MRAPs, Irregular Warfare, and Pentagon Reform

The controversial MRAPs raise two questions. First, does the MRAP experience support the contention that the Pentagon is not sufficiently able to field irregular warfare capabilities? Second, what factors best explain the MRAP failure, whether that failure is determined to be their delayed fielding or the fact that they were fielded at all? We conclude that MRAPs are a valid irregular warfare requirement and that the Pentagon should have been better prepared to field them, albeit not on the scale demanded by events in Iraq. We also argue that the proximate cause of the failure to quickly field MRAPs is not the Pentagon’s acquisition system but rather the requirements process, reinforced by more fundamental organizational factors. These findings suggest that acquisition reform is the wrong target for advancing Secretary Gates’ objective of improving irregular warfare capabilities, and that achieving the objective will require more extensive reforms than many realize

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

Characterization of the Major Ions of Coal Creek Near Cedar City, Utah

The major ions of Coal Creek near Cedar City, in southwest Utah, were measured to determine if there were any differences in ion concentrations in July of 2014 as compared with spring measurements of 2012 and 2013. Past analyses have shown higher ion concentrations in lower regions of Coal Creek despite the apparent lack of water input. This research is aimed to better characterize these abrupt increases in concentration and determine if these trends varied when samples were acquired in the summer vs. in the spring when sample acquisition has occurred in the past. Environmental water samples were collected at evenly spaced locations in Coal Creek from State Route 14 Mile Marker 7 westward to where the creek intersects with Main Street in Cedar City. Ion concentrations were determined in water samples collected every other day for 3 consecutive weeks using Ion Chromatography (IC) and Atomic Absorption Spectroscopy (AA). The spatially intensive sampling revealed two previously unknown low volume springs that are highly concentrated in the major ions and discharge into the creek. Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) was used to characterize trace metal concentrations within the water tributaries, in addition to IC to determine bulk anion content in the creek. The high ion concentrations of springs correlated well with known geologic features near the creek, such as faulted gypsum layers creating springs as well as evaporate deposits, both of which explain the doubling of ion concentrations seen in the examined section of Coal Creek

Treatment compliance and effectiveness of a cognitive behavioural intervention for low back pain : a complier average causal effect approach to the BeST data set

Background: Group cognitive behavioural intervention (CBI) is effective in reducing low-back pain and disability in comparison to advice in primary care. The aim of this analysis was to investigate the impact of compliance on estimates of treatment effect and to identify factors associated with compliance. Methods: In this multicentre trial, 701 adults with troublesome sub-acute or chronic low-back pain were recruited from 56 general practices. Participants were randomised to advice (control n = 233) or advice plus CBI (n = 468). Compliance was specified a priori as attending a minimum of three group sessions and the individual assessment. We estimated the complier average causal effect (CACE) of treatment. Results: Comparison of the CACE estimate of the mean treatment difference to the intention-to-treat (ITT) estimate at 12 months showed a greater benefit of CBI amongst participants compliant with treatment on the Roland Morris Questionnaire (CACE: 1.6 points, 95% CI 0.51 to 2.74; ITT: 1.3 points, 95% CI 0.55 to 2.07), the Modified Von Korff disability score (CACE: 12.1 points, 95% CI 6.07 to 18.17; ITT: 8.6 points, 95% CI 4.58 to 12.64) and the Modified von Korff pain score (CACE: 10.4 points, 95% CI 4.64 to 16.10; ITT: 7.0 points, 95% CI 3.26 to 10.74). People who were non-compliant were younger and had higher pain scores at randomisation. Conclusions: Treatment compliance is important in the effectiveness of group CBI. Younger people and those with more pain are at greater risk of non-compliance

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

What advances may the future bring to the diagnosis, treatment, and care of male sexual and reproductive health?

Over the past 40 years, since the publication of the original WHO Laboratory Manual for the Examination and Processing of Human Semen, the laboratory methods used to evaluate semen markedly changed and benefited from improved precision and accuracy, as well as the development of new tests and improved, standardized methodologies. Herein, we present the impact of the changes put forth in the sixth edition together with our views of evolving technologies that may change the methods used for the routine semen analysis, up-and-coming areas for the development of new procedures, and diagnostic approaches that will help to extend the often-descriptive interpretations of several commonly performed semen tests that promise to provide etiologies for the abnormal semen parameters observed. As we look toward the publication of the seventh edition of the manual in approximately 10 years, we describe potential advances that could markedly impact the field of andrology in the future

Galaxy Star Formation as a Function of Environment in the Early Data Release of the Sloan Digital Sky Survey

We present in this paper a detailed analysis of the effect of environment on the star formation activity of galaxies within the Early Data Release (EDR) of the Sloan Digital Sky Survey (SDSS). We have used the Halpha emission line to derive the star formation rate (SFR) for each galaxy within a volume-limited sample of 8598 galaxies with 0.05 less than or equal to z less than or equal to 0.095 and M (r*) less than or equal to 20.45. We find that the SFR of galaxies is strongly correlated with the local ( projected) galaxy density, and thus we present here a density-SFR relation that is analogous to the density-morphology relation. The effect of density on the SFR of galaxies is seen in three ways. First, the overall distribution of SFRs is shifted to lower values in dense environments compared with the field population. Second, the effect is most noticeable for the strongly star-forming galaxies (Halpha EW > 5 Angstrom) in the 75th percentile of the SFR distribution. Third, there is a break ( or characteristic density) in the density-SFR relation at a local galaxy density of similar to1 h(75)(-2) Mpc(-2). To understand this break further, we have studied the SFR of galaxies as a function of clustercentric radius from 17 clusters and groups objectively selected from the SDSS EDR data. The distribution of SFRs of cluster galaxies begins to change, compared with the field population, at a clustercentric radius of 3-4 virial radii (at the >1sigma statistical significance), which is consistent with the characteristic break in density that we observe in the density-SFR relation. This effect with clustercentric radius is again most noticeable for the most strongly star-forming galaxies. Our tests suggest that the density-morphology relation alone is unlikely to explain the density-SFR relation we observe. For example, we have used the ( inverse) concentration index of SDSS galaxies to classify late-type galaxies and show that the distribution of the star-forming (EW Halpha > 5Angstrom) late-type galaxies is different in dense regions ( within 2 virial radii) compared with similar galaxies in the field. However, at present, we are unable to make definitive statements about the independence of the density-morphology and density-SFR relation. We have tested our work against potential systematic uncertainties including stellar absorption, reddening, SDSS survey strategy, SDSS analysis pipelines, and aperture bias. Our observations are in qualitative agreement with recent simulations of hierarchical galaxy formation that predict a decrease in the SFR of galaxies within the virial radius. Our results are in agreement with recent 2dF Galaxy Redshift Survey results as well as consistent with previous observations of a decrease in the SFR of galaxies in the cores of distant clusters. Taken together, these works demonstrate that the decrease in SFR of galaxies in dense environments is a universal phenomenon over a wide range in density (from 0.08 to 10 h(75)(-2) Mpc(-2)) and redshift (out to z similar or equal to 0.5)