146 research outputs found

    Automatic Detection of Pain from Spontaneous Facial Expressions

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    This paper presents a new approach for detecting pain in sequences of spontaneous facial expressions. The motivation for this work is to accompany mobile-based self-management of chronic pain as a virtual sensor for tracking patients' expressions in real-world settings. Operating under such constraints requires a resource efficient approach for processing non-posed facial expressions from unprocessed temporal data. In this work, the facial action units of pain are modeled as sets of distances among related facial landmarks. Using standardized measurements of pain versus no-pain that are specific to each user, changes in the extracted features in relation to pain are detected. The activated features in each frame are combined using an adapted form of the Prkachin and Solomon Pain Intensity scale (PSPI) to detect the presence of pain per frame. Painful features must be activated in N consequent frames (time window) to indicate the presence of pain in a session. The discussed method was tested on 171 video sessions for 19 subjects from the McMaster painful dataset for spontaneous facial expressions. The results show higher precision than coverage in detecting sequences of pain. Our algorithm achieves 94% precision (F-score=0.82) against human observed labels, 74% precision (F-score=0.62) against automatically generated pain intensities and 100% precision (F-score=0.67) against self-reported pain intensities

    Convergence of energy-dependent incommensurate antiferromagnetic neutron scattering peaks to commensurate resonance in underdoped bilayer cuprates

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    The recently discovered coexistence of incommensurate antiferromagnetic neutron scattering peaks and commensurate resonance in underdoped YBa2_2Cu3_3O6+x_{6+x} is calling for an explanation. Within the t-J model, the doping and energy dependence of the spin dynamics of the underdoped bilayer cuprates in the normal state is studied based on the fermion-spin theory by considering the bilayer interactions. Incommensurate peaks are found at [(1±δ)π,π][(1\pm\delta)\pi,\pi] and [π,(1±δ)π][\pi,(1\pm\delta)\pi] at low energies with δ\delta initially increasing with doping at low dopings and then saturating at higher dopings. These incommensurate peaks are suppressed, and the parameter δ\delta is reduced with increasing energy. Eventually it converges to the [π,π][\pi,\pi] resonance peak. Thus the recently observed coexistence is interpreted in terms of bilayer interactions.Comment: 15 pages, Revtex, five figures are included, accepted for publication in Phys. Rev.

    Methods for Assessing the Impact of Fog Oil Smoke on Availability, Palatability, & Food Quality of Relevant Life Stages of Insects for Threatened and Endangered Species

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    A methodology for quantifying population dynamics and food source value of insect fauna in areas subjected to fog oil smoke was developed. Our approach employed an environmentally controlled re-circulating wind tunnel outfitted with a high-heat vaporization and re-condensation fog oil generator that has been shown to produce aerosols of comparable chemistry and droplet-size distribution as those of field releases of the smoke. This method provides reproducible exposures of insects under realistic climatic and environmental conditions to fog oil aerosols that duplicate chemical and droplet-size characteristics of field releases of the smoke. The responses measured take into account reduction in food sources due to death and to changes in availability of relevant life stages of insects that form the prey base for the listed Threatened and Endangered Species. The influence of key environmental factors, wind speed and canopy structure on these responses were characterized. Data generated using this method was used to develop response functions related to particle size, concentration, wind speed, and canopy structure that will allow military personnel to assess and manage impacts to endangered species from fog oil smoke used in military training

    Fluorescent nanoparticles for sensing

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    Nanoparticle-based fluorescent sensors have emerged as a competitive alternative to small molecule sensors, due to their excellent fluorescence-based sensing capabilities. The tailorability of design, architecture, and photophysical properties has attracted the attention of many research groups, resulting in numerous reports related to novel nanosensors applied in sensing a vast variety of biological analytes. Although semiconducting quantum dots have been the best-known representative of fluorescent nanoparticles for a long time, the increasing popularity of new classes of organic nanoparticle-based sensors, such as carbon dots and polymeric nanoparticles, is due to their biocompatibility, ease of synthesis, and biofunctionalization capabilities. For instance, fluorescent gold and silver nanoclusters have emerged as a less cytotoxic replacement for semiconducting quantum dot sensors. This chapter provides an overview of recent developments in nanoparticle-based sensors for chemical and biological sensing and includes a discussion on unique properties of nanoparticles of different composition, along with their basic mechanism of fluorescence, route of synthesis, and their advantages and limitations
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