Asymmetric Dark Matter from Leptogenesis

We present a new realization of asymmetric dark matter in which the dark matter and lepton asymmetries are generated simultaneously through two-sector leptogenesis. The right-handed neutrinos couple both to the Standard Model and to a hidden sector where the dark matter resides. This framework explains the lepton asymmetry, dark matter abundance and neutrino masses all at once. In contrast to previous realizations of asymmetric dark matter, the model allows for a wide range of dark matter masses, from keV to 10 TeV. In particular, very light dark matter can be accommodated without violating experimental constraints. We discuss several variants of our model that highlight interesting phenomenological possibilities. In one, late decays repopulate the symmetric dark matter component, providing a new mechanism for generating a large annihilation rate at the present epoch and allowing for mixed warm/cold dark matter. In a second scenario, dark matter mixes with the active neutrinos, thus presenting a distinct method to populate sterile neutrino dark matter through leptogenesis. At late times, oscillations and dark matter decays lead to interesting indirect detection signals.Comment: 32 pages + appendix, references added, minor change

Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Neural Network

© 2019, Springer Nature Switzerland AG. In this paper, we present R-CNN, Fast R-CNN and Faster R-CNN methods to automatically detect and recognise the predators in underwater videos. We compare the results of these methods on real data and discuss their strengths and weaknesses. We build a dataset using footage captured from representative environment of the wild and devise a data model with three classes (seal, dolphin, background). Following this, we train R-CNN, Fast R-CNN and Faster R-CNN, then evaluate them on a test dataset compose of challenging objects that had not been seen during training. We perform evaluation on GPU, acquiring information about the AP and IOU for each model and network based on various proposal numbers as well as runtime speeds. Based on the results, we found that the best model of predator detection using visual deep learning models is Faster R-CNN with 2000 proposals

A Vibrational Spectral Maker for Probing the Hydrogen-Bonding Status of Protonated Asp and Glu Residues

Hydrogen bonding is a fundamental element in protein structure and function. Breaking a single hydrogen bond may impair the stability of a protein. We report an infrared vibrational spectral marker for probing the hydrogen-bond number for buried, protonated Asp or Glu residues in proteins. Ab initio computational studies were performed on hydrogen-bonding interactions of a COOH group with a variety of side-chain model compounds of polar and charged amino acids in vacuum using density function theory. For hydrogen-bonding interactions with polar side-chain groups, our results show a strong correlation between the C=O stretching frequency and the hydrogen bond number of a COOH group: ∼1759–1776 cm(−1) for zero, ∼1733–1749 cm(−1) for one, and 1703–1710 cm(−1) for two hydrogen bonds. Experimental evidence for this correlation will be discussed. In addition, we show an approximate linear correlation between the C=O stretching frequency and the hydrogen-bond strength. We propose that a two-dimensional infrared spectroscopy, C=O stretching versus O-H stretching, may be employed to identify the specific type of hydrogen-bonding interaction. This vibrational spectral marker for hydrogen-bonding interaction is expected to enhance the power of time-resolved Fourier transform infrared spectroscopy for structural characterization of functionally important intermediates of proteins