Compiling Process Networks to Interaction Nets

Size: px
Start display at page:

Download "Compiling Process Networks to Interaction Nets"

Transcription

1 Complng Process Networks to Interacton Nets Ian Macke LIX, CNRS UMR 7161, École Polytechnque, Palaseau Cede, France Kahn process networks are a model of computaton based on a collecton of sequental, determnstc processes that communcate by sendng messages through unbounded channels. They are well suted for modellng stream-based computatons, but are n no way restrcted to ths applcaton. Interacton nets are graph rewrtng systems that have many nterestng propertes for mplementaton. In ths paper we show how to encode process networks usng nteracton nets, where we model both networks and messages n the same framework. 1 Introducton We relate two models of computaton: Kahn Process Networks (KPNs) and Interacton Nets. Our am s to encode process networks as Interacton Nets so that we can make use of mplementatons, and also to understand a dfferent way of programmng and computng wthn nteracton nets. Specfcally, we nvestgate encodng a KPN as a system of nteracton nets, so from one perspectve we can see ths work as gvng a complaton. Snce there are many mplementatons of nteracton nets ths gves an mplementaton technque for process networks. Kahn Process Networks [3] model computaton usng tokens travellng around a fed network, and the structure of the network does not change durng computaton. Each node of the network transforms, dependng on ts functon, tokens by consumng and creatng new tokens. Transformaton of tokens happens asynchronously and n parallel n the network: many tokens can be travellng around the network, and dfferent nodes can be transformng dfferent tokens at the same tme. The communcaton channels, where the tokens flow, are buffered: the order of tokens s preserved, so tokens are queued watng to be processed. Each processng element must block f nputs are not yet avalable, but they must not block to output. Only one process can wrte to each channel, and only one process can read each channel. Interacton nets [5] on the other hand are a specfc form of graph rewrtng system. A program s represented as a network, and the net s rewrtten to normal form by graph rewrte rules, called nteracton rules. These rules act locally: no part of the graph can be coped or erased globally, so consequently one nteracton cannot nterfere wth another nteracton. Interacton rules can be appled asynchronously and n parallel; a feature shared wth process networks. Each node of an nteracton net must be connected by a sngle edge port-to-port to another node. Thus there are smlartes between Interacton Nets and KPN. Our goal s to smulate process networks n nteracton nets. We acheve ths by modellng both the fed network and tokens usng nodes n nteracton nets. The frng of a transformaton n the process network becomes an nteracton rule (or several nteracton rules). It s then just a conventon that some nodes n the nteracton net are fed and others are travellng n realty, we are just performng a rewrte rule. Not only does ths gve an mplementaton of data-flow networks wth nteracton nets, but t offers an nterestng programmng style that s frequently more natural (and often more effcent) than other ways. We see ths as an applcaton of nteracton nets where the body of research on parallel mplementaton s becomng stronger. A. Corradn & H. Zantema (Eds.) Computng wth Terms and Graphs (TERMGRAPH 2016) EPTCS 225, 2016, pp. 5 14, do: /eptcs c I. Macke Ths work s lcensed under the Creatve Commons Attrbuton Lcense.

2 6 Complng Process Networks to Interacton Nets Overvew. The rest of ths paper s structured as follows. In the net secton we gve some background materal on process networks and set up the specfc style of nteracton nets that we are usng, ncludng some notaton and conventons that we adopt. In Secton 3 we buld some eample nteracton systems that serve as buldng blocks for our complaton of data-flow networks. Secton 4 gves an outlne of the complaton process, and gves some eamples of process networks n nteracton nets. Secton 5 gves an eample of usng the complaton, and also gves addtonal eamples of programmng wth nteracton nets n the style of process networks. Fnally, we conclude n Secton 6. 2 Background Process networks. A Kahn Process Network [3, 4] s a drected graph G = (V,E) wth sequental processng unts as the vertces V and unbounded buffers as the connectng edges E. A process, whch s a sequental program that could be wrtten n any language (for eample C, Java, Algol, Haskell, etc.), communcates wth another process f there s a connectng edge, called a channel, lnkng the two processes. A communcaton channel (stream or buffer) s a fnte or nfnte sequence of data elements (all the eamples n ths paper are numbers, but other data, ncluded structured, s possble). We use a lst notaton S=[ 1, 2,...], where the empty stream s denoted [], when we need to wrte them down. A process can be understood as a functon mappng a collecton of streams to another collecton of streams. The followng dagram llustrates the dea, where there are m nputs and n outputs: f Each process f s a sequental program that can read from nput streams S 1,...,S m to wrte to output streams U 1,...,U n (we wll assume m,n>0) that can nclude the two prmtves for communcaton: send on U: ths command sends a value (we wll assume all values are ntegers n ths paper) along the channel U. The command s non-blockng, whch s why the connectng edges are unbounded buffers, workng as a FIFO queue. = wat(u): ths command wats for a value on channel U, and assgns the value to the varable. Ths command s blockng: a read on a channel must wat untl the value s there, and only then consumes t. If a process has only wrte nstructons, then t may be the case that m=0 s requred. Such processes are sad to be able to process on empty buffers, and ths s typcally needed to start the computaton gong. Here we wll favour an alternatve and ntalse channels nstead. If a process has repeated processng on empty buffers, then we can smulate ths behavour by creatng a cycle by connectng an nput from an output. The data on ths etra channel can then be used to trgger the process. The process can then read ths nput, and just send t back around the loop to the nput. In ths way, all processes trgger output from nput, but the prce to pay s that we mght need to ntalse some of the channels wth startng values. Wthn a process, thesend andwat nstructons can be used n a number of dfferent ways: A process does not have to read all of the nputs. Thus some channels can accumulate data ndefntely f a process s wrtng to the channel but no process s readng t.

