A Novel Particle Swarm Optimization Approach for Grid Job Scheduling
|
|
- Sandra Garrett
- 5 years ago
- Views:
Transcription
1 A Novel Parcle warm Opmzaon Approach for Grd ob chedulng Hesam Izaan, Behrouz Tor Ladan, Kamran Zamanfar, Ajh Abraham³ Islamc Azad Unversy, Ramsar branch, Ramsar, Iran Deparmen of Compuer Engneerng, Unversy of Isfahan, Isfahan, Iran {Ladan, ³Norwegan Cener of Excellence, Cener of Excellence for Quanfable Qualy of ervce, Norwegan Unversy of cence and Technology, Trondhem, Norway Absrac Ths paper represens a Parcle warm Opmzaon (PO) algorhm, for grd job schedulng. PO s a populaon-based search algorhm based on he smulaon of he socal behavor of brd flocng and fsh schoolng. Parcles fly n problem search space o fnd opmal or near-opmal soluons. In hs paper we used a PO approach for grd job schedulng. The scheduler ams a mnmzng maespan and flowme smulaneously. Expermenal sudes show ha he proposed novel approach s more effcen han he PO approach repored n he leraure.. Inroducon Compuaonal Grd [] s composed of a se of vrual organzaons (VOs). Any VO has s varous resources and servces and on he bass of s polces provdes access o hem and hence grd resources and servces are much dfferen and heerogeneous and are dsrbued n dfferen geographcally areas. A any momen, dfferen resources and servces are added o or removed from grd and as a resul, grd envronmen s hghly dynamc. ervce s an mporan concep n many dsrbued compuaons and communcaons. ervce s used o depc he deals of a resource whn he grd []. Grd servces and resources are regsered whn one or more Grd Informaon ervers (GIs). The end users subm her requess o he Grd Resource Broer (GRB). Dfferen requess demand dfferen requremens and avalable resources have dfferen capables. GRB dscovers proper resources for execung hese requess by queryng n GI and hen schedules hem on he dscovered resources. Unl now a lo of wors has been done n order o schedule jobs n a compuaonal grd. Ye accordng o he new naure of he subjec furher research s requred. Cao [] used agens o schedule grd. In hs mehod dfferen resources and servces are regarded as dfferen agens and grd resource dscovery and adversemen are performed by hese agens. Buyya [] used economc based conceps ncludng commody mare, posed prce modelng, conrac ne models, barganng modelng ec for grd schedulng. As menoned n [8] schedulng s NP-complee. Mea-heursc mehods have been used o solve well nown NP-complee problems. In [0] Yarhanan and Dongarra used smulaed annealng for grd job schedulng. GAs for grd job schedulng s addressed n several wors [], [], [] and [6]. Abraham e al. [5] used fuzzy PO for grd job schedulng.
2 Dfferen crera can be used for evaluang he effcacy of schedulng algorhms and he mos mporan of whch are maespan and flowme. Maespan s he me when grd fnshes he laes job and flowme s he sum of fnalzaon mes of all he jobs. An opmal schedule wll be he one ha opmzes he flowme and maespan [5]. The mehod proposed n [5] ams a smulaneously mnmzng mae span and flowme. In hs paper, a verson of dscree parcle swarm opmzaon (DPO) s proposed for grd job schedulng and he goal of scheduler s o mnmze he wo parameers menoned above smulaneously. Ths mehod s compared o he mehod presened n [5] n order o evaluae s effcacy. The expermenal resuls show he presened mehod s more effcen and hs mehod can be effecvely used for grd schedulng. The remander of hs paper s organzed n he followng manner. In econ, we formulae he problem, n econ he PO paradgm s brefly dscussed and econ descrbes he proposed mehod for grd job schedulng, and econ 5 repors he expermenal resuls. Fnally econ 6 concludes hs wor.. Problem Formulaon GRB s responsble for schedulng by recevng he jobs from he users and queryng her requred servces n GI and hen allocang hese jobs o he dscovered servces. uppose n a specfc me nerval, n jobs {,,..., n} are submed o GRB. Also assume he jobs are ndependen of each oher (wh no ner-as daa dependences) and preempon s no allowed (hey canno change he resource hey has been assgned o). A he me of recevng he jobs by GRB, m nodes { N, N,..., N m} and servces, {,,..., } are whn he grd. Each node has one or more servces and each job requres one servce. If a job requres more han one ndependen servce, hen we can consder as a se of sub-jobs each requrng a servce. In hs paper, schedulng s done a node level and s assumed ha each node uses Frs-Come, Frs-erved (FCF) mehod for performng he receved jobs. We assume ha each node n he grd can esmae how much me s needed o perform each servce ncludes. In addon each node ncludes a me as prevous worload whch s he me requred for performng he jobs gven o n he prevous seps. We used he ETC model o esmae he requred me for execung a job n a node. In ETC model we ae he usual assumpon ha we now he compung capacy of each resource, an esmaon or predcon of he compuaonal needs of each job, and he load of pror wor of each resource. Table shows a smple example of a se of receved jobs by GRB n a specfc me nerval and saus of avalable nodes and servces n he grd. In hs Table GRB receved 5 jobs n a me nerval and he saus of avalable nodes and resources n he grd s as follows: obs :{,,,, 5} ervces {,,, } Nodes : :{ N, N, N } obs Requremens: 5 Table A smple example of a se of jobs and grd saus Nodes saus: Prevous worload N N N 6 98
3 There are servces and nodes; N ncludes 5 me uns of prevous worload whch means ha requres 5 me uns o complee he ass already submed o. Ths node requres 8 me uns o perform, 5 me uns o perform and 75 me uns o perform. nce hs node does no nclude, s no able o perform. Therefore he requred me o perform by hs node s consdered as. In hs Table job requres servce, requres, requres, requres, and 5 requres. chedulng algorhm should be desgned n a way ha each job s allocaed o a node whch ncludes he servces requred by ha job. Assume ha C, ( {,,..., m}, j {,,..., n}) s he compleon me for performng jh job n h j node and W ( {,,..., m}) s he prevous worload of N, hen Eq. () shows he me requred for N o complee he jobs ncluded n. Accordng o he aforemenoned defnon, maespan and flowme can be esmaed usng Equaons. () and () respecvely. + W C () maespan = max{ C {,,..., m} flowme = m C = + W }, () () As menoned n he prevous secon he goal of he scheduler s o mnmze maespan and flowme smulaneously.. Parcle warm Opmzaon Parcle warm Opmzaon (PO) s a populaon based search algorhm nspred by brd flocng and fsh schoolng orgnally desgned and nroduced by Kennedy and Eberhar [9] n 995. In conras o evoluonary compuaon paradgms such as genec algorhm, a swarm s smlar o a populaon, whle a parcle s smlar o an ndvdual. The parcles fly hrough a muldmensonal search space n whch he poson of each parcle s adjused accordng o s own experence and he experence of s neghbors. PO sysem combnes local search mehods (hrough self experence) wh global search mehods (hrough neghborng experence), aempng o balance exploraon and exploaon [5]. In 997 he bnary verson of hs algorhm was presened by Kennedy and Eberhar [6] for dscree opmzaon problems. In hs mehod, each parcle s composed of D elemens, whch ndcae a poenal soluon. In order o evaluae he appropraeness of soluons a fness funcon s always used. Each parcle s consdered as a poson n a D-dmensonal space and each elemen of a parcle poson can ae he bnary value of 0 or n whch means ncluded and 0 means no ncluded. Each elemen can change from 0 o and vse versa. Also each parcle has a D-dmensonal velocy vecor he elemens of whch are n range [ V max, Vmax ]. Veloces are defned n erms of probables ha a b wll be n one sae or he oher. A he begnnng of he algorhm, a number of parcles and her velocy vecors are generaed randomly. Then n some eraon he algorhm ams a obanng he opmal or near-opmal soluons based on s predefned fness funcon. The velocy vecor s updaed n each me sep usng wo bes posons, pbes and nbes, and hen he poson of he parcles s updaed usng velocy vecors.
