A technology to record membrane potential from multiple neurons, simultaneously, in behaving animals will have a transformative impact on neuroscience research 1,2 . Genetically encoded voltage indicators are a promising tool for these purposes, but were so far limited to single-cell recordings with marginal signal to noise ratio (SNR) in vivo [3][4][5] . We developed improved near infrared voltage indicators, high speed microscopes and targeted gene expression schemes which enabled recordings of supra-and subthreshold voltage dynamics from multiple neurons simultaneously in mouse hippocampus, in vivo. The reporters revealed sub-cellular details of Reprints and permissions information is available at www.nature.com/reprintsUsers may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

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In many applications involving incompressible fluid flow, the Stokes system plays an important role. Complex flow problems may require extremely fine resolutions, easily resulting in saddle-point problems with more than a trillion (10 12 ) unknowns. Even on the most advanced supercomputers, the fast solution of such systems of equations is a highly nontrivial and challenging task. In this work we consider a realization of an iterative saddle-point solver which is based mathematically on the Schur-complement formulation of the pressure and algorithmically on the abstract concept of hierarchical hybrid grids. The design of our fast multigrid solver is guided by an innovative performance analysis for the computational kernels in combination with a quantification of the communication overhead. Excellent node performance and good scalability to almost a million parallel threads are demonstrated on different characteristic types of modern supercomputers.1. Introduction. Current leading edge supercomputers can provide performance in the order of several petaflop/s, enabling the development of increasingly complex and accurate computational models having unprecedented size. This is especially relevant in flow simulations that may exhibit many small scale features that must be resolved over large domains. As an example, the problem of earth mantle convection is posed on a thick spherical shell of approximately 3 000 km depth and 6 300 km radius, resulting in an overall volume of close to a trillion, that is, 10 12 km 3 . A high resolution then results automatically in huge algebraic systems.Although finite element (FE) methods are flexible enough to handle different local mesh-sizes, fully adaptive meshing techniques require dynamic data structures and a complex program control flow that incurs significant computational cost. Recent work on parallel adaptive FE techniques can be found, e.g., in [1,2,11,44]. In [10] it is shown that an adaptive parallel FE method can reach locally 1 km resolution for the mantle convection problem on a large scale supercomputer. Here we will demonstrate that such a resolution can even be reached globally.Higher order FE approaches can lead to a better accuracy with the same number of unknowns, but the linear systems are denser. This implies more computational work, more memory access cost, and also higher parallel communication cost, so

The pioneering experiments in linear spectroscopy were performed using flames in the 1800s, but nonlinear optical measurements had to wait until lasers became available in the twentieth century. Because the nonlinear cross-section of materials is very small(1,2), macroscopic bulk samples and pulsed lasers are usually used. Numerous efforts have explored coherent nonlinear signal generation from individual nanoparticles(3-5) or small atomic ensembles(6-8) with millions of atoms. Experiments on a single semiconductor quantum dot have also been reported, albeit with a very small yield(9). Here, we report the coherent nonlinear spectroscopy of a single molecule under continuous-wave single-pass illumination and the switching of a laser beam by on the order of ten pump photons. The sharp molecular transitions and efficient photon-molecule coupling at a tight focus(10) allow for optical switching with less than a handful of pump photons and are thus promising for applications in quantum engineering(11)

The computational complexity of naive, sampling-based uncertainty quantification for 3D partial differential equations is extremely high. Multilevel approaches, such as multilevel Monte Carlo (MLMC), can reduce the complexity significantly when they are combined with a fast multigrid solver, but to exploit them fully in a parallel environment, sophisticated scheduling strategies are needed. We optimize the concurrent execution across the three layers of the MLMC method: parallelization across levels, across samples, and across the spatial grid. In a series of numerical tests, the influence on the overall performance of the "scalability window" of the multigrid solver (i.e., the range of processor numbers over which good parallel efficiency can be maintained) is illustrated. Different homogeneous and heterogeneous scheduling strategies are proposed and discussed. Finally large 3D scaling experiments are carried out including adaptivity.