3 I. Macke 7 The order of nput/output s not constraned n any way. A process can read from some of the channels n any order, then wrte to others. It can also nterleave readng and wrtng. A process has local varables, and thus an nternal state. Ths s used to make the process behave dfferently on future nputs, and also to store past results. In the followng, nteracton nets wll use dfferent prncpal ports to mmc the dfferent behavour. The nternal state of a process wll be represented by the correspondng nternal state of an nteracton net node. A process network s a collecton of processes connected together. The edges are shared channels where communcaton can take place. The network of processes has a lnearty constrant: only one wrter per channel and one recever per channel. A process can duplcate values on a channel (and wrte a value to two dfferent channels) but each channel must be lnear n ths sense. A consequence of these condtons s that processes are determnstc. But they are also amenable to a hgh degree of concurrency: many processes can be actve at one tme, and each processor need only wat for all t s values before proceedngs. For ths reason process networks are a model of dstrbuted computaton. There are many varants of ths model, for eample synchronous processes. There are also many studes of propertes of these networks, such as deadlock. Here we are nterested n mplementng them rather than studyng any propertes. We gve an eample adapted from [3]. In ths network f s a process that alternately receves nput from the left/rght and copes t to the output channel. g s a process that receves an nput and alternately copes t left and rght. h s a process that ntally emts an, then copes the nput to the output channel. U W f h 0 X h 1 g V Y The code for these processes s gven by the followng, where we just show, respectvely, h 0 and f : send 0 on U repeat = wat(v) send on U end bool b = true repeat f b then = wat(u) else = wat(w) send on X b = not b end In ths eample, f we montor the output of f, we get an nfnte alternatng sequence of 0 and 1. Ths process does not deadlock, and no synchronsaton s needed for any of the ndvdual processes. We refer the reader to the lterature (see for eample [3]) for a more detaled descrpton of data-flow networks. Faustn [1] gves an operatonal semantcs of process networks. There are many works mplementng KPN (see for eample [8] where they are encoded n Java), and there are many programmng languages based on the prncples. We gve eamples of these networks usng nteracton nets later. Interacton nets. Analogous to term rewrtng systems, we have a set of user-defned nodes (drawn as crcles and squares n ths paper), and a set of rewrte rules: 1 α n n 1. α β y 1 n.. N y m 1. y 1 y m

4 8 Complng Process Networks to Interacton Nets The set of rewrte rules s constraned: there s at most one rule for each par of nodes; and each name n the dagram occurs eactly twce once n the left, and once n the rght. Ths means that the nterface s preserved by reducton, and a consequence of ths s that duplcaton and erasng must be done eplctly, but the rewrtng system s one-step confluent by constructon. We etend nteracton nets n two ways, and here we wll present ths nformally. Frst, we allow nodes to hold values (just numbers are needed for the eamples n ths paper, but other base types can be ncluded n a smlar way). Consequently, rules can nspect and update these values. It s possble to do ths and stll preserve the same confluence propertes by placng condtons on the rules. Net, we avod ntroducng aulary nodes that are essentally requred because arguments are taken one at a tme (cf. Curryng). We do ths by allowng some nodes to have several prncpal ports, and the rewrte rule requres all to match. Ths offers no new computatonal power, but groups several nteractons whch s sometmes convenent. We llustrate all these deas wth an eample. Consder the two rules whch encode parwse addton on lsts of numbers: y y we can combne these nto a sngle rule n the followng way: y y For the programs we wrte, all the propertes of nteracton nets are preserved, and ths etenson can be mplemented n usual nteracton nets. There are many mplementatons of nteracton nets (see for eample [2, 6]), ncludng parallel ones. We wll be buldng nteracton net systems to smulate process networks, and for ths to work we assume that the mplementaton s far, by whch we mean that no new reductons are done before older ones are completed. Most mplementatons respect ths, so ths avods any starvaton or lve-lock ssues that mght arse. 3 Buldng blocks In addton to the eamples for nteracton nets gven n the last secton, we buld some eamples here that wll be useful later for the complaton, specfcally for the representaton of the communcaton channels (buffers). Lsts and streams. Usng two knds of nodes, we can buld lsts of numbers: nl

5 I. Macke 9 An eample lst wth four elements would look lke ths: nl Streams are just lsts wthout nl. We can wrte smple operatons over these lsts/streams. For eample, applyng an operaton to each element of a lst (cf. map) can be done usng the followng two nteracton rules, where we ncrement each element of the lst: nc nl nl nc 1 nc The followng net rewrtes usng four nteractons to reach a normal form: nc nl rewrtes to nl Other operatons on lsts/streams can be defned n a smlar way. Duplcatng streams. A very mportant operaton, because of the lnearty constrant, s the ablty to eplctly copy lsts and streams. Wth the ntroducton of a new node, we can defne two nteracton rules whch wll copy any lst or stream n the followng way: nl nl nl We can now put both of the above systems together to compute thngs lke the followng (that we leave as an eercse to the reader): nl Usng these components, we can generate an nfnte lst of ntegers, startng from a gven number n wth the followng net: n We call ths cyclc net nts(n). There are varants of ths f we change nc to ncrement by 2, etc. Remark that ths net s non-termnatng. Two nteractons are needed for each new number on the output stream (one to copy the number, and another to ncrement one of the copes). If we start wth n=1, and trace the eecuton (after two nteractons each tme) we have: nc 1 2 nc and then after four more nteractons we have: nc and so on. Thus we can generate cyclc nets, and smulate smple data-flow wth nteracton nets. Ths net generates an nfnte stream of ntegers. Ths s an eample of a process network, but uses our own nterpretaton on the nets: and nc are consdered fed and the numbers are nterpreted as data tems on a stream. In the net secton we take these deas further.

6 10 Complng Process Networks to Interacton Nets 4 Complaton of process networks to nets Our net task s to show how to translate any KPN nto an nteracton net. Not only does ths hghlght some connectons between the formalsms, but t also provdes an mplementaton of process networks. There are two possble ways to proceed: We can defne a set of KPN combnators, by whch we mean a fnte set of processes that can be used to represent all other process networks (thus all computable functons). We then just need to show how to translate ths fed set of processes, together wth the communcaton channels and net structure, to complete the complaton. We can gve a general translaton for each (user-programmed) process. Ths means that we need to f the programmng language used to defne processes, and then gve a translaton of ths language. In ths paper we just sketch the general translaton, and then gve some eample nteracton systems. We adopt a conventon when wrtng nteracton nets (that we have followed already n the prevous eamples): squares wll represent processes, and crcles wll represent data tems on channels (streams). The general form of the complaton s the followng: Communcaton channels are encoded as nteracton net streams, as defned n Secton 3. Each process becomes a set of nteracton net nodes that wll smulate the functonal behavour of each process. We need to defne the nodes and the rewrte rules for each process. Any nternal state of a process becomes the nternal state of an nteracton node. The topologcal structure of a process network and an nteracton net are dentcal, so ths structure s just coped as part of the complaton. Below we gve suffcent detals of how process and channels are translated so that we are able to gve some eamples. Channels. Each channel n a KPN s represented as an nteracton net stream. If the channel s empty, then the stream s represented as an edge n the nteracton net. Otherwse, f S = [ 1,..., n ] then we buld the followng stream: 1 2 n Intally, channels are usually empty at the start (so at the complaton). However, some eceptons to ths rule wll be used. In the complaton above, f we allow other data, then we need to translate that too. Here we just use numbers. Processes. Each process f s represented by a collecton of nteracton net nodes. If f has m nput streams and n output streams: f

7 I. Macke 11 then ths wll be represented by a collecton of m nteracton nodes f 1,..., f m, where each f has the prncpal port at poston correspondng to the th nput stream, and each wth n m 1 aulary ports (m 1 nput and n output): f 1 f 2 f m For each node f we defne the correspondng nteracton rules that smulate the process. The nteracton rule f wth nput on stream becomes the process f j (where = j s a possblty) and possbly outputs somethng on one of the output channels. The nternal state s changed accordngly also. We note that a process network uses nternal state n two very dfferent ways: one to know whch channel to read from, and the other to store values from past nputs. Interacton nets just use the second, because the choce of whch channel to read from comes from the dfferent node used (whch gves the poston of the prncpal port). Note also that f any channel s not read by the process, then there s no need to generate any rules for the correspondng node. Eample 1. Consder a KPN that computes the runnng total from a channel. Ths can smply be mplemented by a sngle process that stores nternally, startng from 0, the accumulatve sum of all the values read on the nput channel. For each data tem on the nput channel, the total s updated, and the new total s wrtten to the output. Followng the deas of the last secton, we need just one node, and one nteracton rule to represent ths system: y s(y) s() y If we start wth the node s(0), we can generate the stream of accumulatng totals read so far. The process keeps a hstory of the nputs (the sum of all the prevous values). Eample 2. Our net eample s a varant on the prevous one, where there s no nternal state n the process. We use the network channels to keep the runnng total, whch llustrates a general dea that we can frequently represent the nternal state usng addtonal channels. Consder the followng process network: U V W send 0 on W repeat = wat(u) w = wat(w) send (w) on V send (w) on W end Ths gets compled nto the followng system. Snce we need to send before read, we need to ntalse the stream W wth 0. The followng s the ntal net generated, and we gve the two rules correspondng to the process:

8 12 Complng Process Networks to Interacton Nets y () 2 () y y We end ths secton by statng a very general relaton between KPN and the compled nteracton net. Theorem 3 (Correctness). If a KPN reads or wrtes on a channel, then the correspondng nteracton net can make the same move. Because we mmc the functonal behavour of the KPN as a system of nteracton nets, then ths result s essentally obtaned by constructon. In a longer verson of ths paper we gve the addtonal detals to justfy ths. Fnally, we remark that nteracton nets are one-step confluent, therefore we get a completeness result also: t doesn t matter whch way we evaluate a generated nteracton net, t wll always output the same thng as the KPN. 5 Representng process networks Each process becomes a node n the nteracton system, and we gve rules that smulate the requred behavour. Channels become streams, and we represent streams of data as gven prevously. In ths secton we gve another eample of the complaton, and also gve some eamples of programmng nteracton nets drectly n the style of process networks. The alternatng bt process gven n Secton 2 can be represented by the followng nteracton net: f h 0 h 1 wth the followng s nteracton rules (there are two rules for h that are dentcal): f 1 f 2 f 2 f 1 g 1 g 1 g 2 g 2 g 1 h h The choce of the prncpal ports drectly encodes the order of readng on channels of the orgnal process. We net show how to wrte process networks drectly. Suppose we wsh to buld a net epressng the followng nput/output behavour that we represent as a bo f shown below. We can thnk of ths as the functon on pars: f = λ,y.,y ). We can represent ths usng the buldng blocks we have already ntroduced, as shown below on the rght:

9 I. Macke 13 y y f y Connectng numbers to the top wll gve the requred behavour. We can terate ths functon by buldng a feedback loop as shown below on the left. Fnally, we can put some startng values n to complete the network, as shown below on the rght: y y n 0 y If we look at the output of the node, we wll get n, 2n, 3n, etc. Ths network wll compute a stream of multples of n. We can also control the computaton by restrctng the teratons by ntroducng a counter. To do ths we need to know when to stop, and how to etract the result. We can do ths by addng n some synchronsaton that can be encoded wth standard deas from nteracton nets. We gve two fnal eamples to llustrate how easly dfferent nets can be bult. These networks compute a stream of factoral numbers and a stream of Fbonacc numbers respectvely: n m 1 1 nc 6 Concluson Interacton nets gve a very easy way of mplementng data-flow networks. There are several parallel mplementatons of nteracton nets now avalable, so ths means that we can also take advantage of the natural parallelsm that arses by wrtng programs n ths way. Programmng algorthms n nteracton nets by encodng a process network can gve a very effcent encodng of the algorthm: the eample above for Fbonacc needs just three knds of nodes and two rewrte rules to generate an nfnte stream of Fbonacc numbers whch s smpler and more effcent than any other way. There are nterestng etensons to the deas presented here that are currently beng nvestgated. For nstance, s s possble to allow KPN to dynamcally create new processes. Ths fts very naturally wth nteracton nets. Notons of second order networks (see for nstance [7]) have been dscussed, and

10 14 Complng Process Networks to Interacton Nets nterestng relatons agan arse wth past work on nteracton nets. We hope to report on some of these deas n a longer verson of ths paper. References [1] Antony A. Faustn (1982): An Operatonal Semantcs for Pure Dataflow. In Mogens Nelsen & Erk Meneche Schmdt, edtors: Automata, Languages and Programmng, 9th Colloquum, Aarhus, Denmark, July 12-16, 1982, Proceedngs, Lecture Notes n Computer Scence 140, Sprnger, pp , do: /bfb [2] Abubakar Hassan, Ian Macke & Shnya Sato (2014): An Implementaton Model for Interacton Nets. In Aart Mddeldorp & Femke van Raamsdonk, edtors: Proceedngs 8th Internatonal Workshop on Computng wth Terms and Graphs, TERMGRAPH 2014, Venna, Austra, July 13, 2014., EPTCS 183, pp , do: /eptcs [3] Glles Kahn (1974): The Semantcs of Smple Language for Parallel Programmng. In: IFIP Congress, pp [4] Glles Kahn & Davd B. MacQueen (1977): Coroutnes and Networks of Parallel Processes. In: IFIP Congress, pp [5] Yves Lafont (1990): Interacton Nets. In: Proceedngs of the 17th ACM Symposum on Prncples of Programmng Languages (POPL 90), ACM Press, pp , do: / [6] Ian Macke & Shnya Sato (2015): Parallel Evaluaton of Interacton Nets: Case Studes and Eperments. ECEASST 73, do: /tuj.eceasst [7] S. G. Matthews (1991): Addng second order functons to Kahn data flow. Techncal Report, Unversty of Warwck. Department of Computer Scence. [8] Thomas M. Parks & Davd Roberts (2003): Dstrbuted Process Networks n Java. In: 17th Internatonal Parallel and Dstrbuted Processng Symposum (IPDPS 2003), Aprl 2003, Nce, France, Proceedngs, IEEE Computer Socety, p. 138, do: /ipdps

Load Balancing for Hex-Cell Interconnection Network

Load Balancing for Hex-Cell Interconnection Network Int. J. Communcatons, Network and System Scences,,, - Publshed Onlne Aprl n ScRes. http://www.scrp.org/journal/jcns http://dx.do.org/./jcns.. Load Balancng for Hex-Cell Interconnecton Network Saher Manaseer,

More information

An Optimal Algorithm for Prufer Codes *

An Optimal Algorithm for Prufer Codes * J. Software Engneerng & Applcatons, 2009, 2: 111-115 do:10.4236/jsea.2009.22016 Publshed Onlne July 2009 (www.scrp.org/journal/jsea) An Optmal Algorthm for Prufer Codes * Xaodong Wang 1, 2, Le Wang 3,

More information

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009.

