The Extremely Luminous Quasar Survey (ELQS) in the SDSS footprint I.: Infrared Based Candidate Selection

Studies of the most luminous quasars at high redshift directly probe the evolution of the most massive black holes in the early Universe and their connection to massive galaxy formation. However, extremely luminous quasars at high redshift are very rare objects. Only wide area surveys have a chance to constrain their population. The Sloan Digital Sky Survey (SDSS) has so far provided the most widely adopted measurements of the quasar luminosity function (QLF) at $z>3$. However, a careful re-examination of the SDSS quasar sample revealed that the SDSS quasar selection is in fact missing a significant fraction of $z\gtrsim3$ quasars at the brightest end. We have identified the purely optical color selection of SDSS, where quasars at these redshifts are strongly contaminated by late-type dwarfs, and the spectroscopic incompleteness of the SDSS footprint as the main reasons. Therefore we have designed the Extremely Luminous Quasar Survey (ELQS), based on a novel near-infrared JKW2 color cut using WISE AllWISE and 2MASS all-sky photometry, to yield high completeness for very bright ($m_{\rm{i}} < 18.0$) quasars in the redshift range of $3.0\leq z\leq5.0$. It effectively uses random forest machine-learning algorithms on SDSS and WISE photometry for quasar-star classification and photometric redshift estimation. The ELQS will spectroscopically follow-up $\sim 230$ new quasar candidates in an area of $\sim12000\,\rm{deg}^2$ in the SDSS footprint, to obtain a well-defined and complete quasars sample for an accurate measurement of the bright-end quasar luminosity function at $3.0\leq z\leq5.0$. In this paper we present the quasar selection algorithm and the quasar candidate catalog.Comment: 16 pages, 8 figures, 9 tables; ApJ in pres

Towards Understanding Adversarial Robustness of Optical Flow Networks

Recent work demonstrated the lack of robustness of optical flow networks to physical, patch-based adversarial attacks. The possibility to physically attack a basic component of automotive systems is a reason for serious concerns. In this paper, we analyze the cause of the problem and show that the lack of robustness is rooted in the classical aperture problem of optical flow estimation in combination with bad choices in the details of the network architecture. We show how these mistakes can be rectified in order to make optical flow networks robust to physical, patch-based attacks. Additionally, we take a look at global white-box attacks in the scope of optical flow. We find that targeted white-box attacks can be crafted to bias flow estimation models towards any desired output, but this requires access to the input images and model weights. Our results indicate that optical flow networks are robust to universal attacks

Sparse and Redundant Representations for Inverse Problems and Recognition

Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented