Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We apply the designed CSPN to two depth estimation tasks given a single image: (1) To refine the depth output from state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth samples to a dense depth map by embedding the depth samples within the propagation procedure. The second task is inspired by the availability of LIDARs that provides sparse but accurate depth measurements. We experimented the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2 and KITTI, where we show that our proposed approach improves in not only quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5 times faster) than prior SOTA methods.Comment: 14 pages, 8 figures, ECCV 201

Cosmological Density and Power Spectrum from Peculiar Velocities: Nonlinear Corrections and PCA

We allow for nonlinear effects in the likelihood analysis of galaxy peculiar velocities, and obtain ~35%-lower values for the cosmological density parameter Om and the amplitude of mass-density fluctuations. The power spectrum in the linear regime is assumed to be a flat LCDM model (h=0.65, n=1, COBE) with only Om as a free parameter. Since the likelihood is driven by the nonlinear regime, we "break" the power spectrum at k_b=0.2 h/Mpc and fit a power law at k>k_b. This allows for independent matching of the nonlinear behavior and an unbiased fit in the linear regime. The analysis assumes Gaussian fluctuations and errors, and a linear relation between velocity and density. Tests using proper mock catalogs demonstrate a reduced bias and a better fit. We find for the Mark3 and SFI data Om_m=0.32+-0.06 and 0.37+-0.09 respectively, with sigma_8*Om^0.6 = 0.49+-0.06 and 0.63+-0.08, in agreement with constraints from other data. The quoted 90% errors include cosmic variance. The improvement in likelihood due to the nonlinear correction is very significant for Mark3 and moderately so for SFI. When allowing deviations from LCDM, we find an indication for a wiggle in the power spectrum: an excess near k=0.05 and a deficiency at k=0.1 (cold flow). This may be related to the wiggle seen in the power spectrum from redshift surveys and the second peak in the CMB anisotropy. A chi^2 test applied to modes of a Principal Component Analysis (PCA) shows that the nonlinear procedure improves the goodness of fit and reduces a spatial gradient of concern in the linear analysis. The PCA allows addressing spatial features of the data and fine-tuning the theoretical and error models. It shows that the models used are appropriate for the cosmological parameter estimation performed. We address the potential for optimal data compression using PCA.Comment: 18 pages, LaTex, uses emulateapj.sty, ApJ in press (August 10, 2001), improvements to text and figures, updated reference

A finite model of two-dimensional ideal hydrodynamics

A finite-dimensional su($N$) Lie algebra equation is discussed that in the infinite $N$ limit (giving the area preserving diffeomorphism group) tends to the two-dimensional, inviscid vorticity equation on the torus. The equation is numerically integrated, for various values of $N$, and the time evolution of an (interpolated) stream function is compared with that obtained from a simple mode truncation of the continuum equation. The time averaged vorticity moments and correlation functions are compared with canonical ensemble averages.Comment: (25 p., 7 figures, not included. MUTP/92/1

Mass equidistribution of Hilbert modular eigenforms

Let F be a totally real number field, and let f traverse a sequence of non-dihedral holomorphic eigencuspforms on GL(2)/F of weight (k_1,...,k_n), trivial central character and full level. We show that the mass of f equidistributes on the Hilbert modular variety as max(k_1,...,k_n) tends to infinity. Our result answers affirmatively a natural analogue of a conjecture of Rudnick and Sarnak (1994). Our proof generalizes the argument of Holowinsky-Soundararajan (2008) who established the case F = Q. The essential difficulty in doing so is to adapt Holowinsky's bounds for the Weyl periods of the equidistribution problem in terms of manageable shifted convolution sums of Fourier coefficients to the case of a number field with nontrivial unit group.Comment: 40 pages; typos corrected, nearly accepted for

Kinematics of the Local Universe XIII. 21-cm line measurements of 452 galaxies with the Nan\c{c}ay radiotelescope, JHK Tully-Fisher relation and preliminary maps of the peculiar velocity field

This paper presents 452 new 21-cm neutral hydrogen line measurements carried out with the FORT receiver of the meridian transit Nan\c{c}ay radiotelescope (NRT) in the period April 2003 -- March 2005. This observational programme is part of a larger project aiming at collecting an exhaustive and magnitude-complete HI extragalactic catalogue for Tully-Fisher applications (the so-called KLUN project, for Kinematics of the Local Universe studies, end in 2008). The whole on-line HI archive of the NRT contains today reduced HI-profiles for ~4500 spiral galaxies of declination delta > -40° (http://klun.obs-nancay.fr). As an example of application, we use direct Tully-Fisher relation in three (JHK) bands in deriving distances to a large catalog of 3126 spiral galaxies distributed through the whole sky and sampling well the radial velocity range between 0 and 8000 km/s. Thanks to an iterative method accounting for selection bias and smoothing effects, we show as a preliminary output a detailed and original map of the velocity field in the Local Universe

Dual mechanism of brain injury and novel treatment strategy in maple syrup urine disease

Maple syrup urine disease (MSUD) is an inherited disorder of branched-chain amino acid metabolism presenting with lifethreatening cerebral oedema and dysmyelination in affected individuals. Treatment requires life-long dietary restriction and monitoring of branched-chain amino acids to avoid brain injury. Despite careful management, children commonly suffer metabolic decompensation in the context of catabolic stress associated with non-specific illness. The mechanisms underlying this decompensation and brain injury are poorly understood. Using recently developed mouse models of classic and intermediate maple syrup urine disease, we assessed biochemical, behavioural and neuropathological changes that occurred during encephalopathy in these mice. Here, we show that rapid brain leucine accumulation displaces other essential amino acids resulting in neurotransmitter depletion and disruption of normal brain growth and development. A novel approach of administering norleucine to heterozygous mothers of classic maple syrup urine disease pups reduced branched-chain amino acid accumulation in milk as well as blood and brain of these pups to enhance survival. Similarly, norleucine substantially delayed encephalopathy in intermediate maple syrup urine disease mice placed on a high protein diet that mimics the catabolic stress shown to cause encephalopathy in human maple syrup urine disease. Current findings suggest two converging mechanisms of brain injury in maple syrup urine disease including: (i) neurotransmitter deficiencies and growth restriction associated with branchedchain amino acid accumulation and (ii) energy deprivation through Krebs cycle disruption associated with branched-chain ketoacid accumulation. Both classic and intermediate models appear to be useful to study the mechanism of brain injury and potential treatment strategies for maple syrup urine disease. Norleucine should be further tested as a potential treatment to prevent encephalopathy in children with maple syrup urine disease during catabolic stress

Debiasing Decisions. Improved Decision Making With A Single Training Intervention

From failures of intelligence analysis to misguided beliefs about vaccinations, biased judgment and decision making contributes to problems in policy, business, medicine, law, and private life. Early attempts to reduce decision biases with training met with little success, leading scientists and policy makers to focus on debiasing by using incentives and changes in the presentation and elicitation of decisions. We report the results of two longitudinal experiments that found medium to large effects of one-shot debiasing training interventions. Participants received a single training intervention, played a computer game or watched an instructional video, which addressed biases critical to intelligence analysis (in Experiment 1: bias blind spot, confirmation bias, and fundamental attribution error; in Experiment 2: anchoring, representativeness, and social projection). Both kinds of interventions produced medium to large debiasing effects immediately (games ≥ -31.94% and videos ≥ -18.60%) that persisted at least 2 months later (games ≥ -23.57% and videos ≥ -19.20%). Games, which provided personalized feedback and practice, produced larger effects than did videos. Debiasing effects were domain-general: bias reduction occurred across problems in different contexts, and problem formats that were taught and not taught in the interventions. The results suggest that a single training intervention can improve decision making. We suggest its use alongside improved incentives, information presentation, and nudges to reduce costly errors associated with biased judgments and decisions

