Resurrected DSCOVR Propulsion System - Challenges and Lessons Learned

The Deep Space Climate Observatory (DSCOVR), formerly known as Triana, is a unique mission, not because of its objectives but because of how long it was in storage before launch. The Triana spacecraft was built in the late 90s and later renamed as DSCOVR, but the project was canceled before the spacecraft was launched. The nearly-complete spacecraft was put in controlled storage for 10 years, until the National Oceanic and Atmospheric Administration (NOAA) provided funding for the National Aeronautics and Space Administration (NASA) to refurbish the spacecraft. On February 11, 2015, DSCOVR was launched on a Falcon 9 v1.1 from launch complex 40 at Cape Canaveral Air Force Station. This paper describes the DSCOVR propulsion system, which utilizes ten 4.5 N thrusters in blowdown mode to perform Midcourse Correction (MCC) maneuvers, Lissajous Orbit Insertion (LOI) at Lagrangian point L1, momentum unloading maneuvers, and station keeping delta-v maneuvers at L1. This paper also describes the testing that was performed, including susbsystem-level and spacecraft-level tests, to verify the propulsion system's integrity for flight. Finally, this paper concludes with a discussion of the challenges and lessons learned during this unique mission, including replacement of a bent thruster and installation of an auxiliary heater over existing propellant line heaters

Bounds on inference

Lower bounds for the average probability of error of estimating a hidden variable X given an observation of a correlated random variable Y, and Fano's inequality in particular, play a central role in information theory. In this paper, we present a lower bound for the average estimation error based on the marginal distribution of X and the principal inertias of the joint distribution matrix of X and Y. Furthermore, we discuss an information measure based on the sum of the largest principal inertias, called k-correlation, which generalizes maximal correlation. We show that k-correlation satisfies the Data Processing Inequality and is convex in the conditional distribution of Y given X. Finally, we investigate how to answer a fundamental question in inference and privacy: given an observation Y, can we estimate a function f(X) of the hidden random variable X with an average error below a certain threshold? We provide a general method for answering this question using an approach based on rate-distortion theory.Comment: Allerton 2013 with extended proof, 10 page

Hiding Symbols and Functions: New Metrics and Constructions for Information-Theoretic Security

We present information-theoretic definitions and results for analyzing symmetric-key encryption schemes beyond the perfect secrecy regime, i.e. when perfect secrecy is not attained. We adopt two lines of analysis, one based on lossless source coding, and another akin to rate-distortion theory. We start by presenting a new information-theoretic metric for security, called symbol secrecy, and derive associated fundamental bounds. We then introduce list-source codes (LSCs), which are a general framework for mapping a key length (entropy) to a list size that an eavesdropper has to resolve in order to recover a secret message. We provide explicit constructions of LSCs, and demonstrate that, when the source is uniformly distributed, the highest level of symbol secrecy for a fixed key length can be achieved through a construction based on minimum-distance separable (MDS) codes. Using an analysis related to rate-distortion theory, we then show how symbol secrecy can be used to determine the probability that an eavesdropper correctly reconstructs functions of the original plaintext. We illustrate how these bounds can be applied to characterize security properties of symmetric-key encryption schemes, and, in particular, extend security claims based on symbol secrecy to a functional setting.Comment: Submitted to IEEE Transactions on Information Theor

Batched differentially private information retrieval

First author draf

Arithmetic and Boolean secret sharing MPC on FPGAs in the data center

Multi-Party Computation (MPC) is an important technique used to enable computation over confidential data from several sources. The public cloud provides a unique opportunity to enable MPC in a low latency environment. Field Programmable Gate Array (FPGA) hardware adoption allows for both MPC acceleration and utilization of low latency, high bandwidth communication networks that substantially improve the performance of MPC applications. In this work, we show how designing arithmetic and Boolean Multi-Party Computation gates for FPGAs in a cloud provide improvements to current MPC offerings and ease their use in applications such as machine learning. We focus on the usage of Secret Sharing MPC first designed by Araki et al [1] to design our FPGA MPC while also providing a comparison with those utilizing Garbled Circuits for MPC. We show that Secret Sharing MPC provides a better usage of cloud resources, specifically FPGA acceleration, than Garbled Circuits and is able to use at least a 10 × less computer resources as compared to the original design using CPUs.Accepted manuscrip

