The Primary Dealer Credit Facility (PDCF) (U.S. GFC)

On March 16, 2008, the Federal Reserve created the Primary Dealer Credit Facility, or PDCF, to provide overnight funding to primary dealers in the tri-party repurchase agreement (repo) market, where lenders had become increasingly risk averse. Loans were fully secured by (initially) investment-grade securities and offered at the primary credit rate by the Federal Reserve Bank of New York. The eligible collateral was significantly expanded in September 2008, after rumors of Lehman Brothers potentially filing for bankruptcy, to include all of the types of instruments that could be pledged at the two major tri-party repo clearing banks. The PDCF was a means for the Federal Reserve to provide lender-of-last-resort funding directly to primary dealers, including the five largest US investment banks, which it could not do before. The program also served to buy time for dealers to find other methods of financing. During its tenure, the facility was actively used, with the highest daily amount of outstanding loans at $130 billion, which occurred in September 2008. Overall, 18 of the 20 primary dealers participated in the program, although, unlike the other major program targeting primary dealers, the Term Securities Lending Facility, most participation was by US firms. The facility was closed on February 1, 2010. All loans extended under this facility were repaid in full, with$593 million in interest and fees collected. It has been credited, with other similar programs, with relieving the severe liquidity stresses on primary dealers during the height of the crisis

How Do Fairness Definitions Fare? Examining Public Attitudes Towards Algorithmic Definitions of Fairness

What is the best way to define algorithmic fairness? While many definitions of fairness have been proposed in the computer science literature, there is no clear agreement over a particular definition. In this work, we investigate ordinary people's perceptions of three of these fairness definitions. Across two online experiments, we test which definitions people perceive to be the fairest in the context of loan decisions, and whether fairness perceptions change with the addition of sensitive information (i.e., race of the loan applicants). Overall, one definition (calibrated fairness) tends to be more preferred than the others, and the results also provide support for the principle of affirmative action.Comment: To appear at AI Ethics and Society (AIES) 201

HOW AGN JETS HEAT the INTRACLUSTER MEDIUM - INSIGHTS from HYDRODYNAMIC SIMULATIONS

© 2016. The American Astronomical Society. All rights reserved. Feedback from active galactic nuclei (AGNs) is believed to prevent catastrophic cooling in galaxy clusters. However, how the feedback energy is transformed into heat, and how the AGN jets heat the intracluster medium (ICM) isotropically, still remain elusive. In this work, we gain insights into the relative importance of different heating mechanisms using three-dimensional hydrodynamic simulations including cold gas accretion and momentum-driven jet feedback, which are the most successful models to date in terms of reproducing the properties of cool cores. We find that there is net heating within two "jet cones" (within ∼30° from the axis of jet precession) where the ICM gains entropy by shock heating and mixing with the hot thermal gas within bubbles. Outside the jet cones, the ambient gas is heated by weak shocks, but not enough to overcome radiative cooling, therefore, forming a "reduced" cooling flow. Consequently, the cluster core is in a process of "gentle circulation" over billions of years. Within the jet cones, there is significant adiabatic cooling as the gas is uplifted by buoyantly rising bubbles; outside the cones, energy is supplied by the inflow of already-heated gas from the jet cones as well as adiabatic compression as the gas moves toward the center. In other words, the fluid dynamics self-adjusts such that it compensates and transports the heat provided by the AGN, and hence no fine-tuning of the heating profile of any process is necessary. Throughout the cluster evolution, turbulent energy is only at the percent level compared to gas thermal energy, and thus turbulent heating is not the main source of heating in our simulation

Extend the shallow part of Single Shot MultiBox Detector via Convolutional Neural Network

Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the current object detection field, which uses fully convolutional neural network to detect all scaled objects in an image. Deconvolutional Single Shot Detector (DSSD) is an approach which introduces more context information by adding the deconvolution module to SSD. And the mean Average Precision (mAP) of DSSD on PASCAL VOC2007 is improved from SSD's 77.5% to 78.6%. Although DSSD obtains higher mAP than SSD by 1.1%, the frames per second (FPS) decreases from 46 to 11.8. In this paper, we propose a single stage end-to-end image detection model called ESSD to overcome this dilemma. Our solution to this problem is to cleverly extend better context information for the shallow layers of the best single stage (e.g. SSD) detectors. Experimental results show that our model can reach 79.4% mAP, which is higher than DSSD and SSD by 0.8 and 1.9 points respectively. Meanwhile, our testing speed is 25 FPS in Titan X GPU which is more than double the original DSSD.Comment: 7 pages, 3 figures, 3 table