Assignment # 2. Farrukh Jabeen Algorithms 510 Assignment #2 Due Date: June 15, 2009. Farrukh Jabeen Algorthms 51 Assgnment #2 Due Date: June 15, 29. Assgnment # 2 Chapter 3 Dscrete Fourer Transforms Implement the FFT for the DFT. Descrbed n sectons 3.1 and 3.2. Delverables: 1. Concse descrpton

More information

CMPS 10 Introduction to Computer Science Lecture Notes

CMPS 10 Introduction to Computer Science Lecture Notes CPS 0 Introducton to Computer Scence Lecture Notes Chapter : Algorthm Desgn How should we present algorthms? Natural languages lke Englsh, Spansh, or French whch are rch n nterpretaton and meanng are not

More information

Parallelism for Nested Loops with Non-uniform and Flow Dependences

Parallelism for Nested Loops with Non-uniform and Flow Dependences Parallelsm for Nested Loops wth Non-unform and Flow Dependences Sam-Jn Jeong Dept. of Informaton & Communcaton Engneerng, Cheonan Unversty, 5, Anseo-dong, Cheonan, Chungnam, 330-80, Korea. seong@cheonan.ac.kr

More information

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following.

Complex Numbers. Now we also saw that if a and b were both positive then ab = a b. For a second let s forget that restriction and do the following. Complex Numbers The last topc n ths secton s not really related to most of what we ve done n ths chapter, although t s somewhat related to the radcals secton as we wll see. We also won t need the materal

More information

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms

Course Introduction. Algorithm 8/31/2017. COSC 320 Advanced Data Structures and Algorithms. COSC 320 Advanced Data Structures and Algorithms Course Introducton Course Topcs Exams, abs, Proects A quc loo at a few algorthms 1 Advanced Data Structures and Algorthms Descrpton: We are gong to dscuss algorthm complexty analyss, algorthm desgn technques

More information

Harvard University CS 101 Fall 2005, Shimon Schocken. Assembler. Elements of Computing Systems 1 Assembler (Ch. 6)

Harvard University CS 101 Fall 2005, Shimon Schocken. Assembler. Elements of Computing Systems 1 Assembler (Ch. 6) Harvard Unversty CS 101 Fall 2005, Shmon Schocken Assembler Elements of Computng Systems 1 Assembler (Ch. 6) Why care about assemblers? Because Assemblers employ some nfty trcks Assemblers are the frst

More information

Array transposition in CUDA shared memory

Array transposition in CUDA shared memory Array transposton n CUDA shared memory Mke Gles February 19, 2014 Abstract Ths short note s nspred by some code wrtten by Jeremy Appleyard for the transposton of data through shared memory. I had some

More information

Intro. Iterators. 1. Access

Intro. Iterators. 1. Access Intro Ths mornng I d lke to talk a lttle bt about s and s. We wll start out wth smlartes and dfferences, then we wll see how to draw them n envronment dagrams, and we wll fnsh wth some examples. Happy

More information

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1)

For instance, ; the five basic number-sets are increasingly more n A B & B A A = B (1) Secton 1.2 Subsets and the Boolean operatons on sets If every element of the set A s an element of the set B, we say that A s a subset of B, or that A s contaned n B, or that B contans A, and we wrte A

More information

AADL : about scheduling analysis

AADL : about scheduling analysis AADL : about schedulng analyss Schedulng analyss, what s t? Embedded real-tme crtcal systems have temporal constrants to meet (e.g. deadlne). Many systems are bult wth operatng systems provdng multtaskng

More information

The Codesign Challenge

The Codesign Challenge ECE 4530 Codesgn Challenge Fall 2007 Hardware/Software Codesgn The Codesgn Challenge Objectves In the codesgn challenge, your task s to accelerate a gven software reference mplementaton as fast as possble.

More information

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz

Compiler Design. Spring Register Allocation. Sample Exercises and Solutions. Prof. Pedro C. Diniz Compler Desgn Sprng 2014 Regster Allocaton Sample Exercses and Solutons Prof. Pedro C. Dnz USC / Informaton Scences Insttute 4676 Admralty Way, Sute 1001 Marna del Rey, Calforna 90292 pedro@s.edu Regster

More information

Assembler. Shimon Schocken. Spring Elements of Computing Systems 1 Assembler (Ch. 6) Compiler. abstract interface.

Assembler. Shimon Schocken. Spring Elements of Computing Systems 1 Assembler (Ch. 6) Compiler. abstract interface. IDC Herzlya Shmon Schocken Assembler Shmon Schocken Sprng 2005 Elements of Computng Systems 1 Assembler (Ch. 6) Where we are at: Human Thought Abstract desgn Chapters 9, 12 abstract nterface H.L. Language

More information

Programming in Fortran 90 : 2017/2018

Programming in Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Programmng n Fortran 90 : 2017/2018 Exercse 1 : Evaluaton of functon dependng on nput Wrte a program who evaluate the functon f (x,y) for any two user specfed values

More information

Hermite Splines in Lie Groups as Products of Geodesics

Hermite Splines in Lie Groups as Products of Geodesics Hermte Splnes n Le Groups as Products of Geodescs Ethan Eade Updated May 28, 2017 1 Introducton 1.1 Goal Ths document defnes a curve n the Le group G parametrzed by tme and by structural parameters n the

More information

Concurrent models of computation for embedded software

Concurrent models of computation for embedded software Concurrent models of computaton for embedded software and hardware! Researcher overvew what t looks lke semantcs what t means and how t relates desgnng an actor language actor propertes and how to represent

More information

Lecture 5: Multilayer Perceptrons

Lecture 5: Multilayer Perceptrons Lecture 5: Multlayer Perceptrons Roger Grosse 1 Introducton So far, we ve only talked about lnear models: lnear regresson and lnear bnary classfers. We noted that there are functons that can t be represented

More information

ELEC 377 Operating Systems. Week 6 Class 3

ELEC 377 Operating Systems. Week 6 Class 3 ELEC 377 Operatng Systems Week 6 Class 3 Last Class Memory Management Memory Pagng Pagng Structure ELEC 377 Operatng Systems Today Pagng Szes Vrtual Memory Concept Demand Pagng ELEC 377 Operatng Systems

More information

Solving two-person zero-sum game by Matlab

Solving two-person zero-sum game by Matlab Appled Mechancs and Materals Onlne: 2011-02-02 ISSN: 1662-7482, Vols. 50-51, pp 262-265 do:10.4028/www.scentfc.net/amm.50-51.262 2011 Trans Tech Publcatons, Swtzerland Solvng two-person zero-sum game by

