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5 results for “Artificial immune system”
Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
FIGURES 2 a-g -AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>The program was implemented in Matlab and tested with several patterns. The first, dataset1<br> consisted of 2 patterns each comprised of 100 points falling on two concentric circles of radii 10<br> and 20 respectively. The second, dataset2 consisted of 3 patterns each of 100 points falling on three<br> concentric circles of radii 10, 15, and 20 respectively. The model was further tested for its<br> capability to find clusters in patterns of open spatial form using dataset3 and dataset4 consisting of<br> 200 and 300 points falling on 2 and 3 concentric semi circles respectively. As shown in the<br> Figure2.a and Figure2.b, the algorithm is capable of determining spatial association of a data point<br> with other data points belonging to its appropriate circle only. The results successfully demonstrated<br> the capability of our model to automatically detect clean clusters of arbitrary shapes in the input<br> data represented in closed spatial form. The model was found even capable of determining clusters<br> of open spatial forms also, as shown in Figure2.c and Figure2.e. However, the output of the<br> algorithm was found affected by the values of the algorithm parameters k and a. In the present<br> experiment, k =8 and a =10 was sufficient for performing correct cluster associations. On the other<br> hand, correct clustering for the dataset2, could be obtained with 10NN estimation i.e. k =10, with<br> a.=15. Moreover setting k =15, with a.=15 was required for dataset4, as clustering error was<br> observed with k =10, with a.=15, as in Figure2.d. Figure2.f and Figure2.g show the correct<br> clustering even in presence of combination of open and closed form of input patterns. In each<br> figure, the first sub-plot shows the original data and the second sub-plot shows the clusters<br> identified by our program.</p>
Figure3. APCs A1-A4 connected within range of cohesion-factor-threshold form members of one ARB-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>Figure3 depicts this process. The<br> model with the above specification then effectively detects self or non-self pathogens. In terms of<br> its application to the task of clustering, this interpretation means making the affinities high within<br> clusters and low across clusters. A pathogen corresponding to an outlier would not stimulate a TCR<br> sufficiently and may not form part of any ARB.</p>
Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization
<p>Data resulting from the experiments described in "Leveraging diversity in computer-aided musical orchestration with an artificial immune system for multi-modal optimization" (https://doi.org/10.1016/j.swevo.2018.12.010). The contents of the files is the following:</p> <ul> <li>CAMO-AIS_SWEVO.zip: All of the data below in a single file</li> <li>CAMO_Iowa.zip: Audio and Data for the orchestrations with the Iowa sound database found at http://theremin.music.uiowa.edu/MIS.html</li> <li>CAMO_Phil.zip: Audio and Data for the orchestrations with the Philharmonia sound database found at https://www.philharmonia.co.uk/explore/sound_samples</li> <li>CAMO_RWC.zip: Audio and Data for the orchestrations with the RWC sound database found at https://staff.aist.go.jp/m.goto/RWC-MDB/rwc-mdb-i.html</li> <li>CAMO_SOL.zip: Audio and Data for the orchestrations with the Studio Online sound database available with Orchids http://forumnet.ircam.fr/product/orchids-en/</li> <li>Listening_Test.zip: Raw data (i.e., perceptual similarity ratings) from the listening test found at http://http://camo.inesctec.pt/. This data is anonymous (each participant is assigned a reference number) so the participants cannot be identified.</li> </ul> <p>See the README.txt file for a detailed description of the contents of each file.</p>
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