Thermospheric nitric oxide from the ATLAS 1 and Spacelab 1 missions

Spectral and spatial images obtained with the Imaging Spectrometric Observatory on the ATLAS 1 and Spacelab 1 missions are used to study the ultraviolet emissions of nitric oxide in the thermosphere. By synthetically fitting the measured NO gamma bands, intensities are derived as a function of altitude and latitude. We find that the NO concentrations inferred from the ATLAS 1 measurements are higher than predicted by our thermospheric airglow model and tend to lie to the high side of a number of earlier measurements. By comparison with synthetic spectral fits, the shape of the NO gamma bands is used to derive temperature as a function of altitude. Using the simultaneous spectral and spatial imaging capability of the instrument, we present the first simultaneously acquired altitude images of NO gamma band temperature and intensity in the thermosphere. The lower thermospheric temperature images show structure as a function of altitude. The spatial imaging technique appears to be a viable means of obtaining temperatures in the middle and lower thermosphere, provided that good information is also obtained at the higher altitudes, as the contribution of the overlying, hotter NO is nonnegligible. By fitting both self-absorbed and nonabsorbed bands of the NO gamma system, we show that the self absorption effects are observable up to 200 km, although small above 150 km. The spectral resolution of the instrument (1.6 A) allows separation of the N(+)(S-5) doublet, and we show the contribution of this feature to the combination of the NO gamma (1, 0) band and the N(+)(S-5) doublet as a function of altitude (less than 10% below 200 km). Spectral images including the NO delta bands support previous findings that the fluorescence efficiency is much higher than that determined from laboratory measurements. The Spacelab 1 data indicate the presence of a significant population of hot NO in the vehicle environment of that early shuttle mission

Charge exchange of metastable 2D oxygen ions with N2 in the thermosphere

Measurements of N2+ and supporting data made on the Atmosphere Explorer-C satellite in the ionosphere are used to study the charge exchange process The equality k = (5 +/- 1.7) x 10-10cm3s-1. This value lies close to the lower limit of experimental uncertainty of the rate coefficient determined in the laboratory. We have also investigated atomic oxygen quenching of O+(2D) and find that the rate coefficient is 2 x 10-11 cm3s-1 to within approximately a factor of two.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/22805/1/0000362.pd

Capturing Label Characteristics in VAEs

We present a principled approach to incorporating labels in VAEs that captures the rich characteristic information associated with those labels. While prior work has typically conflated these by learning latent variables that directly correspond to label values, we argue this is contrary to the intended effect of supervision in VAEs-capturing rich label characteristics with the latents. For example, we may want to capture the characteristics of a face that make it look young, rather than just the age of the person. To this end, we develop the CCVAE, a novel VAE model and concomitant variational objective which captures label characteristics explicitly in the latent space, eschewing direct correspondences between label values and latents. Through judicious structuring of mappings between such characteristic latents and labels, we show that the CCVAE can effectively learn meaningful representations of the characteristics of interest across a variety of supervision schemes. In particular, we show that the CCVAE allows for more effective and more general interventions to be performed, such as smooth traversals within the characteristics for a given label, diverse conditional generation, and transferring characteristics across datapoints.Comment: Accepted to ICLR 202

Optimizing the colour and fabric of targets for the control of the tsetse fly Glossina fuscipes fuscipes

Background: Most cases of human African trypanosomiasis (HAT) start with a bite from one of the subspecies of Glossina fuscipes. Tsetse use a range of olfactory and visual stimuli to locate their hosts and this response can be exploited to lure tsetse to insecticide-treated targets thereby reducing transmission. To provide a rational basis for cost-effective designs of target, we undertook studies to identify the optimal target colour. Methodology/Principal Findings: On the Chamaunga islands of Lake Victoria , Kenya, studies were made of the numbers of G. fuscipes fuscipes attracted to targets consisting of a panel (25 cm square) of various coloured fabrics flanked by a panel (also 25 cm square) of fine black netting. Both panels were covered with an electrocuting grid to catch tsetse as they contacted the target. The reflectances of the 37 different-coloured cloth panels utilised in the study were measured spectrophotometrically. Catch was positively correlated with percentage reflectance at the blue (460 nm) wavelength and negatively correlated with reflectance at UV (360 nm) and green (520 nm) wavelengths. The best target was subjectively blue, with percentage reflectances of 3%, 29%, and 20% at 360 nm, 460 nm and 520 nm respectively. The worst target was also, subjectively, blue, but with high reflectances at UV (35% reflectance at 360 nm) wavelengths as well as blue (36% reflectance at 460 nm); the best low UV-reflecting blue caught 3× more tsetse than the high UV-reflecting blue. Conclusions/Significance: Insecticide-treated targets to control G. f. fuscipes should be blue with low reflectance in both the UV and green bands of the spectrum. Targets that are subjectively blue will perform poorly if they also reflect UV strongly. The selection of fabrics for targets should be guided by spectral analysis of the cloth across both the spectrum visible to humans and the UV region

DGPose: Deep Generative Models for Human Body Analysis

Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in which the body pose and the visual appearance are disentangled. Such a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as pose-transfer without specific training for such a task. Our proposed models, the Conditional-DGPose and the Semi-DGPose, have different characteristics. In the first, body pose labels are taken as conditioners, from a fully-supervised training set. In the second, our structured semi-supervised approach allows for pose estimation to be performed by the model itself and relaxes the need for labelled data. Therefore, the Semi-DGPose aims for the joint understanding and generation of people in images. It is not only capable of mapping images to interpretable latent representations but also able to map these representations back to the image space. We compare our models with relevant baselines, the ClothNet-Body and the Pose Guided Person Generation networks, demonstrating their merits on the Human3.6M, ChictopiaPlus and DeepFashion benchmarks.Comment: IJCV 2020 special issue on 'Generating Realistic Visual Data of Human Behavior' preprint. Keywords: deep generative models, semi-supervised learning, human pose estimation, variational autoencoders, generative adversarial network

Costs Of Using “Tiny Targets” to Control Glossina fuscipes fuscipes, a Vector of Gambiense Sleeping Sickness in Arua District of Uganda

Introduction To evaluate the relative effectiveness of tsetse control methods, their costs need to be analysed alongside their impact on tsetse populations. Very little has been published on the costs of methods specifically targeting human African trypanosomiasis. Methodology/Principal Findings In northern Uganda, a 250 km2 field trial was undertaken using small (0.5 X 0.25 m) insecticide-treated targets (“tiny targets”). Detailed cost recording accompanied every phase of the work. Costs were calculated for this operation as if managed by the Ugandan vector control services: removing purely research components of the work and applying local salaries. This calculation assumed that all resources are fully used, with no spare capacity. The full cost of the operation was assessed at USD 85.4 per km2, of which USD 55.7 or 65.2% were field costs, made up of three component activities (target deployment: 34.5%, trap monitoring: 10.6% and target maintenance: 20.1%). The remaining USD 29.7 or 34.8% of the costs were for preliminary studies and administration (tsetse surveys: 6.0%, sensitisation of local populations: 18.6% and office support: 10.2%). Targets accounted for only 12.9% of the total cost, other important cost components were labour (24.1%) and transport (34.6%). Discussion Comparison with the updated cost of historical HAT vector control projects and recent estimates indicates that this work represents a major reduction in cost levels. This is attributed not just to the low unit cost of tiny targets but also to the organisation of delivery, using local labour with bicycles or motorcycles. Sensitivity analyses were undertaken, investigating key prices and assumptions. It is believed that these costs are generalizable to other HAT foci, although in more remote areas, with denser vegetation and fewer people, costs would increase, as would be the case for other tsetse control techniques