4 Pbes and nbes are D-dmensonal, he elemens of whch are composed of 0 and he same as parcles poson and operae as he memory of he algorhm. The personal bes poson, pbes, s he bes poson he parcle has vsed and nbes s he bes poson he parcle and s neghbors have vsed snce he frs me sep. When all of he populaon sze of he swarm s consdered as he neghbor of a parcle, nbes s called global bes (sar neghborhood opology) and f he smaller neghborhoods are defned for each parcle (e.g. rng neghborhood opology), hen nbes s called local. Equaons and 5 are used o updae he velocy and poson vecors of he parcles respecvely. V ( + ) ( = wv. ( + cr ( pbes ( X ( ) + cr ( nbes ( X ( ) () ( + ) X where, ( = ( + ) f sg( V ( ) > r 0 oherwse j (5) sg ( ) = (6) ( + exp( V ( ) ( ( + ) V + ) In () X ( s jh elemen of h parcle n h sep of he algorhm and V ( s he jh elemen of he velocy vecor of he h parcle n h sep. c and c are posve acceleraon consans whch conrol he nfluence of pbes and nbes on he search process. Also r and r are random values n range [ 0, ] sampled from a unform dsrbuon. w whch s called nera wegh was nroduced by h and Eberhar [7] as a mechansm o conrol he exploraon and exploaon ables of he swarm. Usually w sars wh large values (e.g. 0.9) whch decreases over me o smaller values so ha n he las eraon ends o a small value (e.g. 0.). r j n Eq. (5) s a random number n range [ 0, ] and Eq. (6) shows sgmod funcon.. Proposed PO Algorhm for Grd ob chedulng In hs secon we propose a verson of dscree parcle swarm opmzaon for grd job schedulng. Parcle needs o be desgned o presen a sequence of jobs n avalable grd nodes. Also he velocy has o be redefned. Deals are gven wha follows.. Poson of parcles One of he ey ssues n desgnng a successful PO algorhm s he represenaon sep whch ams a fndng an approprae mappng beween problem soluon and PO parcle. In our mehod soluons are encoded n a m n marx, called poson marx, n whch m s he number of avalable nodes a he me of schedulng and n s he number of jobs. The poson marx of each parcle has he wo followng properes: ) All he elemens of he marces have eher he value of 0 or. In oher words, f X s he poson marx of h parcles, hen:
5 X (, {0,} (,, {,,... m}, j {,,..., n} (7) ) In each column of hese marces only one elemen s and ohers are 0. In poson marx each column represens a job allocaon and each row represens allocaed jobs n a node. In each column s deermned ha a job should be performed by whch node. Assume ha X shows he poson marx of h parcle. If X (, = hen he jh job wll be performed by h node. Fgure shows a poson marx n he example menoned n Table. Ths poson marx shows ha and wll be performed n N ; and 5 wll be performed n N and wll be performed n N. 5 N N N Fg.. Poson marx.. Parcles velocy, pbes and nbes Velocy of each parcle s consdered as a In oher words f V s he velocy marx of h parcle, hen: V ( max max m n marx whose elemens are n range V max, V ]. [ max, [ V, V ] (,, {,,... m}, j {,,..., n} (8) Also Pbes and nbes are m n marces and her elemens are 0 or as poson marces. pbes represens he bes poson ha h parcle has vsed snce he frs me sep and nbes represens he bes poson ha h parcle and s neghbors have vsed from he begnnng of he algorhm. In hs paper we used sar neghborhood opology for nbes. In each me sep pbes and nbes should be updaed; frs fness value of each parcle (for example han he fness value of pbes ( pbes assocaed wh X ) s esmaed and n case s value s greaer X ), pbes s replaced wh X. For updang nbes n each neghborhood, pbess are used so ha f n neghborhood fness value of pbes s greaer han nbes, hen nbes s replaced wh pbes... Parcle updang Equaon (9) s used for updang he velocy marx and hen (0) for poson marx of each parcle. V ( + ) (, = w. V (, + cr ( pbes (, X (, ) + cr ( nbes (, X (, ) (9)
6 X ( + ) (, = ( + ) = ( + ) f ( V j V j (, ) max{ (, )}), {,,... m} 0 oherwse (0) In (9) V (, s he elemen n h row and jh column of he h velocy marx n h me sep of he algorhm and X (, denoes he elemen n h row and jh column of he h poson marx n h me sep. Eq. (0) means ha n each column of poson marx value s assgned o he elemen whose correspondng elemen n velocy marx has he max value n s correspondng column. If n a column of velocy marx here s more han one elemen wh max value, hen one of hese elemens s seleced randomly and assgned o s correspondng elemen n he poson marx... Fness evaluaon In hs paper, maespan and flowme are used o evaluae he performance of scheduler smulaneously. Because maespan and flowme values are n ncomparable ranges and he flowme has a hgher magnude order over he maespan, he value of mean flowme, flowme / m, s used o evaluae flowme where m s he number of avalable nodes. The Fness value of each soluon can be esmaed usng (). fness = ( λ. maespan + ( λ). mean _ λ [0, ] flowme), () λ n () s used o regulae he effecveness of parameers used n hs equaon. The greaer λ, more aenon s pad by he scheduler n mnmzng maespan and vse versa. The smaller maespan and flowme n (), he graer fness value, and hence a beer soluon s regarded..5. Proposed PO algorhm The pseudo code of he proposed PO algorhm s saed as follows: Creae and nalze a m n -dmensonal swarm wh P parcles repea for each parcle =,,P do f f ( X ) > f ( pbes ) hen // f( ) represen he fness funcon pbes = X ; end f f pbes ) > f ( nbes ) hen ( nbes = pbes ; end end for each parcle =,,P do updae he velocy marx usng Eq. (9) updae he poson marx usng Eq. (0) end unl soppng condon s rue; Fg.. Pseudo code of he proposed mehod
