United States Army Research Laboratory

About: United States Army Research Laboratory is a government organization based out in Adelphi, Maryland, United States. It is known for research contribution in the topics: Laser & Radar. The organization has 5003 authors who have published 16859 publications receiving 390489 citations. The organization is also known as: Army Research Laboratory & U.S. Army Research Laboratory.

Nonaqueous liquid electrolytes for lithium-based rechargeable batteries.

Abstract: 2.1. Solvents 4307 2.1.1. Propylene Carbonate (PC) 4308 2.1.2. Ethers 4308 2.1.3. Ethylene Carbonate (EC) 4309 2.1.4. Linear Dialkyl Carbonates 4310 2.2. Lithium Salts 4310 2.2.1. Lithium Perchlorate (LiClO4) 4311 2.2.2. Lithium Hexafluoroarsenate (LiAsF6) 4312 2.2.3. Lithium Tetrafluoroborate (LiBF4) 4312 2.2.4. Lithium Trifluoromethanesulfonate (LiTf) 4312 2.2.5. Lithium Bis(trifluoromethanesulfonyl)imide (LiIm) and Its Derivatives 4313

The FERET evaluation methodology for face-recognition algorithms

Abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.

The Limitations of Deep Learning in Adversarial Settings

Abstract: Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfections in the training phase of deep neural networks make them vulnerable to adversarial samples: inputs crafted by adversaries with the intent of causing deep neural networks to misclassify. In this work, we formalize the space of adversaries against deep neural networks (DNNs) and introduce a novel class of algorithms to craft adversarial samples based on a precise understanding of the mapping between inputs and outputs of DNNs. In an application to computer vision, we show that our algorithms can reliably produce samples correctly classified by human subjects but misclassified in specific targets by a DNN with a 97% adversarial success rate while only modifying on average 4.02% of the input features per sample. We then evaluate the vulnerability of different sample classes to adversarial perturbations by defining a hardness measure. Finally, we describe preliminary work outlining defenses against adversarial samples by defining a predictive measure of distance between a benign input and a target classification.

A Survey of Dynamic Spectrum Access

Abstract: Compounding the confusion is the use of the broad term cognitive radio as a synonym for dynamic spectrum access. As an initial attempt at unifying the terminology, the taxonomy of dynamic spectrum access is provided. In this article, an overview of challenges and recent developments in both technological and regulatory aspects of opportunistic spectrum access (OSA). The three basic components of OSA are discussed. Spectrum opportunity identification is crucial to OSA in order to achieve nonintrusive communication. The basic functions of the opportunity identification module are identified