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

Development of improved structural adhesives Annual summary report, 1 Jul. 1967 - 3 Dec. 1968

Improved structural adhesives for bonding aluminum over low temperature

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

Carbon isotope ratios of Great Plains soils and in wheat-fallow systems

Includes bibliographical references (pages 1076-1077).The purposes of this study were to improve knowledge of regional vegetation patterns of C3 and C4 plants in the North American Great Plains and to use δ13C methodology and long-term research sites to determine contributions of small-grain crops to total soil organic carbon (SOC) now present. Archived and recent soil samples were used. Detailed soil sampling was in 1993 at long-term sites near Akron, CO, and Sidney, NE. After soil sieving, drying, and deliming, SOC and δ13C were determined using an automated C/N analyzer interfaced to an isotope-ratio mass spectrometer. Yield records from long-term experimental sites were used to estimate the amount of C3 plant residue C returned to the soil. Results from δ13C analyses of soils from near Waldheim, Saskatchewan, to Big Springs, TX, showed a strong north to south decrease in SOC derived from C3 plants and a corresponding increase from C4 plants. The δ13C analyses gave evidence that C3 plant residue C (possibly from shrubs) is increasing at the Big Springs, TX, and Lawton, OK, sites. Also, δ13C analyses of subsoil and topsoil layers shows evidence of a regional shift to more C3 species, possibly because of a cooler climate during the past few hundreds to thousands of years. Data from long-term research sites indicate that the efficiency of incorporation of small-grain crop residue C was about 5.4% during 84 year at Akron, CO, and about 10.5% during 20 year at Sidney, NE. The 14C age of the SOC at 0- to 10-cm depth was 193 year and at 30 to 45 cm was 4000 yr; 14C age of nonhydrolyzable C was 2000 and 7000 year for these same two respective depths. Natural partitioning of the 13C isotope by the photosynthetic pathways of C3 and C4 plants provides a potentially powerful tool to study SOC dynamics at both regional and local scales