763/IPCSIT.22.V5.46 A New Generalzed Gronwall-Bellman Tye Ineualy Qnghua Feng School of

More information

Response of MDOF systems

Response of MDOF systems Response of MDOF syses Degree of freedo DOF: he nu nuber of ndependen coordnaes requred o deerne copleely he posons of all pars of a syse a any nsan of e. wo DOF syses hree DOF syses he noral ode analyss

More information

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation

Existence and Uniqueness Results for Random Impulsive Integro-Differential Equation Global Journal of Pure and Appled Mahemacs. ISSN 973-768 Volume 4, Number 6 (8), pp. 89-87 Research Inda Publcaons hp://www.rpublcaon.com Exsence and Unqueness Resuls for Random Impulsve Inegro-Dfferenal

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

Learning Objectives. Self Organization Map. Hamming Distance(1/5) Introduction. Hamming Distance(3/5) Hamming Distance(2/5) 15/04/2015

Learning Objectives. Self Organization Map. Hamming Distance(1/5) Introduction. Hamming Distance(3/5) Hamming Distance(2/5) 15/04/2015 /4/ Learnng Objecves Self Organzaon Map Learnng whou Exaples. Inroducon. MAXNET 3. Cluserng 4. Feaure Map. Self-organzng Feaure Map 6. Concluson 38 Inroducon. Learnng whou exaples. Daa are npu o he syse

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

Random Birth-and-Death Networks

Random Birth-and-Death Networks Rando Birth-and-Death Networs Xiaojun Zhang *, Zheng He, Lez Rayan-Bacchus 3 School of Matheatical Sciences, School of Manageent and Econoics, Uniersity of Electronic Science and Technology of China, Chengdu,

More information

Sklar: Sections (4.4.2 is not covered).

Sklar: Sections (4.4.2 is not covered). COSC 44: Dgal Councaons Insrucor: Dr. Ar Asf Deparen of Copuer Scence and Engneerng York Unversy Handou # 6: Bandpass Modulaon opcs:. Phasor Represenaon. Dgal Modulaon Schees: PSK FSK ASK APK ASK/FSK)

More information

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae

More information

Inverse Joint Moments of Multivariate. Random Variables

Inverse Joint Moments of Multivariate. Random Variables In J Conem Mah Scences Vol 7 0 no 46 45-5 Inverse Jon Momens of Mulvarae Rom Varables M A Hussan Dearmen of Mahemacal Sascs Insue of Sascal Sudes Research ISSR Caro Unversy Egy Curren address: Kng Saud

More information

Water Hammer in Pipes

Water Hammer in Pipes Waer Haer Hydraulcs and Hydraulc Machnes Waer Haer n Pes H Pressure wave A B If waer s flowng along a long e and s suddenly brough o res by he closng of a valve, or by any slar cause, here wll be a sudden

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

Advanced time-series analysis (University of Lund, Economic History Department)

Advanced time-series analysis (University of Lund, Economic History Department) Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng

More information

A New Generalisation of Sam-Solai s Multivariate symmetric Arcsine Distribution of Kind-1*

A New Generalisation of Sam-Solai s Multivariate symmetric Arcsine Distribution of Kind-1* IOSR Journal o Mahemacs IOSRJM ISSN: 78-578 Volume, Issue May-June 0, PP 4-48 www.osrournals.org A New Generalsaon o Sam-Sola s Mulvarae symmerc Arcsne Dsrbuon o Knd-* Dr. G.S. Davd Sam Jayaumar. Dr.A.Solarau.

