Effects of noise upon human information processing

Studies of noise effects upon human information processing are described which investigated whether or not effects of noise upon performance are dependent upon specific characteristics of noise stimulation and their interaction with task conditions. The difficulty of predicting noise effects was emphasized. Arousal theory was considered to have explanatory value in interpreting the findings of all the studies. Performance under noise was found to involve a psychophysiological cost, measured by vasoconstriction response, with the degree of response cost being related to scores on a noise annoyance sensitivity scale. Noise sensitive subjects showed a greater autonomic response under noise stimulation

Immunological Characterization and Neutralizing Ability of Monoclonal Antibodies Directed Against Botulinum Neurotoxin Type H.

BackgroundOnly Clostridium botulinum strain IBCA10-7060 produces the recently described novel botulinum neurotoxin type H (BoNT/H). BoNT/H (N-terminal two-thirds most homologous to BoNT/F and C-terminal one-third most homologous to BoNT/A) requires antitoxin to toxin ratios ≥1190:1 for neutralization by existing antitoxins. Hence, more potent and safer antitoxins against BoNT/H are needed.MethodsWe therefore evaluated our existing monoclonal antibodies (mAbs) to BoNT/A and BoNT/F for BoNT/H binding, created yeast-displayed mutants to select for higher-affinity-binding mAbs by using flow cytometry, and evaluated the mAbs' ability to neutralize BoNT/H in the standard mouse bioassay.ResultsAnti-BoNT/A HCC-binding mAbs RAZ1 and CR2 bound BoNT/H with high affinity. However, only 1 of 6 BoNT/F mAbs (4E17.2A) bound BoNT/H but with an affinity >800-fold lower (equilibrium dissociation binding constant [KD] = 7.56 × 10(-8)M) than its BoNT/F affinity (KD= 9.1 × 10(-11)M), indicating that the N-terminal two-thirds of BoNT/H is immunologically unique. The affinity of 4E17.2A for BoNT/H was increased >500-fold to KD= 1.48 × 10(-10)M (mAb 4E17.2D). A combination of mAbs RAZ1, CR2, and 4E17.2D completely protected mice challenged with 280 mouse median lethal doses of BoNT/H at a mAb dose as low as 5 µg of total antibody.ConclusionsThis 3-mAb combination potently neutralized BoNT/H and represents a potential human antitoxin that could be developed for the prevention and treatment of type H botulism

A New Family of High-Current Cyclotrons for Isotope Production

We have developed a new family of compact cyclotrons designed to accelerate record-high currents of ions with charge-to-mass of 1/2. We have detailed engineering designs for a 5 mA H2+ cyclotron (delivering 10 mA of protons) and are extending this concept to 5 mA of deuterons (D+). The innovations enabling the high currents are: 1) bunching with an RFQ that enables efficient capture and 2) space-charge-mitigated stable bunch formation established in the first few turns. These developments can be applied to cyclotron from 5 to around 60 MeV/amu. A 20 MeV/amu deuteron cyclotron would be effective for 225Ac production via (n,2n) with fast neutrons generated by deuteron breakup in beryllium.Comment: 22 pages, 9 figures, paper to be submitted to the Journal of Radioanalytical and Nuclear Chemistry, as part of the Proceedings of the 11th International Conference on Isotopes, held in Saskatoon, Canada, July 202

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

Dynamic Analysis of Executables to Detect and Characterize Malware

It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life. We examine the use of machine learning algorithms to detect malware using the system calls generated by executables-alleviating attempts at obfuscation as the behavior is monitored rather than the bytes of an executable. We examine several machine learning techniques for detecting malware including random forests, deep learning techniques, and liquid state machines. The experiments examine the effects of concept drift on each algorithm to understand how well the algorithms generalize to novel malware samples by testing them on data that was collected after the training data. The results suggest that each of the examined machine learning algorithms is a viable solution to detect malware-achieving between 90% and 95% class-averaged accuracy (CAA). In real-world scenarios, the performance evaluation on an operational network may not match the performance achieved in training. Namely, the CAA may be about the same, but the values for precision and recall over the malware can change significantly. We structure experiments to highlight these caveats and offer insights into expected performance in operational environments. In addition, we use the induced models to gain a better understanding about what differentiates the malware samples from the goodware, which can further be used as a forensics tool to understand what the malware (or goodware) was doing to provide directions for investigation and remediation.Comment: 9 pages, 6 Tables, 4 Figure

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