Social signaling via bioluminescent blinks determines nearest neighbor distance in schools of flashlight fish Anomalops katoptron

The schooling flashlight fish Anomalops katoptron can be found at dark nights at the water surface in the Indo-Pacific. Schools are characterized by bioluminescent blink patterns of sub-ocular light organs densely-packed with bioluminescent, symbiotic bacteria. Here we analyzed how blink patterns of A. katoptron are used in social interactions. We demonstrate that isolated specimen of A. katoptron showed a high motivation to align with fixed or moving artificial light organs in an experimental tank. This intraspecific recognition of A. katoptron is mediated by blinking light and not the body shape. In addition, A. katoptron adjusts its blinking frequencies according to the light intensities. LED pulse frequencies determine the swimming speed and the blink frequency response of A. katoptron, which is modified by light organ occlusion and not exposure. In the natural environment A. katoptron is changing its blink frequencies and nearest neighbor distance in a context specific manner. Blink frequencies are also modified by changes in the occlusion time and are increased from day to night and during avoidance behavior, while group cohesion is higher with increasing blink frequencies. Our results suggest that specific blink patterns in schooling flashlight fish A. katoptron define nearest neighbor distance and determine intraspecific communication.

Robust Spectrum Sensing via Double-Sided Neighbor Distance Based on Genetic Algorithm in Cognitive Radio Networks

In cognitive radio networks (CRNs), secondary users (SUs) can access vacant spectrum licensed to a primary user (PU). Therefore, accurate and timely spectrum sensing is vital for efficient utilization of available spectrum. The sensing result at each SU is unauthentic due to fading, shadowing, and receiver uncertainty problems. Cooperative spectrum sensing (CSS) provides a solution to these problems. In CSS, false sensing reports at the fusion center (FC) received from malicious users (MUs) drastically degrade the performance of cooperation in PU detection. In this paper, we propose a robust spectrum sensing scheme to minimize the effects of false sensing reports by MUs. The proposed scheme focuses on double-sided neighbor distance (DSND) based on genetic algorithm (GA) in order to filter out the MU sensing reports in CSS. The simulation results show that the sensing results are more accurate and reliable for the proposed GA majority-voting hard decision fusion (GAMV-HDF) and GA weighted soft decision fusion (GAW-SDF) compared to conventional equal gain combination soft decision fusion (EGC-SDF), maximum gain combination soft decision fusion (MGC-SDF), and majority-voting hard decision fusion (MV-HDF) schemes in the presence of MUs.

Social signaling via bioluminescent blinks drives schooling behavior in the flashlight fish Anomalops katoptron

The bioluminescent flashlight fish Anomalops katoptron live in schools of several hundred specimens. To understand how flashlight fish, integrate bioluminescent signaling into their schooling behavior, we analyzed movement profiles and blink frequencies. Isolated specimen of A. katoptron show a high motivation to align with fixed or moving artificial light organs. Depending on presented frequencies A. katoptron responds with a reduction in swimming speed and its own blink frequency. Higher presented blink frequencies reduce the nearest neighbor distance. In the natural environment A. katoptron is changing its blink frequencies and nearest neighbor distance in a context specific manner. Blink frequencies are increased from day to night and during avoidance behavior, while nearest neighbor distance is decreased with increasing blink frequencies. A. katoptron changes its blink frequencies by modifying light organ occlusion. Our results suggest that visually transmitted information via specific blink patterns determine intraspecific communication and group cohesion in schooling A. katoptron.

High Dimensional Clustering with r-nets

Clustering, a fundamental task in data science and machine learning, groups a set of objects in such a way that objects in the same cluster are closer to each other than to those in other clusters. In this paper, we consider a well-known structure, so-called r-nets, which rigorously captures the properties of clustering. We devise algorithms that improve the runtime of approximating r-nets in high-dimensional spaces with1 and `2 metrics from, where . These algorithms are also used to improve a framework that provides approximate solutions to other high dimensional distance problems. Using this framework, several important related problems can also be solved efficiently, e.g.,pproximate kth-nearest neighbor distance-approximate Min-Max clustering,-approximate k-center clustering. In addition, we build an algorithm that-approximates greedy permutations in time O˜((dn+n2−α)·logΦ) where Φ is the spread of the input. This algorithm is used to -approximate k-center with the same time complexity.