More information

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour

6.854 Advanced Algorithms Petar Maymounkov Problem Set 11 (November 23, 2005) With: Benjamin Rossman, Oren Weimann, and Pouya Kheradpour 6.854 Advanced Algorthms Petar Maymounkov Problem Set 11 (November 23, 2005) Wth: Benjamn Rossman, Oren Wemann, and Pouya Kheradpour Problem 1. We reduce vertex cover to MAX-SAT wth weghts, such that the

More information

124 Chapter 8. Case Study: A Memory Component ndcatng some error condton. An exceptonal return of a value e s called rasng excepton e. A return s ssue

124 Chapter 8. Case Study: A Memory Component ndcatng some error condton. An exceptonal return of a value e s called rasng excepton e. A return s ssue Chapter 8 Case Study: A Memory Component In chapter 6 we gave the outlne of a case study on the renement of a safe regster. In ths chapter wepresent the outne of another case study on persstent communcaton;

More information

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique

The Greedy Method. Outline and Reading. Change Money Problem. Greedy Algorithms. Applications of the Greedy Strategy. The Greedy Method Technique //00 :0 AM Outlne and Readng The Greedy Method The Greedy Method Technque (secton.) Fractonal Knapsack Problem (secton..) Task Schedulng (secton..) Mnmum Spannng Trees (secton.) Change Money Problem Greedy

More information

Assembler. Building a Modern Computer From First Principles.

Assembler. Building a Modern Computer From First Principles. Assembler Buldng a Modern Computer From Frst Prncples www.nand2tetrs.org Elements of Computng Systems, Nsan & Schocken, MIT Press, www.nand2tetrs.org, Chapter 6: Assembler slde Where we are at: Human Thought

More information

Meta-heuristics for Multidimensional Knapsack Problems

Meta-heuristics for Multidimensional Knapsack Problems 2012 4th Internatonal Conference on Computer Research and Development IPCSIT vol.39 (2012) (2012) IACSIT Press, Sngapore Meta-heurstcs for Multdmensonal Knapsack Problems Zhbao Man + Computer Scence Department,

More information

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory

Virtual Memory. Background. No. 10. Virtual Memory: concept. Logical Memory Space (review) Demand Paging(1) Virtual Memory Background EECS. Operatng System Fundamentals No. Vrtual Memory Prof. Hu Jang Department of Electrcal Engneerng and Computer Scence, York Unversty Memory-management methods normally requres the entre process

More information

Related-Mode Attacks on CTR Encryption Mode

Related-Mode Attacks on CTR Encryption Mode Internatonal Journal of Network Securty, Vol.4, No.3, PP.282 287, May 2007 282 Related-Mode Attacks on CTR Encrypton Mode Dayn Wang, Dongda Ln, and Wenlng Wu (Correspondng author: Dayn Wang) Key Laboratory

More information

Problem Set 3 Solutions

Problem Set 3 Solutions Introducton to Algorthms October 4, 2002 Massachusetts Insttute of Technology 6046J/18410J Professors Erk Demane and Shaf Goldwasser Handout 14 Problem Set 3 Solutons (Exercses were not to be turned n,

More information

A Binarization Algorithm specialized on Document Images and Photos

A Binarization Algorithm specialized on Document Images and Photos A Bnarzaton Algorthm specalzed on Document mages and Photos Ergna Kavalleratou Dept. of nformaton and Communcaton Systems Engneerng Unversty of the Aegean kavalleratou@aegean.gr Abstract n ths paper, a

More information

Mathematics 256 a course in differential equations for engineering students

Mathematics 256 a course in differential equations for engineering students Mathematcs 56 a course n dfferental equatons for engneerng students Chapter 5. More effcent methods of numercal soluton Euler s method s qute neffcent. Because the error s essentally proportonal to the

More information

Cluster Analysis of Electrical Behavior

Cluster Analysis of Electrical Behavior Journal of Computer and Communcatons, 205, 3, 88-93 Publshed Onlne May 205 n ScRes. http://www.scrp.org/ournal/cc http://dx.do.org/0.4236/cc.205.350 Cluster Analyss of Electrcal Behavor Ln Lu Ln Lu, School

More information

Brave New World Pseudocode Reference

Brave New World Pseudocode Reference Brave New World Pseudocode Reference Pseudocode s a way to descrbe how to accomplsh tasks usng basc steps lke those a computer mght perform. In ths week s lab, you'll see how a form of pseudocode can be

More information

F Geometric Mean Graphs

F Geometric Mean Graphs Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 2 (December 2015), pp. 937-952 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) F Geometrc Mean Graphs A.

More information

PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES

PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES PYTHON IMPLEMENTATION OF VISUAL SECRET SHARING SCHEMES Ruxandra Olmd Faculty of Mathematcs and Computer Scence, Unversty of Bucharest Emal: ruxandra.olmd@fm.unbuc.ro Abstract Vsual secret sharng schemes

More information

On Some Entertaining Applications of the Concept of Set in Computer Science Course

On Some Entertaining Applications of the Concept of Set in Computer Science Course On Some Entertanng Applcatons of the Concept of Set n Computer Scence Course Krasmr Yordzhev *, Hrstna Kostadnova ** * Assocate Professor Krasmr Yordzhev, Ph.D., Faculty of Mathematcs and Natural Scences,

More information

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE

ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Інформаційні технології в освіті ON SOME ENTERTAINING APPLICATIONS OF THE CONCEPT OF SET IN COMPUTER SCIENCE COURSE Yordzhev K., Kostadnova H. Some aspects of programmng educaton

More information

Review of approximation techniques

Review of approximation techniques CHAPTER 2 Revew of appromaton technques 2. Introducton Optmzaton problems n engneerng desgn are characterzed by the followng assocated features: the objectve functon and constrants are mplct functons evaluated

More information

Machine Learning: Algorithms and Applications

Machine Learning: Algorithms and Applications 14/05/1 Machne Learnng: Algorthms and Applcatons Florano Zn Free Unversty of Bozen-Bolzano Faculty of Computer Scence Academc Year 011-01 Lecture 10: 14 May 01 Unsupervsed Learnng cont Sldes courtesy of

More information

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming

Optimization Methods: Integer Programming Integer Linear Programming 1. Module 7 Lecture Notes 1. Integer Linear Programming Optzaton Methods: Integer Prograng Integer Lnear Prograng Module Lecture Notes Integer Lnear Prograng Introducton In all the prevous lectures n lnear prograng dscussed so far, the desgn varables consdered

More information

The stream cipher MICKEY-128 (version 1) Algorithm specification issue 1.0

The stream cipher MICKEY-128 (version 1) Algorithm specification issue 1.0 The stream cpher MICKEY-128 (verson 1 Algorthm specfcaton ssue 1. Steve Babbage Vodafone Group R&D, Newbury, UK steve.babbage@vodafone.com Matthew Dodd Independent consultant matthew@mdodd.net www.mdodd.net

