The Error is the Feature: how to Forecast Lightning using a Model Prediction Error

Despite the progress within the last decades, weather forecasting is still a challenging and computationally expensive task. Current satellite-based approaches to predict thunderstorms are usually based on the analysis of the observed brightness temperatures in different spectral channels and emit a warning if a critical threshold is reached. Recent progress in data science however demonstrates that machine learning can be successfully applied to many research fields in science, especially in areas dealing with large datasets. We therefore present a new approach to the problem of predicting thunderstorms based on machine learning. The core idea of our work is to use the error of two-dimensional optical flow algorithms applied to images of meteorological satellites as a feature for machine learning models. We interpret that optical flow error as an indication of convection potentially leading to thunderstorms and lightning. To factor in spatial proximity we use various manual convolution steps. We also consider effects such as the time of day or the geographic location. We train different tree classifier models as well as a neural network to predict lightning within the next few hours (called nowcasting in meteorology) based on these features. In our evaluation section we compare the predictive power of the different models and the impact of different features on the classification result. Our results show a high accuracy of 96% for predictions over the next 15 minutes which slightly decreases with increasing forecast period but still remains above 83% for forecasts of up to five hours. The high false positive rate of nearly 6% however needs further investigation to allow for an operational use of our approach.Comment: 10 pages, 7 figure

Infinite dimensional Lie algebras in 4D conformal quantum field theory

The concept of global conformal invariance (GCI) opens the way of applying algebraic techniques, developed in the context of 2-dimensional chiral conformal field theory, to a higher (even) dimensional space-time. In particular, a system of GCI scalar fields of conformal dimension two gives rise to a Lie algebra of harmonic bilocal fields, V_m(x,y), where the m span a finite dimensional real matrix algebra M closed under transposition. The associative algebra M is irreducible iff its commutant M' coincides with one of the three real division rings. The Lie algebra of (the modes of) the bilocal fields is in each case an infinite dimensional Lie algebra: a central extension of sp(infty,R) corresponding to the field R of reals, of u(infty,infty) associated to the field C of complex numbers, and of so*(4 infty) related to the algebra H of quaternions. They give rise to quantum field theory models with superselection sectors governed by the (global) gauge groups O(N), U(N), and U(N,H)=Sp(2N), respectively.Comment: 16 pages, with minor improvements as to appear in J. Phys.

Long-lived virus-reactive memory T cells generated from purified cytokine-secreting T helper type 1 and type 2 effectors

Many vaccination strategies and immune cell therapies aim at increasing the numbers of memory T cells reactive to protective antigens. However, the differentiation lineage and therefore the optimal generation conditions of CD4 memory cells remain controversial. Linear and divergent differentiation models have been proposed, suggesting CD4 memory T cell development from naive precursors either with or without an effector-stage intermediate, respectively. Here, we address this question by using newly available techniques for the identification and isolation of effector T cells secreting effector cytokines. In adoptive cell transfers into normal, nonlymphopenic mice, we show that long-lived virus-specific memory T cells can efficiently be generated from purified interferon γ–secreting T helper (Th) type 1 and interleukin (IL)-4– or IL-10–secreting Th2 effectors primed in vitro or in vivo. Importantly, such effector-derived memory T cells were functional in viral challenge infections. They proliferated vigorously, rapidly modulated IL-7 receptor expression, exhibited partial stability and flexibility of their cytokine patterns, and exerted differential effects on virus-induced immunopathology. Thus, cytokine-secreting effectors can evade activation-induced cell death and develop into long-lived functional memory cells. These findings demonstrate the efficiency of linear memory T cell differentiation and encourage the design of vaccines and immune cell therapies based on differentiated effector T cells

Spin-flop transition in Gd5Ge4 observed by x-ray resonant magnetic scattering and first-principles calculations of magnetic anisotropy

X-ray resonant magnetic scattering was employed to study a fully reversible spin-flop transition in orthorhombic Gd5Ge4 and to elucidate details of the magnetic structure in the spin-flop phase. The orientation of the moments at the three Gd sites flop 90° from the c axis to the a axis when a magnetic field, Hsf=9 kOe, is applied along the c axis at T=9 K. The magnetic space group changes from Pnm′a to Pn′m′a′ for all three Gd sublattices. The magnetic anisotropy energy determined from experimental measurements is in good agreement with the calculations of the magnetic anisotropy based on the spin-orbit coupling of the conduction electrons and an estimation of the dipolar interactions anisotropy. No significant magnetostriction effects were observed at the spin-flop transition

Hyperbolic planforms in relation to visual edges and textures perception

We propose to use bifurcation theory and pattern formation as theoretical probes for various hypotheses about the neural organization of the brain. This allows us to make predictions about the kinds of patterns that should be observed in the activity of real brains through, e.g. optical imaging, and opens the door to the design of experiments to test these hypotheses. We study the specific problem of visual edges and textures perception and suggest that these features may be represented at the population level in the visual cortex as a specific second-order tensor, the structure tensor, perhaps within a hypercolumn. We then extend the classical ring model to this case and show that its natural framework is the non-Euclidean hyperbolic geometry. This brings in the beautiful structure of its group of isometries and certain of its subgroups which have a direct interpretation in terms of the organization of the neural populations that are assumed to encode the structure tensor. By studying the bifurcations of the solutions of the structure tensor equations, the analog of the classical Wilson and Cowan equations, under the assumption of invariance with respect to the action of these subgroups, we predict the appearance of characteristic patterns. These patterns can be described by what we call hyperbolic or H-planforms that are reminiscent of Euclidean planar waves and of the planforms that were used in [1, 2] to account for some visual hallucinations. If these patterns could be observed through brain imaging techniques they would reveal the built-in or acquired invariance of the neural organization to the action of the corresponding subgroups.Comment: 34 pages, 11 figures, 2 table

Radio Astronomy

Contains reports on sixteen research projects.National Science Foundation (Grant AST81-21416)National Science Foundation (Grant AST80-22864)National Aeronautics and Space Administration (Contract S-10665-C)National Aeronautics and Space Administration (Contract NAGW373)National Science Foundation (Grant AST79-19553)National Oceanic and Atmospheric Administration (Grant 04-8-M01-1)National Aeronautics and Space Administration (Grant NAG5-10)National Aeronautics and Space Administration (Contract NAS5-22929)Defense Advanced Research Projects Agency (Contract MDA 903-82-K-0521)Intelsat (Contract Intel-188)Joint Services Electronics Program (Contract DAAG29-80-C-0104)Lockheed Missiles and Space Company (Contract LS90B4860F