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Determine the antigen patterns as training set DT ; for each antigen pattern, zp DT do nb = 0; bcell bind = f alse; Randomly select nf free antibodies from set F as subset E; for each free antibody, yj E do Calculate the a nity, fa (zp , yj ); if fa (zp , yj ) < aF then nb + +; Remove free antibody yj from set F; end end for each randomly selected B-Cell, uk , at index position, k, in B-Cell set, B do Calculate the a nity, fa (zp , uk ); if fa (zp , uk ) < aB then bcell bind = true; break; end end if not bcell bind then Initialize new B-Cell, unew , with zp and add to B-Cell set B; Produce fF (zp , unew ) free antibodies and add to free-antibody set F; end else Produce fF ( zp , uk ) free antibodies and add to free-antibody set F Update stimulation level of uk by adding nb ; if stimulation level of uk B then Clone B-Cell, uk , as uk ; Mutate uk as uk ; Add uk to B-Cell set B; Select memory cell, vmin , from M as memory cell with lowest a nity, fa , to uk ; u if fa ( k , vmin ) < aM then Add uk to memory set M; end else if fa (zp , uk ) < fa (zp , vmin ) then Replace memory cell, vmin , with clone, uk , in memory set, M; end end end end end for each cell, xi , in set F B M do if living time of xi > F ,B,M then Remove xi from corresponding set; end end.

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The network theory was rst modeled by Timmis and Neal [846] resulting in the arti cial immune network (AINE) AINE de nes the new concept of arti cial recognition balls (ARBs), which are bounded by a resource limited environment In summary, AINE consists of a population of ARBs, links between the ARBs, a set of antigen training patterns (of which a cross section is used o initialize the ARBs) and some clonal operations for learning An ARB represents a region of antigen space that is covered by a certain type of B-Cell ARBs that are close to each other (similar) in antigen space are connected with weighted edges to form a number of individual network structures The similarity (a nity) between ARBs and between the ARBs and an antigen is measured using Euclidean distance Two ARBs are connected if the a nity between them is below the network a nity threshold, an , calculated as the average distance between all the antigen patterns in the training set, DT Algorithm 195 summarizes AINE For each iteration, all training patterns in set DT are presented to the set of ARBs, A After each iteration, each ARB, wi , calculates its stimulation level, larb,i , and allocates resources (ie B-Cells) based on its stimulation level as de ned in equation (197) The stimulation level, larb,i , of an ARB, wi , is calculated as the summation of the antigen stimulation, la,i , the network stimulation, ln,i , and the network suppression, sn,i The stimulation level of an ARB is de ned as [626] larb,i (wi ) = la,i (wi ) + ln,i (wi ) + sn,i (wi ) with.

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V on P The start time of the free node n V .This determination of the start time is here called end technique, as node n is scheduled at the end of all other nodes scheduled on processor P In Section 61, another echnique is presented the insertion technique which can compute earlier start times, yet with a higher complexity Since ts (n, P) tdr (n, P), ts (n, P) complies with Condition 43 that is, it complies with Condition 42 for all predecessors of node n Furthermore, as ts (n, P) tf (P) (here tf (P) is the nish time of P in the partial schedule Scur ), ts (n, P) cannot violate Condition 41 either An interesting property of the end technique is that it produces a schedule of optimal length if the nodes are scheduled in the right order on the right processor Theorem 51 (End Technique Is Optimal) Let S be a feasible schedule for task graph G = (V, E, w, c) on system P Using simple list scheduling (Algorithm 9), the schedule Send is created employing the end technique (De nition 52), whereby the nodes are scheduled in nondecreasing order of their start times in S and allocated to the same processors as in S Then sl(Send ) sl(S) (52). QR-Code In .NET Framework Using Barcode maker for ASP .Related: Generate Interleaved 2 of 5 .NET , Create EAN-8 Excel , VB.NET Data Matrix Generating

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Dynamically compute the priorities of the free nodes by evaluating the start time for every free node on every processor That node of all free nodes that llows the earliest start time is selected together with the processor on which this time is achieved What is the resulting schedule length 54 Create a linear clustering for the task graph of Exercise 52 Draw the scheduled task graph (De nition 55) for the nal clustering What is its dominant sequence .Research: Experimentally compare single-phase with multiple-phase scheduling heuristics for a limited number of processors For single-phase heuristics, considere variants of list scheduling with different priority schemes, static as well as dynamic For multiphase scheduling, consider the three-phase clustering based algorithm as discussed in Section 531 Employ ifferent clustering heuristics in the rst phase, as well as different cluster-to-processor mappings in the second phase (a) Implement algorithms (b) Experimentally compare them by scheduling a large set of different task graphs (c) Document results in an article (d) Publish article in peer-reviewed conference or journal. Creator In Visual C# Using Barcode maker for VS .Related: Create QR Code Word , VB.NET ITF-14 Generating , Print Code 39 C#

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Algorithm 12 Create Node List 1: Assign a priority to each n V 2: Put source nodes n V : pred(n) = into priority queue Q 3: while Q = do Q only contains free nodes 4: Let n be the node of Q with highest priority; remove n from Q 5: Append n to list L 6: Put {ni succ(n) : pred(ni ) L} into Q 7: end while which costs O(log V) and the determination of the free nodes amortizes o O(E) Removing the node with the highest priority from the heap is O(1) An example for a metric that is not compatible with the precedence order is tl + bl a metric that gives preference to the nodes of the critical path Table 51 displays several node orders of the sample task graph obtained with node level based priority metrics The values of the node levels are given in Table 41 As analyzed in Section 44, calculating these levels has a complexity of O(V + E) For all these metrics the rst part of the simple list scheduling is thus O(V log V + E) (calculating levels: O(V + E) + sorting: O(V log V) or O(V log V + E)) Already the few examples in Table 51 illustrate the large variety of possible node orders In experiments, the relevance of the node order for the schedule length was demonstrated (eg, Adam et al [2], Sinnen and Sousa [177]) Unfortunately, most comparisons of scheduling algorithms (eg, Ahmad et al [6], Gerasoulis and Yang [76], Khan et al [103], Kwok and Ahmad [111], McCreary et al [136]) analyze entire algorithms: that is, the algorithms differ not only in one but many aspects This generally impedes one from drawing conclusions on discrete aspects of the algorithms (eg, on the node order) Still, most algorithms apply orders based on node levels and the bottom level order exhibits good average performance (eg, Adam et al [2], Sinnen and Sousa [177]) Breaking Ties Table 51 also shows examples where the node order is not fully de ned, indicated in the table by parentheses around the respective nodes Incidentally,.

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The algorithm concentrates on the next node of the dynamic sequence nDS , even if it is only partially free The free nodes that are scheduled until nDS becomes free are not llowed to have a negative effect on the scheduling of nDS Hence, DSC implements a list scheduling with a lookahead technique The node priority is the sum of the allocated bottom level and allocated top level of the scheduled task graph, tl(n, Ci 1 ) + bl(n, Ci 1 ) in Gscheduled Note that the bottom level of a node does not change during list scheduling (Section 513), that is, (bl(n, Ci 1 ) in Gscheduled ) = (bl(n) in G) To reduce complexity, the top level is estimated in a form that does not change the behavior of the algorithm, but does not require a complete traversal of the task graph.

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