7 5. Implemenaon and Expermenal Resuls In hs econ, he proposed algorhm s compared o he mehods presened n [5]. Boh approaches were mplemened usng VC++ and run on a Penum IV. GHz PC. In he prelmnary expermen he followng ranges of parameer values were esed: λ = [0, ], c and c = [, ], w = [. 0.0], P = [0, 0], V max = [5, 50], and maxmum eraons = [ 0 m, 00 m] n whch m s he number of nodes. Based on expermenal resuls he proposed PO algorhm performs bes under he followng sengs: λ =0.5, c = c =. 5, w = , P = 8, V max = 0, and maxmum eraon = 00 m. 5.. Comparson of resuls wh he mehod proposed n [5] Abraham e al. [5] used Fuzzy dscree parcle swarm opmzaon [] for grd job schedulng. In her mehod, he poson of each parcle s presened as m n marces n whch m s he number of avalable nodes and n s he number of receved jobs. Each marx represens a poenal soluon whose elemens are n [0, ] nervals n whch he oal sum of he elemens of each column s equal o. The value of s, he elemen n h row and jh column of he poson marx, means he degree of j membershp ha he grd node N j would process he job n he feasble schedule soluon [5]. In he frs me sep of he algorhm one poson marx s generaed usng LFR-FR heursc [6] ha mnmzes he maespan and he flowme smulaneously and ohers are generaed randomly and hen n each me sep hese marces are updaed usng velocy marx whose elemens are real numbers n range [ V max, Vmax ]. Afer updang each poson marx, s normalzed n a way ha each elemen s n range [0, ] and he sum of values of each column equals and hen usng hese obaned marces schedules are generaed. In hs paper, for comparson and evaluaon of he scheduler, maespan and mean flowme are used smulaneously. A random number n he range [ 0, 500], sampled from unform dsrbuon, s assgned o he prevous worload of each node n our ess. One or more servces (a mos servces) of {,,..., } are randomly seleced for each node. The me for execung servces s randomly seleced n range [, 00] f he node has hese servces; oherwse s seleced as. For each job one servce among servces s seleced randomly as he requred servce of ha job. To mprove he effcency of our proposed mehod and he mehod presened n [5] we generae only feasble soluons n nal sep as well as each eraon/generaon. In oher words each job s allocaed o he node whch has he servce requred by ha job. If n grd here s a job ha s correspondng servce does no exs n any of he nodes, hen s allocaed node s consdered as - and hs means ha hs job s no performable n he grd a ha specfc me. In hs case, he requred me for performng he job, s no aen no accoun n fness esmaon so ha he effcency of he mehod does no fade. Nne grd saus of dfferen szes wh number of jobs, n=50, 00, 00 number of nodes, m=0, 0, 0 and number of servces, =0, 80, 60 are generaed. The sascal resuls of over 50 ndependen runs are llusraed n Table.
8 Case udy Grd saus: Number of (obs, Nodes, ervces) Table Comparson of sascal resuls beween our proposed mehod and FDPO proposed n [5] LFR-FR Number of FPO [5] Proposed DPO heursc eraons: (00 m ) maespan flowme maespan flowme maespan flowme I (50,0,0) II (00,0,80) III (00,0,60) IV (50,0,0) V (00,0,80) VI (00,0,60) VII (50,0,0) VIII (00,0,80) IX (00,0,60) As evden, he proposed mehod performs beer han he Fuzzy PO proposed n [5]. Fgures and show a comparson of CPU me requred o acheve resuls and he fness values of each mehod for dfferen case sudes as shown n Table. Tme (seconds) FPO [5] DPO (proposed) I II III IV V VI VII VIII IX Case sudy Fg.. Comparson of convergence me beween our proposed mehod and FPO [5]. Fness value (Eq.) FPO [5] DPO (proposed) I II III IV V VI VII VIII IX Case udy Fg.. Comparson of fness values beween our proposed mehod and FPO [5]. 6. Conclusons Ths paper presened a verson of Dscree Parcle warm Opmzaon (DPO) algorhm for grd job schedulng. cheduler ams a generang feasble soluons whle mnmzng maespan and flowme smulaneously. The performance of he proposed mehod was compared wh he fuzzy PO hrough carryng ou exhausve smulaon ess and dfferen sengs. Expermenal resuls show ha he proposed mehod ouperforms fuzzy PO. In he fuure, we plan o use he proposed mehod for grd job schedulng wh more qualy of servce consrans.
9 References [] I. Foser, C. Kesselman,. Tuece, The Anaomy of he Grd: Enablng calable Vrual Organzaons, Inernaonal ournal of Hgh Performance Compung Applcaons 5 (00) 00-. []. Cao, D.. Kerbyson, G.R. Nudd, Performance Evaluaon of an Agen-Based Resource Managemen Infrasrucure for Grd Compung, n: Proceedngs of s IEEE/ACM Inernaonal ymposum on Cluser Compung and he Grd (00) -8. []. Cao, agen-based resource managemen for grd compung, Ph.D. Thess, Deparmen of Compuer cence Unversy of Warwc, London, 00. [] R. Buyya, Economc-based Dsrbued Resource Managemen and chedulng for Grd Compung, Ph.D. Thess, chool of Compuer cence and ofware Engneerng Monash Unversy, Melbourne, 00. [5] A. alman, I. Ahmad,. Al-Madan, Parcle swarm opmzaon for as assgnmen problem, Mcroprocessors and Mcrosysems 6 (00) 6 7. [6]. Kennedy, R.C. Eberhar, A dscree bnary verson of he parcle swarm algorhm, IEEE nernaonal conference on ysems, Man, and Cybernecs (997) [7] Y. h, R.C. Eberhar, A modfed parcle swarm opmzer, n: proceedngs of he IEEE Congress on Evoluonary Compuaon (998) [8] E.G. Coffman r. (Ed.), Compuer and ob-hop chedulng Theory, Wley, New Yor, NY, 976. [9]. Kennedy, R.C. Eberhar, Parcle swarm opmzaon, n: Proceedngs of he IEEE Inernaonal Conference on Neural Newors (995) [0] A. Yarhan,. Dongarra, Expermens wh schedulng usng smulaed annealng n a grd envronmen, n: rd Inernaonal Worshop on Grd Compung (00). [] W. Pang, K. Wang, C. Zhou, L. Dong,Fuzzy Dscree Parcle warm Opmzaon for olvng Travelng alesman Problem, In: Proceedngs of he Fourh Inernaonal Conference on Compuer and Informaon Technology, IEEE C Press (00) [] V. D Marno, M. Mllo, ub opmal schedulng n a grd usng genec algorhms, Parallel Compung 0 (00) [] D. Lu, Y. Cao, CGA: Chaoc Genec Algorhm for Fuzzy ob chedulng n Grd Envronmen, prnger-verlag Berln Hedelberg (007). [] Y. Gao, H. Rong,.Z. Huangc, Adapve grd job schedulng wh genec algorhms, Fuure Generaon Compuer ysems (005) 5 6. [5] A. Abraham, H. Lu, W. Zhang, T.G. Chang, chedulng obs on Compuaonal Grds Usng Fuzzy Parcle warm Algorhm, prnger-verlag Berln Hedelberg (006) [6] A. Abraham, R. Buyya, B. Nah, Naure s heurscs for schedulng jobs on compuaonal grds, In: 8h IEEE Inernaonal Conference on Advanced Compung and Communcaons (ADCOM 000), Inda, IBN , Taa McGraw-Hll Publshng Co. Ld, New Delh, pp. 5-5, 000.
