Ohio Soil Test Summary 1971-72

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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

Swisher County, Texas : records of wells, drillers' logs, and water analyses, and map showing location of wells (1938)

Map of Swisher County, Texas showing locations of water wells listed. "Prepared in cooperation with the United States Department of the Interior, Geological Survey, the Bureau of Industrial Chemistry of The University of Texas, and the Works Progress Administration."UT Librarie

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

Improved structural adhesives for bonding aluminum over low temperature

Estimation of slow- and fast-cycling soil organic carbon pools from 6N HCI hydrolysis

Includes bibliographical references (pages 238-239).Acid hydrolysis is used to fractionate the soil organic carbon pool into relatively slow- and fast-cycling compartments on soils from Arizona, the Great Plains states and Michigan collected for carbon isotope tracer studies related to soil carbon sequestration, for studies of shifts in C3/C4 vegetation, and for "pre-bomb" soil-carbon inventories. Prior to hydrolysis, soil samples are first treated with cold 0.5-1N HCl to remove soil carbonates if necessary. Samples are then dispersed in a concentrated NaCI solution (p~1.2 g cm-3) and floated plant fragments are skimmed off the surface. After rinsing and drying, all remaining recognizable plant fragments are picked from the soil under 20x magnification. Plant-free soils, and hot, 6NHCl acid-hydrolysis residue and hydrolyzate fractions are analyzed for carbon content, δ 13C and 14C age, and the carbon distribution is verified within 1-2% by stable-carbon isotope mass balance. On average, the recalcitrant residue fraction is 1800 year older and 2.6‰ more 13C-depleted than total soil organic carbon. A test of hydrolysis with fresh plant fragments produced as much as 71-76% in the acid-hydrolysis residue pool. Thus, if plant fragments are not largely removed prior to hydrolysis, the residue fraction may date much younger than it actually is.Publisher version: https://journals.uair.arizona.edu/index.php/radiocarbon/article/view/1903/1904

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

Biological and molecular structure analyses of the controls on soil organic matter dynamics

Includes bibliographical references (page 170).The dynamics of soil organic carbon (SOC) are controlled by the interaction of biological, physical, and chemical parameters. These are best measured by a combination of techniques such as long-term field sites with a C3↔C4 plant switch. Acid hydrolysis and 14C- dating measure the mean residence time (MRT) of the resistant fraction. Long-term incubation allows the in situ biota to identify and decompose the labile SOC components. Statistical analysis (curve fitting) of the CO2 release curves, determines the pool size and of the two labile fractions (1). The effect of chemical structure is measured with pyrolysismolecular beam mass spectrometry (py-MBMS). The dynamics of charcoal, clay and silt are measured with both 13C and 14C