University of California

About: University of California is a education organization based out in Oakland, California, United States. It is known for research contribution in the topics: Population & Layer (electronics). The organization has 55175 authors who have published 52933 publications receiving 1491169 citations. The organization is also known as: UC & University of California System.

Stellar SEDs from 0.3-2.5 Microns: Tracing the Stellar Locus and Searching for Color Outliers in SDSS and 2MASS

Abstract: The Sloan Digital Sky Survey (SDSS) and Two Micron All Sky Survey (2MASS) are rich resources for studying stellar astrophysics and the structure and formation history of the Galaxy. As new surveys and instruments adopt similar filter sets, it is increasingly important to understand the properties of the ugrizJHKs stellar locus, both to inform studies of `normal' main sequence stars as well as for robust searches for point sources with unusual colors. Using a sample of ~600,000 point sources detected by SDSS and 2MASS, we tabulate the position and width of the ugrizJHKs stellar locus as a function of g-i color, and provide accurate polynomial fits. We map the Morgan-Keenan spectral type sequence to the median stellar locus by using synthetic photometry of spectral standards and by analyzing 3000 SDSS stellar spectra with a custom spectral typing pipeline. We develop an algorithm to calculate a point source's minimum separation from the stellar locus in a seven-dimensional color space, and use it to robustly identify objects with unusual colors, as well as spurious SDSS/2MASS matches. Analysis of a final catalog of 2117 color outliers identifies 370 white-dwarf/M dwarf (WDMD) pairs, 93 QSOs, and 90 M giant/carbon star candidates, and demonstrates that WDMD pairs and QSOs can be distinguished on the basis of their J-Ks and r-z colors. We also identify a group of objects with correlated offsets in the u-g vs. g-r and g-r vs. r-i color-color spaces, but subsequent follow-up is required to reveal the nature of these objects. Future applications of this algorithm to a matched SDSS-UKIDSS catalog may well identify additional classes of objects with unusual colors by probing new areas of color-magnitude space.

Bitter to Better — How to Make Bitcoin a Better Currency

Abstract: Bitcoin is a distributed digital currency which has attracted a substantial number of users. We perform an in-depth investigation to understand what made Bitcoin so successful, while decades of research on cryptographic e-cash has not lead to a large-scale deployment. We ask also how Bitcoin could become a good candidate for a long-lived stable currency. In doing so, we identify several issues and attacks of Bitcoin, and propose suitable techniques to address them.

Modified green fluorescent proteins

Abstract: Modifications in the sequence of Aequorea wild-type GFP provide products having markedly different excitation and emission spectra from corresponding products from wild-type GFP. In one class of modifications, the product derived from the modified GFP exhibits an alteration in the ratio of two main excitation peaks observed with the product derived from wild-type GFP. In another class, the product derived from the modified GFP fluoresces at a shorter wavelength than the corresponding product from wild-type GFP. In yet another class of modifications, the product derived from the modified GFP exhibits only a single excitation peak and enhanced emission relative to the product derived from wild-type GFP.

Learning-Based View Synthesis for Light Field Cameras

Abstract: With the introduction of consumer light field cameras, light field imaging has recently become widespread. However, there is an inherent trade-off between the angular and spatial resolution, and thus, these cameras often sparsely sample in either spatial or angular domain. In this paper, we use machine learning to mitigate this trade-off. Specifically, we propose a novel learning-based approach to synthesize new views from a sparse set of input views. We build upon existing view synthesis techniques and break down the process into disparity and color estimation components. We use two sequential convolutional neural networks to model these two components and train both networks simultaneously by minimizing the error between the synthesized and ground truth images. We show the performance of our approach using only four corner sub-aperture views from the light fields captured by the Lytro Illum camera. Experimental results show that our approach synthesizes high-quality images that are superior to the state-of-the-art techniques on a variety of challenging real-world scenes. We believe our method could potentially decrease the required angular resolution of consumer light field cameras, which allows their spatial resolution to increase.