More information

High level vs Low Level. What is a Computer Program? What does gcc do for you? Program = Instructions + Data. Basic Computer Organization

High level vs Low Level. What is a Computer Program? What does gcc do for you? Program = Instructions + Data. Basic Computer Organization What s a Computer Program? Descrpton of algorthms and data structures to acheve a specfc ojectve Could e done n any language, even a natural language lke Englsh Programmng language: A Standard notaton

More information

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces

Range images. Range image registration. Examples of sampling patterns. Range images and range surfaces Range mages For many structured lght scanners, the range data forms a hghly regular pattern known as a range mage. he samplng pattern s determned by the specfc scanner. Range mage regstraton 1 Examples

More information

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics

NAG Fortran Library Chapter Introduction. G10 Smoothing in Statistics Introducton G10 NAG Fortran Lbrary Chapter Introducton G10 Smoothng n Statstcs Contents 1 Scope of the Chapter... 2 2 Background to the Problems... 2 2.1 Smoothng Methods... 2 2.2 Smoothng Splnes and Regresson

More information

Parallel matrix-vector multiplication

Parallel matrix-vector multiplication Appendx A Parallel matrx-vector multplcaton The reduced transton matrx of the three-dmensonal cage model for gel electrophoress, descrbed n secton 3.2, becomes excessvely large for polymer lengths more

More information

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation

An Iterative Solution Approach to Process Plant Layout using Mixed Integer Optimisation 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 An Iteratve Soluton Approach to Process Plant Layout usng Mxed

More information

Module Management Tool in Software Development Organizations

Module Management Tool in Software Development Organizations Journal of Computer Scence (5): 8-, 7 ISSN 59-66 7 Scence Publcatons Management Tool n Software Development Organzatons Ahmad A. Al-Rababah and Mohammad A. Al-Rababah Faculty of IT, Al-Ahlyyah Amman Unversty,

More information

Concurrent Apriori Data Mining Algorithms

Concurrent Apriori Data Mining Algorithms Concurrent Apror Data Mnng Algorthms Vassl Halatchev Department of Electrcal Engneerng and Computer Scence York Unversty, Toronto October 8, 2015 Outlne Why t s mportant Introducton to Assocaton Rule Mnng

More information

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach

Data Representation in Digital Design, a Single Conversion Equation and a Formal Languages Approach Data Representaton n Dgtal Desgn, a Sngle Converson Equaton and a Formal Languages Approach Hassan Farhat Unversty of Nebraska at Omaha Abstract- In the study of data representaton n dgtal desgn and computer

More information

Cordial and 3-Equitable Labeling for Some Star Related Graphs

Cordial and 3-Equitable Labeling for Some Star Related Graphs Internatonal Mathematcal Forum, 4, 009, no. 31, 1543-1553 Cordal and 3-Equtable Labelng for Some Star Related Graphs S. K. Vadya Department of Mathematcs, Saurashtra Unversty Rajkot - 360005, Gujarat,

More information

News. Recap: While Loop Example. Reading. Recap: Do Loop Example. Recap: For Loop Example

News. Recap: While Loop Example. Reading. Recap: Do Loop Example. Recap: For Loop Example Unversty of Brtsh Columba CPSC, Intro to Computaton Jan-Apr Tamara Munzner News Assgnment correctons to ASCIIArtste.java posted defntely read WebCT bboards Arrays Lecture, Tue Feb based on sldes by Kurt

More information

CHAPTER 2 DECOMPOSITION OF GRAPHS

CHAPTER 2 DECOMPOSITION OF GRAPHS CHAPTER DECOMPOSITION OF GRAPHS. INTRODUCTION A graph H s called a Supersubdvson of a graph G f H s obtaned from G by replacng every edge uv of G by a bpartte graph,m (m may vary for each edge by dentfyng

More information

Wishing you all a Total Quality New Year!

Wishing you all a Total Quality New Year! Total Qualty Management and Sx Sgma Post Graduate Program 214-15 Sesson 4 Vnay Kumar Kalakband Assstant Professor Operatons & Systems Area 1 Wshng you all a Total Qualty New Year! Hope you acheve Sx sgma

More information

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints

Sum of Linear and Fractional Multiobjective Programming Problem under Fuzzy Rules Constraints Australan Journal of Basc and Appled Scences, 2(4): 1204-1208, 2008 ISSN 1991-8178 Sum of Lnear and Fractonal Multobjectve Programmng Problem under Fuzzy Rules Constrants 1 2 Sanjay Jan and Kalash Lachhwan

More information

Private Information Retrieval (PIR)

Private Information Retrieval (PIR) 2 Levente Buttyán Problem formulaton Alce wants to obtan nformaton from a database, but she does not want the database to learn whch nformaton she wanted e.g., Alce s an nvestor queryng a stock-market

More information

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky

Improving Low Density Parity Check Codes Over the Erasure Channel. The Nelder Mead Downhill Simplex Method. Scott Stransky Improvng Low Densty Party Check Codes Over the Erasure Channel The Nelder Mead Downhll Smplex Method Scott Stransky Programmng n conjuncton wth: Bors Cukalovc 18.413 Fnal Project Sprng 2004 Page 1 Abstract

More information

User Authentication Based On Behavioral Mouse Dynamics Biometrics

User Authentication Based On Behavioral Mouse Dynamics Biometrics User Authentcaton Based On Behavoral Mouse Dynamcs Bometrcs Chee-Hyung Yoon Danel Donghyun Km Department of Computer Scence Department of Computer Scence Stanford Unversty Stanford Unversty Stanford, CA

More information

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss.

Today s Outline. Sorting: The Big Picture. Why Sort? Selection Sort: Idea. Insertion Sort: Idea. Sorting Chapter 7 in Weiss. Today s Outlne Sortng Chapter 7 n Wess CSE 26 Data Structures Ruth Anderson Announcements Wrtten Homework #6 due Frday 2/26 at the begnnng of lecture Proect Code due Mon March 1 by 11pm Today s Topcs:

More information

Conditional Speculative Decimal Addition*

Conditional Speculative Decimal Addition* Condtonal Speculatve Decmal Addton Alvaro Vazquez and Elsardo Antelo Dep. of Electronc and Computer Engneerng Unv. of Santago de Compostela, Span Ths work was supported n part by Xunta de Galca under grant

More information

Computer models of motion: Iterative calculations

Computer models of motion: Iterative calculations Computer models o moton: Iteratve calculatons OBJECTIVES In ths actvty you wll learn how to: Create 3D box objects Update the poston o an object teratvely (repeatedly) to anmate ts moton Update the momentum

More information

LLVM passes and Intro to Loop Transformation Frameworks

LLVM passes and Intro to Loop Transformation Frameworks LLVM passes and Intro to Loop Transformaton Frameworks Announcements Ths class s recorded and wll be n D2L panapto. No quz Monday after sprng break. Wll be dong md-semester class feedback. Today LLVM passes