Network Security Risk Assessment Based on Node Correlation
Journal of Physcs: Conference Seres PAPER OPE ACCESS ewor Secury Rs Assessmen Based on ode Correlaon To ce hs arcle: Zengguang Wang e al 2018 J. Phys.: Conf. Ser. 1069 012073 Vew he arcle onlne for updaes
More informationMind the class weight bias: weighted maximum mean discrepancy for unsupervised domain adaptation. Hongliang Yan 2017/06/21
nd he class wegh bas: weghed maxmum mean dscrepancy for unsupervsed doman adapaon Honglang Yan 207/06/2 Doman Adapaon Problem: Tranng and es ses are relaed bu under dfferen dsrbuons. Tranng (Source) DA
More informationDeriving Reservoir Operating Rules via Fuzzy Regression and ANFIS
Dervng Reservor Operang Rules va Fuzzy Regresson and ANFIS S. J. Mousav K. Ponnambalam and F. Karray Deparmen of Cvl Engneerng Deparmen of Sysems Desgn Engneerng Unversy of Scence and Technology Unversy
More informationLab 10 OLS Regressions II
Lab 10 OLS Regressons II Ths lab wll cover how o perform a smple OLS regresson usng dfferen funconal forms. LAB 10 QUICK VIEW Non-lnear relaonshps beween varables nclude: o Log-Ln: o Ln-Log: o Log-Log:
More informationNormal Random Variable and its discriminant functions
Normal Random Varable and s dscrmnan funcons Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped sngle prooype
More informationAccuracy of the intelligent dynamic models of relational fuzzy cognitive maps
Compuer Applcaons n Elecrcal Engneerng Accuracy of he nellgen dynamc models of relaonal fuzzy cognve maps Aleksander Jasrebow, Grzegorz Słoń Kelce Unversy of Technology 25-314 Kelce, Al. Tysącleca P. P.
More informationANFIS Based Time Series Prediction Method of Bank Cash Flow Optimized by Adaptive Population Activity PSO Algorithm
Informaon 25, 6, 3-33; do:.339/nfo633 Arcle OPEN ACCESS nformaon ISSN 278-2489 www.mdp.com/journal/nformaon ANFIS Based Tme Seres Predcon Mehod of Bank Cash Flow Opmzed by Adapve Populaon Acvy PSO Algorhm
More informationKeywords: School bus problem, heuristic, harmony search
Journal of Emergng Trends n Compung and Informaon Scences 2009-2013 CIS Journal. All rghs reserved. hp://www.csjournal.org Model and Algorhm for Solvng School Bus Problem 1 Taehyeong Km, 2 Bum-Jn Par 1
More informationEXPLOITING GEOMETRICAL NODE LOCATION FOR IMPROVING SPATIAL REUSE IN SINR-BASED STDMA MULTI-HOP LINK SCHEDULING ALGORITHM
Inernaonal Journal of Technology (2015) 1: 53 62 ISSN 2086 9614 IJTech 2015 EXLOITING GEOMETRICAL NODE LOCATION FOR IMROVING SATIAL REUSE IN SINR-BASED STDMA MULTI-HO LINK SCHEDULING ALGORITHM Nachwan
More informationOnline Technical Appendix: Estimation Details. Following Netzer, Lattin and Srinivasan (2005), the model parameters to be estimated
Onlne Techncal Appendx: Esmaon Deals Followng Nezer, an and Srnvasan 005, he model parameers o be esmaed can be dvded no hree pars: he fxed effecs governng he evaluaon, ncdence, and laen erence componens
More informationImproving Forecasting Accuracy in the Case of Intermittent Demand Forecasting
(IJACSA) Inernaonal Journal of Advanced Compuer Scence and Applcaons, Vol. 5, No. 5, 04 Improvng Forecasng Accuracy n he Case of Inermen Demand Forecasng Dasuke Takeyasu The Open Unversy of Japan, Chba
More informationPFAS: A Resource-Performance-Fluctuation-Aware Workflow Scheduling Algorithm for Grid Computing
PFAS: A Resource-Performance-Flucuaon-Aware Workflow Schedulng Algorhm for Grd Compung Fangpeng Dong and Selm G. Akl School of Compung, Queen's Unversy Kngson, ON Canada, K7L N6 {dong, akl}@cs.queensu.ca
More informationEconomics of taxation
Economcs of axaon Lecure 3: Opmal axaon heores Salane (2003) Opmal axes The opmal ax sysem mnmzes he excess burden wh a gven amoun whch he governmen wans o rase hrough axaon. Opmal axes maxmze socal welfare,
More informationAmerican basket and spread options. with a simple binomial tree
Amercan baske and spread opons wh a smple bnomal ree Svelana orovkova Vre Unverse Amserdam Jon work wh Ferry Permana acheler congress, Torono, June 22-26, 2010 1 Movaon Commody, currency baskes conss of
More informationFairing of Polygon Meshes Via Bayesian Discriminant Analysis
Farng of Polygon Meshes Va Bayesan Dscrmnan Analyss Chun-Yen Chen Insue of Informaon Scence, Academa Snca. Deparmen of Compuer Scence and Informaon Engneerng, Naonal Tawan Unversy. 5, Tawan, Tape, Nankang
More informationChain-linking and seasonal adjustment of the quarterly national accounts
Sascs Denmark Naonal Accouns 6 July 00 Chan-lnkng and seasonal adjusmen of he uarerly naonal accouns The mehod of chan-lnkng he uarerly naonal accouns was changed wh he revsed complaon of daa hrd uarer
More informationThe Virtual Machine Resource Allocation based on Service Features in Cloud Computing Environment
Send Orders for Reprns o reprns@benhamscence.ae The Open Cybernecs & Sysemcs Journal, 2015, 9, 639-647 639 Open Access The Vrual Machne Resource Allocaon based on Servce Feaures n Cloud Compung Envronmen
More informationNumerical Evaluation of European Option on a Non Dividend Paying Stock
Inernaonal Journal of Compuaonal cence and Mahemacs. IN 0974-389 olume Number 3 (00) pp. 6--66 Inernaonal Research Publcaon House hp://www.rphouse.com Numercal Evaluaon of European Opon on a Non Dvdend
More informationIFX-Cbonds Russian Corporate Bond Index Methodology
Approved a he meeng of he Commee represenng ZAO Inerfax and OOO Cbonds.ru on ovember 1 2005 wh amendmens complan wh Agreemen # 545 as of ecember 17 2008. IFX-Cbonds Russan Corporae Bond Index Mehodology
More informationUNN: A Neural Network for uncertain data classification
UNN: A Neural Nework for unceran daa classfcaon Jaq Ge, and Yun Xa, Deparmen of Compuer and Informaon Scence, Indana Unversy Purdue Unversy, Indanapols, USA {jaqge, yxa }@cs.upu.edu Absrac. Ths paper proposes
More informationAn Inclusion-Exclusion Algorithm for Network Reliability with Minimal Cutsets
Amercan Journal of ompuaonal Mahemacs, 202, 2, 6-20 hp://dxdoorg/0426/acm2022404 Publshed Onlne December 202 (hp://wwwscrporg/ournal/acm) An Incluson-Excluson Algorhm for ework Relably wh Mnmal uses Yan-Ru