More information

Lecture 2 M/G/1 queues. M/G/1-queue

Lecture 2 M/G/1 queues. M/G/1-queue Lecure M/G/ queues M/G/-queue Posson arrval process Arbrary servce me dsrbuon Sngle server To deermne he sae of he sysem a me, we mus now The number of cusomers n he sysems N() Tme ha he cusomer currenly

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

Supplementary Online Material

Supplementary Online Material Suppleenary Onlne Maeral In he followng secons, we presen our approach o calculang yapunov exponens. We derve our cenral resul Λ= τ n n pτλ ( A pbt λ( = τ, = A ( drecly fro he growh equaon x ( = AE x (

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β

SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β SARAJEVO JOURNAL OF MATHEMATICS Vol.3 (15) (2007), 137 143 SOME NOISELESS CODING THEOREMS OF INACCURACY MEASURE OF ORDER α AND TYPE β M. A. K. BAIG AND RAYEES AHMAD DAR Absrac. In hs paper, we propose

More information

First-order piecewise-linear dynamic circuits

First-order piecewise-linear dynamic circuits Frs-order pecewse-lnear dynamc crcus. Fndng he soluon We wll sudy rs-order dynamc crcus composed o a nonlnear resse one-por, ermnaed eher by a lnear capacor or a lnear nducor (see Fg.. Nonlnear resse one-por

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

PHYS 705: Classical Mechanics. Canonical Transformation

PHYS 705: Classical Mechanics. Canonical Transformation PHYS 705: Classcal Mechancs Canoncal Transformaon Canoncal Varables and Hamlonan Formalsm As we have seen, n he Hamlonan Formulaon of Mechancs,, are ndeenden varables n hase sace on eual foong The Hamlon

More information

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION

UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 2017 EXAMINATION INTERNATIONAL TRADE T. J. KEHOE UNIVERSITAT AUTÒNOMA DE BARCELONA MARCH 27 EXAMINATION Please answer wo of he hree quesons. You can consul class noes, workng papers, and arcles whle you are workng on he

More information

Chapter 3: Signed-rank charts

Chapter 3: Signed-rank charts Chaer : gned-ran chars.. The hewhar-ye conrol char... Inroducon As menoned n Chaer, samles of fxed sze are aen a regular nervals and he long sasc s hen loed. The queson s: Whch qualy arameer should be

More information

Political Economy of Institutions and Development: Problem Set 2 Due Date: Thursday, March 15, 2019.

Political Economy of Institutions and Development: Problem Set 2 Due Date: Thursday, March 15, 2019. Polcal Economy of Insuons and Developmen: 14.773 Problem Se 2 Due Dae: Thursday, March 15, 2019. Please answer Quesons 1, 2 and 3. Queson 1 Consder an nfne-horzon dynamc game beween wo groups, an ele and

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran

More information

A TWO-LEVEL LOAN PORTFOLIO OPTIMIZATION PROBLEM

A TWO-LEVEL LOAN PORTFOLIO OPTIMIZATION PROBLEM Proceedngs of he 2010 Wner Sulaon Conference B. Johansson, S. Jan, J. Monoya-Torres, J. Hugan, and E. Yücesan, eds. A TWO-LEVEL LOAN PORTFOLIO OPTIMIZATION PROBLEM JanQang Hu Jun Tong School of Manageen

More information

Pavel Azizurovich Rahman Ufa State Petroleum Technological University, Kosmonavtov St., 1, Ufa, Russian Federation

Pavel Azizurovich Rahman Ufa State Petroleum Technological University, Kosmonavtov St., 1, Ufa, Russian Federation VOL., NO. 5, MARCH 8 ISSN 89-668 ARN Journal of Engneerng and Aled Scences 6-8 Asan Research ublshng Nework ARN. All rghs reserved. www.arnjournals.com A CALCULATION METHOD FOR ESTIMATION OF THE MEAN TIME

More information

2/20/2013. EE 101 Midterm 2 Review

2/20/2013. EE 101 Midterm 2 Review //3 EE Mderm eew //3 Volage-mplfer Model The npu ressance s he equalen ressance see when lookng no he npu ermnals of he amplfer. o s he oupu ressance. I causes he oupu olage o decrease as he load ressance

More information

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs

More information

Comparison of Differences between Power Means 1

Comparison of Differences between Power Means 1 In. Journal of Mah. Analyss, Vol. 7, 203, no., 5-55 Comparson of Dfferences beween Power Means Chang-An Tan, Guanghua Sh and Fe Zuo College of Mahemacs and Informaon Scence Henan Normal Unversy, 453007,