More information

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain

AMath 483/583 Lecture 21 May 13, Notes: Notes: Jacobi iteration. Notes: Jacobi with OpenMP coarse grain AMath 483/583 Lecture 21 May 13, 2011 Today: OpenMP and MPI versons of Jacob teraton Gauss-Sedel and SOR teratve methods Next week: More MPI Debuggng and totalvew GPU computng Read: Class notes and references

More information

CS1100 Introduction to Programming

CS1100 Introduction to Programming Factoral (n) Recursve Program fact(n) = n*fact(n-) CS00 Introducton to Programmng Recurson and Sortng Madhu Mutyam Department of Computer Scence and Engneerng Indan Insttute of Technology Madras nt fact

More information

Simulation Based Analysis of FAST TCP using OMNET++

Simulation Based Analysis of FAST TCP using OMNET++ Smulaton Based Analyss of FAST TCP usng OMNET++ Umar ul Hassan 04030038@lums.edu.pk Md Term Report CS678 Topcs n Internet Research Sprng, 2006 Introducton Internet traffc s doublng roughly every 3 months

More information

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search

Sequential search. Building Java Programs Chapter 13. Sequential search. Sequential search Sequental search Buldng Java Programs Chapter 13 Searchng and Sortng sequental search: Locates a target value n an array/lst by examnng each element from start to fnsh. How many elements wll t need to

More information

Agenda & Reading. Simple If. Decision-Making Statements. COMPSCI 280 S1C Applications Programming. Programming Fundamentals

Agenda & Reading. Simple If. Decision-Making Statements. COMPSCI 280 S1C Applications Programming. Programming Fundamentals Agenda & Readng COMPSCI 8 SC Applcatons Programmng Programmng Fundamentals Control Flow Agenda: Decsonmakng statements: Smple If, Ifelse, nested felse, Select Case s Whle, DoWhle/Untl, For, For Each, Nested

More information

A Topology-aware Random Walk

A Topology-aware Random Walk A Topology-aware Random Walk Inkwan Yu, Rchard Newman Dept. of CISE, Unversty of Florda, Ganesvlle, Florda, USA Abstract When a graph can be decomposed nto clusters of well connected subgraphs, t s possble

More information

Chapter 6 Programmng the fnte element method Inow turn to the man subject of ths book: The mplementaton of the fnte element algorthm n computer programs. In order to make my dscusson as straghtforward

More information

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields

A mathematical programming approach to the analysis, design and scheduling of offshore oilfields 17 th European Symposum on Computer Aded Process Engneerng ESCAPE17 V. Plesu and P.S. Agach (Edtors) 2007 Elsever B.V. All rghts reserved. 1 A mathematcal programmng approach to the analyss, desgn and

More information

X- Chart Using ANOM Approach

X- Chart Using ANOM Approach ISSN 1684-8403 Journal of Statstcs Volume 17, 010, pp. 3-3 Abstract X- Chart Usng ANOM Approach Gullapall Chakravarth 1 and Chaluvad Venkateswara Rao Control lmts for ndvdual measurements (X) chart are

More information

A fault tree analysis strategy using binary decision diagrams

A fault tree analysis strategy using binary decision diagrams Loughborough Unversty Insttutonal Repostory A fault tree analyss strategy usng bnary decson dagrams Ths tem was submtted to Loughborough Unversty's Insttutonal Repostory by the/an author. Addtonal Informaton:

More information

Introduction. Leslie Lamports Time, Clocks & the Ordering of Events in a Distributed System. Overview. Introduction Concepts: Time

Introduction. Leslie Lamports Time, Clocks & the Ordering of Events in a Distributed System. Overview. Introduction Concepts: Time Lesle Laports e, locks & the Orderng of Events n a Dstrbuted Syste Joseph Sprng Departent of oputer Scence Dstrbuted Systes and Securty Overvew Introducton he artal Orderng Logcal locks Orderng the Events

More information

Polyhedral Compilation Foundations

Polyhedral Compilation Foundations Polyhedral Complaton Foundatons Lous-Noël Pouchet pouchet@cse.oho-state.edu Dept. of Computer Scence and Engneerng, the Oho State Unversty Feb 8, 200 888., Class # Introducton: Polyhedral Complaton Foundatons

More information

The Shortest Path of Touring Lines given in the Plane

The Shortest Path of Touring Lines given in the Plane Send Orders for Reprnts to reprnts@benthamscence.ae 262 The Open Cybernetcs & Systemcs Journal, 2015, 9, 262-267 The Shortest Path of Tourng Lnes gven n the Plane Open Access Ljuan Wang 1,2, Dandan He

More information

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation

Quality Improvement Algorithm for Tetrahedral Mesh Based on Optimal Delaunay Triangulation Intellgent Informaton Management, 013, 5, 191-195 Publshed Onlne November 013 (http://www.scrp.org/journal/m) http://dx.do.org/10.36/m.013.5601 Qualty Improvement Algorthm for Tetrahedral Mesh Based on

More information

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization

Problem Definitions and Evaluation Criteria for Computational Expensive Optimization Problem efntons and Evaluaton Crtera for Computatonal Expensve Optmzaton B. Lu 1, Q. Chen and Q. Zhang 3, J. J. Lang 4, P. N. Suganthan, B. Y. Qu 6 1 epartment of Computng, Glyndwr Unversty, UK Faclty

More information

K-means and Hierarchical Clustering

K-means and Hierarchical Clustering Note to other teachers and users of these sldes. Andrew would be delghted f you found ths source materal useful n gvng your own lectures. Feel free to use these sldes verbatm, or to modfy them to ft your

More information

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration

Improvement of Spatial Resolution Using BlockMatching Based Motion Estimation and Frame. Integration Improvement of Spatal Resoluton Usng BlockMatchng Based Moton Estmaton and Frame Integraton Danya Suga and Takayuk Hamamoto Graduate School of Engneerng, Tokyo Unversty of Scence, 6-3-1, Nuku, Katsuska-ku,

More information

Support Vector Machines

Support Vector Machines /9/207 MIST.6060 Busness Intellgence and Data Mnng What are Support Vector Machnes? Support Vector Machnes Support Vector Machnes (SVMs) are supervsed learnng technques that analyze data and recognze patterns.