More informationA valuation model of credit-rating linked coupon bond based on a structural model
Compuaonal Fnance and s Applcaons II 247 A valuaon model of cred-rang lnked coupon bond based on a srucural model K. Yahag & K. Myazak The Unversy of Elecro-Communcaons, Japan Absrac A cred-lnked coupon
More informationA Multi-Periodic Optimization Modeling Approach for the Establishment of a Bike Sharing Network: a Case Study of the City of Athens
A Mul-Perodc Opmzaon Modelng Approach for he Esablshmen of a Be Sharng Newor: a Case Sudy of he Cy of Ahens G.K.D Sahards, A. Fragogos and E. Zygour Absrac Ths sudy nroduces a novel mahemacal formulaon
More informationCorrelation of default
efaul Correlaon Correlaon of defaul If Oblgor A s cred qualy deeroraes, how well does he cred qualy of Oblgor B correlae o Oblgor A? Some emprcal observaons are efaul correlaons are general low hough hey
More informationThe Financial System. Instructor: Prof. Menzie Chinn UW Madison
Economcs 435 The Fnancal Sysem (2/13/13) Insrucor: Prof. Menze Chnn UW Madson Sprng 2013 Fuure Value and Presen Value If he presen value s $100 and he neres rae s 5%, hen he fuure value one year from now
More informationPrediction of Oil Demand Based on Time Series Decomposition Method Nan MA * and Yong LIU
2017 2nd Inernaonal Conference on Sofware, Mulmeda and Communcaon Engneerng (SMCE 2017) ISBN: 978-1-60595-458-5 Predcon of Ol Demand Based on Tme Seres Decomposon Mehod Nan MA * and Yong LIU College of
More informationA Hybrid Method to Improve Forecasting Accuracy Utilizing Genetic Algorithm An Application to the Data of Operating equipment and supplies
A Hyrd Mehod o Improve Forecasng Accuracy Ulzng Genec Algorhm An Applcaon o he Daa of Operang equpmen and supples Asam Shara Tax Corporaon Arkne, Shzuoka Cy, Japan, e-mal: a-shara@arkne.nfo Dasuke Takeyasu
More informationA Backbone Formation Algorithm in Wireless Sensor Network Based on Pursuit Algorithm
Ysong Jang, Weren Sh A Backbone Formaon Algorhm n Wreless Sensor Nework Based on Pursu Algorhm YISONG JIANG, WEIREN SHI College of Auomaon Chongqng Unversy No 74 Shazhengje, Shapngba, Chongqng Chna jys398@6com,
More informationDynamic Relationship and Volatility Spillover Between the Stock Market and the Foreign Exchange market in Pakistan: Evidence from VAR-EGARCH Modelling
Dynamc Relaonshp and Volaly pllover Beween he ock Marke and he Foregn xchange marke n Paksan: vdence from VAR-GARCH Modellng Dr. Abdul Qayyum Dr. Muhammad Arshad Khan Inroducon A volale sock and exchange
More informationSection 6 Short Sales, Yield Curves, Duration, Immunization, Etc.
More Tuoral a www.lledumbdocor.com age 1 of 9 Secon 6 Shor Sales, Yeld Curves, Duraon, Immunzaon, Ec. Shor Sales: Suppose you beleve ha Company X s sock s overprced. You would ceranly no buy any of Company
More informationQuarterly Accounting Earnings Forecasting: A Grey Group Model Approach
Quarerly Accounng Earnngs Forecasng: A Grey Group Model Approach Zheng-Ln Chen Deparmen of Accounng Zhongnan Unversy of Economcs and Law # Souh Nanhu Road, Wuhan Cy, 430073 Hube People's Republc of Chna
More informationMULTI-SPECTRAL IMAGE ANALYSIS BASED ON DYNAMICAL EVOLUTIONARY PROJECTION PURSUIT
MULTI-SPECTRAL IMAGE AALYSIS BASED O DYAMICAL EVOLUTIOARY PROJECTIO PURSUIT YU Changhu a, MEG Lngku a, YI Yaohua b, a School of Remoe Sensng Informaon Engneerng, Wuhan Unversy, 39#,Luoyu Road, Wuhan,Chna,430079,
More informationA Novel Approach to Model Generation for Heterogeneous Data Classification
A Novel Approach o Model Generaon for Heerogeneous Daa Classfcaon Rong Jn*, Huan Lu *Dep. of Compuer Scence and Engneerng, Mchgan Sae Unversy, Eas Lansng, MI 48824 rongn@cse.msu.edu Deparmen of Compuer
More informationSkyCube Computation over Wireless Sensor Networks Based on Extended Skylines
Proceedngs of he 2010 IEEE Inernaonal Conference on Informaon and Auomaon June 20-23, Harbn, Chna SkyCube Compuaon over Wreless Sensor Neworks Based on Exended Skylnes Zhqong Wang 1, Zhyue Wang 2, Junchang
More informationSOCIETY OF ACTUARIES FINANCIAL MATHEMATICS. EXAM FM SAMPLE SOLUTIONS Interest Theory
SOCIETY OF ACTUARIES EXAM FM FINANCIAL MATHEMATICS EXAM FM SAMPLE SOLUTIONS Ineres Theory Ths page ndcaes changes made o Sudy Noe FM-09-05. January 4, 04: Quesons and soluons 58 60 were added. June, 04
More informationPricing and Valuation of Forward and Futures
Prcng and Valuaon of orward and uures. Cash-and-carry arbrage he prce of he forward conrac s relaed o he spo prce of he underlyng asse, he rsk-free rae, he dae of expraon, and any expeced cash dsrbuons
More informationMichał Kolupa, Zbigniew Śleszyński SOME REMARKS ON COINCIDENCE OF AN ECONOMETRIC MODEL
M I S C E L L A N E A Mchał Kolupa, bgnew Śleszyńsk SOME EMAKS ON COINCIDENCE OF AN ECONOMETIC MODEL Absrac In hs paper concep of concdence of varable and mehods for checkng concdence of model and varables
More informationFITTING EXPONENTIAL MODELS TO DATA Supplement to Unit 9C MATH Q(t) = Q 0 (1 + r) t. Q(t) = Q 0 a t,
FITTING EXPONENTIAL MODELS TO DATA Supplemen o Un 9C MATH 01 In he handou we wll learn how o fnd an exponenal model for daa ha s gven and use o make predcons. We wll also revew how o calculae he SSE and
More informationAnalysing Big Data to Build Knowledge Based System for Early Detection of Ovarian Cancer
Indan Journal of Scence and Technology, Vol 8(4), DOI: 0.7485/js/205/v84/65745, July 205 ISSN (Prn) : 0974-6846 ISSN (Onlne) : 0974-5645 Analysng Bg Daa o Buld Knowledge Based Sysem for Early Deecon of
More informationDifferences in the Price-Earning-Return Relationship between Internet and Traditional Firms
Dfferences n he Prce-Earnng-Reurn Relaonshp beween Inerne and Tradonal Frms Jaehan Koh Ph.D. Program College of Busness Admnsraon Unversy of Texas-Pan Amercan jhkoh@upa.edu Bn Wang Asssan Professor Compuer
More informationPricing Model of Credit Default Swap Based on Jump-Diffusion Process and Volatility with Markov Regime Shift
Assocaon for Informaon Sysems AIS Elecronc brary (AISe) WICEB 13 Proceedngs Wuhan Inernaonal Conference on e-busness Summer 5-5-13 Prcng Model of Cred Defaul Swap Based on Jump-Dffuson Process and Volaly
More informationThe Proposed Mathematical Models for Decision- Making and Forecasting on Euro-Yen in Foreign Exchange Market
Iranan Economc Revew, Vol.6, No.30, Fall 20 The Proposed Mahemacal Models for Decson- Makng and Forecasng on Euro-Yen n Foregn Exchange Marke Abdorrahman Haer Masoud Rabban Al Habbna Receved: 20/07/24
More informationNoise and Expected Return in Chinese A-share Stock Market. By Chong QIAN Chien-Ting LIN
Nose and Expeced Reurn n Chnese A-share Sock Marke By Chong QIAN Chen-Tng LIN 1 } Capal Asse Prcng Model (CAPM) by Sharpe (1964), Lnner (1965) and Mossn (1966) E ( R, ) R f, + [ E( Rm, ) R f, = β ] + ε
More informationExplaining Product Release Planning Results Using Concept Analysis
Explanng Produc Release Plannng Resuls Usng Concep Analyss Gengshen Du, Thomas Zmmermann, Guenher Ruhe Deparmen of Compuer Scence, Unversy of Calgary 2500 Unversy Drve NW, Calgary, Albera T2N 1N4, Canada
More informationEstimation of Optimal Tax Level on Pesticides Use and its
64 Bulgaran Journal of Agrculural Scence, 8 (No 5 0, 64-650 Agrculural Academy Esmaon of Opmal Ta Level on Pescdes Use and s Impac on Agrculure N. Ivanova,. Soyanova and P. Mshev Unversy of Naonal and
More informationTax Dispute Resolution and Taxpayer Screening
DISCUSSION PAPER March 2016 No. 73 Tax Dspue Resoluon and Taxpayer Screenng Hdek SATO* Faculy of Economcs, Kyushu Sangyo Unversy ----- *E-Mal: hsao@p.kyusan-u.ac.jp Tax Dspue Resoluon and Taxpayer Screenng
More informationShort-Term Load Forecasting using PSO Based Local Linear Wavelet Neural Network
Shor-Term Load Forecasng usng PSO Based Local Lnear Wavele Neural Newor Prasana Kumar Pany DRIEMS, Cuac, Orssa, Inda E-mal : Prasanpany@gmal.com Absrac - Shor-erm forecasng (STLF plays an mporan role n
More informationThe UAE UNiversity, The American University of Kurdistan
MPRA Munch Personal RePEc Archve A MS-Excel Module o Transform an Inegraed Varable no Cumulave Paral Sums for Negave and Posve Componens wh and whou Deermnsc Trend Pars. Abdulnasser Haem-J and Alan Musafa
More informationOptimal Fuzzy Min-Max Neural Network (FMMNN) for Medical Data Classification Using Modified Group Search Optimizer Algorithm
1 Opmal Fuzzy Mn-Max Neural Nework (FMMNN) for Medcal Daa Classfcaon Usng Modfed Group Search Opmzer Algorhm D. Mahammad Raf 1 * Chear Ramachandra Bharah 2 1 Vvekananda Insue of Engneerng & Technology,
More informationHardware-Assisted High-Efficiency Ray Casting of Unstructured Time-Varying Flows Using Temporal Coherence
Hardware-Asssed Hgh-Effcency Ray Casng of Unsrucured Tme-Varyng Flows Usng Temporal Coherence Qanl Ma, Lang Zeng, Huaxun Xu, Wenke Wang, Skun L Absrac Advances n compuaonal power are enablng hgh-precson
More informationUsing Fuzzy-Delphi Technique to Determine the Concession Period in BOT Projects
Usng Fuzzy-Delph Technque o Deermne he Concesson Perod n BOT Projecs Khanzad Mosafa Iran Unversy of Scence and Technology School of cvl engneerng Tehran, Iran. P.O. Box: 6765-63 khanzad@us.ac.r Nasrzadeh
More informationAlbania. A: Identification. B: CPI Coverage. Title of the CPI: Consumer Price Index. Organisation responsible: Institute of Statistics
Albana A: Idenfcaon Tle of he CPI: Consumer Prce Index Organsaon responsble: Insue of Sascs Perodcy: Monhly Prce reference perod: December year 1 = 100 Index reference perod: December 2007 = 100 Weghs
More informationComparing Sharpe and Tint Surplus Optimization to the Capital Budgeting Approach with Multiple Investments in the Froot and Stein Framework.
Comparng Sharpe and Tn Surplus Opmzaon o he Capal Budgeng pproach wh Mulple Invesmens n he Froo and Sen Framework Harald Bogner Frs Draf: Sepember 9 h 015 Ths Draf: Ocober 1 h 015 bsrac Below s shown ha
More informationA Hybrid Method for Forecasting with an Introduction of a Day of the Week Index to the Daily Shipping Data of Sanitary Materials
Journal of Communcaon and Compuer (05) 0-07 do: 0.765/548-7709/05.0.00 D DAVID PUBLISHING A Hyrd Mehod for Forecasng wh an Inroducon of a Day of he Week Inde o he Daly Shppng Daa of Sanary Maerals Dasuke
More informationBaoding, Hebei, China. *Corresponding author
2016 3 rd Inernaonal Conference on Economcs and Managemen (ICEM 2016) ISBN: 978-1-60595-368-7 Research on he Applcably of Fama-French Three-Facor Model of Elecrc Power Indusry n Chnese Sock Marke Yeld
More informationVI. Clickstream Big Data and Delivery before Order Making Mode for Online Retailers
VI. Clcksream Bg Daa and Delvery before Order Makng Mode for Onlne Realers Yemng (Yale) Gong EMLYON Busness School Haoxuan Xu *, Jnlong Zhang School of Managemen, Huazhong Unversy of Scence &Technology
More informationImproving Earnings per Share: An Illusory Motive in Stock Repurchases
Inernaonal Journal of Busness and Economcs, 2009, Vol. 8, No. 3, 243-247 Improvng Earnngs per Share: An Illusory Move n Sock Repurchases Jong-Shn We Deparmen of Inernaonal Busness Admnsraon, Wenzao Ursulne
More informationOpen Access Impact of Wind Power Generation on System Operation and Costs
Send Orders for Reprns o reprns@benhamscence.ae 580 he Open Elecrcal & Elecronc Engneerng Journal, 2014, 8, 580-588 Open Access Impac of nd Power eneraon on Sysem Operaon and oss ang Fe 1,2,*, Pan enxa
More informationTime-domain Analysis of Linear and Nonlinear Circuits
Tme-doman Analyss of Lnear and Nonlnear Crcus Dr. José Erneso Rayas-Sáncez February 4, 8 Tme-doman Analyss of Lnear and Nonlnear Crcus Dr. José Erneso Rayas-Sáncez Inroducon Tme doman analyss can be realzed
More informationOptimal Combination of Trading Rules Using Neural Networks
Vol. 2, No. Inernaonal Busness Research Opmal Combnaon of Tradng Rules Usng Neural Neworks Subraa Kumar Mra Professor, Insue of Managemen Technology 35 Km Mlesone, Kaol Road Nagpur 44 502, Inda Tel: 9-72-280-5000
More informationBank of Japan. Research and Statistics Department. March, Outline of the Corporate Goods Price Index (CGPI, 2010 base)
Bank of Japan Research and Sascs Deparmen Oulne of he Corporae Goods Prce Index (CGPI, 2010 base) March, 2015 1. Purpose and Applcaon The Corporae Goods Prce Index (CGPI) measures he prce developmens of
