57 research outputs found
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
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
Modelling the Influence of Foot-and-Mouth Disease Vaccine Antigen Stability and Dose on the Bovine Immune Response
Foot and mouth disease virus causes a livestock disease of significant global socio-economic importance. Advances in its control and eradication depend critically on improvements in vaccine efficacy, which can be best achieved by better understanding the complex within-host immunodynamic response to inoculation. We present a detailed and empirically parametrised dynamical mathematical model of the hypothesised immune response in cattle, and explore its behaviour with reference to a variety of experimental observations relating to foot and mouth immunology. The model system is able to qualitatively account for the observed responses during in-vivo experiments, and we use it to gain insight into the incompletely understood effect of single and repeat inoculations of differing dosage using vaccine formulations of different structural stability
Large protistan mixotrophs in the North Atlantic Continuous Plankton Recorder time series: associated environmental conditions and trends
Aquatic ecologists are integrating mixotrophic plankton – here defined as microorganisms with photosynthetic and phagotrophic capacity – into their understanding of marine food webs and biogeochemical cycles. Understanding mixotroph temporal and spatial distributions, as well as the environmental conditions under which they flourish, is imperative to understanding their impact on trophic transfer and biogeochemical cycling. Mixotrophs are hypothesized to outcompete strict photoautotrophs and heterotrophs when either light or nutrients are limiting, but testing this hypothesis has been hindered by the challenge of identifying and quantifying mixotrophs in the field. Using field observations from a multi-decadal northern North Atlantic dataset, we calculated the proportion of organisms that are considered mixotrophs within individual microplankton samples. We also calculated a “trophic index” that represents the relative proportions of photoautotrophs (phytoplankton), mixotrophs, and heterotrophs (microzooplankton) in each sample. We found that the proportion of mixotrophs was positively correlated with temperature, and negatively with either light or inorganic nutrient concentration. This proportion was highest during summertime thermal stratification and nutrient limitation, and lowest during the North Atlantic spring bloom period. Between 1958 and 2015, changes in the proportion of mixotrophs coincided with changes in the Atlantic Multi-decadal Oscillation (AMO), was highest when the AMO was positive, and showed a significant uninterrupted increase in offshore regions from 1992-2015. This study provides an empirical foundation for future experimental, time series, and modeling studies of aquatic mixotrophs.</jats:p
Large protistan mixotrophs in the North Atlantic Continuous Plankton Recorder time series: associated environmental conditions and trends
Aquatic ecologists are integrating mixotrophic plankton – here defined as microorganisms with photosynthetic and phagotrophic capacity – into their understanding of marine food webs and biogeochemical cycles. Understanding mixotroph temporal and spatial distributions, as well as the environmental conditions under which they flourish, is imperative to understanding their impact on trophic transfer and biogeochemical cycling. Mixotrophs are hypothesized to outcompete strict photoautotrophs and heterotrophs when either light or nutrients are limiting, but testing this hypothesis has been hindered by the challenge of identifying and quantifying mixotrophs in the field. Using field observations from a multi-decadal northern North Atlantic dataset, we calculated the proportion of organisms that are considered mixotrophs within individual microplankton samples. We also calculated a “trophic index” that represents the relative proportions of photoautotrophs (phytoplankton), mixotrophs, and heterotrophs (microzooplankton) in each sample. We found that the proportion of mixotrophs was positively correlated with temperature, and negatively with either light or inorganic nutrient concentration. This proportion was highest during summertime thermal stratification and nutrient limitation, and lowest during the North Atlantic spring bloom period. Between 1958 and 2015, changes in the proportion of mixotrophs coincided with changes in the Atlantic Multi-decadal Oscillation (AMO), was highest when the AMO was positive, and showed a significant uninterrupted increase in offshore regions from 1992-2015. This study provides an empirical foundation for future experimental, time series, and modeling studies of aquatic mixotrophs
Erosional truncation of uppermost Permian shallow-marine carbonates and implications for Permian-Triassic boundary events
On shallow-marine carbonate buildups in south China, Turkey, and Japan, uppermost Permian skeletal limestones are truncated by an erosional surface that exhibits as much as 10 cm of topography, including overhanging relief. Sedimentary facies, microfabrics, carbon isotopes, and cements together suggest that erosion occurred in a submarine setting. Moreover, biostratigraphic data from south China demonstrate that the surface postdates the uppermost Permian sequence boundary at the global stratotype section and truncates strata within the youngest known Permian conodont zone. The occurrences of similar truncation surfaces at the mass-extinction horizon on carbonate platforms across the global tropics, each overlain by microbial buildups, and their association with a large negative excursion in delta C-13 further suggest a causal link between erosion of shallow-marine carbonates and mass extinction. Previously proposed to account for marine extinctions, the hypothesis of rapid carbon release from sedimentary reservoirs or the deep ocean can also explain the petro-graphic observations. Rapid, unbuffered carbon release would cause submarine carbonate dissolution, accounting for erosion of uppermost Permian skeletal carbonates, and would be followed by a pulse of high carbonate saturation, explaining the precipitation of microbial limestones containing upward-growing carbonate crystal fans. Models for other carbon-release events suggest that at least 5 x 1018 g of carbon, released in < 100 key., would be required. Of previously hypothesized Permian-Triassic boundary scenarios, thermogenic methane production from heating of coals during Siberian Traps emplacement best accounts for petrographic characteristics and depositional environment of the truncation surface and overlying microbial limestone, as well as an associated carbon isotope excursion and physiologically selective extinction in the marine realm
Data from: Elevated CO2 and water addition enhance nitrogen turnover in grassland plants with implications for temporal stability
Temporal variation in soil nitrogen (N) availability affects growth of grassland communities that differ in their use and reuse of N. In a seven-year-long climate change experiment in a semiarid grassland, the temporal stability of plant biomass production varied with plant N turnover (reliance on externally acquired N relative to internally recycled N). Species with high N turnover were less stable in time compared to species with low N turnover. In contrast, N turnover at the community level was positively associated with asynchrony in biomass production, which in turn increased community temporal stability. Elevated CO2 and summer irrigation, but not warming, enhanced community N turnover and stability, possibly because treatments promoted greater abundance of species with high N turnover. Our study highlights the importance of plant N turnover for determining the temporal stability of individual species and plant communities affected by climate change
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