More information

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall

8. Queueing systems lect08.ppt S Introduction to Teletraffic Theory - Fall 8. Queueg sysems lec8. S-38.45 - Iroduco o Teleraffc Theory - Fall 8. Queueg sysems Coes Refresher: Smle eleraffc model M/M/ server wag laces M/M/ servers wag laces 8. Queueg sysems Smle eleraffc model

More information

Outline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model

Outline. Probabilistic Model Learning. Probabilistic Model Learning. Probabilistic Model for Time-series Data: Hidden Markov Model Probablsc Model for Tme-seres Daa: Hdden Markov Model Hrosh Mamsuka Bonformacs Cener Kyoo Unversy Oulne Three Problems for probablsc models n machne learnng. Compung lkelhood 2. Learnng 3. Parsng (predcon

More information

Graduate Macroeconomics 2 Problem set 5. - Solutions

Graduate Macroeconomics 2 Problem set 5. - Solutions Graduae Macroeconomcs 2 Problem se. - Soluons Queson 1 To answer hs queson we need he frms frs order condons and he equaon ha deermnes he number of frms n equlbrum. The frms frs order condons are: F K

More information

II The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions

II The Z Transform. Topics to be covered. 1. Introduction. 2. The Z transform. 3. Z transforms of elementary functions II The Z Trnsfor Tocs o e covered. Inroducon. The Z rnsfor 3. Z rnsfors of eleenry funcons 4. Proeres nd Theory of rnsfor 5. The nverse rnsfor 6. Z rnsfor for solvng dfference equons II. Inroducon The

More information

Track Properities of Normal Chain

Track Properities of Normal Chain In. J. Conemp. Mah. Scences, Vol. 8, 213, no. 4, 163-171 HIKARI Ld, www.m-har.com rac Propes of Normal Chan L Chen School of Mahemacs and Sascs, Zhengzhou Normal Unversy Zhengzhou Cy, Hennan Provnce, 4544,

More information

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading

Online Supplement for Dynamic Multi-Technology. Production-Inventory Problem with Emissions Trading Onlne Supplemen for Dynamc Mul-Technology Producon-Invenory Problem wh Emssons Tradng by We Zhang Zhongsheng Hua Yu Xa and Baofeng Huo Proof of Lemma For any ( qr ) Θ s easy o verfy ha he lnear programmng

More information

A Cell Decomposition Approach to Online Evasive Path Planning and the Video Game Ms. Pac-Man

A Cell Decomposition Approach to Online Evasive Path Planning and the Video Game Ms. Pac-Man Cell Decomoson roach o Onlne Evasve Pah Plannng and he Vdeo ame Ms. Pac-Man reg Foderaro Vram Raju Slva Ferrar Laboraory for Inellgen Sysems and Conrols LISC Dearmen of Mechancal Engneerng and Maerals

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence

More information

e-journal Reliability: Theory& Applications No 2 (Vol.2) Vyacheslav Abramov

e-journal Reliability: Theory& Applications No 2 (Vol.2) Vyacheslav Abramov June 7 e-ournal Relably: Theory& Applcaons No (Vol. CONFIDENCE INTERVALS ASSOCIATED WITH PERFORMANCE ANALYSIS OF SYMMETRIC LARGE CLOSED CLIENT/SERVER COMPUTER NETWORKS Absrac Vyacheslav Abramov School

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION

A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION S19 A NEW TECHNIQUE FOR SOLVING THE 1-D BURGERS EQUATION by Xaojun YANG a,b, Yugu YANG a*, Carlo CATTANI c, and Mngzheng ZHU b a Sae Key Laboraory for Geomechancs and Deep Underground Engneerng, Chna Unversy