More informationInterest Rate Derivatives: More Advanced Models. Chapter 24. The Two-Factor Hull-White Model (Equation 24.1, page 571) Analytic Results
Ineres Rae Dervaves: More Advanced s Chaper 4 4. The Two-Facor Hull-Whe (Equaon 4., page 57) [ θ() ] σ 4. dx = u ax d dz du = bud σdz where x = f () r and he correlaon beween dz and dz s ρ The shor rae
More informationA Novel Application of the Copula Function to Correlation Analysis of Hushen300 Stock Index Futures and HS300 Stock Index
A Novel Applcaon of he Copula Funcon o Correlaon Analyss of Hushen3 Sock Index Fuures and HS3 Sock Index Fang WU *, 2, Yu WEI. School of Economcs and Managemen, Souhwes Jaoong Unversy, Chengdu 63, Chna
More informationOptimal procurement strategy for uncertain demand situation and imperfect quality by genetic algorithm
Inernaonal Conference on Mechancal, Indusral and Maerals Engneerng 2015 (ICMIME2015) 11-13 December, 2015, RUET, Rajshah, Bangladesh. Paper ID: IE-44 Opmal procuremen sraegy for unceran demand suaon and
More informationRecall from last time. The Plan for Today. INTEREST RATES JUNE 22 nd, J u n e 2 2, Different Types of Credit Instruments
Reall from las me INTEREST RATES JUNE 22 nd, 2009 Lauren Heller Eon 423, Fnanal Markes Smple Loan rnpal and an neres paymen s pad a maury Fxed-aymen Loan Equal monhly paymens for a fxed number of years
More informationTruth Discovery in Data Streams: A Single-Pass Probabilistic Approach
Truh Dscovery n Daa Sreams: A Sngle-Pass Probablsc Approach Zhou Zhao, James Cheng and Wlfred Ng Deparmen of Compuer Scence and Engneerng, Hong Kong Unversy of Scence and Technology Deparmen of Compuer
More informationManagement of financial and consumer satisfaction risks in supply chain design
Managemen of fnancal and consumer sasfacon rss n suly chan desgn G. Gullén(), F. D. Mele(), M. Bagaewcz(), A. Esuña(), and L. Puganer()(#) ()Unversdad Polècnca de Caalunya, Chemcal Engneerng Dearmen, ETSEIB,
More informationAutomatic Clustering Using an Improved Particle Swarm Optimization
Journal of Indusral and Inellgen Informaon Vol. 1, o. 1, March 013 Auomac Cluserng Usng an Imroved Parcle Swarm Omzaon R. J. Kuo and Feran E. Zulva aonal Tawan Unversy of Scence and Technology, Tae, Tawan
More informationFinancial Innovation and Asset Price Volatility. Online Technical Appendix
Fnancal Innovaon and Asse Prce Volaly Onlne Techncal Aendx Felx Kubler and Karl Schmedders In hs echncal aendx we derve all numbered equaons dslayed n he aer Equaons For he wo models n he aer, he frs se
More informationVolatility Modeling for Forecasting Stock Index with Fixed Parameter Distributional Assumption
Journal of Appled Fnance & Banng, vol. 3, no. 1, 13, 19-1 ISSN: 179-5 (prn verson), 179-599 (onlne) Scenpress Ld, 13 Volaly Modelng for Forecasng Soc Index wh Fxed Parameer Dsrbuonal Assumpon Md. Mosafzur
More informationTerms and conditions for the MXN Peso / US Dollar Futures Contract (Physically Delivered)
The Englsh verson of he Terms and Condons for Fuures Conracs s publshed for nformaon purposes only and does no consue legal advce. However, n case of any Inerpreaon conroversy, he Spansh verson shall preval.
More informationOptimum Reserve Capacity Assessment and Energy and Spinning Reserve Allocation Based on Deterministic and Stochastic Security Approach
Ausralan Journal of Basc and Appled Scences, 4(9): 4400-4412, 2010 ISS 1991-8178 Opmum Reserve Capacy Assessmen and Enery and Spnnn Reserve Allocaon Based on Deermnsc and Sochasc Secury Approach Farzad
More informationFugit (options) The terminology of fugit refers to the risk neutral expected time to exercise an
Fug (opons) INTRODUCTION The ermnology of fug refers o he rsk neural expeced me o exercse an Amercan opon. Invened by Mark Garman whle professor a Berkeley n he conex of a bnomal ree for Amercan opon hs
More informationOnline appendices from Counterparty Risk and Credit Value Adjustment a continuing challenge for global financial markets by Jon Gregory
Onlne appendces fro Counerpary sk and Cred alue Adusen a connung challenge for global fnancal arkes by Jon Gregory APPNDX A: Dervng he sandard CA forula We wsh o fnd an expresson for he rsky value of a
More informationMultiagent System Simulations of Sealed-Bid Auctions with Two-Dimensional Value Signals
Deparmen Dscusson Paper DDP77 ISSN 94-2838 Deparmen of Economcs Mulagen Sysem Smulaons of Sealed-Bd Aucons wh Two-Dmensonal Value Sgnals Alan Mehlenbacher Deparmen of Economcs, Unversy of Vcora Vcora,
More informationAn improved segmentation-based HMM learning method for Condition-based Maintenance
An mproved segmenaon-based HMM learnng mehod for Condon-based Manenance T Lu 1,2, J Lemere 1,2, F Carella 1,2 and S Meganck 1,3 1 ETRO Dep., Vre Unverse Brussel, Plenlaan 2, 1050 Brussels, Belgum 2 FMI
More informationOPTIMIZED CALIBRATION OF CURRENCY MARKET STRATEGIES Mustafa Onur Çağlayan 1, János D. Pintér 2
Inernaonal Conference 24h Mn EURO Conference Connuous Opmzaon and Informaon-Based Technologes n he Fnancal Secor (MEC EurOPT 2010) June 23 26, 2010, Izmr, TURKEY ISBN 978-9955-28-598-4 R. Kasımbeyl, C.
More informationUnified Unit Commitment Formulation and Fast Multi-Service LP Model for Flexibility Evaluation in Sustainable Power Systems
IEEE Transacons on Susanable Energy Acceped for publcaon, November 2015 1 Unfed Un Commmen Formulaon and Fas Mul-Servce LP Model for Flexbly Evaluaon n Susanable Power Sysems Lngx Zhang, Suden Member,
More informationCointegration between Fama-French Factors
1 Conegraon beween Fama-French Facors Absrac Conegraon has many applcaons n fnance and oher felds of scence researchng me seres and her nerdependences. The analyss s a useful mehod o analyse non-conegraon
More informationTrade, Growth, and Convergence in a Dynamic Heckscher-Ohlin Model*
Federal Reserve Ban of Mnneapols Research Deparmen Saff Repor 378 Ocober 8 (Frs verson: Sepember 6) Trade, Growh, and Convergence n a Dynamc Hecscher-Ohln Model* Clausre Bajona Ryerson Unversy Tmohy J.
