Nowadays, data is represented by vectors. Retrieving those vectors, among millions and billions, that are similar to a given query is a ubiquitous problem, known as similarity search, of relevance for a wide range of applications. Graph-based indices are currently the best performing techniques for billion-scale similarity search. However, their random-access memory pattern presents challenges to realize their full potential. In this work, we present new techniques and systems for creating faster and smaller graph-based indices. To this end, we introduce a novel vector compression method, Locally-adaptive Vector Quantization (LVQ), that uses per-vector scaling and scalar quantization to improve search performance with fast similarity computations and a reduced effective bandwidth, while decreasing memory footprint and barely impacting accuracy. LVQ, when combined with a new high-performance computing system for graph-based similarity search, establishes the new state of the art in terms of performance and memory footprint. For billions of vectors, LVQ outcompetes the second-best alternatives: (1) in the low-memory regime, by up to 20.7x in throughput with up to a 3x memory footprint reduction, and (2) in the high-throughput regime by 5.8x with 1.4x less memory.

McAfee isn't known for its work on adversarial machine learning on autonomous vehicles. Yet at the new McAfee Advanced Threat Research Lab in Hillsboro, Oregon, automotive research is on full display. The lab is equipped with sensors used for vehicle autonomy, as well as an operational dashboard for an electronic vehicle. The lab even has two full-sized garage doors to roll in cars for live demos. Automotive attacks are "certainly an area we may be interested in looking into, and it's certainly an area that's emerging as a significant attack vector," Steve Povolny, head of Advanced Threat Research at McAfee, told ZDNet.

Job Requirements • Master's degree or equivalent work experience in machine learning • Strong hands on experience solving complex problems using unsupervised and supervised machine learning algorithms • Proficiency in feature selection and feature engineering • Strong experience with big data tools and techniques, like Hadoop and Spark • Broad knowledge of machine learning algorithms, with ability to select and apply appropriate algorithms to specific problem domains • Ability to collaborate with domain experts to efficiently and effectively identify and extract previously unfamiliar domain knowledge Preferred • Knowledge in Natural Language Processing, especially named entity recognition • Experience in problems associated with people-centric data, like name parsing, name comparison, address parsing etc. • Experience with frameworks and techniques in deep learning and deep neural networks • Experience with computer vision, particularly facial recognition and comparison About SureID SureID, Inc. integrates leading edge products and services into solutions that combine identity enrollment, authentication, background screening, and access management to make facilities, assets, and people safer and more secure. Using SureID's patented programs, highly secure facilities – such as military installations, government buildings, manufacturing and distribution sites, ports, and commercial builds – can increase security and streamline access for authorized personnel. SureID has a proven track record for successfully servicing government, military and commercial clients. The RAPIDGate Program already serves thousands of companies and hundreds of thousands of RAPIDGate badge-holders who enjoy streamlined access into Department of Defense and Homeland Security facilities. SureID is a privately-held company founded in November 2001 and headquartered in Hillsboro, OR.