More information

A Novel Curiosity-Driven Perception-Action Cognitive Model

A Novel Curiosity-Driven Perception-Action Cognitive Model Inernaonal Conference on Arfcal Inellgence: Technologes and Applcaons (ICAITA 6) A Novel Curosy-Drven Percepon-Acon Cognve Model Jng Chen* Bng L and L L School of Inforaon Technology Engneerng Tanjn Unversy

More information

MODELS OF PRODUCTION RUNS FOR MULTIPLE PRODUCTS IN FLEXIBLE MANUFACTURING SYSTEM

MODELS OF PRODUCTION RUNS FOR MULTIPLE PRODUCTS IN FLEXIBLE MANUFACTURING SYSTEM Yugoslav Journal of Oeraons Research (0), Nuer, 307-34 DOI: 0.98/YJOR0307I MODELS OF PRODUCTION RUNS FOR MULTIPLE PRODUCTS IN FLEXIBLE MANUFACTURING SYSTEM Olver ILIĆ, Mlć RADOVIĆ Faculy of Organzaonal

More information

On Local Existence and Blow-Up of Solutions for Nonlinear Wave Equations of Higher-Order Kirchhoff Type with Strong Dissipation

On Local Existence and Blow-Up of Solutions for Nonlinear Wave Equations of Higher-Order Kirchhoff Type with Strong Dissipation Inernaonal Journal of Modern Nonlnear Theory and Alcaon 7 6-5 h://wwwscrorg/journal/jna ISSN Onlne: 67-987 ISSN Prn: 67-979 On Local Exsence and Blow-U of Soluons for Nonlnear Wave Euaons of Hgher-Order

More information

A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE

A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE S13 A DECOMPOSITION METHOD FOR SOLVING DIFFUSION EQUATIONS VIA LOCAL FRACTIONAL TIME DERIVATIVE by Hossen JAFARI a,b, Haleh TAJADODI c, and Sarah Jane JOHNSTON a a Deparen of Maheacal Scences, Unversy

More information

National Exams December 2015 NOTES: 04-BS-13, Biology. 3 hours duration

National Exams December 2015 NOTES: 04-BS-13, Biology. 3 hours duration Naonal Exams December 205 04-BS-3 Bology 3 hours duraon NOTES: f doub exss as o he nerpreaon of any queson he canddae s urged o subm wh he answer paper a clear saemen of any assumpons made 2 Ths s a CLOSED

More information

NPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management

NPTEL Project. Econometric Modelling. Module23: Granger Causality Test. Lecture35: Granger Causality Test. Vinod Gupta School of Management P age NPTEL Proec Economerc Modellng Vnod Gua School of Managemen Module23: Granger Causaly Tes Lecure35: Granger Causaly Tes Rudra P. Pradhan Vnod Gua School of Managemen Indan Insue of Technology Kharagur,

More information

A New Method for Computing EM Algorithm Parameters in Speaker Identification Using Gaussian Mixture Models

A New Method for Computing EM Algorithm Parameters in Speaker Identification Using Gaussian Mixture Models 0 IACSI Hong Kong Conferences IPCSI vol. 9 (0) (0) IACSI Press, Sngaore A New ehod for Comung E Algorhm Parameers n Seaker Idenfcaon Usng Gaussan xure odels ohsen Bazyar +, Ahmad Keshavarz, and Khaoon

More information

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5 TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres

More information

A Modified Genetic Algorithm Comparable to Quantum GA

A Modified Genetic Algorithm Comparable to Quantum GA A Modfed Genec Algorh Coparable o Quanu GA Tahereh Kahookar Toos Ferdows Unversy of Mashhad _k_oos@wal.u.ac.r Habb Rajab Mashhad Ferdows Unversy of Mashhad h_rajab@ferdows.u.ac.r Absrac: Recenly, researchers

More information

Transcription: Messenger RNA, mrna, is produced and transported to Ribosomes

Transcription: Messenger RNA, mrna, is produced and transported to Ribosomes Quanave Cenral Dogma I Reference hp//book.bonumbers.org Inaon ranscrpon RNA polymerase and ranscrpon Facor (F) s bnds o promoer regon of DNA ranscrpon Meenger RNA, mrna, s produced and ranspored o Rbosomes

More information

Comb Filters. Comb Filters

Comb Filters. Comb Filters The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of

More information

Scattering at an Interface: Oblique Incidence

Scattering at an Interface: Oblique Incidence Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may

More information

MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES. Institute for Mathematical Research, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia

MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES. Institute for Mathematical Research, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia Malaysan Journal of Mahemacal Scences 9(2): 277-300 (2015) MALAYSIAN JOURNAL OF MATHEMATICAL SCIENCES Journal homeage: h://ensemumedumy/journal A Mehod for Deermnng -Adc Orders of Facorals 1* Rafka Zulkal,

More information

Performance Analysis for a Network having Standby Redundant Unit with Waiting in Repair

Performance Analysis for a Network having Standby Redundant Unit with Waiting in Repair TECHNI Inernaonal Journal of Compung Scence Communcaon Technologes VOL.5 NO. July 22 (ISSN 974-3375 erformance nalyss for a Nework havng Sby edundan Un wh ang n epar Jendra Sngh 2 abns orwal 2 Deparmen

More information

Sampling Procedure of the Sum of two Binary Markov Process Realizations

Sampling Procedure of the Sum of two Binary Markov Process Realizations Samplng Procedure of he Sum of wo Bnary Markov Process Realzaons YURY GORITSKIY Dep. of Mahemacal Modelng of Moscow Power Insue (Techncal Unversy), Moscow, RUSSIA, E-mal: gorsky@yandex.ru VLADIMIR KAZAKOV

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

Survival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System

Survival Analysis and Reliability. A Note on the Mean Residual Life Function of a Parallel System Communcaons n Sascs Theory and Mehods, 34: 475 484, 2005 Copyrgh Taylor & Francs, Inc. ISSN: 0361-0926 prn/1532-415x onlne DOI: 10.1081/STA-200047430 Survval Analyss and Relably A Noe on he Mean Resdual

More information

Relative controllability of nonlinear systems with delays in control

Relative controllability of nonlinear systems with delays in control Relave conrollably o nonlnear sysems wh delays n conrol Jerzy Klamka Insue o Conrol Engneerng, Slesan Techncal Unversy, 44- Glwce, Poland. phone/ax : 48 32 37227, {jklamka}@a.polsl.glwce.pl Keywor: Conrollably.

More information

Department of Economics University of Warsaw Warsaw, Poland Długa Str. 44/50.

Department of Economics University of Warsaw Warsaw, Poland Długa Str. 44/50. MIGRATIOS OF HETEROGEEOUS POPULATIO OF DRIVERS ACROSS CLASSES OF A BOUS-MALUS SYSTEM BY WOJCIECH OTTO Dearmen of Economcs Unversy of Warsaw 00-24 Warsaw Poland Długa Sr. 44/50 woo@wne.uw.edu.l . ITRODUCTIO

More information

Advanced Macroeconomics II: Exchange economy

Advanced Macroeconomics II: Exchange economy Advanced Macroeconomcs II: Exchange economy Krzyszof Makarsk 1 Smple deermnsc dynamc model. 1.1 Inroducon Inroducon Smple deermnsc dynamc model. Defnons of equlbrum: Arrow-Debreu Sequenal Recursve Equvalence

More information

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours NATONAL UNVERSTY OF SNGAPORE PC5 ADVANCED STATSTCAL MECHANCS (Semeser : AY 1-13) Tme Allowed: Hours NSTRUCTONS TO CANDDATES 1. Ths examnaon paper conans 5 quesons and comprses 4 prned pages.. Answer all

More information

Robustness Experiments with Two Variance Components

Robustness Experiments with Two Variance Components Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference

More information

2 Aggregate demand in partial equilibrium static framework

2 Aggregate demand in partial equilibrium static framework Unversy of Mnnesoa 8107 Macroeconomc Theory, Sprng 2009, Mn 1 Fabrzo Perr Lecure 1. Aggregaon 1 Inroducon Probably so far n he macro sequence you have deal drecly wh represenave consumers and represenave

More information

ON THE WEAK LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS

ON THE WEAK LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS ON THE WEA LIMITS OF SMOOTH MAPS FOR THE DIRICHLET ENERGY BETWEEN MANIFOLDS FENGBO HANG Absrac. We denfy all he weak sequenal lms of smooh maps n W (M N). In parcular, hs mples a necessary su cen opologcal

More information

M. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria

M. Y. Adamu Mathematical Sciences Programme, AbubakarTafawaBalewa University, Bauchi, Nigeria IOSR Journal of Mahemacs (IOSR-JM e-issn: 78-578, p-issn: 9-765X. Volume 0, Issue 4 Ver. IV (Jul-Aug. 04, PP 40-44 Mulple SolonSoluons for a (+-dmensonalhroa-sasuma shallow waer wave equaon UsngPanlevé-Bӓclund

More information

Speech recognition in noise by using word graph combinations

Speech recognition in noise by using word graph combinations Proceedngs of 0 h Inernaonal Congress on Acouscs, ICA 00 3-7 Augus 00, Sydney, Ausrala Seech recognon n by usng word grah cobnaons Shunsuke Kuraaa, Masaharu Kao and Tesuo Kosaka Graduae School of Scence

More information

Fourier Analysis Models and Their Application to River Flows Prediction

Fourier Analysis Models and Their Application to River Flows Prediction The s Inernaonal Appled Geologcal ongress, Deparen of Geology, Islac Azad Unversy - Mashad Branch, Iran, 6-8 Aprl Fourer Analyss Models and Ther Applcaon o Rver Flows Predcon ohel Ghareagha Zare - Mohaad

More information

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal

More information

PHYS 1443 Section 001 Lecture #4

PHYS 1443 Section 001 Lecture #4 PHYS 1443 Secon 001 Lecure #4 Monda, June 5, 006 Moon n Two Dmensons Moon under consan acceleraon Projecle Moon Mamum ranges and heghs Reerence Frames and relae moon Newon s Laws o Moon Force Newon s Law

More information

Volatility Interpolation

Volatility Interpolation Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local

More information

Bernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field

Bernoulli process with 282 ky periodicity is detected in the R-N reversals of the earth s magnetic field Submed o: Suden Essay Awards n Magnecs Bernoull process wh 8 ky perodcy s deeced n he R-N reversals of he earh s magnec feld Jozsef Gara Deparmen of Earh Scences Florda Inernaonal Unversy Unversy Park,

More information

Chapters 2 Kinematics. Position, Distance, Displacement

Chapters 2 Kinematics. Position, Distance, Displacement Chapers Knemacs Poson, Dsance, Dsplacemen Mechancs: Knemacs and Dynamcs. Knemacs deals wh moon, bu s no concerned wh he cause o moon. Dynamcs deals wh he relaonshp beween orce and moon. The word dsplacemen

More information

EE241 - Spring 2003 Advanced Digital Integrated Circuits

EE241 - Spring 2003 Advanced Digital Integrated Circuits EE4 EE4 - rn 00 Advanced Dal Ineraed rcus Lecure 9 arry-lookahead Adders B. Nkolc, J. Rabaey arry-lookahead Adders Adder rees» Radx of a ree» Mnmum deh rees» arse rees Loc manulaons» onvenonal vs. Ln»

More information

A Deza Frankl type theorem for set partitions

A Deza Frankl type theorem for set partitions A Deza Frankl ype heorem for se parons Cheng Yeaw Ku Deparmen of Mahemacs Naonal Unversy of Sngapore Sngapore 117543 makcy@nus.edu.sg Kok Bn Wong Insue of Mahemacal Scences Unversy of Malaya 50603 Kuala

More information

New M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study)

