New spectral classification technique for X-ray sources: quantile analysis

We present a new technique called "quantile analysis" to classify spectral properties of X-ray sources with limited statistics. The quantile analysis is superior to the conventional approaches such as X-ray hardness ratio or X-ray color analysis to study relatively faint sources or to investigate a certain phase or state of a source in detail, where poor statistics does not allow spectral fitting using a model. Instead of working with predetermined energy bands, we determine the energy values that divide the detected photons into predetermined fractions of the total counts such as median (50%), tercile (33% & 67%), and quartile (25% & 75%). We use these quantiles as an indicator of the X-ray hardness or color of the source. We show that the median is an improved substitute for the conventional X-ray hardness ratio. The median and other quantiles form a phase space, similar to the conventional X-ray color-color diagrams. The quantile-based phase space is more evenly sensitive over various spectral shapes than the conventional color-color diagrams, and it is naturally arranged to properly represent the statistical similarity of various spectral shapes. We demonstrate the new technique in the 0.3-8 keV energy range using Chandra ACIS-S detector response function and a typical aperture photometry involving background subtraction. The technique can be applied in any energy band, provided the energy distribution of photons can be obtained.Comment: 11 pages, 9 figures, accepted for publication in Ap

3.5Å cryoEM structure of hepatitis B virus core assembled from full-length core protein.

The capsid shell of infectious hepatitis B virus (HBV) is composed of 240 copies of a single protein called HBV core antigen (HBc). An atomic model of a core assembled from truncated HBc was determined previously by X-ray crystallography. In an attempt to obtain atomic structural information of HBV core in a near native, non-crystalline environment, we reconstructed a 3.5Å-resolution structure of a recombinant core assembled from full-length HBc by cryo electron microscopy (cryoEM) and derived an atomic model. The structure shows that the 240 molecules of full-length HBc form a core with two layers. The outer layer, composed of the N-terminal assembly domain, is similar to the crystal structure of the truncated HBc, but has three differences. First, unlike the crystal structure, our cryoEM structure shows no disulfide bond between the Cys61 residues of the two subunits within the dimer building block, indicating such bond is not required for core formation. Second, our cryoEM structure reveals up to four more residues in the linker region (amino acids 140-149). Third, the loops in the cryoEM structures containing this linker region in subunits B and C are oriented differently (~30° and ~90°) from their counterparts in the crystal structure. The inner layer, composed of the C-terminal arginine-rich domain (ARD) and the ARD-bound RNAs, is partially-ordered and connected with the outer layer through linkers positioned around the two-fold axes. Weak densities emanate from the rims of positively charged channels through the icosahedral three-fold and local three-fold axes. We attribute these densities to the exposed portions of some ARDs, thus explaining ARD's accessibility by proteases and antibodies. Our data supports a role of ARD in mediating communication between inside and outside of the core during HBV maturation and envelopment

Hotels-50K: A Global Hotel Recognition Dataset

Recognizing a hotel from an image of a hotel room is important for human trafficking investigations. Images directly link victims to places and can help verify where victims have been trafficked, and where their traffickers might move them or others in the future. Recognizing the hotel from images is challenging because of low image quality, uncommon camera perspectives, large occlusions (often the victim), and the similarity of objects (e.g., furniture, art, bedding) across different hotel rooms. To support efforts towards this hotel recognition task, we have curated a dataset of over 1 million annotated hotel room images from 50,000 hotels. These images include professionally captured photographs from travel websites and crowd-sourced images from a mobile application, which are more similar to the types of images analyzed in real-world investigations. We present a baseline approach based on a standard network architecture and a collection of data-augmentation approaches tuned to this problem domain