Can Heavy WIMPs Be Captured by the Earth?

If weakly interacting massive particles (WIMPs) in bound solar orbits are systematically driven into the Sun by solar-system resonances (as Farinella et al. have shown is the case for many Earth-crossing asteroids), then the capture of high-mass WIMPs by the Earth would be affected dramatically because high-mass WIMPs are captured primarily from bound orbits. WIMP capture would be eliminated for M_x>630 GeV and would be highly suppressed for M_x>~150 GeV. Annihilation of captured WIMPs and anti-WIMPs is expected to give rise to neutrinos coming from the Earth's center. The absence of such a neutrino signal has been used to place limits on WIMP parameters. At present, one does not know if typical WIMP orbits are in fact affected by these resonances. Until this question is investigated and resolved, one must (conservatively) assume that they are. Hence, limits on high-mass WIMP parameters are significantly weaker than previously believed.Comment: 8 pages + 1 figure. Submitted to Ap

Orientation of Demagnetized Bees

The orientation of honey bee dances is affected by the earth's magnetic field. Honey bees possess localized, well-oriented, stable and superparamagnetic domains of magnetite. Four lines of evidence suggest that the superparamagnetic domains of bees are more likely to be involved in magnetic field detectors than the stable domains. (1) Although the stable domains vary widely in size and number between bees, approximately 2×10^8 superparamagnetic domains are found reliably in all bees, and are restricted to there latively narrow size range of 300–350 Å. This suggests that the superparamagnetic domains are more likely to have a biological function. (2) Behavioural observations of dances in null fields are difficult to reconcile with astable-domain detector but are clearly predicted by many superparamagnetic detector models. (3) When honey bees are demagnetized, their ability to orient to the earth's field is unaffected. This suggests that the detector either utilizes the super paramagnetic domains or depends on aligned anisotropic stable domains processed without regard to magneticpolarity. (4) Bees that have only superparamagnetic domains are able nevertheless to orient to the earth's magnetic field, a phenomenon which indicates that permanent domains may not be required for detection

Simple model of self-organized biological evolution as completely integrable dissipative system

The Bak-Sneppen model of self-organized biological evolution of an infinite ecosystem of randomly interacting species is represented in terms of an infinite set of variables which can be considered as an analog to the set of integrals of motion of completely integrable system. Each of this variables remains to be constant but its influence on the evolution process is restricted in time and after definite moment its value is excluded from description of the system dynamics.Comment: LaTeX, 7 page

Superpixel Convolutional Networks using Bilateral Inceptions

In this paper we propose a CNN architecture for semantic image segmentation. We introduce a new 'bilateral inception' module that can be inserted in existing CNN architectures and performs bilateral filtering, at multiple feature-scales, between superpixels in an image. The feature spaces for bilateral filtering and other parameters of the module are learned end-to-end using standard backpropagation techniques. The bilateral inception module addresses two issues that arise with general CNN segmentation architectures. First, this module propagates information between (super) pixels while respecting image edges, thus using the structured information of the problem for improved results. Second, the layer recovers a full resolution segmentation result from the lower resolution solution of a CNN. In the experiments, we modify several existing CNN architectures by inserting our inception module between the last CNN (1x1 convolution) layers. Empirical results on three different datasets show reliable improvements not only in comparison to the baseline networks, but also in comparison to several dense-pixel prediction techniques such as CRFs, while being competitive in time.Comment: European Conference on Computer Vision (ECCV), 201