An application of the requirement vs capability analysis to estimating design reliability of solid rocket motors

Design reliability parameters for solid propellant rocket engine

The Organs of the Parietal Fossa in Elasmobranchs

Davidson, in a paper on the musculature of Heptanchus maculatus (1918), mentions a small shield-shaped organ to be found in the parietal fossa, and in connection with it a pair of small muscles having their origin on the cranium and dorsal longitudinal muscles. He believes that these muscles constrict this sac-like organ

Self-Updating Models with Error Remediation

Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.Comment: 17 pages, 13 figures, published in the proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II conference in the SPIE Defense + Commercial Sensing, 2020 symposiu

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

Predicting Graph Categories from Structural Properties

Complex networks are often categorized according to the underlying phenomena that they represent such as molecular interactions, re-tweets, and brain activity. In this work, we investigate the problem of predicting the category (domain) of arbitrary networks. This includes complex networks from different domains as well as synthetically generated graphs from five different network models. A classification accuracy of 96.6% is achieved using a random forest classifier with both real and synthetic networks. This work makes two important findings. First, our results indicate that complex networks from various domains have distinct structural properties that allow us to predict with high accuracy the category of a new previously unseen network. Second, synthetic graphs are trivial to classify as the classification model can predict with near-certainty the network model used to generate it. Overall, the results demonstrate that networks drawn from different domains (and network models) are trivial to distinguish using only a handful of simple structural properties

International Evidence on the Impact of Health-Justice Partnerships: A Systematic Scoping Review

BACKGROUND: Health-justice partnerships (HJPs) are collaborations between healthcare and legal services which support patients with social welfare issues such as welfare benefits, debt, housing, education and employment. HJPs exist across the world in a variety of forms and with diverse objectives. This review synthesizes the international evidence on the impacts of HJPs. METHODS: A systematic scoping review of international literature was undertaken. A wide-ranging search was conducted across academic databases and grey literature sources, covering OECD countries from January 1995 to December 2018. Data from included publications were extracted and research quality was assessed. A narrative synthesis approach was used to analyze and present the results. RESULTS: Reported objectives of HJPs related to: prevention of health and legal problems; access to legal assistance; health improvement; resolution of legal problems; improvement of patient care; support for healthcare services; addressing inequalities; and catalyzing systemic change. There is strong evidence that HJPs: improve access to legal assistance for people at risk of social and health disadvantage; positively influence material and social circumstances through resolution of legal problems; and improve mental wellbeing. A wide range of other positive impacts were identified for individuals, services and communities; the strength of evidence for each is summarized and discussed. CONCLUSION: HJPs are effective in tackling social welfare issues that affect the health of disadvantaged groups in society and can therefore form a key part of public health strategies to address inequalities

Increasing secondary resistance to fluoroquinolones amongst Helicobacter pylori in Western Australia

Background: The Australian Therapeutic Guidelines does not endorse culture and susceptibility testing prior to salvage therapy for Helicobacter pylorieradication. We wished to determine whether this remains appropriate. Aim: To determine the sensitivity (as minimum inhibitory concentrations, MIC) of H pylorito a range of antibiotics used in salvage therapy over time. Methods: From 2012 to 2017, gastric or duodenal biopsy samples were obtained from 154 patients receiving H pylorieradication therapy. MIC for amoxicillin, clarithromycin, tetracycline, metronidazole, rifampicin and levofloxacinwere measured using standard laboratory techniques. Results: A significant increase from zero to 28% in secondary resistance to levofloxacin amongst H. pyloriin Western Australia was noted over the study period. No corresponding trend was seen with the other antibiotics. Conclusions: These findings suggest that selective use of culture and susceptibility testing may be warranted prior to initiating salvage therapy with levofloxacin

A NICER Discovery of a Low-Frequency Quasi-Periodic Oscillation in the Soft-Intermediate State of MAXI J1535-571

We present the discovery of a low-frequency $\approx 5.7$ Hz quasi-periodic oscillation (QPO) feature in observations of the black hole X-ray binary MAXI J1535-571 in its soft-intermediate state, obtained in September-October 2017 by the Neutron Star Interior Composition Explorer (NICER). The feature is relatively broad (compared to other low-frequency QPOs; quality factor $Q\approx 2$) and weak (1.9% rms in 3-10 keV), and is accompanied by a weak harmonic and low-amplitude broadband noise. These characteristics identify it as a weak Type A/B QPO, similar to ones previously identified in the soft-intermediate state of the transient black hole X-ray binary XTE J1550-564. The lag-energy spectrum of the QPO shows increasing soft lags towards lower energies, approaching 50 ms at 1 keV (with respect to a 3-10 keV continuum). This large phase shift has similar amplitude but opposite sign to that seen in Rossi X-ray Timing Explorer data for a Type B QPO from the transient black hole X-ray binary GX 339-4. Previous phase-resolved spectroscopy analysis of the Type B QPO in GX 339-4 pointed towards a precessing jet-like corona illuminating the accretion disk as the origin of the QPO signal. We suggest that this QPO in MAXI J1535-571 may have the same origin, with the different lag sign depending on the scale height of the emitting region and the observer inclination angle.Comment: Accepted for publication in ApJ Letter