Observations of shallow convective clouds generated by solar heating of dark smoke plumes

The SEVIRI instrument on the Meteosat Second Generation satellite with both fine spatial and temporal resolution allows to detect and follow the dynamics of fast developing meteorological events like spreading smoke plumes and the lifecycles of convective clouds. Smoke plumes have the ability to change the atmospheric heat content due to absorption and reduced reflection of solar radiation. By these means they can trigger formation of shallow convective clouds at their edge. A heavy smoke plume emerging from burning Lebanese oil tanks and spreading over adjacent deserts on 17 July 2006 has been observed as an example of such an effect. This study suggests a physical explanation of the observed convection along the edge of the smoke plume, namely the strong thermal contrast resulting from solar heating of the smoke layer

Coupled Cluster Channels in the Homogeneous Electron Gas

We discuss diagrammatic modifications to the coupled cluster doubles (CCD) equations, wherein different groups of terms out of rings, ladders, crossed-rings and mosaics can be removed to form approximations to the coupled cluster method, of interest due to their similarity with various types of random phase approximations. The finite uniform electron gas is benchmarked for 14- and 54-electron systems at the complete basis set limit over a wide density range and performance of different flavours of CCD are determined. These results confirm that rings generally overcorrelate and ladders generally undercorrelate; mosaics-only CCD yields a result surprisingly close to CCD. We use a recently developed numerical analysis [J. J. Shepherd and A. Gr\"uneis, Phys. Rev. Lett. 110, 226401 (2013)] to study the behaviours of these methods in the thermodynamic limit. We determine that the mosaics, on forming the Brueckner Hamltonian, open a gap in the effective one-particle eigenvalues at the Fermi energy. Numerical evidence is presented which shows that methods based on this renormalisation have convergent energies in the thermodynamic limit including mosaic-only CCD, which is just a renormalised MP2. All other methods including only a single channel, namely ladder-only CCD, ring-only CCD and crossed-ring-only CCD, appear to yield divergent energies; incorporation of mosaic terms prevents this from happening.Comment: 9 pages, 4 figures, 1 table. Comments welcome: [email protected]

Characterization of Superabsorbent Polymers in Aluminum Solutions

Over the past few decades, super absorbent polymers (SAPs) have been the topic of research projects all around the world due to their incredible ability to absorb water. They have applications in everything from disposable diapers to high performance concrete. In concrete, aqueous cations permeate the polymer network, reducing swelling and altering properties. One of these ions, aluminum, alters SAP properties by creating a stiff outer shell and greatly reducing absorbency, but these effects have not been well characterized. One method of characterizing the effects of aluminum on SAP hydrogels was performing gravimetric swelling tests to determine equilibrium water capacity at different aluminum ion concentrations. Compressive strength was also determined for swollen particles using a rheometer to perform compression tests. Results from this testing show that low concentration solutions take several hours to permeate the polymer network and reduce swelling capacity, while high concentration solutions are able to limit swelling immediately. The compressive strength of the gel was increased greatly in polymers containing mostly poly(acrylic acid), while SAPs containing more poly(acrylamide) did not have their strength as greatly influenced by the aluminum ions. These results help elucidate the negative effects that may be caused by multivalent cations in concrete. Further research will include studying the interactions of aluminum ions with polymer strands using polymer brushes on a quartz crystal microbalance. This will hopefully reveal the mechanism and kinetics of salt absorption in polymer networks

Intrinsic dimension of data representations in deep neural networks

Deep neural networks progressively transform their inputs across multiple processing layers. What are the geometrical properties of the representations learned by these networks? Here we study the intrinsic dimensionality (ID) of data-representations, i.e. the minimal number of parameters needed to describe a representation. We find that, in a trained network, the ID is orders of magnitude smaller than the number of units in each layer. Across layers, the ID first increases and then progressively decreases in the final layers. Remarkably, the ID of the last hidden layer predicts classification accuracy on the test set. These results can neither be found by linear dimensionality estimates (e.g., with principal component analysis), nor in representations that had been artificially linearized. They are neither found in untrained networks, nor in networks that are trained on randomized labels. This suggests that neural networks that can generalize are those that transform the data into low-dimensional, but not necessarily flat manifolds

Training deep neural density estimators to identify mechanistic models of neural dynamics

Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-- trained using model simulations-- to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features, and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics

Retinal metric: a stimulus distance measure derived from population neural responses

The ability of the organism to distinguish between various stimuli is limited by the structure and noise in the population code of its sensory neurons. Here we infer a distance measure on the stimulus space directly from the recorded activity of 100 neurons in the salamander retina. In contrast to previously used measures of stimulus similarity, this "neural metric" tells us how distinguishable a pair of stimulus clips is to the retina, given the noise in the neural population response. We show that the retinal distance strongly deviates from Euclidean, or any static metric, yet has a simple structure: we identify the stimulus features that the neural population is jointly sensitive to, and show the SVM-like kernel function relating the stimulus and neural response spaces. We show that the non-Euclidean nature of the retinal distance has important consequences for neural decoding.Comment: 5 pages, 4 figures, to appear in Phys Rev Let

Linear-scaling density-functional simulations of charged point defects in Al2O3 using hierarchical sparse matrix algebra

We present calculations of formation energies of defects in an ionic solid (Al(2)O(3)) extrapolated to the dilute limit, corresponding to a simulation cell of infinite size. The large-scale calculations required for this extrapolation are enabled by developments in the approach to parallel sparse matrix algebra operations, which are central to linear-scaling density-functional theory calculations. The computational cost of manipulating sparse matrices, whose sizes are determined by the large number of basis functions present, is greatly improved with this new approach. We present details of the sparse algebra scheme implemented in the ONETEP code using hierarchical sparsity patterns, and demonstrate its use in calculations on a wide range of systems, involving thousands of atoms on hundreds to thousands of parallel processes

3D-Laser-Scanning Technique Applied to Bulk Density Measurements of Apollo Lunar Samples

In order to better interpret gravimetric data from orbiters such as GRAIL and LRO to understand the subsurface composition and structure of the lunar crust, it is import to have a reliable database of the density and porosity of lunar materials. To this end, we have been surveying these physical properties in both lunar meteorites and Apollo lunar samples. To measure porosity, both grain density and bulk density are required. For bulk density, our group has historically utilized sub-mm bead immersion techniques extensively, though several factors have made this technique problematic for our work with Apollo samples. Samples allocated for measurement are often smaller than optimal for the technique, leading to large error bars. Also, for some samples we were required to use pure alumina beads instead of our usual glass beads. The alumina beads were subject to undesirable static effects, producing unreliable results. Other investigators have tested the use of 3d laser scanners on meteorites for measuring bulk volumes. Early work, though promising, was plagued with difficulties including poor response on dark or reflective surfaces, difficulty reproducing sharp edges, and large processing time for producing shape models. Due to progress in technology, however, laser scanners have improved considerably in recent years. We tested this technique on 27 lunar samples in the Apollo collection using a scanner at NASA Johnson Space Center. We found it to be reliable and more precise than beads, with the added benefit that it involves no direct contact with the sample, enabling the study of particularly friable samples for which bead immersion is not possibl

Solar radiative transfer simulations in Saharan dust plumes: particle shapes and 3-D effect

Radiative fields of three-dimensional inhomogeneous Saharan dust clouds have been calculated at solar wavelength (0.6 μm) by means of a Monte Carlo radiative transfer model. Scattering properties are taken from measurements in the SAMUM campaigns, from light scattering calculations for spheroids based on the MIESCHKA code, from Mie theory for spheres and from the geometric optics method assuming irregular shaped particles. Optical properties of different projected area equivalent shapes are compared. Large differences in optical properties are found especially in the phase functions. Results of radiative transfer calculations based on the Monte Carlo method are shown exemplarily for one dust cloud simulated by the cloud resolving atmospheric circulation model LM-MUSCAT-DES. Shape-induced differences in the radiation fluxes are pronounced, for example, the domain averaged normalized radiance is about 30% lower in the case of a dust plume consisting of spheroids or irregular particles compared to spheres. The effect of net horizontal photon transport (3-D effect) on the reflected radiance fields is only notable at the largest gradients in optical thickness. For example, the reflectance at low sun position differs locally about 15% when horizontal photon transport is accounted for. ‘Sharp edges' due to 1-D calculations are smoothed out in the 3-D case

Internal structure and physical properties of the Asteroid 2008 TC3 inferred from a study of the Almahata Sitta meteorites

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