More informationDecision Support for Service Transition Management
Decson Suppor for Servce Transon Managemen Enforce Change Schedulng by Performng Change Rsk and Busness Impac Analyss Thomas Sezer Technsche Unversä München Char of Inerne-based Informaon Sysems 85748
More informationThe Effects of Nature on Learning in Games
The Effecs of Naure on Learnng n Games C.-Y. Cynha Ln Lawell 1 Absrac Ths paper develops an agen-based model o nvesgae he effecs of Naure on learnng n games. In parcular, I exend one commonly used learnng
More informationDEA-Risk Efficiency and Stochastic Dominance Efficiency of Stock Indices *
JEL Classfcaon: C61, D81, G11 Keywords: Daa Envelopmen Analyss, rsk measures, ndex effcency, sochasc domnance DEA-Rsk Effcency and Sochasc Domnance Effcency of Sock Indces * Marn BRANDA Charles Unversy
More informationEstimating intrinsic currency values
Esmang nrnsc currency values Forex marke praconers consanly alk abou he srenghenng or weakenng of ndvdual currences. In hs arcle, Jan Chen and Paul Dous presen a new mehodology o quanfy hese saemens n
More informationReal-Time Traffic over the IEEE Medium Access Control Layer
Real-Tme Traffc over he IEEE 82. Medum Access Conrol Layer João L. Sobrnho and A. S. Krshnaumar Ths paper proposes mulple access procedures o ranspor real-me raffc over IEEE 82. wreless local area newors
More informationHFR Risk Parity Indices
HFR Rsk Pary Indces Defned Formulac Mehodology 2018 2018 Hedge Fund Research, Inc. - All rghs reserved. HFR, HFRI, HFRX, HFRQ, HFRU, HFRL, HFR PorfoloScope, WWW.HEDGEFUNDRESEARCH.COM, HEDGE FUND RESEARCH,
More informationCryptographic techniques used to provide integrity of digital content in long-term storage
RB/3/2011 Crypographc echnques used o provde negry of dgal conen n long-erm sorage REPORT ON THE PROBLEM Problem presened by Marn Šmka Paweł Wojcechowsk Polsh Secury Prnng Works (PWPW) 1 Repor auhors Małgorzaa
More informationBatch Processing for Incremental FP-tree Construction
Inernaonal Journal of Compuer Applons (975 8887) Volume 5 No.5, Augus 21 Bach Processng for Incremenal FP-ree Consrucon Shashkumar G. Toad Deparmen of CSE, GMRIT, Rajam, Srkakulam Dsrc AndraPradesh, Inda.
More informationAssociating Absent Frequent Itemsets with Infrequent Items to Identify Abnormal Transactions
Assocang Absen Frequen Iemses wh Infrequen Iems o Idenfy Abnormal Transacons L-Jen Kao Deparmen of Compuer Scence and Informaon Engneerng Hwa Hsa Insue of Technology New Tape Cy, Tawan 23568 ljenkao@cc.hwh.edu.w
More informationAdjusted-Productivity Growth for Resource Rents: Kuwait Oil Industry
Appled Economcs and Fnance Vol. 3, No. 2; May 2016 ISSN 2332-7294 E-ISSN 2332-7308 Publshed by Redfame Publshng URL: hp://aef.redfame.com Adjused-Producvy Growh for Resource Rens: Kuwa Ol Indusry 1 Acng
More informationFloating rate securities
Caps and Swaps Floang rae secures Coupon paymens are rese perodcally accordng o some reference rae. reference rae + ndex spread e.g. -monh LIBOR + 00 bass pons (posve ndex spread 5-year Treasury yeld 90
More informationA Markov Copulae Approach to Pricing and Hedging of Credit Index Derivatives and Ratings Triggered Step Up Bonds
A Markov Copulae Approach o Prcng and Hedgng of Cred Index Dervaves and Rangs Trggered Sep Up Bonds Tomasz R. Beleck, Andrea Vdozz, Luca Vdozz Absrac The paper presens seleced resuls from he heory of Markov
More informationNBER WORKING PAPER SERIES TRADE, GROWTH, AND CONVERGENCE IN A DYNAMIC HECKSCHER-OHLIN MODEL. Claustre Bajona Timothy J. Kehoe
NBER WORKING PAPER SERIES TRADE, GROWTH, AND CONVERGENCE IN A DYNAMIC HECKSCHER-OHLIN MODEL Clausre Bajona Tmohy J. Kehoe Worng Paper 567 hp://www.nber.org/papers/w567 NATIONAL BUREAU OF ECONOMIC RESEARCH
More informationImpact of Stock Markets on Economic Growth: A Cross Country Analysis
Impac of Sock Markes on Economc Growh: A Cross Counry Analyss By Muhammad Jaml Imporance of sock markes for poolng fnancal resources ncreased snce he las wo decades. Presen sudy analyzed mpac of sock markes
More informationThe impact of intellectual capital on returns and stock prices of listed companies in Tehran Stock Exchange
Appled Scence Repors www.pscpub.com/asr -SSN: 231-944 / P-SSN: 2311-139 DO: 1.15192/PSCP.ASR.214.4.3.1516 App. Sc. Repor. 4 (3), 214: 15-16 PSC Publcaons The mpac of nellecual capal on reurns and sock
More informationSocially Responsible Investments: An International Empirical Study
Workng Paper n : 24-53-3 Socally Responsble Invesmens: An Inernaonal Emprcal Sudy Hachm Ben Ameur a,, Jérôme Senanedsch b a INSEEC Busness School, 27 avenue Claude Vellefaux 75 Pars, France b INSEEC Busness
More informationReturn Calculation Methodology
Reurn Calculaon Mehodology Conens 1. Inroducon... 1 2. Local Reurns... 2 2.1. Examle... 2 3. Reurn n GBP... 3 3.1. Examle... 3 4. Hedged o GBP reurn... 4 4.1. Examle... 4 5. Cororae Acon Facors... 5 5.1.
More informationData Quality Inference
Daa Qualy Inference Raymond K. Pon and Alfonso F. Cárdenas UCLA Compuer Scence Boeler Hall 4829 Los Angeles, CA 90095 (310) 825-1770 {rpon, cardenas}@cs.ucla.edu ABSTRACT In he feld of sensor neworks,
More informationMORNING SESSION. Date: Wednesday, May 4, 2016 Time: 8:30 a.m. 11:45 a.m. INSTRUCTIONS TO CANDIDATES
SOCIETY OF ACTUARIES Exam QFICORE MORNING SESSION Dae: Wednesday, May 4, 016 Tme: 8:30 a.m. 11:45 a.m. INSTRUCTIONS TO CANDIDATES General Insrucons 1. Ths examnaon has a oal of 100 pons. I consss of a
More informationOnline Adaboost-Based Parameterized Methods for Dynamic Distributed Network Intrusion Detection
Onlne Adaboos-Based Parameerzed Mehods or Dnamc Dsrbued Nework Inruson Deecon Wemng Hu, Jun Gao, Yanguo Wang, and Ou Wu (Naonal Laboraor o Paern Recognon, Insue o Auomaon, Chnese Academ o Scences, Beng
More information