New M-Estimator Objective Function. in Simultaneous Equations Model. (A Comparative Study) Inernaonal Mahemacal Forum, Vol. 8, 3, no., 7 - HIKARI Ld, www.m-hkar.com hp://dx.do.org/.988/mf.3.3488 New M-Esmaor Objecve Funcon n Smulaneous Equaons Model (A Comparave Sudy) Ahmed H. Youssef Professor

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ

More information

THERMODYNAMICS 1. The First Law and Other Basic Concepts (part 2)

THERMODYNAMICS 1. The First Law and Other Basic Concepts (part 2) Company LOGO THERMODYNAMICS The Frs Law and Oher Basc Conceps (par ) Deparmen of Chemcal Engneerng, Semarang Sae Unversy Dhon Harano S.T., M.T., M.Sc. Have you ever cooked? Equlbrum Equlbrum (con.) Equlbrum

More information

( ) [ ] MAP Decision Rule

( ) [ ] MAP Decision Rule Announcemens Bayes Decson Theory wh Normal Dsrbuons HW0 due oday HW o be assgned soon Proec descrpon posed Bomercs CSE 90 Lecure 4 CSE90, Sprng 04 CSE90, Sprng 04 Key Probables 4 ω class label X feaure

More information

Epistemic Game Theory: Online Appendix

Epistemic Game Theory: Online Appendix Epsemc Game Theory: Onlne Appendx Edde Dekel Lucano Pomao Marcano Snscalch July 18, 2014 Prelmnares Fx a fne ype srucure T I, S, T, β I and a probably µ S T. Le T µ I, S, T µ, βµ I be a ype srucure ha

More information

THEORETICAL STUDY ON PIPE OF TAPERED THICKNESS WITH AN INTERNAL FLOW TO ESTIMATE NATURAL FREQUENCY

THEORETICAL STUDY ON PIPE OF TAPERED THICKNESS WITH AN INTERNAL FLOW TO ESTIMATE NATURAL FREQUENCY Inernaonal Journal of Mechancal Engneerng and Technology (IJMET) Volue 7, Issue, March-Arl 6,., Arcle ID: IJMET_7 Avalable onlne a h://www.aee.co/ijmet/ssues.as?jtye=ijmet&vtye=7&itye= Journal Iac Facor

More information

Transient Response in Electric Circuits

Transient Response in Electric Circuits Transen esponse n Elecrc rcus The elemen equaon for he branch of he fgure when he source s gven by a generc funcon of me, s v () r d r ds = r Mrs d d r (')d' () V The crcu s descrbed by he opology equaons

More information

Imperfect Information

Imperfect Information Imerfec Informaon Comlee Informaon - all layers know: Se of layers Se of sraeges for each layer Oucomes as a funcon of he sraeges Payoffs for each oucome (.e. uly funcon for each layer Incomlee Informaon

More information

Motion in Two Dimensions

Motion in Two Dimensions Phys 1 Chaper 4 Moon n Two Dmensons adzyubenko@csub.edu hp://www.csub.edu/~adzyubenko 005, 014 A. Dzyubenko 004 Brooks/Cole 1 Dsplacemen as a Vecor The poson of an objec s descrbed by s poson ecor, r The

More information

Cubic Bezier Homotopy Function for Solving Exponential Equations

Cubic Bezier Homotopy Function for Solving Exponential Equations Penerb Journal of Advanced Research n Compung and Applcaons ISSN (onlne: 46-97 Vol. 4, No.. Pages -8, 6 omoopy Funcon for Solvng Eponenal Equaons S. S. Raml *,,. Mohamad Nor,a, N. S. Saharzan,b and M.

More information

Size and Weight of Shortest Path Trees with Exponential Link Weights

Size and Weight of Shortest Path Trees with Exponential Link Weights Sze and Wegh of Shores Pah Trees wh Exponenal Ln Weghs Reco van der Hofsad Gerard Hooghesra Pe Van Meghe Sepeber 7, 2003 Absrac We derve he dsrbuon of he nuber of lns and he average wegh for he shores

More information