More information

Smoothing Spline ANOVA for variable screening

Smoothing Spline ANOVA for variable screening Smoothng Splne ANOVA for varable screenng a useful tool for metamodels tranng and mult-objectve optmzaton L. Rcco, E. Rgon, A. Turco Outlne RSM Introducton Possble couplng Test case MOO MOO wth Game Theory

More information

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) ,

VRT012 User s guide V0.1. Address: Žirmūnų g. 27, Vilnius LT-09105, Phone: (370-5) , Fax: (370-5) , VRT012 User s gude V0.1 Thank you for purchasng our product. We hope ths user-frendly devce wll be helpful n realsng your deas and brngng comfort to your lfe. Please take few mnutes to read ths manual

More information

Active Contours/Snakes

Active Contours/Snakes Actve Contours/Snakes Erkut Erdem Acknowledgement: The sldes are adapted from the sldes prepared by K. Grauman of Unversty of Texas at Austn Fttng: Edges vs. boundares Edges useful sgnal to ndcate occludng

More information

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices

An Application of the Dulmage-Mendelsohn Decomposition to Sparse Null Space Bases of Full Row Rank Matrices Internatonal Mathematcal Forum, Vol 7, 2012, no 52, 2549-2554 An Applcaton of the Dulmage-Mendelsohn Decomposton to Sparse Null Space Bases of Full Row Rank Matrces Mostafa Khorramzadeh Department of Mathematcal

More information

Solving Route Planning Using Euler Path Transform

Solving Route Planning Using Euler Path Transform Solvng Route Plannng Usng Euler Path ransform Y-Chong Zeng Insttute of Informaton Scence Academa Snca awan ychongzeng@s.snca.edu.tw Abstract hs paper presents a method to solve route plannng problem n

More information

5.1 The ISR: Overvieui. chapter

5.1 The ISR: Overvieui. chapter chapter 5 The LC-3 n Chapter 4, we dscussed the basc components of a computer ts memory, ts processng unt, ncludng the assocated temporary storage (usually a set of regsters), nput and output devces, and

More information

Transaction-Consistent Global Checkpoints in a Distributed Database System

Transaction-Consistent Global Checkpoints in a Distributed Database System Proceedngs of the World Congress on Engneerng 2008 Vol I Transacton-Consstent Global Checkponts n a Dstrbuted Database System Jang Wu, D. Manvannan and Bhavan Thurasngham Abstract Checkpontng and rollback

More information

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task

Term Weighting Classification System Using the Chi-square Statistic for the Classification Subtask at NTCIR-6 Patent Retrieval Task Proceedngs of NTCIR-6 Workshop Meetng, May 15-18, 2007, Tokyo, Japan Term Weghtng Classfcaton System Usng the Ch-square Statstc for the Classfcaton Subtask at NTCIR-6 Patent Retreval Task Kotaro Hashmoto

More information

Petri Net Based Software Dependability Engineering

Petri Net Based Software Dependability Engineering Proc. RELECTRONIC 95, Budapest, pp. 181-186; October 1995 Petr Net Based Software Dependablty Engneerng Monka Hener Brandenburg Unversty of Technology Cottbus Computer Scence Insttute Postbox 101344 D-03013

More information

Sequences, Datalog, and Transducers

Sequences, Datalog, and Transducers Journal of Computer and System Scences 57, 234259 (1998) Artcle No. SS981562 Sequences, Datalog, and Transducers Anthony Bonner Department of Computer Scence, Unversty of Toronto, Toronto, Canada M5S 1A1

More information

Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems Why System Evaluaton? Performance Evaluaton of Informaton Retreval Systems Many sldes n ths secton are adapted from Prof. Joydeep Ghosh (UT ECE) who n turn adapted them from Prof. Dk Lee (Unv. of Scence

More information

UB at GeoCLEF Department of Geography Abstract

UB at GeoCLEF Department of Geography   Abstract UB at GeoCLEF 2006 Mguel E. Ruz (1), Stuart Shapro (2), June Abbas (1), Slva B. Southwck (1) and Davd Mark (3) State Unversty of New York at Buffalo (1) Department of Lbrary and Informaton Studes (2) Department

More information

Report on On-line Graph Coloring

Report on On-line Graph Coloring 2003 Fall Semester Comp 670K Onlne Algorthm Report on LO Yuet Me (00086365) cndylo@ust.hk Abstract Onlne algorthm deals wth data that has no future nformaton. Lots of examples demonstrate that onlne algorthm

More information

Ontology Generator from Relational Database Based on Jena

Ontology Generator from Relational Database Based on Jena Computer and Informaton Scence Vol. 3, No. 2; May 2010 Ontology Generator from Relatonal Database Based on Jena Shufeng Zhou (Correspondng author) College of Mathematcs Scence, Laocheng Unversty No.34

More information

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data

A Fast Content-Based Multimedia Retrieval Technique Using Compressed Data A Fast Content-Based Multmeda Retreval Technque Usng Compressed Data Borko Furht and Pornvt Saksobhavvat NSF Multmeda Laboratory Florda Atlantc Unversty, Boca Raton, Florda 3343 ABSTRACT In ths paper,

More information

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance

Tsinghua University at TAC 2009: Summarizing Multi-documents by Information Distance Tsnghua Unversty at TAC 2009: Summarzng Mult-documents by Informaton Dstance Chong Long, Mnle Huang, Xaoyan Zhu State Key Laboratory of Intellgent Technology and Systems, Tsnghua Natonal Laboratory for

More information

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an

the nber of vertces n the graph. spannng tree T beng part of a par of maxmally dstant trees s called extremal. Extremal trees are useful n the mxed an On Central Spannng Trees of a Graph S. Bezrukov Unverstat-GH Paderborn FB Mathematk/Informatk Furstenallee 11 D{33102 Paderborn F. Kaderal, W. Poguntke FernUnverstat Hagen LG Kommunkatonssysteme Bergscher

More information

Math Homotopy Theory Additional notes

Math Homotopy Theory Additional notes Math 527 - Homotopy Theory Addtonal notes Martn Frankland February 4, 2013 The category Top s not Cartesan closed. problem. In these notes, we explan how to remedy that 1 Compactly generated spaces Ths

More information

A Facet Generation Procedure. for solving 0/1 integer programs

A Facet Generation Procedure. for solving 0/1 integer programs A Facet Generaton Procedure for solvng 0/ nteger programs by Gyana R. Parja IBM Corporaton, Poughkeepse, NY 260 Radu Gaddov Emery Worldwde Arlnes, Vandala, Oho 45377 and Wlbert E. Wlhelm Teas A&M Unversty,

More information

Wightman. Mobility. Quick Reference Guide THIS SPACE INTENTIONALLY LEFT BLANK

Wightman. Mobility. Quick Reference Guide THIS SPACE INTENTIONALLY LEFT BLANK Wghtman Moblty Quck Reference Gude THIS SPACE INTENTIONALLY LEFT BLANK WIGHTMAN MOBILITY BASICS How to Set Up Your Vocemal 1. On your phone s dal screen, press and hold 1 to access your vocemal. If your

More information

A NOTE ON FUZZY CLOSURE OF A FUZZY SET

A NOTE ON FUZZY CLOSURE OF A FUZZY SET (JPMNT) Journal of Process Management New Technologes, Internatonal A NOTE ON FUZZY CLOSURE OF A FUZZY SET Bhmraj Basumatary Department of Mathematcal Scences, Bodoland Unversty, Kokrajhar, Assam, Inda,

More information