Efficient Monitoring of Parametric Context Free Patterns

Recent developments in runtime verification and monitoring show that parametric regular and temporal logic specifications can be efficiently monitored against large programs. However, these logics reduce to ordinary finite automata, limiting their expressivity. For example, neither can specify structured properties that refer to the call stack of the program. While context-free grammars (CFGs) are expressive and well-understood, existing techniques of monitoring CFGs generate massive runtime overhead in real-life applications. This paper shows for the first time that monitoring parametric CFGs is practical (on the order of 10% or lower for average cases, several times faster than the state-of-the-art). We present a monitor synthesis algorithm for CFGs based on an LR(1) parsing algorithm, modified with stack cloning to account for good prefix matching. In addition, a logic-independent mechanism is introduced to support partial matching, allowing patterns to be checked against fragments of execution traces

Do the Poor Pay More for Healthy Food? An Empirical Economic Analysis

Food Consumption/Nutrition/Food Safety,

Laser-induced Breakdown Spectroscopy (LIBS) for Detecting Metal Particles Released from Energetic Reactions

The objective of this work is to characterize emissions from solid propellants using a non-intrusive optical diagnostic method, primarily focusing on the release of the metallic species of aluminum (Al), copper (Cu), lead (Pb), and mercury (Hg) during energetic reactions. The primary motivation for developing such diagnostic methods is that particulate matter released to the air from energetic reactions can cause adverse health effects, such as pulmonary and cardiovascular disease, particulate matter-induced allergy, and cancer. The enabling technology used for this research study is Laser-Induced Breakdown Spectroscopy (LIBS), which is an elemental, analytical technique that uses high-intensity laser pulses to generate a plasma in a medium where the composition is to be detected. Light emitted from this plasma is then collected and dispersed using a spectrometer onto a CCD array. The elemental composition can be determined based on characteristic spectral lines detected and their relative intensities. Two LIBS schemes were used during the current experiments: one using a 10-nanosecond (ns) pulse-duration, 10- Hz repetition-rate, neodymium-doped yttrium aluminum garnet (Nd:YAG) laser and the other using an 80-femtosecond (fs) pulse-duration, 1-kHz repetition-rate, amplified Ti:Sapphire laser system. Before attempting to detect the metallic species in the gas-phase exhaust region during the combustion of laboratory-scale propellant sticks, initial experiments of laser pulse energy dependence and plasma decay time were performed using solid target plates of Al, Cu and Pb. These initial experiments were conducted to determine the optimum laser parameters and signal collection conditions. Subsequent experiments were conducted during combustion events of hydroxyl-terminated polybutadiene/ammonium perchlorate (HTPB/AP) propellant samples doped with known quantities of metals. The ns-LIBS scheme was capable of detecting Al LIBS signals corresponding to the samples with predetermined quantities of Al in the 5–16% range by mass. An aluminum metal concentration study was also performed, which showed that a propellant strand with a higher mass percentage of aluminum is more likely to have a LIBS signal until up to a point where the gas-phase reaction zone begins to act like a homogeneous medium. A comparison of LIBS detection between a ns Nd:YAG laser and fs Ti:Sapphire laser was also performed. While the LIBS scheme using the 10-ns, 10-Hz Nd:YAG laser pulses could not detect any other metal species besides aluminum, the 80-fs, 1-kHz Ti:Sapphire laser was able to detect characteristic signals from the metallic additives: aluminum, copper, lead (from the base metal as well as from lead stearate [(C17H35COO)2Pb], a common additive for altering the reaction rate), and mercury chloride (Hg2Cl2) at mass percentages in the range of 2–16% by mass in the initial propellant mix

