Polarization effects in attosecond photoelectron spectroscopy

We study the influence of polarization effects in streaking by combined atto- and femtosecond pulses. The polarization-induced terms alter the streaking spectrum. The normal streaking spectrum, which maps to the vector potential of the femtosecond pulse, is modified by a contribution following the field instead. We show that polarization effects may lead to an apparent temporal shift, that needs to be properly accounted for in the analysis. The effect may be isolated and studied by angle-resolved photoelectron spectroscopy from oriented polar molecules. We also show that polarization effects will lead to an apparent temporal shift of 50 as between photoelectrons from a 2p and 1s state in atomic hydrogen.Comment: 4 pages, 3 figure

The Effects of Dissolved Methane upon Liquid Argon Scintillation Light

In this paper we report on measurements of the effects of dissolved methane upon argon scintillation light. We monitor the light yield from an alpha source held 20 cm from a cryogenic photomultiplier tube (PMT) assembly as methane is injected into a high-purity liquid argon volume. We observe significant suppression of the scintillation light yield by dissolved methane at the 10 part per billion (ppb) level. By examining the late scintillation light time constant, we determine that this loss is caused by an absorption process and also see some evidence of methane-induced scintillation quenching at higher concentrations (50-100 ppb). Using a second PMT assembly we look for visible re-emission features from the dissolved methane which have been reported in gas-phase argon methane mixtures, and we find no evidence of visible re-emission from liquid-phase argon methane mixtures at concentrations between 10 ppb and 0.1%.Comment: 18 pages, 11 figures Updated to match published versio

Microscopic correlation between chemical and electronic states in epitaxial graphene on SiC(000-1)

We present energy filtered electron emission spectromicroscopy with spatial and wave-vector resolution on few layer epitaxial graphene on SiC$(000-1) grown by furnace annealing. Low energy electron microscopy shows that more than 80% of the sample is covered by 2-3 graphene layers. C1s spectromicroscopy provides an independent measurement of the graphene thickness distribution map. The work function, measured by photoelectron emission microscopy (PEEM), varies across the surface from 4.34 to 4.50eV according to both the graphene thickness and the graphene-SiC interface chemical state. At least two SiC surface chemical states (i.e., two different SiC surface structures) are present at the graphene/SiC interface. Charge transfer occurs at each graphene/SiC interface. K-space PEEM gives 3D maps of the k_|| pi - pi* band dispersion in micron scale regions show that the Dirac point shifts as a function of graphene thickness. Novel Bragg diffraction of the Dirac cones via the superlattice formed by the commensurately rotated graphene sheets is observed. The experiments underline the importance of lateral and spectroscopic resolution on the scale of future electronic devices in order to precisely characterize the transport properties and band alignments

Extravehicular activities limitations study. Volume 1: Physiological limitations to extravehicular activity in space

This report contains the results of a comprehensive literature search on physiological aspects of EVA. Specifically, the topics covered are: (1) Oxygen levels; (2) Optimum EVA work; (3) Food and Water; (4) Carbon dioxide levels; (5) Repetitive decompressions; (6) Thermal, and (7) Urine collection. The literature was assessed on each of these topics, followed by statements on conclusions and recommended future research needs

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

Effect of Rotor- and Stator-Blade Modifications on Surge Performance of an 11-Stage Axial-Flow Compressor. I - Original Production Compressor of XJ40-WE-6 Engine

An investigation to increase the compressor surge-limit pressure ratio of the XJ40-WE-6 turbojet engine at high equivalent speeds was conducted at the NACA Lewis altitude wind tunnel. This report evaluates the compressor modifications which were restricted to (1) twisting rotor blades (in place) to change blade section angles and (2) inserting new stator diaphragms with different blade angles. Such configuration changes could be incorporated quickly and easily in existing engines at overhaul depots. It was found that slight improvements in the compressor surge limit were possible by compressor blade adjustment. However, some of the modifications also reduced the engine air flow and hence penalized the thrust. The use of a mixer assembly at the compressor outlet improved the surge limit with no appreciable thrust penalty

Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US