Spiders are ecologically important predators with complex venom and extraordinarily tough silk that enables capture of large prey. Here we present the assembled genome of the social velvet spider and a draft assembly of the tarantula genome that represent two major taxonomic groups of spiders. The spider genomes are large with short exons and long introns, reminiscent of mammalian genomes. Phylogenetic analyses place spiders and ticks as sister groups supporting polyphyly of the Acari. Complex sets of venom and silk genes/proteins are identified. We find that venom genes evolved by sequential duplication, and that the toxic effect of venom is most likely activated by proteases present in the venom. The set of silk genes reveals a highly dynamic gene evolution, new types of silk genes and proteins, and a novel use of aciniform silk. These insights create new opportunities for pharmacological applications of venom and biomaterial applications of silk.

This paper studies the consensus problem for a class of general second-order multi-agent systems (MASs) with communication delay. We first consider the delay-free case and obtain a necessary and sufficient condition for consensus. Then, based on the obtained conditions for the delay-free case, we deduce an explicit formula for the delay margin of the consensus for the case with time delay using the relationship between the roots of the characteristic equation and the time delay parameter. In addition, we consider the special case where the second-order model is a double integrator. For this case, simpler consensus conditions under communication delay are provided.

Stone" "Mohawk hairstyle" "Without makeup" "Cute cat" "Lion" "Gothic church" * Equal contribution, ordered alphabetically. Code and video are available on https://github.com/orpatashnik/StyleCLIP

The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimator leads to the well-known unscented Kalman filter (UKF). Although the UKF works very well in Gaussian noises, its performance may deteriorate significantly when the noises are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises. To improve the robustness of the UKF against impulsive noises, a new filter for nonlinear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF). In MCUF, the UT is applied to obtain the prior estimates of the state and covariance matrix, and a robust statistical linearization regression based on the maximum correntropy criterion (MCC) is then used to obtain the posterior estimates of the state and covariance. The satisfying performance of the new algorithm is confirmed by two illustrative examples.

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Abstract:The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE) criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable) noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm.

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