Tying the knots: the nationalization of wedding rituals in antebellum America

As middle-class culture became increasingly influential in the years before the Civil War, the white wedding became a powerful symbol of that culture, embodying both bourgeois, entrepreneurial values and a companionate view of marriage. In their weddings, antebellum Americans expressed their willingness or reluctance to view their relationships through a middle-class lens. Diverse groups of people alternately embraced a bourgeois, companionate identity for their relationships and their communities, or crafted counter-ideologies hearkening back to what many saw as America's more stable, powerful aristocratic and patriarchal past. The weddings of middle-class New Yorkers, wealthy southerners, enslaved African Americans, and Mormon pioneers all reflected these conflicts. New Yorkers centered their weddings around the marrying couple's love for each other, suggesting that marriage was not an economic arrangement but a romantic one. Outside the northeast, however, Americans struggled to comprehend and, often, to counter the growing cultural dominance of the middle class, and crafted ideological and ritual responses. The weddings of southern slaveholders and Mormon separatists both asserted different visions of America as a patriarchal nation, beating back the specter of gender equality with paeans to powerful masculinity. And southern slaveholders imposed their vision of patriarchy on the marriages of slaves, using ritual to undermine blacks' claims to patriarchal man- or womanhood. In exploring these disparate rituals, I offer a vision of an America marked by intense debates over what form its interpersonal relationships, its gender roles, its economy, its spiritual future, and its national identity should take. Understanding these conflicting desires--to partake of the national culture as equals, yet to differentiate themselves as social and political actors--helps illuminate the halting, equivocal paths Americans walked toward sectional division, and toward their eventual accession to middle-class values

Robustness of Energy Landscape Control to Dephasing

As shown in previous work, in some cases closed quantum systems exhibit a non-conventional trade-off in performance and robustness in the sense that controllers with the highest fidelity can also provide the best robustness to parameter uncertainty. As the dephasing induced by the interaction of the system with the environment guides the evolution to a more classically mixed state, it is worth investigating what effect the introduction of dephasing has on the relationship between performance and robustness. In this paper we analyze the robustness of the fidelity error, as measured by the logarithmic sensitivity function, to dephasing processes. We show that introduction of dephasing as a perturbation to the nominal unitary dynamics requires a modification of the log-sensitivity formulation used to measure robustness about an uncertain parameter with non-zero nominal value used in previous work. We consider controllers optimized for a number of target objectives ranging from fidelity under coherent evolution to fidelity under dephasing dynamics to determine the extent to which optimizing for a specific regime has desirable effects in terms of robustness. Our analysis is based on two independent computations of the log-sensitivity: a statistical Monte Carlo approach and an analytic calculation. We show that despite the different log sensitivity calculations employed in this study, both demonstrate that the log-sensitivity of the fidelity error to dephasing results in a conventional trade-off between performance and robustness.Comment: 11 pages, four figures, and three table

Paying per-label attention for multi-label extraction from radiology reports

Funding: This work is part of the Industrial Centre for AI Research in digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690].Training medical image analysis models requires large amounts of expertly annotated data which is time-consuming and expensive to obtain. Images are often accompanied by free-text radiology reports which are a rich source of information. In this paper, we tackle the automated extraction of structured labels from head CT reports for imaging of suspected stroke patients, using deep learning. Firstly, we propose a set of 31 labels which correspond to radiographic findings (e.g. hyperdensity) and clinical impressions (e.g. haemorrhage) related to neurological abnormalities. Secondly, inspired by previous work, we extend existing state-of-the-art neural network models with a label-dependent attention mechanism. Using this mechanism and simple synthetic data augmentation, we are able to robustly extract many labels with a single model, classified according to the radiologist's reporting (positive, uncertain, negative). This approach can be used in further research to effectively extract many labels from medical text.PostprintPostprin

Language transfer for early warning of epidemics from social media

Statements on social media can be analysed to identify individuals who are experiencing red flag medical symptoms, allowing early detection of the spread of disease such as influenza. Since disease does not respect cultural borders and may spread between populations speaking different languages, we would like to build multilingual models. However, the data required to train models for every language may be difficult, expensive and time-consuming to obtain, particularly for low-resource languages. Taking Japanese as our target language, we explore methods by which data in one language might be used to build models for a different language. We evaluate strategies of training on machine translated data and of zero-shot transfer through the use of multilingual models. We find that the choice of source language impacts the performance, with Chinese-Japanese being a better language pair than English-Japanese. Training on machine translated data shows promise, especially when used in conjunction with a small amount of target language data.PostprintPeer reviewe