Inflation, cold dark matter, and the central density problem

A problem with high central densities in dark halos has arisen in the context of LCDM cosmologies with scale-invariant initial power spectra. Although n=1 is often justified by appealing to the inflation scenario, inflationary models with mild deviations from scale-invariance are not uncommon and models with significant running of the spectral index are plausible. Even mild deviations from scale-invariance can be important because halo collapse times and densities depend on the relative amount of small-scale power. We choose several popular models of inflation and work out the ramifications for galaxy central densities. For each model, we calculate its COBE-normalized power spectrum and deduce the implied halo densities using a semi-analytic method calibrated against N-body simulations. We compare our predictions to a sample of dark matter-dominated galaxies using a non-parametric measure of the density. While standard n=1, LCDM halos are overdense by a factor of 6, several of our example inflation+CDM models predict halo densities well within the range preferred by observations. We also show how the presence of massive (0.5 eV) neutrinos may help to alleviate the central density problem even with n=1. We conclude that galaxy central densities may not be as problematic for the CDM paradigm as is sometimes assumed: rather than telling us something about the nature of the dark matter, galaxy rotation curves may be telling us something about inflation and/or neutrinos. An important test of this idea will be an eventual consensus on the value of sigma_8, the rms overdensity on the scale 8 h^-1 Mpc. Our successful models have values of sigma_8 approximately 0.75, which is within the range of recent determinations. Finally, models with n>1 (or sigma_8 > 1) are highly disfavored.Comment: 13 pages, 6 figures. Minor changes made to reflect referee's Comments, error in Eq. (18) corrected, references updated and corrected, conclusions unchanged. Version accepted for publication in Phys. Rev. D, scheduled for 15 August 200

Simulating the Formation of the Local Galaxy Population

We simulate the formation and evolution of the local galaxy population starting from initial conditions with a smoothed linear density field which matches that derived from the IRAS 1.2 Jy galaxy survey. Our simulations track the formation and evolution of all dark matter haloes more massive than 10e+11 solar masses out to a distance of 8000 km/s from the Milky Way. We implement prescriptions similar to those of Kauffmann et al. (1999) to follow the assembly and evolution of the galaxies within these haloes. We focus on two variants of the CDM cosmology: an LCDM and a tCDM model. Galaxy formation in each is adjusted to reproduce the I-band Tully-Fisher relation of Giovanelli et al. (1997). We compare the present-day luminosity functions, colours, morphology and spatial distribution of our simulated galaxies with those of the real local population, in particular with the Updated Zwicky Catalog, with the IRAS PSCz redshift survey, and with individual local clusters such as Coma, Virgo and Perseus. We also use the simulations to study the clustering bias between the dark matter and galaxies of differing type. Although some significant discrepancies remain, our simulations recover the observed intrinsic properties and the observed spatial distribution of local galaxies reasonably well. They can thus be used to calibrate methods which use the observed local galaxy population to estimate the cosmic density parameter or to draw conclusions about the mechanisms of galaxy formation. To facilitate such work, we publically release our z=0 galaxy catalogues, together with the underlying mass distribution.Comment: 25 pages, 20 figures, submitted to MNRAS. High resolution copies of figures 1 and 3, halo and galaxy catalogues can be found at http://www.mpa-garching.mpg.de/NumCos/CR/index.htm

Massively distributed authorship of academic papers

Wiki-like or crowdsourcing models of collaboration can provide a number of benefits to academic work. These techniques may engage expertise from different disciplines, and potentially increase productivity. This paper presents a model of massively distributed collaborative authorship of academic papers. This model, developed by a collective of thirty authors, identifies key tools and techniques that would be necessary or useful to the writing process. The process of collaboratively writing this paper was used to discover, negotiate, and document issues in massively authored scholarship. Our work provides the first extensive discussion of the experiential aspects of large-scale collaborative researc