Secret sharing MPC on FPGAs in the datacenter

Multi-Party Computation (MPC) is a technique enabling data from several sources to be used in a secure computation revealing only the result while protecting the orig- inal data, facilitating shared utilization of data sets gathered by different entities. The presence of Field Programmable Gate Array (FPGA) hardware in datacenters can provide accelerated computing as well as low latency, high bandwidth communication that bolsters the performance of MPC and lowers the barrier to using MPC for many applications. In this work, we propose a Secret Sharing FPGA design based on the protocol described by Araki et al. [1]. We compare our hardware design to the original authors’ software implementations of Secret Sharing and to work accelerating MPC protocols based on Garbled Circuits with FPGAs. Our conclusion is that Secret Sharing in the datacenter is competitive and when implemented on FPGA hardware was able to use at least 10× fewer computer resources than the original work using CPUs.Accepted manuscrip

Multimodal Social Media Analysis for Gang Violence Prevention

Gang violence is a severe issue in major cities across the U.S. and recent studies [Patton et al. 2017] have found evidence of social media communications that can be linked to such violence in communities with high rates of exposure to gang activity. In this paper we partnered computer scientists with social work researchers, who have domain expertise in gang violence, to analyze how public tweets with images posted by youth who mention gang associations on Twitter can be leveraged to automatically detect psychosocial factors and conditions that could potentially assist social workers and violence outreach workers in prevention and early intervention programs. To this end, we developed a rigorous methodology for collecting and annotating tweets. We gathered 1,851 tweets and accompanying annotations related to visual concepts and the psychosocial codes: aggression, loss, and substance use. These codes are relevant to social work interventions, as they represent possible pathways to violence on social media. We compare various methods for classifying tweets into these three classes, using only the text of the tweet, only the image of the tweet, or both modalities as input to the classifier. In particular, we analyze the usefulness of mid-level visual concepts and the role of different modalities for this tweet classification task. Our experiments show that individually, text information dominates classification performance of the loss class, while image information dominates the aggression and substance use classes. Our multimodal approach provides a very promising improvement (18% relative in mean average precision) over the best single modality approach. Finally, we also illustrate the complexity of understanding social media data and elaborate on open challenges

Hospital Preparedness and SARS

On May 23, 2003, Toronto experienced the second phase of a severe acute respiratory syndrome (SARS) outbreak. Ninety cases were confirmed, and >620 potential cases were managed. More than 9,000 persons had contact with confirmed or potential case-patients; many required quarantine. The main hospital involved during the second outbreak was North York General Hospital. We review this hospital’s response to, and management of, this outbreak, including such factors as building preparation and engineering, personnel, departmental workload, policies and documentation, infection control, personal protective equipment, training and education, public health, management and administration, follow-up of SARS patients, and psychological and psychosocial management and research. We also make recommendations for other institutions to prepare for future outbreaks, regardless of their origin

Nepali migrant workers and the need for pre-departure training on mental health: a qualitative study

Every year around 1,000 Nepali migrant workers die abroad. Every one in three females and one in ten males commit suicide, reflecting a high mental health risk among Nepali migrant workers. This study aims to identify triggers of mental ill-health among Nepali migrant workers and their perception on need of mental health components in the pre-departure orientation programme. We conducted five focus group discussions (FGD) and seven in-depth interviews with Nepali migrant workers and eight semi-structured interviews with stakeholders working for migrants. Participants were invited at Kathmandu’s international airport on return from abroad, at hotels or bus stations near the airport, through organisations working for migrants, and participants’ network. All FGD and interviews were conducted in Kathmandu and audio recorded, transcribed and translated into English. Data were analyzed thematically. High expectations from families back home, an unfair treatment at work, poor arrangements of accommodation, loneliness and poor social life abroad were frequently reported factors for poor mental health. Access to mental health services abroad by Nepali migrant was also poor. We found little on mental health in the pre-departure orientation. We need to improve our knowledge of mental health risks to provide better, more focused and more up-to-date pre-departure training to new migrant workers leaving Nepal